Photovoltaic (PV) power forecasting is critical for the operation of solar power plants and the coordination of energy within power grids. This work aims to predict future PV power time series by leveraging multimodal data. While recent studies have incorporated numerical modalities such as satellite image sequences and numerical weather prediction (NWP) time series, they often overlook textual modalities—such as the spatio-temporal context of PV plants—and the potential of pretrained large language models (LLMs). In this paper, we build upon existing numerical inputs and further explore the use of spatio-temporal text prompts, generated based on plant coordinates and forecast start time, to enhance the forecasting process. We propose PV-LLM, a satellite-text-prompted framework that integrates a pretrained LLM to improve PV power forecasting. The framework consists of three key components: Text Prompt Construction, Modality-Specific Encoding, and Adaptive Prompt Tuning. First, the Text Prompt Construction module generates spatio-temporal prompts that offer high-level semantic guidance. Next, the Modality-Specific Encoding module encodes each modality according to its unique characteristics, capturing modality-specific patterns while managing varying context lengths. Finally, the Adaptive Prompt Tuning module fine-tunes the LLM to integrate multimodal embeddings, while an adaptive gating mechanism retains its pretrained knowledge. We validate the effectiveness of the proposed framework on a real-world dataset containing multiple PV plants. Experimental results demonstrate that our approach outperforms existing state-of-the-art methods.

Masked image generation (MIG) has demonstrated remarkable efficiency and high-fidelity images by enabling parallel token prediction. Existing methods typically rely solely on the model itself to learn semantic dependencies among visual token sequences. However, directly learning such semantic dependencies from data is challenging because the individual tokens lack clear semantic meanings, and these sequences are usually long. To address this limitation, we propose a novel Knowledge-Augmented Masked Image Generation framework, named KA-MIG, which introduces explicit knowledge of token-level semantic dependencies (i.e., extracted from the training data) as priors to learn richer representations for improving performance. In particular, we explore and identify three types of advantageous token knowledge graphs, including two positive and one negative graphs (i.e., the co-occurrence graph, the semantic similarity graph, and the position-token incompatibility graph). Based on three prior knowledge graphs, we design a graph-aware encoder to learn token and position-aware representations. After that, a lightweight fusion mechanism is introduced to integrate these enriched representations into the existing MIG methods. Resorting to such prior knowledge, our method effectively enhances the model's ability to capture semantic dependencies, leading to improved generation quality. Experimental results demonstrate that our method improves upon existing MIG for class-conditional image generation on ImageNet.

Image inpainting is an important image generation task, which aims to restore corrupted image from partial visible area. Recently, diffusion Schrödinger bridge methods effectively tackle this task by modeling the translation between corrupted and target images as a diffusion Schrödinger bridge process along a noising schedule path. Although these methods have shown superior performance, in this paper, we find that 1) existing methods suffer from a schedule-restoration mismatching issue, i.e., the theoretical schedule and practical restoration processes usually exist a large discrepancy, which theoretically results in the schedule not fully leveraged for restoring images; and 2) the key reason causing such issue is that the restoration process of all pixels are actually asynchronous but existing methods set a synchronous noise schedule to them, i.e., all pixels shares the same noise schedule. To this end, we propose a schedule-Asynchronous Diffusion Schrödinger Bridge (AsyncDSB) for image inpainting. Our insight is preferentially scheduling pixels with high frequency (i.e., large gradients) and then low frequency (i.e., small gradients). Based on this insight, given a corrupted image, we first train a network to predict its gradient map in corrupted area. Then, we regard the predicted image gradient as prior and design a simple yet effective pixel-asynchronous noise schedule strategy to enhance the diffusion Schrödinger bridge. Thanks to the asynchronous schedule at pixels, the temporal interdependence of restoration process between pixels can be fully characterized for high-quality image inpainting. Experiments on real-world datasets show that our AsyncDSB achieves superior performance, especially on FID with around 3% ∼ 14% improvement over state-of-the-art baseline methods.

Long-sequence precipitation forecasting is critical for both meteorological science and smart city applications. The primary objective of this task is to predict future radar echo sequences, which provide high resolution and timely references for atmospheric precipitation distribution based on current observations. However, the chaotic nature of precipitation systems poses significant challenges in extending reliable forecast horizons. Most existing methods struggle with accuracy and clarity when extended to long-sequence predictions, such as three-hour forecasts. This is primarily due to the insufficiency of spatio-temporal information within a single modality over time. In this paper, we propose a cascading forecasting framework that adaptively extracts and integrates multimodal spatio-temporal information to support accurate and realistic long-sequence radar forecasting. Our framework includes a temporal adaptive predictor and a flow-based precipitation distribution adaptor. The predictor utilizes a multi-branch encoder-decoder architecture. This design allows it to extract meteorological sequences from multiple sources at varying scales, resulting in an initial global precipitation estimate. The core component is a carefully designed cross-attention module with a temporal adaptive layer to enhance multi-modality alignment. The initial estimate is then refined by the flow-based adaptor, which adjusts the prediction to match the target precipitation distribution, enhancing local details and correcting extreme precipitation patterns. We validated our method using real multi-source dataset for long-sequence forecasting, and the experimental results demonstrate that our approach outperforms existing state-of-the-art methods.

Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by identifying sensitive parameters and then leveraging its sparse characteristic to combine unstructured and structured tuning for PEFT. However, existing methods only focus on the sparse characteristic of sensitive parameters but overlook its distribution characteristic, which results in additional storage burden and limited performance improvement. In this paper, we find that the distribution of sensitive parameters is not chaotic, but concentrates on a small number of rows or columns in each parameter matrix. Inspired by this fact, we propose a Compact Dynamic-Rank Adaptation-based tuning method for Sensitivity-aware Parameter efficient fine-Tuning, called CDRA-SPT. Specifically, we first identify the sensitive parameters that require tuning for each down-stream task. Then, we reorganize the sensitive parameters by following its row and column into a compact sub-parameter matrix. Finally, a dynamic-rank adaptation is designed and applied at sub-parameter matrix level for PEFT. Its advantage is that the dynamic-rank characteristic of sub-parameter matrix can be fully exploited for PEFT. Extensive experiments show that our method achieves superior performance over previous state-of-the-art methods.

Image quantization is a crucial technique in image generation, aimed at learning a codebook that encodes an image into a discrete token sequence. Recent advancements have seen researchers exploring learning multi-modal codebook (i.e., text-aligned codebook) by utilizing image caption semantics, aiming to enhance codebook performance in cross-modal tasks. However, existing image-text paired datasets exhibit a notable flaw in that the text descriptions tend to be overly concise, failing to adequately describe the images and provide sufficient semantic knowledge, resulting in limited alignment of text and codebook at a fine-grained level. In this paper, we propose a novel Text-Augmented Codebook Learning framework, named TA-VQ, which generates longer text for each image using the visual-language model for improved text-aligned codebook learning. However, the long text presents two key challenges: how to encode text and how to align codebook and text. To tackle two challenges, we propose to split the long text into multiple granularities for encoding, i.e., word, phrase, and sentence, so that the long text can be fully encoded without losing any key semantic knowledge. Following this, a hierarchical encoder and novel sampling-based alignment strategy are designed to achieve fine-grained codebook-text alignment. Additionally, our method can be seamlessly integrated into existing VQ models. Extensive experiments in reconstruction and various downstream tasks demonstrate its effectiveness compared to previous state-of-the-art approaches.

Precipitation nowcasting involves using current radar observation sequences to predict future radar sequences and determine future precipitation distribution, which is crucial for disaster warning, traffic planning, and agricultural production. Despite numerous advancements, challenges persist in accurately predicting both the location and intensity of precipitation, as these factors are often interdependent, with complex atmospheric dynamics and moisture distribution causing position and intensity changes to be intricately coupled. Inspired by the fact that in the frequency domain, phase variations are shown to correspond to changes in the position of precipitation, while amplitude variations are linked to intensity changes, we propose an amplitude-phase disentanglement model called AlphaPre, which separately learn the position and intensity changes of precipitation. AlphaPre comprises three key components: a phase network, an amplitude network, and an AlphaMixer. The phase network captures positional changes by learning phase variations, and the amplitude network models intensity changes by alternating between the frequency and spatial domains. The AlphaMixer then integrates these components to produce a refined precipitation forecast. Extensive experiments on four datasets demonstrate the effectiveness and superiority of our method over state-of-the-art approaches. Our code is publicly available at https://github.com/linkenghong/AlphaPre.

Most current precipitation nowcasting methods aim to capture the underlying spatiotemporal dynamics of precipitation systems by minimizing the mean square error (MSE). However, these methods often neglect effective constraints on the data distribution, leading to unsatisfactory prediction accuracy and image quality, especially for long forecast sequences. To address this limitation, we propose a precipitation nowcasting model incorporating perceptual constraints. This model reformulates precipitation nowcasting as a posterior MSE problem under such constraints. Specifically, we first obtain the posteriori mean sequences of precipitation forecasts using a precipitation estimator. Subsequently, we construct the transmission between distributions using rectified flow. To enhance the focus on distant frames, we design a frame sampling strategy that gradually increases the corresponding weights. We theoretically demonstrate the reliability of our solution, and experimental results on two publicly available radar datasets demonstrate that our model is effective and outperforms current state-of-the-art models.

Precipitation nowcasting plays a pivotal role in urban planning and disaster mitigation, where extending forecast horizons offers critical advantages for proactive decision-making. Most data-driven methods focus on modeling radar echo sequences through end-to-end spatiotemporal predictive learning, yielding precise short-term predictions; however, they fundamentally neglect the inherent physical mechanism governing precipitation system. Moreover, approaches relying solely on single-modality radar observations suffer from persistent information bottlenecks, severely limiting their temporal generalizability for extended forecasting. To address these challenges, we propose PiMMNet, a Physics-informed Multi-Modal Network. It is constructed based on the advection-diffusion principle from fluid dynamics, explicitly modeling the precipitation evolution as a spatiotemporal transport processes characterized by the deterministic advection and the stochastic source. We carefully design a multi-model motion estimation network and a motion-guided diffusion model to describe the deterministic and stochastic terms, respectively. The core innovation of our method lies in jointly estimating a physics-constrained velocity field from multi-modal inputs (radar and satellite data). In this case, we naturally align the motion evolution among modalities into a unified representation, inherently mitigating cross-modal distribution biases. Experimental evaluations on two real-world multi-modal meteorological datasets demonstrate the efficacy of our approach, showcasing significant improvements in accuracy and robustness for longer-range precipitation nowcasting. Our code are available at https://github.com/DeminYu98/PiMMNet.

Nowcasting weather extremes poses significant challenges due to the complex evolution of their dynamical systems. State space models (SSMs) excel in sequence modeling, offering a promising avenue to address this issue. However, existing SSMs typically rely on first-order ordinary differential equations (ODEs), limiting their capacity to capture higher order dynamics. To eliminate these limitations, we propose a second-order state space (S3) architecture, where the weather motion is decomposed as position and momentum in latent spaces to find multi-order behaviors. For sequential weather observations, we provide a recurrent counterpart of S3, which can be further parallelized through fast Fourier transform (FFT) for computational efficiency. Building on these S3 blocks, we develop a unified framework, S3Cast, for spatiotemporal sequence extrapolation. Empirical results demonstrate that S3Cast matches or exceeds the performance of state-of-the-art methods in the prediction of weather extremes, such as lightning, hail, and heavy precipitation.

