Negative sampling (NS) strategies play a crucial role in knowledge graph representation. In order to overcome the limitations of existing negative sampling strategies, such as vulnerability to false negatives, limited generalization, and lack of control over sample hardness, we propose DANS-KGC (Diffusion-based Adaptive Negative Sampling for Knowledge Graph Completion). DANS-KGC comprises three key components: the Difficulty Assessment Module (DAM), the Adaptive Negative Sampling Module (ANS), and the Dynamic Training Mechanism (DTM). DAM evaluates the learning difficulty of entities by integrating semantic and structural features. Based on this assessment, ANS employs a conditional diffusion model with difficulty-aware noise scheduling, leveraging semantic and neighborhood information during the denoising phase to generate negative samples of diverse hardness. DTM further enhances learning by dynamically adjusting the hardness distribution of negative samples throughout training, enabling a curriculum-style progression from easy to hard examples. Extensive experiments on six benchmark datasets demonstrate the effectiveness and generalization ability of DANS-KGC, with the method achieving state-of-the-art results on all three evaluation metrics for the UMLS and YAGO3-10 datasets.

Ultrasound is the preferred early cancer screening modality due to non-ionizing radiation, cost-effectiveness, and real-time imaging, yet conventional diagnosis relies heavily on physician expertise, causing significant subjectivity and limited efficiency. Vision-Language Models (VLMs) show promise but lack ultrasound-specific knowledge and multi-organ generalization. We propose EchoVLM, the first open-source 10-billion-parameter ultrasound-tailored VLM with a Mixture-of-Experts (MoE) architecture. It is infused with knowledge across seven anatomical systems, trained on 208,941 clinical cases, 1.47 million ultrasound key-frame images, and over 100 diseases or imaging findings. Supporting clinical report generation, diagnosis prediction, and Visual Question Answering (VQA), it outperforms Qwen2-VL by 7.58 BLEU-1 and 3.45 ROUGE-1 points in report generation. This work shows substantial potential for establishing a general-purpose ultrasound VLM and lays a technical foundation for clinical translation. Source code and model weights are available at https://github.com/Asunatan/EchoVLM.

Fault tolerance to interference and interpretability are crucial for computer-aided diagnosis (CAD) systems. In clinical practice, deep learning models are often not accepted by doctors and patients due to their lack of diagnostic reasoning capabilities. Moreover, ultrasound imaging data contain significant noise and artifacts, making deep models vulnerable to interference, which frequently leads to misclassification. To address this issue, this study leverages fuzzy logic to achieve fault-tolerant perception of image content and employs a fuzzy trustworthy brain-inspired reasoning framework for diagnostic decision-making. In the fast-thinking module, we innovatively introduce the fuzzification of deep classification probabilities and construct a fault-tolerant perception set based on medical feature classification information. In the slow-thinking module, we establish a tensor decomposition-based knowledge graph, where medical feature information and membership degrees from the fault-tolerant perception set are jointly embedded to form a trustworthy factor matrix. The reasoning space reconstructed under this matrix guides the reasoning process from medical features toward the correct diagnostic outcome. To validate the effectiveness of the proposed model, we conducted diagnostic experiments on thyroid nodules and breast tumors, achieving AUC scores of 0.9549 and 0.9751, respectively. Experimental results demonstrate that our approach exhibits outstanding diagnostic performance on real-world ultrasound data.

Multi-modal fake news detection (MFND) has attracted increasing attention. However, due to information loading failure or access restriction, incomplete modality makes joint multi-modal information extraction be challenging. Existing MFND methods with missing-modality focus on specific missing-modality cases, and how to flexibly deal with various missing-modality cases of news in the real world has not been well studied. In this paper, we propose a novel fake news detection approach named Complementary Graph learning and Prompt-based cross-modal Generation network (CG-PG), which contains two main modules: a complementary graph learning module and a prompt-based cross-modal generation module. The complementary graph learning module explores structural complementary information in image and text graphs to implement cross-modal information propagation. To further recover the information loss caused by missing modalities, the prompt-based cross-modal generation module generates representations of the missing modality from available modalities and imposes task-related constraints on the representations with the missing-aware prompts. Experimental results on the public Weibo and Fakeddit datasets under various missing-modality cases show that CG-PG outperforms state-of-the-art related works.

Underwater areas have harsh environments with poor light, limited visibility, and high levels of noise. Humans have a weak perception of position, surroundings, and exterior information when staying underwater, which makes it difficult for humans to carry out complex underwater tasks, such as rescue, observation, and construction. Wearable devices have shown good results in enhancing human sensory function on land, thus they could potentially play a role in enhancing human underwater perception ability. This perspective aims to analyze the state-of-the-art of underwater wearable systems for human perception enhancement. This work discusses the core technology and challenges of human underwater perceptual enhancement, including wearable underwater navigation, underwater environment reconstruction, and underwater sensorial information delivery. Future research could focus on designing waterproof flexible human-machine interfaces for sensing and feedback, exploiting advanced sensors and fusion algorithms for wearable underwater positioning, and studying multimodal information interaction strategies of wearable systems.

For the diagnosis of liver cancer, conventional brightness mode (B-mode) can only provide morphological information. Multi-modal ultrasound, including shear-wave elastography (SWE) and contrast enhanced ultrasound (CEUS), can provide comprehensive diagnostic information on tumor microenvironment and tissue perfusion . The challenge is to effectively explore the multi-modal features of ultrasound. Besides, there are many fusion strategies currently available, but there is a lack of systematic comparative research on the various fusion strategies. In this study, we designed 'Lesions Pairing' to construct the dataset, addressing the challenge of small sample sizes in multi-modal learning. We then compared the effectiveness of different strategies and proposed hybrid-fusion strategies based on the combination of conventional layer-level fusion (i.e. early-fusion, mid-fusion and late-fusion), which can efficiently extract intra-/inter- modal information. Specifically, we first systematically compared different deep-learning-based fusion strategies for multi-modal ultrasound in the diagnosis of liver cancer. Secondly, based on the comparison results of a multimodal framework that integrates B-mode, SWE, CEUS ultrasound data, and clinical data simultaneously, we propose a hybrid-fusion strategies for the diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma . The experimental results showed that the area under the curve of the early-late fusion strategy combined with clinical data was 0.9854, which was superior to other single mode and other fusion strategies, increasing by 13.8–25.88 % and 2.22 %-9.79 %, respectively.

