ABSTRACT Knowledge graph embedding (KGE) offers a more intuitive approach to discovering potential relations between known entities. However, current models are associated with challenges such as a large number of training parameters and low training efficiency and fail to provide in‐depth analysis of the impact of embedding dimensionality on entities and relations in link prediction performance. Therefore, we investigate the impact of entity and relation embedding dimensions on their interaction and assess how these dimensions affect the performance of KGE models. Based on these insights, we propose a novel dual encoder‐decoder model, NDcRE, which includes decoders MlpD and AttnMlpD, designed to capture long‐distance interactions and improve link prediction performance with fewer parameters. Evaluated on four benchmarks, WN18RR, FB15k‐237, DB100k, and YAGO3‐10, NDcRE significantly improves model efficiency by utilizing fewer parameters and dimensions, thereby enhancing both its utility and convenience. In particular, the AttnMlpD decoder further reduces the model's training parameters, enabling it to deliver strong performance even in environments with limited computational resources.

Precipitation nowcasting is a highly challenging task in weather forecasting and plays a crucial role in protecting lives and property. However, autoregressive methods encounter training difficulties and are prone to error accumulation, whereas non-autoregressive models struggle to effectively utilize temporal information. To address these issues, this paper proposes a novel decoupled spatiotemporal network, TPDTC-Net, specifically for precipitation nowcasting. TPDTC-Net introduces an encoder–temporal predictor–decoder architecture based on a non-autoregressive model to prevent error accumulation, combining a time predictor and an adaptive dynamic weighting module that integrates the strengths of Transformer and convolutional neural networks, thereby improving the accuracy of precipitation nowcasting. From the spatial perspective, TPDTC-Net utilizes a multi-scale self-attention module in the encoder to extract global spatial features and employs a multiplicative convolution module to capture detailed features. In particular, the proposed adaptive dynamic weighting module effectively combines global and local features, allowing the encoder to dynamically adjust the weights based on different inputs and scenarios while learning feature fusion strategies. This enhances the supplementary role of convolution-extracted detail features to the global features extracted by the self-attention mechanism. From the temporal perspective, the time predictor adopts a Fourier self-attention mechanism and reshapes the feature maps passed from the encoder into abstract multivariate time series prediction tasks. By transforming unordered temporal information into ordered sequences, the time predictor effectively captures temporal dependencies, enabling the decoder to generate more accurate predictions. Extensive experiments on benchmark datasets show that TPDTC-Net significantly outperforms state-of-the-art networks in precipitation nowcasting. Specifically, on the KNMI dataset, compared to the second-best baseline model LPT-QPN (r≥ 10), the critical success index (CSI) and Heidke skill score (HSS) of TPDTC-Net increase by 10.35% and 9.37%, respectively. Besides, the balanced mean square error (BMSE) and balanced mean absolute error (BMAE) of TPDTC-Net decrease to 15.4787 and 1.1535. Similarly, on the CIKM AnalytiCup 2017 dataset, TPDTC-Net also delivers the best performance, with comparable performance trends observed. These results demonstrate the superior performance in terms of prediction accuracy of the proposed TPDTC-Net.

Precipitation nowcasting is a long-standing challenge due to the inherent unpredictability, which often lead to significant risks and damage. Traditional approaches that model nonlinear relationships between initial and future precipitation states often fail to accurately capture precipitation dynamics, including distribution and intensity patterns. Current data-driven methods are limited in their ability to represent the chaotic nature of precipitation without guidance from physical theory. To address this, we present Rainfusion, a generative model that integrates Prandtl’s mixing length theory from fluid dynamics with computer vision diffusion models. This integration accounts for nonlinear interactions between large-scale evolution and turbulent fluctuations in precipitation, generating physically plausible predictions. Rainfusion significantly improves forecasting skill on two benchmark dataset over the next 3 hours. Furthermore, we enhance Rainfusion with a control network trained on multi-source data, particularly lightning observations, enabling more accurate and controllable predictions of precipitation’s spatial-temporal patterns. Weather forecasters can utilize Rainfusion to guide predictions toward either growth or decay based on their domain expertise. Our approach advances precipitation nowcasting, offering a robust framework that bridges physical theory with modern deep learning techniques.