Online continual learning (OCL), aimed at developing a neural network that continuously learns new data from a single pass over an online data stream, generally suffers from catastrophic forgetting (CF). Existing replay-based methods alleviate forgetting by replaying partial old data in a proxy-based or contrastive-based replay manner, each with its own shortcomings. Our previous work proposes a novel replay-based method called proxy-based contrastive replay (PCR), which handles the shortcomings by achieving complementary advantages of both replay manners. In this work, we further conduct gradient and limitation analysis of PCR. The analysis results show that PCR still can be further improved in feature extraction, generalization, and anti-forgetting capabilities of the model. Hence, we developed a more advanced method named holistic PCR (HPCR). HPCR consists of three components, each tackling one of the limitations of PCR. The contrastive component conditionally incorporates anchor-to-sample pairs to PCR, improving the feature extraction ability. The second is a temperature component that decouples the temperature coefficient into two parts based on their gradient impacts and sets different values for them to enhance the generalization ability. The third is a distillation component that constrains the learning process with additional loss terms to improve the anti-forgetting ability. Experiments on four datasets consistently demonstrate the superiority of HPCR over various state-of-the-art methods.

Accurate prediction of meteorological elements, such as temperature and relative humidity, is important to human livelihood, early warning of extreme weather, and urban governance. Recently, neural network-based methods have shown impressive performance in this field. However, most of them are overcomplicated and impenetrable. In this paper, we propose a straightforward and interpretable differential framework, where the key lies in explicitly estimating the evolutionary trends. Specifically, three types of trends are exploited. (1) The proximity trend simply uses the most recent changes. It works well for approximately linear evolution. (2) The sequential trend explores the global information, aiming to capture the nonlinear dynamics. Here, we develop an attention-based trend unit to help memorize long-term features. (3) The flow trend is motivated by the nature of evolution, i.e., the heat or substance flows from one region to another. Here, we design a flow-aware attention unit. It can reflect the interactions via performing spatial attention over flow maps. Finally, we develop a trend fusion module to adaptively fuse the above three trends. Extensive experiments on two datasets demonstrate the effectiveness of our method.

Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial intelligence. Gradient-based meta-learning effectively addresses the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (called meta-optimizer), while the inner-loop process leverages it to optimize a task-specific base learner with few examples. Although these methods have shown superior performance on FSL, the outer-loop process requires calculating second-order derivatives along the inner-loop path, which imposes considerable memory burdens and the risk of vanishing gradients. This degrades meta-learning performance. Inspired by recent diffusion models, we find that the inner-loop gradient descent process can be viewed as a reverse process (i.e., denoising) of diffusion where the target of denoising is the weight of base learner but origin data. Based on this fact, we propose to model the gradient descent algorithm as a diffusion model and then present a novel conditional diffusion-based meta-learning, called MetaDiff, that effectively models the optimization process of base learner weights from Gaussian initialization to target weights in a denoising manner. Thanks to the training efficiency of diffusion models, our MetaDiff does not need to differentiate through the inner-loop path such that the memory burdens and the risk of vanishing gradients can be effectively alleviated for improving FSL. Experimental results show that our MetaDiff outperforms state-of-the-art gradient-based meta-learning family on FSL tasks.

Precipitation nowcasting is an important spatiotemporal prediction task to predict the radar echoes sequences based on current observations, which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems, it is a very challenging problem. Previous studies address the problem either from the perspectives of deterministic modeling or probabilistic modeling. However, their predictions suffer from the blurry, high-value echoes fading away and position inaccurate issues. The root reason of these issues is that the chaotic evolutionary precipitation systems are not appropriately modeled. Inspired by the nature of the systems, we propose to decompose and model them from the perspective of global deterministic motion and local stochastic variations with residual mechanism. A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion, which effectively tackles the shortcomings of previous methods. Extensive experimental results on four publicly available radar datasets demonstrate the effectiveness and superiority of the proposed framework, compared to state-of-the-art techniques. Our code is publicly available at https://github.com/DeminYu98/DiffCast.

Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effectively address this problem by learning a discrete codebook from scratch and in a code-independent manner to quantize continuous representations into discrete tokens. However, learning a codebook from scratch and in a code-independent manner is highly challenging, which may be a key reason causing codebook collapse, i.e., some code vectors can rarely be optimized without regard to the relationship between codes and good codebook priors such that die off finally. In this paper, inspired by pretrained language models, we find that these language models have actually pretrained a superior codebook via a large number of text corpus, but such information is rarely exploited in VQIM. To this end, we propose a novel codebook transfer framework with part-of-speech, called VQCT, which aims to transfer a well-trained codebook from pretrained language models to VQIM for robust codebook learning. Specifically, we first introduce a pretrained codebook from language models and part-of-speech knowledge as priors. Then, we construct a vision-related codebook with these priors for achieving codebook transfer. Finally, a novel codebook transfer network is designed to exploit abundant semantic relationships between codes contained in pretrained codebooks for robust VQIM codebook learning. Experimental results on four datasets show that our VQCT method achieves superior VQIM performance over previous state-of-the-art methods.

Long-term time series forecasting (LTSF) aims to predict future values for a long time based on historical data. The period term is an essential component of the time series, which is complex yet important for LTSF. Although existing studies have achieved promising results, they still have limitations in modeling dynamic complicated periods. Most studies only focus on static periods with fixed time steps, while very few studies attempt to capture dynamic periods in the time domain. In this paper, we dissect the original time series in time and frequency domains and empirically find that changes in periods are more easily captured and quantified in the frequency domain. Based on this observation, we propose to explore dynamic period features using rotation in the frequency domain. To this end, we develop the frequency-based rotation network (FRNet), a novel LTSF method to effectively capture the features of the dynamic complicated periods. FRNet decomposes the original time series into period and trend components. Based on the complex-valued linear networks, it leverages a period frequency rotation module to predict the period component and a patch frequency rotation module to predict the trend component, respectively. Extensive experiments on seven real-world datasets consistently demonstrate the superiority of FRNet over various state-of-the-art methods. The source code is available at https://github.com/SiriZhang45/FRNet.

Vector quantization (VQ) is a key technique in high-resolution and high-fidelity image synthesis, which aims to learn a codebook to encode an image with a sequence of discrete codes and then generate an image in an auto-regression manner. Although existing methods have shown superior performance, most methods prefer to learn a single-modal codebook (\emph{e.g.}, image), resulting in suboptimal performance when the codebook is applied to multi-modal downstream tasks (\emph{e.g.}, text-to-image, image captioning) due to the existence of modal gaps. In this paper, we propose a novel language-guided codebook learning framework, called LG-VQ, which aims to learn a codebook that can be aligned with the text to improve the performance of multi-modal downstream tasks. Specifically, we first introduce pre-trained text semantics as prior knowledge, then design two novel alignment modules (\emph{i.e.}, Semantic Alignment Module, and Relationship Alignment Module) to transfer such prior knowledge into codes for achieving codebook text alignment. In particular, our LG-VQ method is model-agnostic, which can be easily integrated into existing VQ models. Experimental results show that our method achieves superior performance on reconstruction and various multi-modal downstream tasks.

Long-term spatial-temporal frame prediction focuses on predicting future image frames precisely, which has numerous applications in real-world scenarios. Existing deep learning prediction models mainly rely on advanced neural network architectures to model complicated spatial-temporal features, which make few efforts to explore high-order correlations to better capture long-term dynamics. Their prediction on long-term frames suffers from inaccurate visual and motion detail issue. In this article, we propose a high-order prediction model for long-term frame prediction, which improves the appearance and motion details by designing special high-order correlation modules in two aspects. First, to enhance the appearance details of predicted frames, we propose a high-order appearance encoder module, where high-order appearance features can be effectively captured with a carefully designed Non-local ConvLSTM. Second, to guarantee the motion accuracy of predicted sequences, we carefully design a high-order motion encoder module, which can accurately capture and preserve the high-order motion patterns with adaptive motion extractors and progressive memory banks, respectively. Comprehensive experiments are conducted on six challenging datasets from real-world scenarios, which demonstrate the effectiveness and superiority of our proposed method over state-of-the-art methods.

Global weather forecast is an important spatial-temporal prediction problem, which can provide numerous societal benefits such as extreme weather forewarning, traffic scheduling, and agricultural planning. Though many spatial-temporal prediction models have been proposed, they suffer from two drawbacks for global weather forecasts, namely (i) ignoring the physical mechanism and spherical characteristics and (ii) not effectively exploiting the global and local correlations. To address the above drawbacks, in this paper, we formalize global weather state dynamics as partial differential equations (PDEs) in spherical space and infer the state of the global weather system by solving these PDEs. Specifically, we use Green’s function method to solve the PDEs and find that the solution of the spherical PDEs can be obtained by the spherical convolution. We further proposed a novel Spherical Neural Operator, SNO, which consists of spherical convolution and vanilla convolution. The former is used to solve these PDEs and model the global correlations in spherical space, and the latter is used to capture the local correlations. Upon the operator, a global weather prediction model is developed. Extensive experimental results demonstrate the effectiveness and superiority of our method over state-of-the-art approaches.

Weather prediction plays a crucial role in human development. Recently, deep learning has demonstrated promising prospects in weather forecasting by integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, two main challenges still exist in multiple weather condition prediction. The first challenge considers multiple weather condition correlations in predictions. The second challenge is how to model long- and short-range spatial dependencies under multiple weather conditions. A novel operator named as tensor-based long- and short-range convolution (TLS-Conv) is proposed to address these challenges. Within this operator, the node & relation attention is utilized to identify the contributions of spatial grid points and weather conditions for prediction. Additionally, the adaptive tensor graph convolution (ATGCN) is tailored to dynamically capture long-range spatial dependencies within multiple weather conditions. Finally, the traditional convolution is integrated with the ATGCN to model both long- and short-range spatial dependencies and weather condition correlations. Building upon the TLS-Conv, the tensor-based long- and short-range convolution for multiple weather prediction (TLS-MWP) model is proposed to predict multiple weather conditions. Extensive experiments are conducted under real-world weather conditions to evaluate its performance. These results unequivocally demonstrate that TLS-MWP surpasses previous methods. The code is available on GitHub at: https://github.com/xuguangning1218/TLS_MWP.

Detecting and eliminating clouds is a crucial step in remote sensing image (RSI) preprocessing. The removal of clouds can significantly enhance the performance of subsequent remote sensing applications. Existing deep learning (DL)-based cloud detection methods extract semantic information to improve feature representation and, subsequently, detection performance. However, these methods do not fully utilize the potential of context semantic information. Besides, to capture semantics from large receptive fields, they employ convolution operators with large kernel sizes, which results in high computational costs. Thus, these computationally heavy models are not suitable for resource-limited devices, particularly satellites. To address this issue, we propose a cloud detection model, LGCNet. This model efficiently extracts both local and global contextual information, fully utilizing semantics while reducing resource usage. LGCNet is built on an encoder-decoder structure. Specifically, the encoder extracts local scale-aware semantics through proposed local semantic blocks (LSBs), which are then skip-connected to the decoder. This approach provides adaptive and diverse local contextual information. On the top of the encoder, the high-level global semantics are captured via the proposed global feature TransBlock (GFTB). A variety of extracted semantics ensure improved detection performance. We evaluate the proposed method using two public datasets: LandSat8 and Moderate-Resolution Imaging Spectroradiometer (MODIS). We conducted experiments on both a server and an edge computing device. Our extensive experiments revealed that LGCNet outperforms other lightweight cloud detection and semantic segmentation methods in terms of performance and computational load.