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction.

Feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for the classification algorithm, thus affecting the classification performance. For a traditional rule-based binary classification algorithm, classification rules are usually deterministic, which results in the fuzzy information contained in the rules being ignored. To do so, this article employs iterative feature selection in fuzzy rule-based binary classification. The proposed algorithm combines feature selection based on fuzzy correlation family with rule mining based on biclustering. It first conducts biclustering on the dataset after feature selection. Then it conducts feature selection again for the biclusters according to the feedback of biclusters evaluation. In this way, an iterative feature selection framework is built. During the iteration process, it stops until the obtained bicluster meets the requirements. In addition, the rule membership function is introduced to extract vectorized fuzzy rules from the bicluster and construct weak classifiers. The weak classifiers with good classification performance are selected by adaptive boosting and the strong classifier is constructed by “weighted average.” Finally, we perform the proposed algorithm on different datasets and compare it with other peers. Experimental results show that it achieves good classification performance and outperforms its peers.

The ultra-low-frequency (ULF) electromagnetic anomaly has been considered one of the earthquake precursory signals with the potential for short-term prediction. As such, effectively detecting ULF anomalies is important for mitigating earthquake disasters. Given the comprehensive coverage of geomagnetic networks in Japan, the 2011 Tohoku-Oki earthquake (M9.0) provided a tremendous opportunity for investigating the characteristics and mechanisms of ULF anomalies. Previous studies reporting detections of ULF anomalies triggered by the Tohoku-Oki earthquake have been questioned on the grounds of insufficient reliability due to technological limitations. In this study, we employ a multi-reference station data-quality-weighted method to detect the ULF anomaly. Comparison with traditional single-reference station method demonstrates the robustness of our detection results. Statistical test also indicates that the ULF anomaly appearing approximately two months before the earthquake was driven by physical processes. Geomagnetic storm analysis further rules solar activity as the cause of the anomaly. Spatial distribution of the anomaly amplitude reveals a decrease in the anomaly energy with increasing epicentral distance, implying a strong association between the anomaly and the earthquake. Our study is helpful for understanding the possible connection between the ULF electromagnetic precursors and the seismogenic processes of strong earthquakes, which contributes to achieving the ultimate goal of short-term earthquake prediction.

Deciphering coordinated movements is integral to understanding the daily activities and interactions between the nervous system and muscles, especially in robot-assisted rehabilitation. This study proposes a novel temporal-guided adaptive graph learning (TAGL) network to recognize coordinated movements from functional near-infrared spectroscopy (fNIRS) data. The temporal-guided node construction module is designed to build graph nodes while considering spatiotemporal and causal dependencies. Given the brain network's affinity for learning asymmetric structures, an adaptive edge learning module is devised, integrating a multihead attention mechanism for the tailored acquisition of directional edge connections among nodes. The TAGL model undergoes evaluation on both a proprietary fNIRS dataset featuring eight circular finger movements and a public fNIRS dataset involving three distinct actions. Comparative experiments with state-of-the-art methods reveal its superior performance, showcasing its potential in deciphering coordinated movements effectively.

Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field.

It is quite challenging to visually identify skin lesions with irregular shapes, blurred boundaries and large scale variances. Convolutional Neural Network (CNN) extracts more local features with abundant spatial information, while Transformer has the powerful ability to capture more global information but with insufficient spatial details. To overcome the difficulties in discriminating small or blurred skin lesions, we propose a Bi-directionally Fused Boundary Aware Network (BiFBA-Net). To utilize complementary features produced by CNNs and Transformers, we design a dual-encoding structure. Different from existing dual-encoders, our method designs a Bi-directional Attention Gate (Bi-AG) with two inputs and two outputs for crosswise feature fusion. Our Bi-AG accepts two kinds of features from CNN and Transformer encoders, and two attention gates are designed to generate two attention outputs that are sent back to the two encoders. Thus, we implement adequate exchanging of multi-scale information between CNN and Transformer encoders in a bi-directional and attention way. To perfectly restore feature maps, we propose a progressive decoding structure with boundary aware, containing three decoders with six supervised losses. The first decoder is a CNN network for producing more spatial details. The second one is a Partial Decoder (PD) for aggregating high-level features with more semantics. The last one is a Boundary Aware Decoder (BAD) proposed to progressively improve boundary accuracy. Our BAD uses residual structure and Reverse Attention (RA) at different scales to deeply mine structural and spatial details for refining lesion boundaries. Extensive experiments on public datasets show that our BiFBA-Net achieves higher segmentation accuracy, and has much better ability of boundary perceptions than compared methods. It also alleviates both over-segmentation of small lesions and under-segmentation of large ones.

In this article, we propose a new unsupervised feature selection method named pseudo-label guided structural discriminative subspace learning (PSDSL). Unlike the previous methods that perform the two stages independently, it introduces the construction of probability graph into the feature selection learning process as a unified general framework, and therefore the probability graph can be learned adaptively. Moreover, we design a pseudo-label guided learning mechanism, and combine the graph-based method and the idea of maximizing the between-class scatter matrix with the trace ratio to construct an objective function that can improve the discrimination of the selected features. Besides, the main existing strategies of selecting features are to employ -norm for feature selection, but this faces the challenges of sparsity limitations and parameter tuning. For addressing this issue, we employ the -norm constraint on the learned subspace to ensure the row sparsity of the model and make the selected feature more stable. Effective optimization strategy is given to solve such NP-hard problem with the determination of parameters and complexity analysis in theory. Ultimately, extensive experiments conducted on nine real-world datasets and three biological ScRNA-seq genes datasets verify the effectiveness of the proposed method on the data clustering downstream task.