The C-band Advanced Scatterometer (ASCAT) has the advantages of good spatial-temporal coverage and low sensitivity to nonextreme rainfall. While the perceived wind speed underestimation issues of ASCAT sea surface wind (SSW) retrievals can be mitigated using appropriate high wind speed scalings, the low spatial resolution in ASCAT remains a challenge, which implicitly leads to the blurring effect in tropical cyclone (TC) inner-core regions. To overcome this issue, the 2-D variational (2DVAR) analysis method is modified from 12.5 to 1.8 km grid size, where the latter allows super-resolution (SR) spatial structure functions, empirically trained on synthetic aperture radar (SAR) data, to enhance TC structure retrievals of ASCAT. The method first employs triple collocation analysis to estimate observation and background errors under different TC categories. After that, the relevant spatial parameters during the data assimilation process are determined and linked to TC features. These analyses contribute to constructing SAR-learned structure functions, complementing ASCAT-observed TC characteristics, and then achieving TC vortex reconstruction and wind field SR. Validation studies demonstrate that the SR products possess the correct small-scale properties of TC inner-core structures, such as radius of maximum wind (RMW), TC asymmetry, and wind variability. Notably, the proposed SR approach can achieve a significant reduction in error standard deviations (SDs) of ($l,t$) wind components (by 37% and 33%, respectively) when compared to spatial interpolated results. The encouraging results suggest the feasibility of the method in enhancing the abundant but lower resolution scatterometer winds, potentially contributing to future advancements in TC advisories.

Time series forecasting is crucial, yet the challenge of escalating errors in chaotic data and natural phenomena prediction endures. Existing methods for recursive strategies face difficulties in Multi-Input Multi-Output scenarios. A unified learning framework addressing error growth alongside these models is lacking, despite advanced neural networks. While dynamical system theory has inspired research in time series forecasting, these approaches struggle to estimate and mitigate error growth adequately. To address these gaps, we introduce Phase-Space-Guided Forecasting (PSGF), rooted in dynamical system theory. PSGF transforms data into high-dimensional phase space, quantifies error growth rates, and incorporates them into the neural network via Error Growth Awareness Loss (EGAL). PSGF enhances the utilization of dynamical constraints, reducing the need for additional feature engineering or hyperparameter tuning. Experimental results on chaotic systems and real-world climate data demonstrate PSGF’s significant accuracy improvements on diverse deep learning models.

Accurate knowledge of Tropical Cyclone (TC) inner-core structures contributes to a better understanding of TC thermodynamics. The Advanced Scatterometer (ASCAT) can measure ocean surface winds at a good spatial-temporal coverage, but the TC inner structures are largely blurred by its 20-km footprint. In this study, the Two-Dimensional Variational (2DVAR) scheme is considered to enhance the TC inner-core structure, by "learning" background spatial error covariances from high-resolution Synthetic Aperture Radar (SAR) winds. We find that the length scales of the stream function are close to the radii of maximum wind speeds and length scales of the velocity potential are dependent on TC asymmetry scales. All these parameters can be provided by ASCAT data. Experimental results prove that the proposed method can enhance TC inner-core structures and thus achieve super-resolution. The promising results contribute to our long-term goal of developing a general method for providing TC inner-core structures from all scatterometer winds available for nowcasting, allowing temporal monitoring of TC winds.

In the field of numerical weather prediction, fine-grained precipitation fields play a crucial role in forecasting and analyzing the spatial distribution and intensity of the precipitation. Historically, it is customary to employ the interpolation technique to downscale the low-resolution initial field output by assimilation systems, aligning with the requirements of a high-resolution forecasting model. Currently, data-driven deep learning methods offer novel solutions to address this challenge. In this letter, we propose a spatial downscaling algorithm for precipitation data generated from the North American Land Data Assimilation System (NLDAS), called Gaussian-perceptual-based generative adversarial network (GP-GAN). Specifically, the GP-GAN introduces a Siamese Gaussian-perceptual module (SGPM) which maps the data reconstructed from the generator and ground-truth to Gaussian latent space to learn the distribution of precipitation. Moreover, the adaptive weighted loss function (AWLF) is proposed to strengthen the emphasis and understanding of extreme precipitation events. Experimental results on the RainNet dataset comprising hourly precipitation over the USA demonstrate that GP-GAN provides better performance than other generative adversarial networks (GANs) and diffusion models in improving spatial resolution.

Accurate and timely estimation of tropical cyclone (TC) wind radii is significant for characterizing wind structure, disaster prevention, and mitigation. The existing methods have not sufficiently considered and utilized multimodal (i.e., multisource heterogeneous) data for wind radii estimation. Meanwhile, complete modalities (i.e., all used modalities) can hardly be available simultaneously, especially in real-time monitoring scenarios, which restricts the applicability of multimodal estimation models. It is challenging to maintain the accuracy of wind radii estimates when confronted with the issue of missing-modality. Therefore, to address these issues, this article aims to achieve robust TC wind radii estimation under both conditions with complete modalities and missing-modality. We first present a multimodal fusion network, MT-TCNet, for estimating TC wind radii under conditions with complete modalities. MT-TCNet benefits from multimodal data including satellite infrared (IR) images, reanalysis of wind fields, and the physical parameter maximum sustained wind (MSW) speed. MSW, which reflects TC intensity, is incorporated to embed the implicit relationship between TC intensity and wind radii. It is capable of providing superior and robust wind radii estimates in scenarios without time constraints, and can be used to generate long-term historical results. Furthermore, this article proposes MT-TCNet-Distill to alleviate the issue of missing-modality caused by delays in ERA5 reanalysis wind fields through generalized distillation and missing modality imputation. MT-TCNet-Distill broadens the applicability of MT-TCNet, which heavily relies on reanalysis data, enabling robust wind radii estimation in real-time scenarios. Comprehensive experiments demonstrate the superior performance of MT-TCNet and MT-TCNet-Distill compared to state-of-the-art methods.