Precipitation nowcasting plays an important role in our life. Many deep learning-based methods are proposed for precipitation nowcasting by predicting radar echo sequence over the past years, and achieving better performance than traditional approaches. However, all of them are based on a static model, which is trained in offline learning and does not adapt to real-time changing precipitation data. Recently, online incremental learning (OIL) has been proposed to dynamically update the model by continually learning new data and preventing the forgetting of historical knowledge in an online fashion. While effective, existing OIL approaches that focus on a classification task are not suitable for the regression task of precipitation nowcasting. To fill this gap, we try to propose a novel OIL framework for precipitation nowcasting. By analyzing its characteristics, we find three challenges: 1) the distributions of radar echo maps in different rainfall events are different; 2) in each rainfall event, there is always an inevitable delay between the timestamps of the training and testing samples; and 3) the real-time requirement for model prediction is very high, which has strict limitations on the training speed of the model. Based on these observations, we propose a practical OIL framework based on gradient activation mapping (GAM). It can be mainly divided into three components: 1) recall training strategy (RTS) is used to eliminate the interference caused by distributions of different events; 2) iterative approximation training (IAT) is designed to align the timestamps of the training and testing; and 3) moreover, we propose gradient activation mapping weight (GAMW) to improve the training effectiveness. Extensive experiments show that the proposed framework can improve the performance of the model stably and effectively. Especially in those heavy rainfall regions where it usually causes more threat to human activity, the improvement is significant.

Cross-modal hashing plays a pivotal role in large-scale remote sensing (RS) ship image retrieval. RS ship images often exhibit similar overall appearance with subtle differences. Existing hashing methods typically employ feature non-interaction strategies to generate common hash codes, which may not effectively capture the correlations between cross-modal ship images to reduce intermodality discrepancies. To address this issue, we propose a novel cross-modal hashing approach based on feature semi-interaction and semantic ranking (FSISR) for RS ship image retrieval. Our FSISR approach not only captures intricate correlations between different ship image modalities, but also enables the construction of hash tables for large-scale retrieval. FSISR comprises a feature semi-interaction module and a semantic ranking objective function. The semi-interaction module utilizes clustering centers from one modality to learn the correlations between two modalities and generate robust shared representations. The objective function optimizes these representations in a common Hamming space, consisting of a shared semantic alignment loss and a margin-free ranking loss. The alignment loss employs a shared semantic layer to preserve label-level similarity, while the ranking loss incorporates hard examples to establish a margin-free loss that captures similarity ranking relationships. We evaluate the performance of our method on benchmark datasets and demonstrate its effectiveness for cross-modal RS ship image retrieval.https://github.com/sunyuxi/FSISR.

Passive microwave (PMW) radiometers have been widely utilized for quantitative precipitation estimation (QPE) by leveraging the relationship between brightness temperature (Tb) and rain rate. Nevertheless, accurate precipitation estimation remains a challenge due to the intricate relationship between them, which is influenced by a diverse range of complex atmospheric and surface properties. In addition, the inherent skew distribution of rainfall values prevents models from correctly addressing extreme precipitation events, leading to a significant underestimation. This article presents a novel model called the multiscale and multilevel feature fusion network (MSMLNet), consisting of two essential components: a multiscale feature extractor and a multilevel regression predictor. The feature extractor is specifically designed to extract characteristics from multiple scales, enabling the model to incorporate various meteorological conditions, as well as atmospheric and surface information in the surrounding environment. The regression predictor first assesses the probabilities of multiple rainfall levels for each observed pixel and then extracts features of different levels separately. The multilevel features are fused according to the predicted probabilities. This approach allows each submodule only to focus on a specific range of precipitation, avoiding the undesirable effects of skew distributions. To evaluate the performance of MSMLNet, various deep learning methods are adapted for the precipitation retrieval task, and a PWM-based product from the global precipitation measurement (GPM) mission is also used for comparison. Extensive experiments show that MSMLNet surpasses GMI-based products and the most advanced deep learning approaches by 17.9% and 2.5% in root mean square error (RMSE), and 54.2% and 4.0% in CSI-10, respectively. Moreover, we demonstrate that MSMLNet significantly mitigates the propensity for underestimating heavy precipitation events and has a consistent and outstanding performance in estimating precipitation across various levels.

A tropical cyclone is a highly destructive extreme weather phenomenon. Estimating the intensity of a tropical cyclone can help provide early warnings, guiding specific disaster defense measures. However, two main challenges hinder performance improvement. The first challenge is how to combine heterogeneous tropical cyclone data into a latent space so that the model can leverage the cloud structure of satellite imagery and the comprehensive meteorological information from reanalysis or forecast data for intensity estimation. The second challenge lies in detecting multiple pseudo-fine-grained areas for the final estimation since tropical cyclones are highly diverse extreme weather phenomena. Neglecting any pseudo-fine-grained areas or relying solely on a single one can potentially result in subpar estimation performance. To address the challenges mentioned above, a fine-grained heterogeneous data fusion framework named FHDTIE is proposed. Two key components in this framework can address the aforementioned challenges. One component is the HDF, which offers shape matching and channel fusing strategies for heterogeneous data fusion. The other component is called the fine-grained cluster features integrator (FCFI). It utilizes a clustering method to identify multiple pseudo-fine-grained areas. Within these areas, the U-Net is used to automatically learn pseudo-fine-grained area representations, and then the graph neural network handles information interaction across these representations. Extensive experiments were conducted to demonstrate the robustness and superiority of the proposed fine-grained heterogeneous data fusion framework. The code is available at GitHub:https://github.com/xuguangning1218/FHDTIE.

The accurate detection of mesoscale convective systems (MCSs) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena, such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the static characteristics and ignores the temporal evolution in the life cycle of MCS. In this article, we propose a novel encoder-decoder neural network named mesoscale convective system detection network (MCSDNet) to detect MCS regions. MCSDNet has a simple architecture and is easy to expand. Different from the previous models, MCSDNet targets on multiframes detection and leverages multiscale spatiotemporal information in remote sensing imagery (RSI). As far as we know, it is the first work to utilize multiscale spatiotemporal information to detect MCS regions. First, we design a multiscale spatiotemporal information module to extract multilevel semantic from different encoder levels, which makes our models can extract more detail spatiotemporal features. Second, spatiotemporal mix unit (STMU), a dual spatiotemporal attention, is introduced to MCSDNet to capture both intraframe features and interframe. Finally, we present MCS remote sensing image (MCSRSI) the first publicly available dataset for multiframes MCS detection based on FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieves the best performance on MCS detection task when comparing with other baseline methods. We hope that the combination of our open-access dataset and promising results will encourage the future research for MCS detection task and provide a robust framework for related tasks in atmospheric science. Our code is available at:https://github.com/250HandsomeLiang/MCSDNet.git

Radar image sequence prediction (RISP) aims to predict future radar images based on historical observations. In the past few years, neural network-based methods have shown impressive performance for RISP. However, two limitations stills exist. 1) They fail to exploit variation information when capturing spatial dependencies. 2) They neglect to analyze and interpret the model. In this article, we propose a variation-aware prediction model for the first limitation, and develop a relevance propagation technique for the second one. Specifically, 1) we recustomize the vanilla convolution by introducing a variation-aware item. The new convolution unit yields two advantages when capturing spatial dependencies, i.e., exploiting variation information and offering spatially-varying kernels. As a result, it can learn the diverse and complex radar echo patterns. By equipping the unit into a typical network (PredRNN), we propose a novel prediction model, dubbed as VA-PredRNN. 2) As for analyzing our model, we propagate the output backward layer by layer till the input. Hence, we can reveal the relevance between the output and the intermediate states. To the best of the authors' knowledge, this is the first work to study the interpretability of a multilayer RISP model. We conduct extensive experiments on two datasets, and the results demonstrate the effectiveness of our VA-PredRNN. We also carry out a series of analyses using the proposed relevance propagation technique. According to the results, we discover the importance of different states.

Satellite image sequence prediction aims to precisely infer future satellite image frames with historical observations, which is a significant and challenging dense prediction task. Though existing deep learning models deliver promising performance for satellite image sequence prediction, the methods suffer from quite expensive training costs, especially in training time and GPU memory demand, due to the inefficiently modeling for temporal variations. This issue seriously limits the lightweight application in satellites such as space-borne forecast models. In this article, we propose a lightweight prediction framework TinyPredNet for satellite image sequence prediction, in which a spatial encoder and decoder model the intra-frame appearance features and a temporal translator captures inter-frame motion patterns. To efficiently model the temporal evolution of satellite image sequences, we carefully design a multi-scale temporal-cascaded structure and a channel attention-gated structure in the temporal translator. Comprehensive experiments are conducted on FengYun-4A (FY-4A) satellite dataset, which show that the proposed framework achieves very competitive performance with much lower computation cost compared to state-of-the-art methods. In addition, corresponding interpretability experiments are conducted to show how our designed structures work. We believe the proposed method can serve as a solid lightweight baseline for satellite image sequence prediction.

The massive amounts of online user behavior data on social networks allow for the investigation of information diffusion prediction, which is essential to comprehend how information propagates among users. The main difficulty in diffusion prediction problem is to effectively model the complex social factors in social networks and diffusion cascades. However, existing methods are mainly based on Euclidean space, which cannot well preserve the underlying hierarchical structures that could better reflect the strength of user influence. Meanwhile, existing methods cannot accurately model the obvious asymmetric features of the diffusion process. To alleviate these limitations, we utilize rotation transformation in the hyperbolic to model complex diffusion patterns. The modulus of representations in the hyperbolic space could effectively describe the strength of the user's influence. Rotation transformations could represent a variety of complex asymmetric features. Further, rotation transformation could model various social factors without changing the strength of influence. In this paper, we propose a novel hyperbolic rotation representation model RotDiff for the diffusion prediction problem. Specifically, we first map each social user to a Lorentzian vector and use two groups of transformations to encode global social factors in the social graph and the diffusion graph. Then, we combine attention mechanism in the hyperbolic space with extra rotation transformations to capture local diffusion dependencies within a given cascade. Experimental results on five real-world datasets demonstrate that the proposed model RotDiff outperforms various state-of-the-art diffusion prediction models.

Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i.e., forgetting historical knowledge of old classes. Existing replay-based methods effectively alleviate this issue by saving and replaying part of old data in a proxy-based or contrastive-based replay manner. Although these two replay manners are effective, the former would incline to new classes due to class imbalance issues, and the latter is unstable and hard to converge because of the limited number of samples. In this paper, we conduct a comprehensive analysis of these two replay manners and find that they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR). The key operation is to replace the contrastive samples of anchors with corresponding proxies in the contrastive-based way. It alleviates the phenomenon of catastrophic forgetting by effectively addressing the imbalance issue, as well as keeps a faster convergence of the model. We conduct extensive experiments on three real-world benchmark datasets, and empirical results consistently demonstrate the superiority of PCR over various state-of-the-art methods11https://github.com/FelixHuiweiLin/PCR.

Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in existing methods tend to generate biased dot-product logits that prefer to the classes of current data, which is known as a bias issue and a phenomenon of forgetting. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in online fashion. In this paper, we try to address the bias issue by a more straightforward and more efficient method. By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits. On the contrary, the norm factor abandoned by existing methods helps remember historical knowledge. Based on this observation, we intuitively propose to leverage the norm factor to balance the new and old knowledge for addressing the bias. To this end, we develop a heuristic approach called unbias experience replay (UER). UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors. Extensive experiments on three datasets show that UER achieves superior performance over various state-of-the-art methods. The code is in https://github.com/FelixHuiweiLin/UER.

Satellite image sequence prediction is a challenging and significant task. Existing deep learning methods for the task make predictions mainly based on low-level pixel-wise features, which fail to model the sophisticated spatial-temporal features of satellite image sequences and deliver unsatisfactory performance. In this paper, we present a Hierarchical Spatial-Temporal network (HSTnet) for satellite image sequence prediction. With a carefully designed hierarchical feature extraction mechanism, HSTnet can learn effective spatial-temporal features from both pixel level and patch level. In addition, to better capture patch-level spatial-temporal dynamics, a dual-branch Transformer is proposed to model patch-level spatial and temporal features, respectively. Comprehensive experiments on the FY-4A satellite dataset demonstrate the superiority and effectiveness of our proposed method HSTnet over state-of-the-art approaches.

Few-shot learning (FSL) aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes marginal improvements. In this paper, 1) we figure out the reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative features for seen attributes as priors. Second, a part/attribute transfer network is designed to learn to infer the representative features for unseen attributes as supplementary priors. Finally, a prototype completion network is devised to learn to complete prototypes with these priors. Moreover, to avoid the prototype completion error, we further develop a Gaussian based prototype fusion strategy that fuses the mean-based and completed prototypes by exploiting the unlabeled samples. At last, we also develop an economic prototype completion version for FSL, which does not need to collect primitive knowledge, for a fair comparison with existing FSL methods without external knowledge. Extensive experiments show that our method: i) obtains more accurate prototypes; ii) achieves superior performance on both inductive and transductive FSL settings.

Recently, the topic of influential community search has gained much attention. Given a graph, it aims to find communities of vertices with high importance values from it. Existing works mainly focus on conventional homogeneous networks, where vertices are of the same type. Thus, they cannot be applied to heterogeneous information networks (HINs) like bibliographic networks and knowledge graphs, where vertices are of multiple types and their importance values are of heterogeneity (i.e., for vertices of different types, their importance meanings are also different). In this paper, we study the problem of influential community search over large HINs. We introduce a novel community model, called heterogeneous influential community (HIC), or a set of closely connected vertices that are of the same type and high importance values, using the meta-path-based core model. An HIC not only captures the importance of vertices in a community, but also considers the influence on meta-paths connecting them. To search the HICs, we mainly consider meta-paths with two and three vertex types. Then, we develop basic algorithms by iteratively peeling vertices with low importance values, and further propose advanced algorithms by identifying the key vertices and designing pruning strategies that allow us to quickly eliminate vertices with low importance values. Extensive experiments on four real large HINs show that our solutions are effective for searching HICs, and the advanced algorithms significantly outperform baselines.

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery, which aims to learn transferable knowledge from base property prediction tasks with sufficient data for predicting novel properties with few labeled molecules. Its key challenge is how to alleviate the data scarcity issue of novel properties. Pretrained Graph Neural Network (GNN) based FSMPP methods effectively address the challenge by pre-training a GNN from large-scale self-supervised tasks and then finetuning it on base property prediction tasks to perform novel property prediction. However, in this paper, we find that the GNN finetuning step is not always effective, which even degrades the performance of pretrained GNN on some novel properties. This is because these molecule-property relationships among molecules change across different properties, which results in the finetuned GNN overfits to base properties and harms the transferability performance of pretrained GNN on novel properties. To address this issue, in this paper, we propose a novel Adaptive Transfer framework of GNN for FSMPP, called ATGNN, which transfers the knowledge of pretrained and finetuned GNNs in a task-adaptive manner to adapt novel properties. Specifically, we first regard the pretrained and finetuned GNNs as model priors of target-property GNN. Then, a task-adaptive weight prediction network is designed to leverage these priors to predict target GNN weights for novel properties. Finally, we combine our ATGNN framework with existing FSMPP methods for FSMPP. Extensive experiments on four real-world datasets, i.e., Tox21, SIDER, MUV, and ToxCast, show the effectiveness of our ATGNN framework.

Recently, explainable artificial intelligence has received considerable attention. Most existing studies are focusing on the tasks of CNNs-based image classification and RNNs-based time series analysis. In this paper, we pay attention to the more complicated spatiotemporal predictive learning task (SPLT), where both the spatial and temporal information play important roles. To explain the internal mechanism of spatiotemporal prediction models, we propose a comprehensive analysis method. Specifically, with a typical encoder-decoder framework, we focus on two core issues of SPLT: image generation and spatiotemporal dynamics. For the first issue, we develop aquantitative channel perturbationmethod to explore the importance of features to prediction. Furthermore, we propose a technique called thesynthesis of multiple independent componentsto analyze how these features generate the prediction. According to the experimental results, thecoarse- and fine-grainedsynthesis (CFGS) mechanism is drawn for image generation in SPLT. For the second issue, we propose astate decompositiontechnique and astate expansiontechnique to disentangle coupled signals in the spatiotemporal dynamical system. This helps us to explore the mechanism of forming motion. Moreover, to diagnose the movement of a particular region during analysis, we propose a fluorescent stamp-based technique. By observing extensive experimental results, we summarize a collaboration mechanism to explain how the motion is formed in SPLT, namely, theextending the present and erasing the past (EPEP)mechanism. To the best of our knowledge, this is the first work to interpret the internal mechanism of SPLT models.

Online class-incremental learning (OCIL) studies the problem of mitigating the phenomenon of catastrophic forgetting while learning new classes from a continuously non-stationary data stream. Existing approaches mainly constrain the updating of parameters to prevent the drift of previous classes that reflects the movement of samples in the embedding space. Although this kind of drift can be relieved to some extent by existing approaches, it is usually inevitable. Therefore, only prevention of drift is not enough, and we also need to further compensate for it. To this end, for each previous class, we exploit the sample with the smallest loss value as its anchor, which can representatively characterize the corresponding class. Based on the assistance of anchors, we present a novel Anchor Assisted Experience Replay (AAER) method that not only prevents the drift but also compensates for the inevitable drift to overcome the catastrophic forgetting. Specifically, we design a Drift-Prevention with Anchor (DPA) operation, which plays a preventive role by reducing the drift implicitly as well as encouraging the samples with the same label cluster tightly. Moreover, we propose a Drift-Compensation with Anchor (DCA) operation that contains two remedy mechanisms: one is Forward-offset which keeps embedding of previous data but estimates new classification centers; the other is just the opposite named Backward-offset, which keeps the old classification centers unchanged but updates the embedding of previous data. We conduct extensive experiments on three real-world datasets, and empirical results consistently demonstrate the superior performance of AAER over various state-of-the-art methods.

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-learning and representation learning. However, few works focus on the interpretability of FSL decision process. In this paper, we take a step towards the interpretable FSL by proposing a novel meta-learning based decision tree framework, namely, MetaDT. In particular, the FSL interpretability is achieved from two aspects, i.e., a concept aspect and a visual aspect. On the concept aspect, we first introduce a tree-like concept hierarchy as FSL prior. Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree. The advantage of such design is that a sequence of high-level concept decisions that lead up to a final class prediction can be obtained, which clarifies the FSL decision process. On the visual aspect, a set of subtask-specific classifiers with visual attention mechanism is designed to perform decision at each node of the decision tree. As a result, a subtask-specific heatmap visualization can be obtained to achieve the decision interpretability of each tree node. At last, to alleviate the data scarcity issue of FSL, we regard the prior of concept hierarchy as an undirected graph, and then design a graph convolution-based decision tree inference network as our meta-learner to infer parameters of the decision tree. Extensive experiments on performance comparison and interpretability analysis show superiority of our MetaDT.

As an extremely challenging spatial-temporal sequence prediction task, satellite image sequence prediction has various and significant applications in real-world scenarios. Although lots of deep learning prediction models are developed for spatial-temporal sequence prediction, the methods still deliver unsatisfactory performance in terms of keeping spatial-temporal consistency, which leads to inaccurate and blurry satellite image sequence predictions. To maintain spatial-temporal consistency and achieve high-quality satellite image sequence prediction, we propose a novel and effective spatial-temporal consistency network (STCNet). In STCNet, a multi-level motion memory-based predictor is proposed to accurately predict motion patterns of satellite image sequences to ensure temporal consistency. Then, a time-variant frame discriminator is carefully designed and proposed, which can enhance the perception quality of predicted frames to guarantee spatial consistency and simultaneously maintain the motion coherency of predicted sequences. Moreover, a scheduled sampling strategy is proposed to reduce the optimizing difficulty and better train the proposed method. Comprehensive experiments conducted on satellite image sequences from FY-4A meteorological satellite verify the effectiveness, applicability, and adaptability of our proposed method compared to state-of-the-art approaches under challenging scenarios.

In Remote Sensing Image (RSI) pre-processing steps, detecting and removing cloudy areas is a critical task. Recently, cloud detection methods based on deep neural networks achieve outstanding performance over traditional methods. Current approaches mostly focus on cloud detection on a single image captured by polar-orbiting satellites. However, there is another type of meteorological satellite - geostationary satellite, which can capture temporal consecutive frames of a particular location. Therefore, the cloud detection task targeting at geostationary satellite can be treated as a video cloud detection task. And in addition to extracting features on a single image, extracting and making full use of the relations between sequential frames is also important. To tackle this problem, we design a deep learning video cloud detection model: Transformer Network for Video Cloud Detection (TRCDNet). The proposed network is based on the encoder-decoder structure. In the encoder, the module ContextGhostLayer is proposed to encode more semantic information to tackle the challenging problems like thin cloud in RSIs. Besides, we design a transformer-based Video Sequence Transformer (VSTR) block. Based on attention mechanism, VSTR can fully extract the across-frame relations. In the proposed decoder, the cloud masks are recovered gradually to the same scale as the input image. To evaluate the methods, we create a Video Cloud Detection dataset based on the captured videos from Fengyun 4 (FY-4) satellite: Fengyun4aCloud. Extensive experiments of current cloud detection methods, semantic segmentation methods, and video semantic segmentation methods indicate that the designed TRCDNet achieves state-of-art performance in video cloud detection.