Ultrasound imaging plays an essential role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for preventing, diagnosing, and treating vascular diseases. However, existing methods have inaccurate localization of the vascular wall boundary. Segmentation errors occur in discontinuous vascular wall boundaries and dark boundaries. To overcome these problems, we propose a new boundary-delineation network (BDNet). First, we design the feature extraction module to prevent the feature information loss of low-quality images by multi-scale information fusion and multi-receptive field feature fusion . Secondly, we generate the initial coarse prediction results based on the extracted features. Based on the coarse prediction results, we use the boundary refinement module to obtain the boundary point locations and re-delineate the boundary points to prevent the boundary points from the offset. Finally, we use region mutual information loss and our designed global pixel relationship loss to model the relationship between pixels from global and neighbourhood aspects using the structural features of the vessel wall to help the model extract important structured information. To facilitate clinical applications, we design the model to be lightweight. Experimental results show that our model achieves the best segmentation results and significantly reduces memory consumption compared to existing models.

Abstract Segmentations provide important clues for diagnosing diseases. U‐shaped neural networks with skip connections have become one of popular frameworks for medical image segmentation. Skip connections really reduce loss of spatial details caused by down‐sampling, but they cannot handle well semantic gaps between low‐ and high‐level features. It is quite challenging to accurately separate out long, narrow, and small organs from human bodies. To solve these problems, the authors propose a Multiple Gated Boosting Network (MGB‐Net). To boost spatial accuracy, the authors first adopt Gated Recurrent Units (GRU) to design multiple Gated Skip Connections (GSC) at different levels, which efficiently reduce the semantic gap between the shallow and deep features. The Update and Reset gates of GRUs enhance features beneficial to segmentation and suppress information adverse to final results in a recurrent way. To obtain more scale invariances, the authors propose a module of Multi‐scale Weighted Channel Attention (MWCA). The module first uses convolutions with different kernel sizes and group numbers to generate multi‐scale features, and then adopts learnable weights to emphasize the importance of each scale for capturing attention features. Blocks of Transformer Self‐Attention (TSA) are sequentially stacked to extract long‐range dependency features. To effectively fuse and boost the features of MWCA and TSA, the authors use GRUs again to propose a Gated Dual Attention module (GDA), which enhances beneficial features and suppresses adverse information in a gated learning way. Experiments show that the authors’ method achieves an average Dice coefficient of 80.66% on the Synapse multi‐organ segmentation dataset. The authors’ method outperforms the state‐of‐the‐art methods on medical images. In addition, the authors’ method achieves a Dice segmentation accuracy of 62.77% on difficult objects such as pancreas, significantly exceeding the current average accuracy, so multiple gated boosting (MGB) methods are reliably effective for improving the ability of feature representations. The authors’ code is publicly available at https://github.com/DAgalaxy/MGB‐Net .

The inversion of airborne electromagnetic (AEM) data suffers from severe non-uniqueness of the solution. Bayesian inference provides the means to estimate structural uncertainty with a rich suite of statistical information. However, conventional Bayesian methods are computationally demanding in nonlinear inversions, especially considering the huge volumes of observational data, and thus are not feasible in practice. In this study, we develop a fast Bayesian inversion operator based on the invertible neural network (INN) to fully explore the posterior distribution and quantitatively evaluate the model uncertainty. The INN uses a latent variable to capture the information loss during measurement and constructs bijective mappings between AEM data and the resistivity model. We also introduce another noise variable into the INN to account for data uncertainties. Synthetic tests demonstrate that the INN can effectively recover the posterior distribution by a relatively small ensemble of predicted resistivity models whose AEM responses show a significant agreement with the true signal. We also apply the INN inversion operator to a field data set and obtain results consistent with previous studies. The INN shows considerable adaptability to field observations and strong noise robustness. Meanwhile, the INN delivers the inversion result with posterior model distribution for 23366 AEM time series in 20 seconds on a common PC. The inversion efficiency can be further improved for large data set due to its natural parallelizability. The proposed INN method can support fast Bayesian inversion of AEM data and offer tremendous potential for near real-time uncertainty evaluation of underground structures.

The geoelectric data contain important anomalous information for short-term earthquake prediction. Timely and accurate identification of seismic electric anomalies is important for disaster prevention. However, identifying anomalies is challenging due to the huge volumes of data and noise disturbance. In this study, we develop a real-time automatic search engine (RASE) that incorporates an unsupervised convolutional denoising network (UCN) module and a supervised LSTM network (SLN) prediction module to automatically search for important anomalous signals in real-time. Experiments demonstrate that the RASE provides excellent detection accuracy and efficiency for synthetic and field data, which takes only dozens of seconds for a common PC to provide accurate detection results for data collected over a 24-hour period. The RASE has excellent flexibility and developability, as its internal modules can be adapted by more suitable technologies for better performance in various application scenarios. Comparison of multiple module combinations shows that the RASE configured with UCN and SLN has the highest detection accuracy. Our proposed search engine can reduce the human labor required for complex and repetitive detection work and fully realize the potential of geoelectric field observation in earthquake monitoring and disaster prevention.