Accurate retrieval of ocean surface wind speed (OSWS) has a vital impact on maritime transportation planning and extreme weather forecast. Current models leveraging deep-learning (DL) techniques have demonstrated considerable potential for satellite remote-sensing wind retrieval. However, these models tend to focus on synchronizing the retrieved wind speeds with the label, neglecting the inherent absolute error (AE) embedded within the label and thus resulting in retrieval errors. To mitigate the disruptive impact of AE on retrieval accuracy, we introduce a novel network called TCNet, which retrieves observations of cyclone global navigation satellite system (CYGNSS) as OSWS. The network constructs an AE module (AEM), guided by triple collocation (TC) method for improved accuracy in real-time wind retrieval by calculating the AE as loss value. These calculations guide the network training process, thereby enhancing retrieval accuracy. Meanwhile, the wind speed dataset imbalance and inherent averaging characteristics of networks frequently result in wind speed uncertaintines in extremes. Notably, this occurs as a gross underestimation of high-speed winds. Therefore, TCNet incorporates an adaptive penalty module (APM) to solve this problem. By assigning higher penalty factors to high-speed winds, the sensitivity of network to its retrieval is improved. Experimentally, the APM in TCNet exhibited a remarkable reduction of AE in high-speed wind retrieval and mitigates the understating of high-speed scenarios while maintaining an overall error that is not significantly increased. Importantly, TCNet demonstrated notable resistance to noise and portrayed excellent generalizability, providing fresh insights into weather forecasting, climate research, and other marine applications.

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models.

Global sea surface wind speed is a key parameter for weather forecasting and climate studies. However, retrieving it from Global Navigation Satellite System Reflectometry (GNSS-R) signals reflected from the ocean surface requires complex data processing and modelling. In addition, conventional GNSS-R methods have limited accuracy in the relatively high wind speed range of 12-20m/s. A novel model - GSWR-DARN - is presented here that combines data augmentation techniques with a residual network to improve the performance of GNSS-R wind speed retrieval. The model transforms one-dimensional (1D) data from the Cyclone Global Navigation Satellite System (CYGNSS) into two-dimensional (2D) data that can be fed into a Convolutional Neural Network (CNN), while enhancing the interconnectivity between different physical variables. By adding a residual network module, the model achieves higher accuracy and better distribution of wind speed estimates in the 0–20 m/s range than traditional methods. The model is shown to reduce the average Root Mean Square Error (RMSE) by 22.48% and increase the average Pearson correlation coefficient by 22%. It is also shown that the model significantly reduces the error distribution in the range of 12-20m/s wind speed range. The model is compared with other models of varying complexity - including Artificial Neural Networks (ANN), the CNN model, the ResNet18 model, the ResNet34 model and the Vision Transformer (Vit) model - and it is found that accuracy decreases with increasing number of residual blocks due to inherent characteristics of CYGNSS data.

Quantitative precipitation nowcasting (QPN) is a highly challenging task in weather forecasting. The ability to provide precise, immediate, and detailed QPN products is necessary for a variety of situations, including storm warnings, air travel, and large gatherings. To address this challenge, this article proposes a new transformer lightweight physics-informed transformer (LPT)-QPN for QPN tasks, utilizing vertical cumulative liquid water content (VIL) products. This model adopts novel transformer modules to model the long-term evolution of precipitation and incorporates multihead squared attention (MHSA) to model its highly nonlinear relationships while reducing computational complexity. The results of experimental evaluations demonstrate the superiority of LPT-QPN when compared to existing state-of-the-art QPN models. In particular, the LPT-QPN model demonstrates greater accuracy for long lead time and in high-intensity areas, confirmed in both quantitative and qualitative evaluations. In addition, through three customized fine-tuning schemes, we are able to further improve the predictability of the LPT-QPN model for specific precipitation events. By incorporating the physical constraints of the convection-diffusion equation, our approach offers novel perspectives for future explorations that combine physical prior knowledge and deep-learning (DL) techniques.