Cross-view geo-localization is a critical task in various applications, such as smart city management and disaster monitoring. Current methods typically divide a satellite image into patches and use these patches to identify the geographic location of a query image. However, these methods can only provide the location of an image rather than the location of a specific object of interest. This makes it difficult to link these methods to GeoDatabases to obtain detailed information about a target object, such as its name and construction time. To overcome this limitation, we propose a novel problem of cross-view object geo-localization in a local region with high-resolution satellite images. This problem includes two main challenges: accurately identifying the location of an object and distinguishing the target object from others in satellite images. To address these challenges, we present a new Detection-based Geo-localization method called DetGeo, which consists of an object detection-based framework with a two-branch encoder and a query-aware cross-view fusion module. DetGeo uses cross-view images as input to the detector to provide object-level geo-localization. The fusion module employs cross-view spatial attention to focus on relevant areas of target objects during cross-view feature fusion. To evaluate our method, we constructed a new Cross-View Object Geo-Localization dataset called CVOGL, which comprises ground-view or drone-view images as query images and satellite-view images as geo-tagged reference images. Comprehensive experiments are conducted to demonstrate the effectiveness of our method on CVOGL. https://github.com/sunyuxi/DetGeo.

Cross-modal hashing aims to retrieve similar images from large-scale Earth Observation (EO) data archives, which typically contain multiple satellite sources of remote sensing (RS) images. However, existing cross-modal hashing methods primarily focus on dual-source RS images and often face two main limitations when retrieving multi-source RS images. Firstly, these methods exhibit significant redundancy as they require handling all possible dual-source combinations in multi-source RS images. Secondly, they often rely on pairwise or triplet image sources to construct objective functions, which are not significantly effective in reducing the discrepancies among multiple RS image sources. To address these limitations, we propose a novel Consistency Center-based deep cross-modal Hashing method called C2Hash for multi-source RS image retrieval. Our C2Hash employs a multi-branch hashing network to directly encode multi-source RS images into unified hash codes, thereby offering higher processing efficiency. Furthermore, C2Hash introduces consistency centers to construct a novel objective function. The consistency center represents the shared semantic features among similar multi-source RS images and is generated by a label hashing network. The objective function encourages similar multi-source RS images to approach the same consistency center to align all image sources in a unified Hamming space. Our method can effectively reduce the discrepancies across multiple image sources and generate unified hash codes. To evaluate its effectiveness, we construct a new Multi-Source RS Image dataset called MSRSI, comprising five different types of image sources. We conduct comprehensive experiments to demonstrate the superior performance of our method on the MSRSI dataset. https://github.com/sunyuxi/C2Hash.

Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing HDA studies focus on the single-source scenario. In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains. In this article, we study the multisource HDA problem and propose a conditional weighting adversarial network (CWAN) to address it. The proposed CWAN adversarially learns a feature transformer, a label classifier, and a domain discriminator. To quantify the importance of different source domains, CWAN introduces a sophisticated conditional weighting scheme to calculate the weights of the source domains according to the conditional distribution divergence between the source and target domains. Different from existing weighting schemes, the proposed conditional weighting scheme not only weights the source domains but also implicitly aligns the conditional distributions during the optimization process. Experimental results clearly demonstrate that the proposed CWAN performs much better than several state-of-the-art methods on four real-world datasets.

Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predicting novel classes via a cosine nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. To this end, we propose a novel meta-learning based prototype optimization framework to rectify prototypes, i.e., introducing a meta-optimizer to optimize prototypes. Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, i.e., the mean-based gradient estimation is also biased on sparse data. To address the issue, we regard the gradient and its flow as meta-knowledge and then propose a novel Neural Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes, called MetaNODE. In this meta-optimizer, we first view the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural ODE. A gradient flow inference network is carefully designed to learn to estimate the continuous gradient flow for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE. Extensive experiments on miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our method.

Few-shot learning (FSL) aims to recognize a novel class with very few instances, which is a challenging task since it suffers from a data scarcity issue. One way to effectively alleviate this issue is introducing explicit knowledge summarized from human past experiences to achieve knowledge transfer for FSL. Based on this idea, in this paper, we introduce the explicit knowledge of class hierarchy (i.e., the hierarchy relations between classes) as FSL priors and propose a novel hyperbolic knowledge transfer framework for FSL, namely, HyperKT. Our insight is, in the hyperbolic space, the hierarchy relation between classes can be well preserved by resorting to the exponential growth characters of hyperbolic volume, so that better knowledge transfer can be achieved for FSL. Specifically, we first regard the class hierarchy as a tree-like structure. Then, 1) a hyperbolic representation learning module and a hyperbolic prototype inference module are employed to encode/infer each image and class prototype to the hyperbolic space, respectively; and 2) a novel hierarchical classification and relation reconstruction loss are carefully designed to learn the class hierarchy. Finally, the novel class prediction is performed in a nearest-prototype manner. Extensive experiments on three datasets show our method achieves superior performance over state-of-the-art methods, especially on 1-shot tasks.

Ground object retrieval from a large-scale remote sensing image is very important for lots of applications. We present a novel problem of visual grounding in remote sensing images. Visual grounding aims to locate the particular objects (in the form of the bounding box or segmentation mask) in an image by a natural language expression. The task already exists in the computer vision community. However, existing benchmark datasets and methods mainly focus on natural images rather than remote sensing images. Compared with natural images, remote sensing images contain large-scale scenes and the geographical spatial information of ground objects (e.g., longitude, latitude). The existing method cannot deal with these challenges. In this paper, we collect a new visual grounding dataset, called RSVG, and design a new method, namely GeoVG. In particular, the proposed method consists of a language encoder, image encoder, and fusion module. The language encoder is used to learn numerical geospatial relations and represent a complex expression as a geospatial relation graph. The image encoder is applied to learn large-scale remote sensing scenes with adaptive region attention. The fusion module is used to fuse the text and image feature for visual grounding. We evaluate the proposed method by comparing it to the state-of-the-art methods on RSVG. Experiments show that our method outperforms the previous methods on the proposed datasets. https://sunyuxi.github.io/publication/GeoVG

Deep learning-based methods have been widely applied in remote sensing scene classification tasks. Recently, researchers focus more on clarifying the basis of a decision. For example, class activation mapping (CAM) can provide us the evidence by highlighting the related area in an image. However, the interpretability of remote sensing scene classification is more challenging than natural images, since remote sensing images usually contain more complicated objects. As a result, the CAM visual interpretation with traditional convolutional neural networks cannot accurately locate all target objects, which leads to some important objects are ignored. In this letter, we propose a novel model, named encoder-classifier-reconstruction CAM (ECR-CAM) neural network, to provide a more precise visual explanation. Specifically, ECR-CAM consists of four modules: an encoder module, a classifier module, a reconstruction module, and a CAM module. Encoder module is utilized to extract image features, and classifier module accounts for generating predictions. The reconstruction module is the key to locate more target objects. It employs the extracted features to reconstruct the input images, which is a pixel-level process. The reconstruction process allows the features to retain important information about all objects, which cannot be achieved by the classification task alone. Finally, the CAM module can show more target objects with more informative features. Experimental results show that our model not only improves the classification performance but also can locate the target objects more accurately.

Precipitation nowcasting is an important task in weather forecast. The key challenge of the task lies at radar echo map extrapolation. Recent studies show that a convolutional recurrent neural network (ConvRNN) is a promising direction to solve the problem. However, the extrapolation results of the existing ConvRNN methods tend to be blurring and unrealistic. Recent studies show that generative adversarial network (GAN) is a promising tool to address the drawback, while it suffers from the instability for training. In this letter, we build a novel ConvRNN model based on the energy-based GAN for radar echo map extrapolation. The method can alleviate the blurring and unrealistic issues and is more stable. We have conducted experiments on a real-world data set, and the results show that the proposed method outperforms several existing models, including optical flow, convolution gated recurrent unit (ConvGRU), and generative adversarial ConvGRU (GA-ConvGRU).

Cross-modal hashing is an important tool for retrieving useful information from very-high-resolution (VHR) optical images and synthetic aperture radar (SAR) images. Dealing with the intermodal discrepancies, including both spatial–spectral and visual semantic aspects, between VHR and SAR images is extremely vital to generate high-quality common hash codes in the Hamming space. However, existing cross-modal hashing methods ignore the spatial–spectral discrepancy when representing VHR and SAR images. Moreover, existing methods employ derived supervised signals, such as pairwise training images, to implicitly guide hashing learning, which fails to effectively deal with the visual semantic discrepancy, i.e., cannot adequately preserve the intraclass similarity and interclass discrimination between VHR and SAR images. To address these drawbacks, this article proposes a multisensor fusion and explicit semantic preserving-based deep Hashing method, termed as MsEspH, which can effectively deal with the discrepancies. Specifically, we design a novel cross-modal hashing network to eliminate the spatial–spectral discrepancies by fusing extra multispectral images (MSIs), which are generated in real time by a generative adversarial network. Then, we propose an explicit semantic preserving-based objective function by analyzing the connection between classification and hash learning. The objective function can preserve the intraclass similarity and interclass discrimination with class labels directly. Moreover, we theoretically verify that hash learning and classification can be unified into a learning framework under certain conditions. To evaluate our method, we construct and release a large-scale VHR-SAR image dataset. Extensive experiments on the dataset demonstrate that our method outperforms various state-of-the-art cross-modal hashing methods.

Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial-temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this paper, we propose a novel Multi-Scale Time Conditional Generative Adversarial Network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multi-scale generator is designed with the multi-head axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on FengYun-4A satellite data set, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

Due to the degeneration problem of sensors, calibration becomes a prerequisite step to retrieve consistent satellite images, especially for the ones from long-term time series. Relative calibration is an economic manner to address the problem. Previous studies leverage the identified no-change pixels (NCPs) between two images for relative calibration. However, the identification of NCPs itself is a very hard task and the inferior detection quality affects the performances significantly. Inspired by the great success of deep learning techniques, in this article, we first develop a convolutional neural network (CNN)-based relative calibration method, which bypasses the NCP detection. In particular, the ratio of sensor sensitivity coefficients at two time points is directly estimated by feeding the corresponding image pair into our developed CNN regressor. A polynomial function is fitted upon the estimated ratios in time series. We train the CNN regressor based on the multisite calibration results and then conduct experiments on FengYun-3A (FY-3A), FengYun-3B (FY-3B), and FengYun-3C (FY-3C). The results validate the effectiveness of the proposed method, and it outperforms state-of-the-art NCP-based methods.

Cloud detection is the task of detecting cloud areas in remote sensing images, and it has attracted extensive research interest. Recently, deep learning-based methods have been proposed and achieved great performance for cloud detection. However, due to the satellite’s limitation in storage and memory, existing deep learning approaches, which suffer from extensive computation and large model size, are almost impossible to be deployed on satellites. To fill this gap, we target at studying effective and efficient cloud detection solutions that are suitable for satellites. In this paper, we develop a lightweight autoencoder-based cloud detection method, namely LWCDnet. In the encoder part, the designed novel lightweight dual-branch block (LWDBB) in the backbone extracts spatial and contextual information concurrently. Moreover, a lightweight feature pyramid module (LWFPM) is proposed to capture high-level multi-scale contextual information. In the decoder part, the lightweight feature fusion module (LWFFM) compensates for the missing spatial and detail information from the encoder to the high-level feature maps. We evaluate the proposed method on two public datasets: LandSat8 and MODIS. Extensive experiments demonstrate that the proposed LWCDnet achieves comparable accuracy as the-state-of-art cloud detection methods and lightweight semantic segmentation algorithms. Meantime LWCDnet has much less computation burden with smaller model size.