Upon obtaining a relatively low false discovery rate (FDR) of alarms and a low false negative rate (FNR) of earthquakes, several previous long-term statistical researches concluded that ionospheric perturbations recorded by satellites are statistically related to earthquakes. However, overly large time-space windows for correlating perturbations with earthquakes will also contribute to low FDR and FNR. In this study, a new score - the number of non-randomly successful alarms - is used to quantitatively describe the sensitivity of Electron Density Perturbations (EDPs) recorded by the DEMETER satellite to global earthquakes withM≥4.8. Results show that the EDPs are significantly related to global medium-to-strong earthquakes and that optimal parameters for removing EDPs which are non-related to earthquakes and the optimal time-space windows for correlating earthquakes and EDPs are variable in space. Moreover, our results show that the intensity of EDPs makes little contribution to distinguishing the perturbations related to earthquakes with different magnitudes and perturbations non-related to earthquakes, while theKpindex is effective for improving the Signal/Noise ratio of our model, where Signal/Noise refers to the EDPs related/non-related to earthquakes. Finally, using the optimal time-space windows for correlating EDPs and earthquake, we construct several earthquake prediction models and quantitatively evaluate their power. We find that these EDP-based earthquake predictions are better than the spatially variable Poisson model showing the great potential of predicting earthquakes based on satellite-based Earth observation techniques. However, the spatio-temporal accuracy of these models for predicting earthquakes is not satisfactory, as the alerted time-space volume is big.

We developed a 3-D joint modeling algorithm of controlled source electromagnetic (CSEM) responses with unstructured meshes. The crucial component is to construct an approximating matrix from the stiffness matrix based on coupled potential formulation. Through an incomplete Cholesky decomposition of the approximating matrix and application of the mapping matrix connecting the secondary electric fields and coupled potentials, we obtain an efficient preconditioner for iterative solution of the linear system based on the secondary field formulation. We first demonstrate the accuracy of the algorithm by comparison with analytic solutions for a three-layered earth model. The linear system of equations can be solved efficiently by a quasi-minimum residual (QMR) method with the newly developed preconditioner, while the iterative solution process converges much slower if we use a preconditioner constructed from the original stiffness matrix. Besides, the divergence correction (DC) technique extensively applied for modeling with rectangular meshes is inefficient for modeling with unstructured meshes. We further verify the reliability of the new algorithm by comparison with published algorithms for complex earth models. The new modeling algorithm could provide accurate numerical solutions for complex 3-D earth model within affordable computation resources. We also compare the numerical solutions of the joint modeling algorithms for unstructured meshes and structured meshes. There are observable large differences for the numerical solutions of complex models, which may confirm the privilege of the numerical modeling algorithms with unstructured meshes.

Seismic electric signals (SESs) are essential short-term precursors of earthquakes. Accurate and efficient detection of SESs is significant to short-term predictions of earthquakes. However, SESs are usually disturbed by various noises and are thus difficult to recognize. Although conventional techniques have made substantial efforts in improving the SES detection accuracy, the success rates of SES detection at certain stations are still less satisfactory due to the complexity and diversity of noises. In this study, we apply deep learning to extract SESs and develop a novel deep learning network based on geoelectric field characteristics by combining the long short-term memory (LSTM) blocks with an autoencoder structure and a time-step attention module. The detection results of both synthetic and real data demonstrate that our proposed network yields superior performance in detecting embedded SESs in the presence of severe noise interference compared with traditional methods and several well-known networks. Moreover, our novel network shows the excellent ability of massive data processing, generalization and migration, which can process one-day’s worth of data in only milliseconds, adapt to SESs whose durations and amplitudes are different from those of the training set and be easily transferred to newly acquired data. The proposed novel method can provide more efficient and accurate detection results, which will broaden the data availability of hazard mitigation based on SESs.

Cross-modal hashing has achieved significant progress in recent years. However, how to effectively learn more discriminative hash codes of each modality and simultaneous alleviate the loss of modality information is still a challenging problem. Focusing on this problem, in this paper, we propose a novel cross-modal hashing approach named Semantic Preserving Generative Adversarial Network (SPGAN). The overall network architecture consists of two sub-networks, i.e., a semantic preserving generative adversarial network module and a discriminative hashing module. The generator maps text features into the image feature space. And the discriminator judges whether the feature representations are real image features or generated image features. The adversarial learning process can effectively reduce modality difference and preserve information of the image modality as much as possible. The discriminative hashing module projects the real and generated image features into a Hamming space to obtain hash codes, and explores semantic similarities for enhancing the discriminant ability of hash codes. Experiments on two widely used datasets demonstrate that SPGAN can outperform state-of-the-art related works.

Medical diagnosis through artificial intelligence has been drawing increasing attention currently. For breast lesions, the clinical ultrasound reports are the most commonly used data in the diagnosis of breast cancer. Nevertheless, the input reports always encounter the inevitable issue of data missing. Unfortunately, despite the efforts made in previous approaches that made progress on tackling data imprecision, nearly all of these approaches cannot accept inputs with data missing. A common way to alleviate the data missing issue is to fill the missing values with artificial data. However, the data filling strategy actually brings in additional noises that do not exist in the raw data. Inspired by the advantage of open world assumption, we regard the missing data in clinical ultrasound reports as non-observed terms of facts, and propose a Knowledge Graph embedding based model KGSeD with the capability of tolerating data missing, which can successfully circumvent the pollution caused by data filling. Our KGSeD is designed via an encoder-decoder framework, where the encoder incorporates structural information of the graph via embedding, and the decoder diagnose patients by inferring their links to clinical outcomes. Comparative experiments show that KGSeD achieves noticeable diagnosis performances. When data missing occurred, KGSeD yields the most stable performance over those of existing approaches, showing better tolerance to data missing.

Groundwater flow could produce an observable self-potential (SP) signal; it is, therefore, useful to use SP signal to investigate the condition of water flow, which is meaningful to the processes to which the water flow is critical, such as landslide warning, contaminant transport, and volcanic eruption. Most of the SP data inversion methods are based on prior in situ electrical resistivity information, which is difficult to be accurately obtained in the continuous monitoring of the vadose zone. We suggest a fusion scheme of integrating the electric charge occurrence probability (ECOP) tomography and the continuous complex wavelet transform (CCWT) method to process and interpret SP data when it lacks prior electrical structure information on the background media. A model of fracture flow with precipitation recharge is used to testify our proposed method. The results indicate that the combination scheme can locate the source of SP anomalies with improved accuracy. The proposed fusion probability tomography can locate the fracture flow with an explicit region of probability anomaly and suppress the irrelevant noises.