Underwater acoustic signal recognition systems face challenges in achieving high accuracy when processing complex data with low signal-to-noise ratio (SNR) in underwater environments, leading to limited noise robustness. Conventional approaches typically employ pre-trained denoising models for preprocessing noisy signals. However, due to disparate optimization goals between denoising and recognition models, denoising methods might introduce signal distortion, hampering effective enhancement of system accuracy. To address this issue, this paper proposes a novel joint training framework with cross-attention fusion for robust underwater acoustic signal recognition (UASR), called CAF-JT. CAF-JT consists of a denoising module, a recognition module, and the CAF module. It addresses the mismatch problem arising from different optimization directions by jointly training the denoising frontend and the recognition backend. Additionally, inspired by the multi-condition training (MCT) method, the CAF module is designed to fuse characteristics from both denoised and noisy audio, thus incorporating noise information. This fusion mechanism enables the model to better adapt to the characteristics of the noisy environment and enhance its noise robustness. Furthermore, to improve the performance of UASR, TF-Transformer blocks are incorporated into both the denoising module and the recognition module to capture the spatio-temporal distribution of spectral features. The proposed approach is evaluated on two open-source underwater acoustic signal datasets, namely ShipsEar and DeepShip. Extensive experimental demonstrate the superiority of CAF-JT over conventional joint training approaches, showcasing its improved noise robustness. Particularly in low SNR conditions, CAF-JT achieves the best average recognition rates of 94.84% and 93.61% on the two datasets, respectively.

Dimensionality reduction is a fundamental technique to address the curse of dimensionality problem in real-world big datasets. However, most existing methods either only target raw datasets that contain explicit relationships between data points, or construct the complete neighborhood graph of the dataset by calculating pairwise similarities, and then generate contexts of data points by random walking to measure the structure of the dataset, which are computationally expensive. In this paper, we propose a fast nonlinear locality-preserving dimensionality reduction approach called FVec2vec, which extends the Skip-gram model to embedding representation of general numerical matrices. Specifically, instead of constructing neighborhood graph by calculating pairwise similarities between data points, we approximate the k-nearest neighbors (kNN) of each data point in matrices by exploring its neighbors’ neighbors first. Then, we design a novel sampling algorithm to randomly sample on the kNN to depict the structure of the dataset. Experimental results show that FVec2vec is faster than most existing methods while achieving acceptable accuracy, and the accuracy is even higher than the state-of-the-art method under certain similarity metrics.

Sea Surface Temperature (SST) prediction has attracted increasing attention due to its critical role in climate change. Traditional SST prediction methods can be mainly divided into two types, the physics-based numerical methods and the data-driven methods. However, the above methods have certain limitations, the former type can not perform well when the physical prior information is incomplete, while latter type can not perform well when the training data is insufficient. This paper uses a deep neural network to extract some valuable information from the data, and then introduces the space-time partial differential equation (PDE) to model the prior physical information referring to SST. By incorporating them together, a new Space-Time PDE-guided Neural Network (STPDE-NET), which can better deal with the prior physical information incompleteness and data insufficiency problems mentioned above is proposed. In the experiments, we compare our STPDE-NET with several famous or state-of-the-art SST prediction methods. The experimental results show that STPDE-NET outperforms the compared methods in most SST prediction circumstances, especially when the training data is insufficient.

Accurate knowledge of wind directions plays a critical role in atmospheric dynamics exploration, numerical weather prediction and Tropical Cyclone (TC) research. This study proposes a new method for wind direction retrieval from TC Synthetic Aperture Radar (SAR) images. Unlike conventional approaches, which estimate wind directions from singlepolarization imagery, the method utilizes dual-polarized (VV and VH) signals to obtain continuous wind directions across moderate and extreme wind speed regimes. The technique is developed based on the Histogram of Oriented Gradient descriptor and the Hann window function. In addition, the neighbouring information is introduced to alleviate sharp directional variations. As case studies, the wind directions in TCs Karl and Maria are derived and subsequently verified by simultaneous dropsonde and ASCAT (ambiguity-removed) measurements. The encouraging results suggest that the wind direction retrieval method based on dual-polarization SAR imagery can be useful in extracting wind direction and contribute to further exploitation of SAR images in TC studies.

Predicting the Arctic sea ice concentration (SIC) has an essential guiding role in understanding climate change trends, resource extraction and route planning. Existing deep learning models still have the problem that it is challenging to utilize the global spatial information of SIC, and the predictions of boundary regions are not accurate enough. In this paper, we propose a new deep learning model, namely ConvLSTM-CRF, to predict the monthly sea ice concentration in the Arctic. We add a dense conditional random fields to the ConvLSTM, which further extracts global spatial information and can predict SIC more accurately. The experimental results show that compared with ConvLSTM, our model has a great improvement in the overall prediction accuracy and has more accurate predictions in the SIC boundary region, especially in the melting and freezing seasons when the SIC changes drastically. Our model also shows better prediction performance when making iterative predictions. In addition, ConvLSTM-CRF can be applied to similar time series forecasting problems, such as precipitation forecasting and snowfall forecasting.

Estimating wind speed with parametric models is one of the important methods for the tropical cyclone prediction and risk assessment. In this study, high spatial resolution cross-polarized synthetic aperture radar (SAR) observations are used to investigate the asymmetric structure of hurricanes. Then, a modified asymmetric hurricane parametric (MAHP) model composed of tangential wind profile model and asymmetric distribution mode is proposed to reconstruct the asymmetric wind speed distribution of hurricanes. Compared to other existing models, the new model has less parameters but can better fit to SAR and other observations.