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage Convolutional Recurrent Neural Network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses and the results tend to have inaccurate appearances, shapes and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization, and systematically compare four types of Generative Adversarial Networks (GANs), which are the combinations of GAN/Wasserstein GAN and its multi-scale version. Extensive experiments on a real-world radar data set and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority circumstances. Moreover, we find that the Wasserstein GAN regularization often delivers better results than the GAN regularization due to its robustness, and the Multi-scale Wasserstein GAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at: https://github.com/luochuyao/MultiScaleGAN and the test system at: http://39.97.217.145:80/.

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then use these features to construct supervised information (e.g., pseudo-labels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudo-labels. In addition, existing methods only generate a pseudo single-label to annotate each RS image, which cannot accurately represent multiple scenes in a RS image. To address these drawbacks, this paper proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudo-labels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then pseudo multi-labels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multi-semantic hash loss by using the Kull-back–Leibler (KL) divergence to preserve the semantic similarity of these pseudo multi-labels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. https://github.com/sunyuxi/MrHash.

Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to address the FSRSSC problem by following few-shot natural image classification methods. These existing methods have made promising progress and achieved superior performance. However, they all overlook two unique characteristics of remote sensing images: (i)object co-occurrencethat multiple objects tend to appear together in a scene image and (ii)object spatial correlationthat these co-occurrence objects are distributed in the scene image following some spatial structure patterns. Such unique characteristics are very beneficial for FSRSSC, which can effectively alleviate the scarcity issue of labeled remote sensing images since they can provide more refined descriptions for each scene class. To fully exploit these characteristics, we propose a novel scene graph matching-based meta-learning framework for FSRSSC, called SGMNet. In this framework, a scene graph construction module is carefully designed to represent each test remote sensing image or each scene class as a scene graph, where the nodes reflect these co-occurrence objects meanwhile the edges capture the spatial correlations between these co-occurrence objects. Then, a scene graph matching module is further developed to evaluate the similarity score between each test remote sensing image and each scene class. Finally, based on the similarity scores, we perform the scene class prediction via a nearest neighbor classifier. We conduct extensive experiments on UCMerced LandUse, WHU19, AID, and NWPU-RESISC45 datasets. The experimental results show that our method obtains superior performance over the previous state-of-the-art methods.

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful; 2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes; (ii) out-performs state-of-the-art techniques by 2%~9% in terms of classification accuracy. Our code is available online1.

Multi-label classification addresses the problem that each instance is associated with multiple labels simultaneously. In this paper, we propose a multi-label crotch ensemble (MLCE) model for multi-label classification, which takes label correlations into consideration. In MLCE, a multi-label cluster tree is first constructed. Then, we incorporate all multi-label crotch predictors of the tree into a classifier, where the multi-label crotch predictor is the crotch formed by an inner node of the tree and its children. Finally, a flexible weighted voting scheme is designed to produce the classification output. We perform experiments on 11 benchmark datasets. Experimental results clearly demonstrate the MLCE significantly outperforms six well-established multi-label classification approaches, in terms of the widely used evaluation metrics.

Poverty prediction is an important task for developing countries that lack the key measures of economic development. The prediction can help governments to allocate scarce resources for sustainable development. Nighttime satellite imagery offers an opportunity to address the task. However, as the nighttime satellite data contain a large amount of noise, directly leveraging it is not very effective. Previous studies have shown that relying on deep learning techniques nighttime satellite data can be a good proxy between daytime satellite imagery and the poverty index. In this letter, based on the proxy, we leverage four deep learning approaches, namely, VGG-Net, Inception-Net, ResNet, and DenseNet, to extract deep features from daytime satellite imagery and then apply least absolute shrinkage and selection operator (LASSO) regression for poverty prediction. To further enhance the performance, we also integrate the squeeze and excitation (SE) module and focal loss into ResNet and DenseNet. Experimental results demonstrate the effectiveness of the investigated approaches, and the DenseNet with SE module and focal loss performs the best.

Stance detection is an important task, which aims to classify the attitude of an opinionated text towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, annotating sufficient data is labor-intensive, which establishes significant barriers for generalizing the stance classifier to the data with new targets. In this paper, we proposed a Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets. Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags. Then, the learned semantic-emotion graph representation, which serves as prior knowledge bridging the gap between the source and target domains, is fully integrated into the bidirectional long short-term memory (BiLSTM) stance classifier by adding a novel knowledge-aware memory unit to the BiLSTM cell. Extensive experiments on a large real-world dataset demonstrate the superiority of SEKT against the state-of-the-art baseline methods.

The SEIR model is widely used in simulating the spread of infectious diseases. COVID-19 virus is a very severe infectious disease. Some studies leverage the SEIR or SEIRD model to simulate the spread and estimate the number of infected and recovered people as time goes on. However, these models suffer from two key deficiencies: (i) conventional SEIRD does not update its model parameters w.r.t. time; (ii) it focuses on predicting the trend, instead of the actual number of infections in the future. In this paper, we propose a cascade SEIRD model. The model learns and updates its parameters every day. Moreover, it is able to predict the number of infection cases, recovered cases and deaths. Specifically, we leverage a machine learning like approach to dynamically estimate the parameters of infection rate, incubation rate, recovery rate and death rate, which can be updated by gradient descent algorithm. Once the nature of the parameters w.r.t. time is determined, ARIMA model is adopted to characterize the dynamics of the parameters and predict their future changes. To validate the effectiveness of the proposed cascade SEIRD model, we conduct experiments on five data sets of different scales of regions (China, Hubei, Wuhan, Shenzhen, US). Experimental results show that the proposed cascade SEIRD achieves the most accurate prediction and outperforms state-of-the-art techniques.

Graph Convolutional Networks (GCNs) have been extensively studied in recent years. Most of existing GCN approaches are designed for the homogenous graphs with a single type of relation. However, heterogeneous graphs of multiple types of relations are also ubiquitous and there is a lack of methodologies to tackle such graphs. Some previous studies address the issue by performing conventional GCN on each single relation and then blending their results. However, as the convolutional kernels neglect the correlations across relations, the strategy is sub-optimal. In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs. In particular, our multi-dimension convolution operator extends the graph spectral analysis into the eigen-decomposition of a Laplacian tensor. And the eigen-decomposition is formulated with a generalized tensor product, which can correspond to any unitary transform instead of limited merely to Fourier transform. We conduct comprehensive experiments on four real-world multi-relational graphs to solve the semi-supervised node classification task, and the results show the superiority of MR-GCN against the state-of-the-art competitors.

The research of reinforcement learning (RL) based recommendation method has become a hot topic in recommendation community, due to the recent advance in interactive recommender systems. The existing RL recommendation approaches can be summarized into a unified framework with three components, namely embedding component (EC), state representation component (SRC) and policy component (PC). We find that EC cannot be nicely trained with the other two components simultaneously. Previous studies bypass the obstacle through a pre-training and fixing strategy, which makes their approaches unlike a real end-to-end fashion. More importantly, such pre-trained and fixed EC suffers from two inherent drawbacks: (1) Pre-trained and fixed embeddings are unable to model evolving preference of users and item correlations in the dynamic environment; (2) Pre-training is inconvenient in the industrial applications. To address the problem, in this paper, we propose an End-to-end Deep Reinforcement learning based Recommendation framework (EDRR). In this framework, a supervised learning signal is carefully designed for smoothing the update gradients to EC, and three incorporating ways are introduced and compared. To the best of our knowledge, we are the first to address the training compatibility between the three components in RL based recommendations. Extensive experiments are conducted on three real-world datasets, and the results demonstrate the proposed EDRR effectively achieves the end-to-end training purpose for both policy-based and value-based RL models, and delivers better performance than state-of-the-art methods.

Precipitation nowcasting is an important task in operational weather forecasts. The key challenge of the task is the radar echo map extrapolation. The problem is mainly solved by an optical-flow method in existing systems. However, the method cannot model rapid and nonlinear movements. Recently, a convolutional gated recurrent unit (ConvGRU) method is developed, which aims to model such movements based on deep learning techniques. Despite the promising performance, ConvGRU tends to yield blurring extrapolation images and fails to multi-modal and skewed intensity distribution. To overcome the limitations, we propose in this letter a generative adversarial ConvGRU (GA-ConvGRU) model. The model is composed of two adversarial learning systems, which are a ConvGRU-based generator and a convolution neural network-based discriminator. The two systems are trained by playing a minimax game. With the adversarial learning scheme, GA-ConvGRU can yield more realistic and more accurate extrapolation. Experiments on real data sets have been conducted and the results demonstrate that the proposed GA-ConvGRU significantly outperforms state-of-the-art extrapolation methods ConvGRU and optical flow.

Aspect-based (aspect-level) sentiment analysis is an important task in fine-grained sentiment analysis, which aims to automatically infer the sentiment towards an aspect in its context. Previous studies have shown that utilizing the attention-based method can effectively improve the accuracy of the aspect-based sentiment analysis. Despite the outstanding progress, aspect-based sentiment analysis in the real-world remains several challenges. (1) The current attention-based method may cause a given aspect to incorrectly focus on syntactically unrelated words. (2) Conventional methods fail to identify the sentiment with the special sentence structure, such as double negatives. (3) Most of the studies leverage only one vector to represent context and target. However, utilizing one vector to represent the sentence is limited, as the natural languages are delicate and complex. In this paper, we propose a knowledge guided capsule network (KGCapsAN), which can address the above deficiencies. Our method is composed of two parts, a Bi-LSTM network and a capsule attention network. The capsule attention network implements the routing method by attention mechanism. Moreover, we utilize two prior knowledge to guide the capsule attention process, which are syntactical and n-gram structures. Extensive experiments are conducted on six datasets, and the results show that the proposed method yields the state-of-the-art.

Road extraction from aerial images is a hot research topic. With the advancement of convolutional neural network (CNN), several CNN-based road detection methods have been developed. However, most of them do not make full use of the hierarchical features from the original aerial images. In this paper, we propose a novel residual dense U-Net (RDUN), a semantic segmentation network which combines the strengths of residual learning, DenseNet, and U-Net, to overcome the drawback. Our proposed RDUN can fully exploit the hierarchical features from all the convolutional layers, which utilizes the residual dense blocks (RDB) to build up a U-Net architecture. The benefits of our model are two-fold. First, by using the RDB abundant local features can be extracted and fused effectively. Second, based the local features, hierarchical features are constructed by shortcut connections between layers in RDB. Extensive experiments are carried out on a real-world road detection dataset and the results demonstrate the proposed RDUN outperforms state-of-the-art competitors.

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for addressing the HDA problem. The proposed STN not only aligns the discriminative directions of domains but also matches both the marginal and conditional distributions across domains. To circumvent negative transfer, STN aligns the conditional distributions by using the soft-label strategy of unlabeled target data, which prevents the hard assignment of each unlabeled target data to only one category that may be incorrect. Further, STN introduces an adaptive coefficient to gradually increase the importance of the soft-labels since they will become more and more accurate as the number of iterations increases. We perform experiments on the transfer tasks of image-to-image, text-to-image, and text-to-text. Experimental results testify that the STN significantly outperforms several state-of-the-art approaches.