Smoke has semi-transparency property leading to highly complicated mixture of background and smoke. Sparse or small smoke is visually inconspicuous, and its boundary is often ambiguous. These reasons result in a very challenging task of separating smoke from a single image. To solve these problems, we propose a Classification-assisted Gated Recurrent Network (CGRNet) for smoke semantic segmentation. To discriminate smoke and smoke-like objects, we present a smoke segmentation strategy with dual classification assistance. Our classification module outputs two prediction probabilities for smoke. The first assistance is to use one probability to explicitly regulate the segmentation module for accuracy improvement by supervising a cross-entropy classification loss. The second one is to multiply the segmentation result by another probability for further refinement. This dual classification assistance greatly improves performance at image level. In the segmentation module, we design an Attention Convolutional GRU module (Att-ConvGRU) to learn the long-range context dependence of features. To perceive small or inconspicuous smoke, we design a Multi-scale Context Contrasted Local Feature structure (MCCL) and a Dense Pyramid Pooling Module (DPPM) for improving the representation ability of our network. Extensive experiments validate that our method significantly outperforms existing state-of-art algorithms on smoke datasets, and also obtain satisfactory results on challenging images with inconspicuous smoke and smoke-like objects.

MOTIVATION: Detecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients. RESULTS: By incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/JianingXi/DriverSub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hyperspectral remote sensing image (HRSI) can provide additional spectral information of objects and have been widely used in many fields. However, due to the complex environment of the HRSI gathering area, collecting the labeled samples of HRSI is time-consuming and labor-intensive. The scarcity of labeled samples is one of the major difficulties for HRSI analysis and processing. In this letter, a spatial-spectral prototypical network (SSPN) for HRSI is proposed for solving the problem of lack of labeled samples. The contribution of this letter is threefold. First, we design a novel local pattern coding algorithm to combine the spatial and spectral information of HRSI pixels based on spatial neighborhood correlation. Then, a spatial-spectral feature extraction algorithm based on 1-D convolutional neural network (1-D-CNN) is suggested to learn the spatial-spectral metric space where HRSI pixels can be correctly classified with only a few labeled samples. Finally, a novel prototype representation for HRSI in spatial-spectral metric space is proposed to better classify the mixed pixels existing in HRSI. The experimental results on three popular HRSI data sets demonstrate that the proposed SSPN is significantly better than the traditional algorithms.

Historical financial data are frequently used in technical analysis to identify patterns that can be exploited to achieve trading profits. Although technical analysis using a variety of technical indicators has proven to be useful for the prediction of price trends, it is difficult to use them to formulate trading rules that could be used in an automatic trading system due to the vague nature of the rules. Moreover, it is challenging to determine a specified combination of technical indicators that can be used to detect good trading points and trading rules since different stock may be affected by different set of factors. In this paper, we propose a novel trading point forecasting framework that incorporates a bicluster mining technique to discover significant trading patterns, a method to establish the fuzzy rule base, and a fuzzy inference system optimized for trading point prediction. The proposed method (called BM-FM) was tested on several historical stock datasets and the average performance was compared with the conventional buy-and-hold strategy and five previously reported intelligent trading systems. Experimental results demonstrated the superior performance of the proposed trading system.

Smoke density estimation from a single image is a totally new but highly ill-posed problem. To solve the problem, we stack several convolutional encoder-decoder structures together to propose a wave-shaped neural network, termed W-Net. Stacking encoder-decoders directly increases the network depth, leading to the enlargement of receptive fields for encoding more semantic information. To maximize the degrees of feature re-usage, we copy and resize the outputs of encoding layers to corresponding decoding layers, and then concatenate them to implement short-cut connections for improving spatial accuracy. The crests and troughs of W-Net are special structures containing abundant localization and semantic information, so we also use short-cut connections between these structures and decoding layers. Estimated smoke density is useful in many applications, such as smoke segmentation, smoke detection, disaster simulation. Experimental results show that our method outperforms existing methods on both smoke density estimation and segmentation. It also achieves satisfying results in visual detection of auto exhausts.

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.

Breast cancer is now considered as one of the leading causes of deaths among women all over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions, computer-aided diagnosis (CAD) system is of increasing interest in breast cancer detection and analysis nowadays. In this paper, a novel computer-aided diagnosis scheme with human-in-the-loop is proposed to help clinicians identify the benign and malignant breast tumors in ultrasound. In this framework, feature acquisition is performed by a user-participated feature scoring scheme that is based on Breast Imaging Reporting and Data System (BI-RADS) lexicon and experience of doctors. Biclustering mining is then used as a useful tool to discover the column consistency patterns on the training data. The patterns frequently appearing in the tumors with the same label can be regarded as a potential diagnostic rule. Subsequently, the diagnostic rules are utilized to construct component classifiers of the Adaboost algorithm via a novel rules combination strategy which resolves the problem of classification in different feature spaces (PC-DFS). Finally, the AdaBoost learning is performed to discover effective combinations and integrate them into a strong classifier. The proposed approach has been validated using a large ultrasounic dataset of 1,062 breast tumor instances (including 418 benign cases and 644 malignant cases) and its performance was compared with several conventional approaches. The experimental results show that the proposed method yielded the best prediction performance, indicating a good potential in clinical applications.