Typical speech separation systems usually operate in the time-frequency (T-F) domain by enhancing the magnitude response and leaving the phase response unaltered. Recent studies, however, suggest that phase is important for perceptual quality, leading some researchers to consider magnitude and phase spectrum enhancements. The merging of the complex ideal ratio masking (cIRM) estimation and training with deep neural network (DNN) has been proved to be an effective way to improve speech separation. Furthermore, the label ambiguity (or permutation) problem has become a major barrier for speaker-independent multi-talker source separation, which prompts us to come up with new solutions. In this paper, to solve the problem of speaker-independent monaural source separation, we propose a novel method called pcIRM, which creatively achieves the cIRM estimation with the utterance-level permutation invariant training (uPIT). Specifically, pcIRM is implemented with the deep bidirectional LSTM (Bi-LSTM) RNN network, and evaluated with the WSJ0-2mix datasets. We report separation results for the proposed method and compare them to that of the existing state-of-the-art methods. Extensive experimental results demonstrate the advantages of our proposed pcIRM method in terms of the signal-to-distortion ratio (SDR) metric.

Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific collaboration, the movements of input and intermediate datasets across geographically distributed datacenters may cause intolerable latency that would hinder efficient execution of large-scale data-intensive scientific workflows. To address the problem, in this article we propose a novel multi-level K-cut graph partitioning algorithm to minimize the volume of data transfer across datacenters while satisfying load balancing and fixed data constraints. The algorithm first contracts the fixed input datasets in the same datacenter and their consuming tasks, and coarsens the contracted graph to a predefined scale in a level-by-level manner. Then, a K-cut algorithm is used to partition the resulted graph into K parts such that the cut size is minimized. After that, the partitioned graph is projected back to the original workflow graph, during which the load balancing constraint is maintained. We evaluate our algorithm using three real-world workflow applications and the results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Close set is a hypothesis utilized by the majority of machine-learning-based (ML-based) recognition algorithms, assuming all testing classes are known at training time. In real world, the more practical model is Open Set Recognition (OSR), which allows the presence of unknown classes at testing time, but requires the rejection ability of the model. The compact abating probability (CAP) model, which assumes the probability of class membership decreases in value (abates) as points move from known data toward open space, is first raised in traditional ML-based OSR method and soon become the basis of majority of later developed works. Most of convolutional-neural-network-based (CNN-based) OSR methods also adopted this model as their basis. During our exploration, however, we find that the application of CAP model to the CNN-based OSR method is restricted by the difference of its feature space from that of ML-based method. To the best of our knowledge, we are the first group who find this gap. To fill this gap, we propose a method called OpenSoftMax to transform the CNN-based methods' features by the process of SoftMax. In order to investigate performance, we further implement quantitative comparison between our OpenSoftMax method and the well-known CNN-based method OpenMax on caltech256 datasets. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposals.

Typhoon (TC) is one of the most powerful and destructive natural disasters. The analysis and determination of TC intensity is of great importance for disaster prevention [1] - [3] . Satellite remote sensing has become an effective means of monitoring TCs based on its high temporal and spatial resolution and large coverage. It is possible to estimate TC intensity using these satellite measurements when direct measurements are not available [4] . Microwave observations from polar-orbiting satellites can play a crucial role in revealing convective organization and eyewall structure that would otherwise be obscured by cloud tops [5] . Passive microwave sensors, such as SSM/I, TRMM/TMI, have been used to estimate TC intensity [6] - [10] . Besides, scatterometer also has allowed for continuous observation of ocean surface vector winds. Thus, scatterometer is a potential alternative for monitoring TCs. However, scatterometer measured wind speed range 2-24m/s, which make it very difficult to directly obtain the intensity of TCs [11] .

Summary The skyline query over uncertain data streams, as an important aspect of big data analysis, plays a significant role in domains such as environment monitoring, decision‐making, and data mining. The skyline query over uncertain data streams with sliding window model always focuses on the most recent N streaming items, which cannot meet the query requirements of different window scales at the same time. To improve the query flexibility and efficiency, we propose an efficient parallel method for processing uncertain n‐of‐N skyline queries; that is, computing the skyline for the most recent n (∀n ≤ N) items in parallel. Specifically, we first propose a framework for parallelizing the query computation for uncertain n‐of‐N skylines. Furthermore, we put forward a sliding window partitioning strategy as well as a streaming items mapping strategy to realize the load balance for each node. In addition, we define a spatial index structure RST based on R‐tree to organize the elements within each individual sliding window and candidate set in each which can significantly improve the dominance tests. Most importantly, we provide an encoding interval scheme to transform the n‐of‐N query into stabbing query in each compute node, which can greatly minimize the query scope and improve the query efficiency. In addition, we use a red‐black tree named RBI to store all stabbing intervals. Extensive experimental results demonstrate that the proposals are efficient and can greatly meet the query requirement of users in real applications.