Finding customer groups from transaction data is very important for retail and e-commerce companies. Recently, a "Purchase Tree" data structure is proposed to compress the customer transaction data and a local PurTree spectral clustering method is proposed to cluster the customer transaction data. However, in the PurTree distance, the node weights for the children nodes of a parent node are set as equal and the differences between different nodes are not distinguished. In this paper, we propose a two-level subspace weighting spectral clustering (TSW) algorithm for customer transaction data. In the new method, a PurTree subspace metric is proposed to measure the dissimilarity between two customers represented by two purchase trees, in which a set of level weights are introduced to distinguish the importance of different tree levels and a set of sparse node weights are introduced to distinguish the importance of different tree nodes in a purchase tree. TSW learns an adaptive similarity matrix from the local distances in order to better uncover the cluster structure buried in the customer transaction data. Simultaneously, it learns a set of level weights and a set of sparse node weights in the PurTree subspace distance. An iterative optimization algorithm is proposed to optimize the proposed model. We also present an efficient method to compute a regularization parameter in TSW. TSW was compared with six clustering algorithms on ten benchmark data sets and the experimental results show the superiority of the new method.

Road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods to solve the problem. However, these approaches may suffer from the well-known problem of propagation errors. In this paper, we propose a novel deep learning model, recurrent convolution neural network U-Net (RCNN-UNet), to tackle the aforementioned problem. Our proposed RCNN-UNet has three distinct advantages. First, the end-to-end deep learning scheme eliminates the propagation errors. Second, a carefully designed RCNN unit is leveraged to build our deep learning architecture, which can better exploit the spatial context and the rich low-level visual features. Thereby, it alleviates the detection problems caused by noises, occlusions, and complex backgrounds of roads. Third, as the tasks of road detection and centerline extraction are strongly correlated, a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Extensive experiments were carried out based on two publicly available benchmark data sets, and nine state-of-the-art baselines were used in a comparative evaluation. Our experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks.

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Books, as a representative of lengthy documents, convey rich semantics. Traditional document modeling methods, such as bag-of-words models, have difficulty capturing such rich semantics when only considering term-frequency features. In order to explore term spatial distributions over a book, a tree-structured book representation is investigated in this paper. Moreover, an efficient learning framework, Tree2Vector, is introduced for mapping tree-structured book data into vectorial space. In particular, we present two types of locality reconstruction (LR) models: Euclidean-type and cosine-type, during the transformation process of tree structures into vectorial representations. The LR is used for modeling the reconstruction process, in which each parent node in a tree is supposed to be reconstructed by its child nodes. The prominent advantage of this Tree2Vector framework is that it solely utilizes the local information within a single book tree. In addition, extensive experimental results demonstrate that Tree2Vector is able to deliver comparable or better performance in comparison to methods that consider the information of all trees in a database globally. Experimental results also suggest that cosine-type LR consistently performs better than Euclidean-type LR in applications of book and author recommendations.

Opportunistic mobile social network (OMSN) enables users to form an instant social network for information sharing (e.g., people watching the same soccer game can share their instant comments). OMSN is ad hoc in nature, thus relies on the cooperation of members regarding message transmission. However, some uncooperative or malicious behavior from abnormal members may reduce network performance, even damage the entire network. Currently, there does not exist effective mechanisms to detect selfish and malicious nodes. To tackle this problem, we propose a dynamic trust framework to facilitate a node to derive a trust value of another node based on the behavior of the latter. The novelty of our framework includes the following: 1) we design a new metric for a trust value of a node and 2) we propose a “two-hop feedback method” that requires intermediate nodes in a forwarding path to generate ACK messages to verify a node's honesty if they are two hops away. In most existing trust models, final ACK messages are considered as critical factors. In OMSN, nodes are not fully connected and final ACK messages cannot be reliably received. In order to avoid the problem that few final ACK messages can be received, we propose a “two-hop feedback method.” Simulation results show that our approach is able to detect a majority of abnormal nodes including malicious nodes, selfish nodes, and those nodes launching conspiracy attacks. Thus, the entire network efficiency can be improved without negative impact of abnormal nodes. Besides, our trust framework can be easily applied to the current popular routing protocols of opportunistic networks.

With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data. We propose integrating total variation into low-rank tensor completion (LRTC) to address the drawback. As LRTC can be formulated by both tensor unfolding and tensor decomposition, we develop correspondingly two methods, namely LRTC-TV-I and LRTC-TVII, and their iterative solvers. Extensive experimental results on color image and medical image inpainting tasks show the effectiveness and superiority of the two methods against state-of-the-art competitors.

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose a generative model with hypergraph regularizers algorithm, called GMHR, for predicting proteins with multiple functions. The GMHR algorithm integrates all data sources that are available, including protein attribute features, interaction networks, label correlations, and unlabeled data. Experimental results on the real-world datasets predicting the functions of proteins demonstrate the superiority of our proposed method compared with the state-of-the-art baselines.

Learning object detection models from weakly labeled data is an important topic in computer vision. Among various types of weak annotations, image-level object labeling is a natural one that tells the existence, but not the precise locations, of object instances in images. Learning object detectors from image-level labels can be naturally cast as a multiple instance learning (MIL) problem. Existing MIL approaches for object detection still suffer from high false positive rates due to the lack of advanced instances selection techniques. In this study, a subspace-based generative model is proposed to select positive instances by minimizing rank of the coefficient matrix associated with the subspace models. An incoherence term between the subspace model and some “hard” negative instances in then modeled by an ε-insensitive loss function. To further improve the discriminative ability, an ensemble strategy is proposed by employing multiple subspace models. Rigorous experiments are performed on several datasets, and the promising experimental results demonstrate that the proposed approach is superior to the state-of-the-art weakly supervised learning algorithms in terms of precision, recall, and F-score.

With the advancement of data acquisition techniques, tensor (multidimensional data) objects are increasingly accumulated and generated, for example, multichannel electroencephalographies, multiview images, and videos. In these applications, the tensor objects are usually nonnegative, since the physical signals are recorded. As the dimensionality of tensor objects is often very high, a dimension reduction technique becomes an important research topic of tensor data. From the perspective of geometry, high-dimensional objects often reside in a low-dimensional submanifold of the ambient space. In this paper, we propose a new approach to perform the dimension reduction for nonnegative tensor objects. Our idea is to use nonnegative Tucker decomposition (NTD) to obtain a set of core tensors of smaller sizes by finding a common set of projection matrices for tensor objects. To preserve geometric information in tensor data, we employ a manifold regularization term for the core tensors constructed in the Tucker decomposition. An algorithm called manifold regularization NTD (MR-NTD) is developed to solve the common projection matrices and core tensors in an alternating least squares manner. The convergence of the proposed algorithm is shown, and the computational complexity of the proposed method scales linearly with respect to the number of tensor objects and the size of the tensor objects, respectively. These theoretical results show that the proposed algorithm can be efficient. Extensive experimental results have been provided to further demonstrate the effectiveness and efficiency of the proposed MR-NTD algorithm.

In this paper, we present a new framework for online video advertising by targeting clothes of principal actors. This framework aims to embed both character-relevant and content-relevant advertisements within a video stream. We identify the main actors automatically in a given video by using face clustering techniques. Specifically, we propose a new clustering algorithm based on face verification. Then we can find the main characters by counting the number of faces of each class. The video frames that include main characters are the best options to advertising. We segment the image patches of main characters from these video frames by object detection, and carry out different strategies for different sizes of images to match clothes ads. Experimental results demonstrate the effectiveness of our proposed method.

Country disaster rescue is becoming more and more important and it requires a rapid response for disaster rescue. The key component for disaster rescue is to plan the optimal rescue path. Traditionally, the optimal rescue path seriously relies on the recognition on images of the damaged areas and the corresponding recognition algorithms are proposed for analyzing satellite images. However, due to its low updating frequency satellite images are not suitable for disaster rescue. Therefore, unmanned aerial vehicle is a good alternative approach to acquire real time images on damaged areas. Techniques are then needed to recognize the UAV images. To cope with this situation, we first extract UAV videos to images, segments these images into fixed size pieces, and manually labeled these data. We then study whether the conventional methods such as mathematical morphology, Hough transform and P-value segmentation approaches can be used to extract roads from UAV images. At last, we propose to adopt SVM and combine it with GA to improve the performance of this approach. Empirical studies are performed on data sets both collected by us and collected from the Internet. Experimental results demonstrate that our approach works well on these data sets when compared with conventional approaches. This indicates that UAV could be able to facilitate the country disaster rescue.

Semi-supervised clustering is one of the most active research area in machine learning and pattern recognition, which can improve the performance of unsupervised clustering efficiently. This paper focuses on exploiting both the label information of a few labeled samples and the spatial distribution information of large amount of unlabeled samples. We proposed a new semi-supervised clustering method, named Bottleneck Distance based Semi-supervised Clustering (BDSC), which is based on the idea of label propagation and can perform clustering with no parameters. BDSC works by firstly obtaining small amount of labeled samples for each class. Then, a minimum spanning tree is constructed from both labeled and unlabeled samples, where the distances between an unlabeled sample and labeled samples are computed to get the bottleneck distance for each unlabeled sample. Finally, labels are propagated by comparing the bottleneck distances. Experimental results demonstrate that the proposed technique outperforms classical clustering algorithms with respect to the precision and the capability of recognizing nonspherical-shaped clusters.

A new triple wing harmonium (TWH) model that integrates text metadata into a low-dimensional semantic space is proposed for the application of content-based movie recommendation. The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model projecting these multiple textual features into low-dimensional latent topics with different probability distribution assumptions. A contrastive divergence (CD) algorithm is used for efficient learning and inference. Experimental results suggest that the proposed method performs better than the state-of-the-art algorithms for movie recommendation.

In this paper, we propose and develop a multi-visual-concept ranking (MultiVCRank) scheme for image retrieval. The key idea is that an image can be represented by several visual concepts, and a hypergraph is built based on visual concepts as hyperedges, where each edge contains images as vertices to share a specific visual concept. In the constructed hypergraph, the weight between two vertices in a hyperedge is incorporated, and it can be measured by their affinity in the corresponding visual concept. A ranking scheme is designed to compute the association scores of images and the relevance scores of visual concepts by employing input query vectors to handle image retrieval. In the scheme, the association and relevance scores are determined by an iterative method to solve limiting probabilities of a multi-dimensional Markov chain arising from the constructed hypergraph. The convergence analysis of the iteration method is studied and analyzed. Moreover, a learning algorithm is also proposed to set the parameters in the scheme, which makes it simple to use. Experimental results on the MSRC, Corel, and Caltech256 data sets have demonstrated the effectiveness of the proposed method. In the comparison, we find that the retrieval performance of MultiVCRank is substantially better than those of HypergraphRank, ManifoldRank, TOPHITS, and RankSVM.

A content-based movie recommender by using a Triple Wing Harmonium (TWH) model is proposed. TWH integrates text metadata into a low dimensional semantic space. movie synopsis, actor list and user comments are considered as the text metadata. A new TWH model is developed by projecting these multiple textual features into low dimensional latent topics. We have used a contrastive divergence algorithm for efficient learning and inference. Experimental results show that the proposed method performs better than the state-of-the-art content-based algorithms for movie recommendation.

Deriving a representative sample from a big social network is essential for many Internet services that rely on accurate analysis of big social data. A good sampling method for social network should be able to generate small sample networks with similar structures as original big network. In this paper, we propose SGP, a new big social network sampling algorithm based on graph partition. In SGP, original network is firstly partitioned into several sub-networks that will be sampled evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. We have evaluated SGP on several well-known data sets. The experimental results show that SGP outperforms six state-of-the-art methods.