Skeleton-based action recognition has been widely applied in intelligent video surveillance and human behavior analysis. Previous works have successfully applied Convolutional Neural Networks (CNN) to learn spatio-temporal characteristics of the skeleton sequence. However, they merely focus on the coordinates of isolated joints, which ignore the spatial relationships between joints and only implicitly learn the motion representations. To solve these problems, we propose an effective method to learn comprehensive representations from skeleton sequences by using Geometric Algebra. Firstly, a frontal orientation based spatio-temporal model is constructed to represent the spatial configuration and temporal dynamics of skeleton sequences, which owns the robustness against view variations. Then the shape-motion representations which mutually compensate are learned to describe skeleton actions comprehensively. Finally, a multi-stream CNN model is applied to extract and fuse deep features from the complementary shape-motion representations. Experimental results on NTU RGB+D and Northwestern-UCLA datasets consistently verify the superiority of our method.

Semi-supervised multi-view feature learning (SMFL) is a feasible solution for webpage classification. However, how to fully extract the complementarity and correlation information effectively under semi-supervised setting has not been well studied. In this paper, we propose a semi-supervised multi-view individual and sharable feature learning (SMISFL) approach, which jointly learns multiple view-individual transformations and one sharable transformation to explore the view-specific property for each view and the common property across views. We design a semi-supervised multi-view similarity preserving term, which fully utilizes the label information of labeled samples and similarity information of unlabeled samples from both intra-view and inter-view aspects. To promote learning of diversity, we impose a constraint on view-individual transformation to make the learned view-specific features to be statistically uncorrelated. Furthermore, we train a linear classifier, such that view-specific and shared features can be effectively combined for classification. Experiments on widely used webpage datasets demonstrate that SMISFL can significantly outperform state-of-the-art SMFL and webpage classification methods.

Spatial–spectral representation plays an important role in hyperspectral images (HSIs) classification. However, many of the existing local feature algorithms for HSIs are based on the two‐dimensional image and do not take full advantage of the information hidden in HSI, such as spatial–spectral locality correlation information, thereby reducing the robustness of these algorithms. In response to these problems, this study presents a robust multi‐view spatial–spectral representation method with the characteristics of HSIs. There are two key techniques in this representation method, called spatial–spectral locality constrained linear coding (SSLLC) and spatial–spectral pyramid matching model (SSPM). Firstly, SSLLC applies the locality information of the feature points and visual words and uses the discriminant information provided by the nearest‐neighbouring spatial–spectral feature points in HSIs. Secondly, SSPM works by partitioning the image into increasingly fine sub‐cubes and uses the cubes to match the local features of the HSIs. The multi‐view representation is tolerant to illumination change, image rotation, affine distortion etc. To assess the validity of authors' algorithm, the authors compared their results with several existing approaches, including a deep learning method. The experimental results show that this representation method can effectively improve the accuracy of HSIs classification.

It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit‐sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1–1 bitwise matching number to the 0–0 bitwise matching number and the 1–0 number to the 0–1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi‐scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

The analysis of gene expression data is useful for detecting the biological information of genes. Biclustering of microarray data has been proposed as a powerful computational tool to discover subsets of genes that exhibit consistent expression patterns along subsets of conditions. In this paper, we propose a novel biclustering algorithm called the bi-phase evolutionary biclustering algorithm. The first phase is for the evolution of rows and columns, and the other is for the evolution of biclusters. The interaction of the two phases ensures a reliable search direction and accelerates the convergence to good solutions. Furthermore, the population is initialized using a conventional hierarchical clustering strategy to discover bicluster seeds. We also developed a seed-based parallel implementation of evolutionary searching to search biclusters more comprehensively. The performance of the proposed algorithm is compared with several popular biclustering algorithms using synthetic datasets and real microarray datasets. The experimental results show that the algorithm demonstrates a significant improvement in discovering biclusters.

This paper presents a human skin inspired automatic robotic ultrasound (US) system for three-dimensional (3-D) imaging. A depth camera was adopted to capture the point cloud of the skin surface. According to the 3-D contour of the skin surface, the scan range and scan path for the US probe could be automatically determined. Then, we used a normal-vector-based method to determine the pose of the robotic arm corresponding to each scan point in the scan path. In addition, two force sensors could feedback the contact force between the scanned tissue and the emission plane of the probe for fine-tuning the pose of the robotic arm. After the scanning, the system could realize 3-D US reconstruction. Experimental results validate the feasibility of the proposed system. It is expected that the proposed system will be useful in clinical practices.

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years, and the high-resolution estimation of signal parameter via rotational invariance technique (ESPRIT) was developed in the spherical harmonics domain. However, the spherical harmonics ESPRIT (SHESPRIT) cannot estimate the DOA when the elevation approaches 90°. To solve this problem, we present a two-step SHESPRIT (TS-SHESPRIT) based on two new recurrence relations of complex spherical harmonics. Furthermore, we develop a real-valued two-step SHESPRIT (RTS-SHESPRIT) that exploits a unitary matrix to obtain a real-valued relation between the signal subspace and the steering matrix to further reduce the computational complexity. However, the number of sources that are estimated by RTS-SHESPRIT is limited. Therefore, we propose the semi-RTS-SHESPRIT method, which reduces the computational complexity associated with eigenvalue decomposition (EVD) and avoids the limitations of RTS-SHESPRIT. Relative to SHESPRIT and TS-SHESPRIT, RTS-SHESPRIT and semi-RTS-SHESPRIT reduce the computational burden by 75% during EVD. Furthermore, we derive the mean square errors (MSEs) of the above algorithms and significantly simplify the MSE expressions. Different expressions for the MSEs are due to different recurrence relations used by different SHESPRIT-type algorithms. All proposed two-step SHESPRIT-type algorithms have higher accuracy than traditional SHESPRIT. The simulation results demonstrate the satisfactory performance of our methods.