Summary The Atmospheric General Circulation Model (AGCM) as one of the most important components of Climate System Model (CSM), has been proved to be an effective way for weather forecasting and climate prediction. Although lots of efforts have been conducted to improve the computing efficiency of AGCMs, such as exploit parallel algorithms, migrating codes, and even redesigning systems to adapt to the emerging computer architectures, it is not enough to match the real requirement, due to the limited scalability of the parallel algorithms themselves. Therefore, we design and implement a scalable parallel spectral‐based atmospheric circulation mode called PAGCM in this paper. Specifically, we first analyze the data dependencies of the dimensions in different spaces according to the calculation characteristics of spectral models, and based on which we propose a two‐dimensional decomposition algorithm in PAGCM to effectively increase the involving cores for the parallel computing, and thus reduce the overall computing time. Furthermore, to adapt to the novel data decomposition in each computing stage of dynamic framework, we propose three‐dimensional data transposition algorithms and data collection algorithms correspondingly, by considering of load balancing and communication optimization. Extensive experiments are conducted on Tianhe‐2 to validate the effectiveness and scalability of our proposals.

It's difficult to retrieve low to moderate wind speeds from Synthetic aperture radar (SAR) data at cross-polarization because of low signal to noise ratio. In this paper, we proposed a new approach for retrieving sea surface low to moderate wind speeds utilizing C-band dual-polarized SAR observations from the Sentinel-I. Dual-polarized SAR images and in situ buoy winds were collected and used to analyze the relations of the normalized radar cross section (NRCS) with the sea surface wind speed and incidence angle. The cross-polarized NRCS is hardly dependent on wind speed under 5m/s but is strongly dependent on incidence. Finally, a new cost function which combines co- and cross-polarized geophysical model functions was proposed. The resulting bias of -0.9 m/s and correlation coefficient of 0.846 demonstrate the effectiveness of the proposed cost function for wind speed under 20m/s. Furthermore, comparisons show that low wind speed retrieval from cross-polarized SAR data is hard, but it can contribute to that from co-polarized SAR data.

In this paper, we performed a comparison of wind speeds from the Quick Scatterometer (QuikSCAT), the MetOp-A Advanced Scatterometer (ASCAT), the WindSat Polarimetric Radiometer (WindSat) and ERA-Interim reanalysis using in situ ship measurements. The comparison was made over the China Sea during a 12-month period from January to December in 2008. The mean bias and Root Mean Square Error (RMSE) were calculated for the matchup dataset. The ASCAT wind speed product was observed more accurate and suitable for the China Sea during the research period with a relatively lower mean bias and RMSE. We also analyzed the accuracy of surface winds in different wind speed ranges. The statistical results show that the wind speeds of all products agree well in the ranges from 5m/s to 10m/s. However, underestimation at high wind speeds and overestimation at low wind speeds have been observed. Furthermore, the rain effects on the scattermeter wind measurements were considered, and ASCAT shows slightly better results and less affected by rain because of its C-band configuration compared with Ku-band QSCAT.

The modular datacenter networks (MDCN) comprise inter- and intra-container networks. Although it simplifies the construction and maintenance of mega-datacenters, interconnecting hundreds of containers and supporting online data-intensive services is still challenging. In this paper, we present meGautz, which is the first inter-container network that isolates inter- and intra-container traffic, and it has the following advantages. First, meGautz offers uniform high capacity among servers in the different containers, and balances loads at the container, switch, and server levels. Second, it achieves traffic isolation and allocates bandwidth evenly. Therefore, even under an all-to-all traffic pattern, the inter- and intra-container networks can deal with their own flows without interfering with each other, and both can gain high throughput. meGautz hence improves the performance of both the entire MDCN and individual servers, for there is no performance loss caused by resource competition. Third, meGautz is the first to achieve as graceful performance degradation as computation and storage do. Results from theoretical analysis and experiments demonstrate that meGautz is a high-capacity, fault-tolerant, and traffic isolated inter-container network.

For the accurate derivation of biophysical parameters based on surface reflectance, atmospheric correction is a necessary step to remove scattering and absorption effects by multiple atmospheric components. However, the huge amount of data and complex algorithms pose great computing challenges for massive operational tasks. Towards the global atmospheric correction for Medium Resolution Spectral Imager (MERSI) data onboard FY-3A and FY-3B, this paper describes an atmospheric correction algorithm considering the directional properties of the observed surface, and exploits its parallel implementations on multi-core and many-core architectures. The algorithm was developed with Open Multiprocessing (OpenMP) for multi-core processors and Compute Unified Device Architecture (CUDA) for Graphics Processing Units (GPU). Experimental results show the runtime was reduced from 187.19s to 42.63s and 10.11s when implemented on a multi-core processor and NVIDIA Tesla K80 respectively.