Recommender system is a core component in many intelligent service systems. A good personalized recommender is an important service to users. Collaborative Filtering (CF), an effective approach to recommendation, has been widely used in many real-life systems. Matrix Factorization (MF) is an important approach to CF, because MF has flexibility in dealing with various data aspects and other application-specific requirements. However, the large computational burden required by MF poses a challenge of speeding up the MF process. In the past few years, Graphics Processing Unit (GPU) has evolved into a very flexible and powerful many-core processor. By transforming the traditional MF model, we can exploit the large-scale parallelization features of a massively multithreaded GPU. The results on various types of data show that the proposed algorithm can be well suited for the massively parallel GPU architecture.

Multilabel learning aims to predict labels of unseen instances by learning from training samples that are associated with a set of known labels. In this paper, we propose to use a hierarchical tree model for multilabel learning, and to develop the ML-Tree algorithm for finding the tree structure. ML-Tree considers a tree as a hierarchy of data and constructs the tree using the induction of one-against-all SVM classifiers at each node to recursively partition the data into child nodes. For each node, we define a predictive label vector to represent the predictive label transmission in the tree model for multilabel prediction and automatic discovery of the label relationships. If two labels co-occur frequently as predictive labels at leaf nodes, these labels are supposed to be relevant. The amount of predictive label co-occurrence provides an estimation of the label relationships. We examine the ML-Tree method on 11 real data sets of different domains and compare it with six well-established multilabel learning algorithms. The performances of these approaches are evaluated by 16 commonly used measures. We also conduct Friedman and Nemenyi tests to assess the statistical significance of the differences in performance. Experimental results demonstrate the effectiveness of our method.

Most existing approaches for the data stream classification focus on single-label data in non-stationary environment. In these methods, each instance can only be tagged with one label. However, in many realistic applications, each instance should be tagged with more than one label. To address the challenge of classifying multi-label stream in evolving environment, we propose a novel Multi-Label Dynamic Ensemble (MLDE) approach. The proposed MLDE integrates a number of Multi-Label Cluster-based Classifiers (MLCCs). MLDE includes an adaptive ensemble method and an ensemble voting method with two important weights, subset accuracy weight and similarity weight. Experimental results reveal that MLDE achieves better performance than state-of-the-art multi-label stream classification algorithms.

In recent years much effort has been devoted to Collective Classification (CC) techniques for predicting labels of linked instances. Given a large number of labeled data, conventional CC algorithms make use of local labeled neighbours to increase accuracy. However, in many real-world applications, labeled data are limited and very expensive to obtain. In this situation, most of the data have no connection to labeled data, and supervision knowledge cannot be obtained from the local connections. Recently, Semi-Supervised Collective Classification (SSCC) has been examined to leverage unlabeled data for enhancing the classification performance of CC. In this paper we propose a probabilistic generative model with network regularization (GMNR) for SSCC. Our main idea is to compute label probability distributions for unlabeled instances by maximizing both the log-likelihood in the generative model and the label smoothness on the network topology of data. The proposed generative model is based on the Probabilistic Latent Semantic Analysis (PLSA) method using attribute features of all instances. A network regularizer is employed to smooth the label probability distributions on the network topology of data. Finally, we develop an effective EM algorithm to compute the label probability distributions for label prediction. Experimental results on three real sparsely-labeled network datasets show that the proposed model GMNR outperforms state-of-the-art CC algorithms and other SSCC algorithms.

BACKGROUND: Automated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are time-consuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction. RESULTS: In this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data. CONCLUSION: Experimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks.

The main aim of this paper is to develop a community discovery scheme in a multi-dimensional network for data mining applications. In online social media, networked data consists of multiple dimensions/entities such as users, tags, photos, comments, and stories. We are interested in finding a group of users who interact significantly on these media entities. In a co-citation network, we are interested in finding a group of authors who relate to other authors significantly on publication information in titles, abstracts, and keywords as multiple dimensions/entities in the network. The main contribution of this paper is to propose a framework (MultiComm)to identify a seed-based community in a multi-dimensional network by evaluating the affinity between two items in the same type of entity (same dimension)or different types of entities (different dimensions)from the network. Our idea is to calculate the probabilities of visiting each item in each dimension, and compare their values to generate communities from a set of seed items. In order to evaluate a high quality of generated communities by the proposed algorithm, we develop and study a local modularity measure of a community in a multi-dimensional network. Experiments based on synthetic and real-world data sets suggest that the proposed framework is able to find a community effectively. Experimental results have also shown that the performance of the proposed algorithm is better in accuracy than the other testing algorithms in finding communities in multi-dimensional networks.

Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation. In this paper, a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation. First, a set of new objective functions for clustering is developed. Based on these objective functions, the corresponding updating rules for the algorithms are then derived analytically. The properties and performances of these algorithms are investigated on several synthetic and real-life data sets. Experimental studies demonstrate that our proposed algorithms outperform the state-of-the-art kmeans-type clustering algorithms with respect to four metrics: accuracy, RandIndex, Fscore, and normal mutual information.

Automatically assigning functions for unknown proteins is a key task in computational biology. Proteins in nature have multiple classes according to the functions they perform. Many efforts have been made to cast the protein function prediction into a multi-label learning problem. This paper proposes a novel Cluster Tree based Multi-label Learning algorithm (CTML) for protein function prediction. The main idea is to compute a set of predictive labels associated at each node for multi-label prediction by using the k-means clustering techniques and the predictive functions via the learning data at the nodes. With the propagation of the predictive labels from the root node to the leaf node, the correlations between labels can be preserved. Experimental results on benchmark data (genbase and yeast datasets) show that the proposed CTML algorithm is effective in predicting protein functions. Moreover, the classification performance of the CTML algorithm is competitive against the other baseline multi-label learning algorithms.

In this paper, we consider the problem of in-depth document analysis. In particular, we propose a novel document analysis method, named multidimensional latent semantic analysis (MDLSA), which enables us to mine local information efficiently from a document with respect to term associations and spatial distributions. MDLSA works by first partitioning each document into paragraphs and building a term affinity graph, which represents the frequency of term cooccurrence in a paragraph. We then conduct a 2-D principal component analysis to achieve an optimal semantic mapping. This analysis involves finding the leading eigenvectors of the sample covariance matrix of a training set to characterize the lower dimensional semantic space. A hybrid document similarity measure is designed to further improve the performance of this framework. Our algorithm is examined in two document applications: retrieval and classification. Experimental results demonstrate that the proposed technique outperforms current algorithms with respect to accuracy and computational efficiency.

This paper proposes TW-k-means, an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables. In this algorithm, a view weight is assigned to each view to identify the compactness of the view and a variable weight is also assigned to each variable in the view to identify the importance of the variable. Both view weights and variable weights are used in the distance function to determine the clusters of objects. In the new algorithm, two additional steps are added to the iterative k-means clustering process to automatically compute the view weights and the variable weights. We used two real-life data sets to investigate the properties of two types of weights in TW-k-means and investigated the difference between the weights of TW-k-means and the weights of the individual variable weighting method. The experiments have revealed the convergence property of the view weights in TW-k-means. We compared TW-k-means with five clustering algorithms on three real-life data sets and the results have shown that the TW-k-means algorithm significantly outperformed the other five clustering algorithms in four evaluation indices.

Module detection is an important task in bioinformatics which aims at finding a set of cells/genes that interact together to be responsible for some biological functionalities. In this paper, we propose a novel tensor factorization approach to finding modules from higher-order gene expression profiles with the time dimension, e.g., gene × condition × time data. The main idea is to incorporate a total variation regularization term for the time dimension during the tensor factorization, and then use the factorization results to identify the modules. Experimental results on two real gene × condition × time datasets have shown the effectiveness of the proposed method.

In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scores. The main contribution of this paper is to (i) propose a framework (HAR) that can compute the hub, authority and relevance scores by solving limiting probabilities arising from multi-relational data, and can incorporate input query vectors to handle query-specific search; (ii) show existence and uniqueness of such limiting probabilities so that they can be used for query search effectively; and (iii) develop an iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probabilities. Extensive experimental results on TREC and DBLP data sets suggest that the proposed method is very effective in obtaining relevant results to the querying inputs. In the comparison, we find that the performance of HAR is better than those of HITS, SALSA and TOPHITS.

In web mining, there are situations in which only few data is labeled which imposes difficulties on traditional web page classification algorithms. Active learning scheme is then proposed to sample the most representative unlabeled data, which are then annotated by external oracles. Most present active methods are based on series-mode query strategy, which deduces the process of active learning inefficient and unstable. In this paper, we propose a novel text oriented active semi-supervised classification model, which is so-called active SSC. Comparing with other active approaches, our model has the characteristic of comprehensibility, and thus it is easy to design a batch-mode query strategy. Experimental results on public text data showed our method is an effect and stable active approach.

For high dimensional genome-wide association (GWA) case-control data of complex disease, there are usually a large portion of single-nucleotide polymorphisms (SNPs) that are irrelevant with the disease. A simple random sampling method in random forest using default mtry parameter to choose feature subspace, will select too many subspaces without informative SNPs. Exhaustive searching an optimal mtry is often required in order to include useful and relevant SNPs and get rid of vast of non-informative SNPs. However, it is very time-consuming and not favorable in GWA study for high- dimensional data. This paper proposes a stratified sampling method for feature subspace selection to generate decision trees in a random forest for GWA high-dimensional data. We employ two genome-wide SNP data sets (Parkinson case- control data comprised of 408,803 SNPs and Alzheimer case- control data comprised of 380,157 SNPs) to demonstrate that the proposed stratified sampling method is effective, and it can generate better random forest with higher accuracy and lower error bound than those by Breiman's random forest generation method.

The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.

This paper presents a changing window approach to exploring gene expression patterns in "snapshot windows". A snapshot window is a sub-matrix of co-expressed microarray data representing certain expression pattern. In this approach, we use a feature weighting k-means subspace clustering algorithm to generate a set of clusters and each cluster defines a set of "snapshot windows" which are characterized by different sets of ordered sample weights that were assigned by the clustering algorithm. We define an accumulated weighting threshold (AWT) as the sum of weights of samples in the "snapshot window". Given a cluster, different "snapshot windows" can be obtained by changing AWT to explore all possible local expression patterns in the cluster. Experiment results have shown our approach is effective and flexible in exploring various expression patterns and identifying novel ones.

This paper outlines the feature-based unsupervised clustering for supplier categorization. Traditionally, when categorizing suppliers, companies have considered factors such as price, quality, flexibility etc. An enterprise is considered for design and manufacture with the objective of acquiring & developing a sophisticated technological base for systems and enlarging & expanding production of components. In this scenario, our intuition lies in supplier categorization based upon the selected features of suppliers. Lastly, we present results of segmentation of supplier data.

This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting offers a new technique for dealing with high dimensional data that occurs in many new applications such as text mining and bioinformatics. In this paper, the variable weighting mechanism is incorporated in the fuzzy k-means algorithm to cluster high dimensional data with overlapping clusters. Experiments on real data sets have shown that the variable weighting fuzzy k-means produced better clustering results than the fuzzy k-means without variable weighting.