Traffic videos are often recorded by vehicle-mounted cameras. Compared with videos recorded by handheld cameras, traffic videos suffer from more challenges, such as higher frequency and more violent jitters, dynamic scenes, large moving objects, and parallax, which can result in significant visual quality degradation. To address these challenges for traffic videos, we propose a special stabilization method. The key aspect of our method is a feedback strategy that divides the extracted feature trajectories into background trajectories and foreground trajectories by feeding back the previous trajectory classification results. The method can perform robustly, even in the case of large moving objects and parallax. Furthermore, our method maintains the number of available background trajectories within a reasonable range via two refinement strategies. One strategy attempts to reliably recover background trajectories from misjudged foreground trajectories when there are an insufficient number of background trajectories. The other strategy can adaptively adjust the number of feature points in each frame to efficiently avoid too many or too few background trajectories. With the obtained background trajectories, a homography matrix between each frame and its stabilized view is computed and implemented to warp the frame image to produce smooth videos. Experimental results confirm that our method is both effective in stabilizing traffic videos and quite robust against large moving objects and parallax.

Recently, the growing emphasis on medical ultrasound (US) has led to a rapid development of US extended field-of-view (EFOV) techniques. US EFOV techniques can be classified into three categories: 2-D US EFOV, 3-D US, and 3-D US EFOV. In this paper, we propose a novel EFOV method called 2.5-D US EFOV that combines both the advantages of the 2-D US EFOV and the 3-D US by generating a panorama on a curved image plane guided by a curved scanning trajectory of the US probe. In 2.5-D US EFOV, the real-time position and orientation of the US image plane can be recorded via an electromagnetic spatial sensor attached to the probe. The scanning direction is not necessarily straight and can be curved according to the regions of interest (ROI). To form the curved panorama, an image cutting method is proposed. Finally, the curved panorama is rendered in a 3-D space using a surface rendering based on a texture mapping technique. This allows 3-D measurements of lines and angles. Phantom experiments demonstrated that 2.5-D US EFOV images could show anatomical structures of ROI accurately and rapidly. The overall average errors for the distance and angle measurements are -0.097 ± 0.128 cm (-1% ± 1.2%) and 1.50° ± 1.60° (1.9% ± 2%), respectively. A typical extended US image can be reconstructed from 321 B-scans images within 3 s. The satisfying quantitative result on the spinal tissues of a scoliosis subject demonstrates that our system has potential applications in the assessment of musculoskeletal issues.

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years. In this paper, we develop a unitary matrix to transform the complex spherical harmonics into real ones and obtain a real-valued covariance matrix after forward-backward (FB) averaging. Based on this transformation, a two-stage decoupled approach (TSDA) is proposed to decouple the estimation of the elevation and the azimuth. First, we propose a unitary spherical harmonics estimation of signal parameter via rotational invariance technique using one recurrence relation of real spherical harmonics to obtain the elevation estimation. Because of the limitation of using another recurrence relation to estimate the azimuth; second, we propose a unitary spherical harmonics root multiple signal classification (U-SHRMUSIC) to obtain the azimuth. Moreover, using the phase shift characteristic of real spherical harmonics, we also propose a low-complexity U-SHRMUSIC that can further decrease the computational load in the second stage. The proposed TSDA can not only achieve more accurate DOA estimation via FB averaging and two-stage estimation after decoupling but also reduce the computational complexity by exploiting real operations. Computer simulations, especially simulations of the real environment, validate the effectiveness of the proposed method.

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

Freehand 3-D ultrasound (US) produces 3-D volume data of anatomical objects from a sequence of irregularly located 2-D B-mode US images (B-scans). In 3-D US, the voxel intensities are calculated by interpolating those pixels from raw B-scans. Current interpolation algorithms do not consider sparsity of the raw data and are time consuming in computation. In this paper, we aim to perform the 3-D reconstruction of freehand US with sparse raw data in a more efficient manner. A novel interpolation algorithm takes advantage of Bezier curves. A single sweep of raw B-scans is collected, and the third-order Bezier curves are employed for approximating the voxels located in a control window. In in vitro and in vivo experiments, a fetus phantom and a subject's forearm were scanned using the freehand 3-D US system and reconstructed using the proposed Bezier interpolation algorithm and three popular interpolation algorithms, respectively. The results showed that the proposed algorithm significantly outperformed the other three algorithms when the raw B-scans were relatively sparse and the interpolation error in gray level can be reduced by 0.51-5.07. The speed for 3-D reconstruction can be improved by 90.6-97.2 because a single third-order Bezier curve using four control points (i.e., the pixel points) is able to estimate more than four voxels, whereas the estimation of a voxel value often requires a number of pixels in conventional techniques.

The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest. However, the vast quantity of videos also turns the finding process into a difficult task. In this article, we address the problem of providing personalized video recommendation for users. Rather than only exploring the user-video bipartite graph that is formulated using click information, we first combine the clicks and queries information to build a tripartite graph. In the tripartite graph, the query nodes act as bridges to connect user nodes and video nodes. Then, to further enrich the connections between users and videos, three subgraphs between the same kinds of nodes are added to the tripartite graph by exploring content-based information (video tags and textual queries). We propose an iterative propagation algorithm over the enhanced graph to compute the preference information of each user. Experiments conducted on a dataset with 1,369 users, 8,765 queries, and 17,712 videos collected from a commercial video search engine demonstrate the effectiveness of the proposed method.

A tensor is used to describe head-related transfer functions (HRTFs) dependent on frequencies, sound directions and anthropometric parameters. It can represent the multi-dimensional structure of measured HRTFs. To construct a personalization model, high-order singular value decomposition (HOSVD) is firstly applied to extract individual core tensor as the outputs of the model. Some important anthropometric parameters are selected by Laplacian score and correlation analysis between all measured parameters and the individual core tensor. They act as the inputs of the personalization model. Then a nonlinear model is constructed based on radial basis function (RBF) neural network to predict individual HRTFs according to the measured anthropometric parameters. Compared with back-propagation (BP) neural network method, simulation results demonstrate the better performance for predicting individual HRTFs in the midsaggital plane at high elevations.