Summary The increasing volume of uncertain data has resulted in a dire need for supporting efficient uncertain data management. The skyline query as an important aspect of data management has received considerable attention in recent years, because of its importance in making intelligent decisions over complex data. Moreover, data collection and storage have become increasingly distributed, which makes the central assembly of data for storage and query infeasible and inefficient. Although many research efforts have been conducted to address the skyline query problem in various distributed scenarios, we still lack algorithms to address the queries over interval data, which is a special kind of attribute‐level uncertain data that widely exists in many applications. In this paper, we extensively study the skyline query over distributed interval data. We model the skyline query problem and define the distributed skyline query over interval data. Particularly, 2 efficient algorithms are proposed to retrieve the skylines progressively from distributed local sites with a highly optimized feedback framework. Moreover, we exploit 2 strategies for further improving the queries. Extensive experiments on synthetic and real datasets with real deployment are conducted to validate the effectiveness and efficiency of our proposals.

Efficient scheduling algorithm is critical for DAG-based applications to obtain high-performance in heterogeneous computing systems. In comparison with heuristic-based algorithms, meta-heuristic based scheduling algorithms can produce better results by searching in a guided manner. Biogeography-based optimization (BBO) is a recently proposed optimization technique which has shown less parameters, faster convergency, and superior performance than existing meta-heuristics. In this article, we introduce this novel optimization technique into the field of DAG scheduling. To reduce scheduling overhead, the proposed algorithm only encodes task mapping while using a heuristic strategy to determine task ordering. Moreover, it uses heuristic-based algorithms as baseline algorithms to obtain better results. We evaluate the BBO-based scheduling algorithm using three real world DAG-based applications under various parameter settings. The results show that the BBO-based scheduling algorithm outperforms the state-of-the-art meta-heuristic based algorithms.

Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads.

SUMMARY Due to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

Nowadays, wireless sensor networks are widely used to monitor real time events and answer the ad hoc queries from a certain member node. However, computing and maintaining the information of aggregate queries in event monitoring wireless sensor networks incurs high spatial and temporal overhead for storage and transmission where potentially high volumes of unnecessary data may run through with changing time. Failure of processing that data can lead to unsuccessful event detection which can be very dangerous and costly in real world application. In order to reduce the overhead caused by unnecessary data for aggregate, suppression techniques such as data fusion, data sharing, data prediction, lossless data compression and base station side query rewriting are widely discussed in the WSN research community. In this paper, a technique which makes use of the spatial data relationship of local sensor nodes collaboratively is proposed to rein the detection of composite events with data aggregate. An empirical study is carried out to show the efficiency of the new technique. In addition, the new algorithm is compared to the previous event detection algorithms without spatial data suppression technique to demonstrate the significant performance gains.

Due to the advantages of cost-effectiveness, on-demand resource provision and easy for sharing, cloud computing has grown in popularity with research community for deploying scientific applications such as workflows. When such interest continues growing and workflows are widely performed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place the application data across globally distributed data centers and schedule tasks according to the data layout to reduce both the latency and make span for workflow execution. In this paper, by utilising dependencies among datasets and tasks, we propose an efficient data and task co scheduling strategy that can place input datasets in a load balance way and meanwhile group the mostly related datasets and tasks together. We build a simulation environment on Tianhe supercomputer to evaluate the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.

Although semantic-based composition approaches have brought some comprehensive advantages such as higher precisions and recalls, they are far from the real practice and hard to be applied in real-world applications due to the several challenging issues such as performance issues of time-consuming ontology reasoning, exponentially expanded searching time in large service repositories, lack of available and consensus ontologies, and higher using thresholds for users who do not have much semantic knowledge. To reduce these issues, in this paper, we present an innovative composition technique by building an Extended Quick Service Query List (EQSQL) for supporting more efficient and more realistic service composition. In EQSQL, data structures are specially designed to record service information and their associated semantic concepts by in advance processing semantic-related computing during service publication period. Particularly, WordNet and semantic similarities among multiple heterogeneous ontologies are exploited in our developed algorithms for forming EQSQL. As a result, EQSQL-based planning algorithm can not only achieve a quick response for a composition request, but guarantee the semantic composition quality as well. More importantly, our approaches can be scalable to the large service repositories and also significantly alleviate users or developers from the burden of using complicated semantic service composition, thus making service composition easier and more realistic. Our final experiments further demonstrate the feasibility and the efficiency of our proposed approaches.