This demonstration presents an innovative instant mobile video search system based on layered audio-video indexing, called "LAVES." Through the system, users can discover videos by simply pointing their phones at a screen to capture a very few seconds of what they are watching. Unlike most existing mobile video search applications which simply send the original video query to the cloud, the proposed mobile system is one of the first attempts towards instant and progressive video search leveraging the light-weight computing capacity of mobile devices. The system is able to index large-scale video data using the layered audio-video indexing technique on the cloud, as well as extract light-weight joint audio-video signatures in real time and perform bipartite-graph-based progressive search process on the devices. On a 600 hours video dataset, the system can outperform the state-of-the-arts by achieving 90.79% precision when the query video is less than 10 seconds.

As the clinical application grows, there is a rapid technical development of 3-D ultrasound imaging. Compared with 2-D ultrasound imaging, 3-D ultrasound imaging can provide improved qualitative and quantitative information for various clinical applications. In this paper, we proposed a novel tracking method for a freehand 3-D ultrasound imaging system with improved portability, reduced degree of freedom, and cost. We designed a sliding track with a linear position sensor attached, and it transmitted positional data via a wireless communication module based on Bluetooth, resulting in a wireless spatial tracking modality. A traditional 2-D ultrasound probe fixed to the position sensor on the sliding track was used to obtain real-time B-scans, and the positions of the B-scans were simultaneously acquired when moving the probe along the track in a freehand manner. In the experiments, the proposed method was applied to ultrasound phantoms and real human tissues. The results demonstrated that the new system outperformed a previously developed freehand system based on a traditional six-degree-of-freedom spatial sensor in phantom and in vivo studies, indicating its merit in clinical applications for human tissues and organs.

The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest to them. Video search, such as Google, Youtube, Bing, is a popular way to help users to find desired videos. However, it is still very challenging to discover new video contents for users. In this paper, we address the problem of providing personalized video suggestions for users. Rather than only exploring the user-video graph that is formulated using the click-through information, we also investigate other two useful graphs, the user-query graph indicating if a user ever issues a query, and the query-video graph indicating if a video appears in the search result of a query. The two graphs act as a bridge to connect users and videos, and have a large potential to improve the recommendation as the queries issued by a user essentially imply his interest. As a result, we reach a tripartite graph over (user, video, query). We develop an iterative propagation scheme over the tripartite graph to compute the preference information of each user. Experimental results on a dataset of 2,893 users, 23,630 queries and 55,114 videos collected during Feb. 1-28, 2011 demonstrate that the proposed method outperforms existing state-of-the-art approaches, co-views and random walks on the user-video bipartite graph.

The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.

Prior to pattern recognition, feature selection is often used to identify relevant features and discard irrelevant ones for obtaining improved analysis results. In this paper, we aim to develop an unsupervised feature ranking algorithm that evaluates features using discovered local coherent patterns, which are known as biclusters. The biclusters (viewed as submatrices) are discovered from a data matrix. These submatrices are used for scoring relevant features from two aspects, i.e., the interdependence of features and the separability of instances. The features are thereby ranked with respect to their accumulated scores from the total discovered biclusters before the pattern classification. Experimental results show that this proposed method can yield comparable or even better performance in comparison with the well-known Fisher score, Laplacian score, and variance score using three UCI data sets, well improve the results of gene expression data analysis using gene ontology annotation, and finally demonstrate its advantage of unsupervised feature ranking for high-dimensional data.

A reduced order model for large numbers of head-related impulse responses (HRIRs) is proposed for real-time three-dimensional (3D) sound rendering. Independent spatial features are firstly extracted from measured HRIRs using independent component analysis (ICA). These spatial feature vectors are not only mutually statistical independent but independent from all the measured azimuths. Therefore filtering sound sources with numerous HRIRs is transformed into filtering them using the extracted lower-dimensional feature vectors. Furthermore balanced model truncation (BMT) method in a state space is adopted to reduce the order of each independent feature vector. Simulation results demonstrate that our proposed algorithm not only acquires better approximated accuracy but has significantly lower computational complexity.

Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use pyramid histogram of oriented gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly available dataset demonstrated that the PHOG with a significantly shorter vector length could achieve as high a recognition rate as the Gabor features did. Furthermore, the feature selection conducted by an AdaBoost algorithm was not needed when using the PHOG features. To further improve the recognition performance, we combined these two feature extraction methods and achieved the best smile recognition rate, indicating a good value of the PHOG features for smile recognitions.

Single channel music separation is a useful but difficult problem in audio signal processing field. In this paper a new method is proposed. The method consists of three stages: estimating the harmonic structure of each source in every frame based on iteration of the mixed spectral peaks, clustering the estimated harmonics into the signals they belong to with pitch and formant information, and synthesizing the music source in time domain. Moreover, the method can solve the octave overlapping problem which is a tough one in the single channel source separation area. The experimental results show that our algorithm can separate the mixed signal and obtains a good subjective audio quality.

In this paper, we aim to propose an unsupervised feature ranking algorithm for evaluating features using discovered biclusters which are local patterns extracted from a data matrix. The biclusters can be expressed as sub-matrices which are used for scoring relevant features from two aspects, i.e. the interdependence of features and the separability of instances. The features are thereby ranked with respect to their accumulated scores from the total discovered biclusters before the pattern classification. Experimental results show that this proposed algorithm can yield comparable or even better performance in comparison with the well-known Fisher Score, Laplacian Score and Variance Score using several UCI data sets.

The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.

A subset of genes sharing compatible expression patterns under a subset of conditions can be found from DNA microarray data using biclustering algorithms. In this paper, we present a novel geometrical biclustering algorithm in combination with gene ontology annotations to identify the gene functional biclusters. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometrical interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our bottom-up biclustering algorithm, the well-known Hough transform is first employed in pair-column spaces to reduce the computation complexity and then the resulting patterns are merged step by step into large-size biclusters incorporated with gene functional modules. The algorithm integrates the numerical characteristics in a gene expression matrix and the gene functions in the biological activities. Our experiments on real data show that the new algorithm outperforms most existing methods for mining gene functional biclusters.