Improving MPI foundational software to suit multicore systems is a key issue for developing effective parallel software on high performance communication domain. Towards this issue, in this paper, we propose a novel technique, called MPI Accelerator or MPIActor in short, which is a transparent middleware to enhance conventional MPI libraries. The main idea is to optimize MPI routines for multicore systems by adopting threaded MPI mechanism and multicore architecture aware collectives in MPIActor. With the join of MPIActor, on one hand, all MPI processes in each node are mapped to several threads in one process. As a result, the overhead of intra-node point-to-point communications can greatly decrease. On the other hand, the collective routines are implemented by the cooperation of individual intra - and inter-node collective subroutines, and the intra-node collective subroutines can be further optimized by multicore architecture aware collective algorithms. Based on above idea, a framework involving an MPI_Reduce routine and a set of point-to-point communication routines has been implemented and evaluated on a 256 cores Nehalem platform. When compared to the performance of MVAPICH2, the final experimental results show that the performance by MPIActor can be significantly improved whatever by using OSU_LATENCY benchmark for point-to-point communications or IMB Reduce benchmark for reduction collectives. Especially, the performance results of using OSU_LATENCY benchmark even can be improved up to 321%.

A critical issue in service composition area is how to achieve an optimized overall end-to-end quality of service(QoS) requirements by effectively coordinating QoS constraints for individual service. However, this issue has not yet been well addressed. In this paper, we propose a novel method by employing a recursive bargaining Strategy to gradually remove QoS constraint violations for Optimizing service composition execution Path. Our method mainly exploits the hidden market competitive relationships which widely exist in real business world for developing a novel bargaining strategy. Based on this strategy, concessions can be made by service providers to offer better QoS values. By recursively using bargaining strategy, an initial execution path built by a local optimization policy for service composition, can be continually updated to be close to the optimal one by reselecting better service providers for meeting overall end-to-end QoS requirements. An experiment and evaluation have been made to demonstrate the feasibility and effectiveness of our proposed method.

Abstract A service‐based scientific workflow can be exposed as a composite service that consists of a set of logically connected sub‐services. A critical issue in this area is how to achieve overall optimized end‐to‐end QoS requirements by effectively coordinating individual QoS constraints of single service. Unfortunately, this issue has not been well addressed. In this paper, we propose a Reverse Order‐based approach to gradually remove QoS Constraint violations for building an optimized path to execute a scientific workflow. With our approach, an initial execution path for a scientific workflow is first built by employing the local optimization policy without considering user‐defined end‐to‐end QoS constraints. Based on this path, global QoS computing models can be used to calculate the global QoS values for each quality attribute. Then, QoS constraint violations can be detected by comparing global QoS values with end‐to‐end user‐defined QoS constraints. For each violation, a Reverse Order‐based correction algorithm by gradually removing QoS constraint violations is proposed to recursively correct it by reselecting critical service execution instances. As a result, an optimized execution path can be rebuilt to meet overall end‐to‐end QoS requirements. Comparison and simulation further demonstrate the feasibility and performance of our approach. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Scientific workflow is emerging as a promising scientific computing paradigm to offer the convenience for the scientists to resolve complex scientific problems. To successfully execute a scientific workflow, the workflow creation by depending on service discovery techniques should be made in the first place. Particularly, semantics have been proposed as a key to automatically solve service discovery issue for facilitating users to create a workflow. However, most of the semantic service discovery methods still remain at a low‐efficiency stage because they generally involve a large number of ontology reasoning that is often time consuming. To address this issue, we present an efficient service discovery method by building Quick Service Query list (QSQL) to support automated service discovery for creating a workflow. QSQL based on graph storage theory is an efficient service index list that is dynamically built by service publication algorithm. In QSQL, semantic relationships between the published services and all related ontology concepts can be processed in advance so that a large number of ontology reasoning can be avoided during service discovery. Further, our proposed discovery algorithm can efficiently select service models from QSQL to match a user query. The final experiments further demonstrate the feasibility and the efficiency of our proposed method. Copyright © 2009 John Wiley & Sons, Ltd.

Current existing semantic composition methods mainly rely on ontology reasoning to support automated service composition. However, in reality, ontologies are generally unavailable or ontology reasoning is time-consuming; thus existing semantic composition methods are becoming impractical in the general service integration field. To address this problem, in this paper, we present an innovative composition technique by combining Wordnet with ontologies together to build an extended quick service query list (EQSQL) for supporting automated service composition. In EQSQL, data structures are designed particularly to record service information and their associated semantic concepts by previously processing semantic-related computing during service publication stage. Based on EQSQL, not only a quick response can be achieved, but the semantically-similar composition quality as well even if there is a lack of concrete domain-dependent ontologies for a user query.

Current service composition and coordination still remain at large amount of manual processing stage, which has brought about low efficiency. In this paper, we present an efficient algorithm for abstract service discovery and a service instance selection method. Our algorithm firstly builds up the special data structures of ontology concepts based on graph storage theories when publishing abstract services. Then, these data structures form a quick service query list. In our algorithm, the large number of ontology reasoning is processed at service publication stage, thus we can make sure the quick query response in service discovery without much reasoning. In addition, our service instance selection methods based on OWL QoS ontology can enable grid resource sharing and coordination more flexible.