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The autonomous driving algorithm studied in this paper makes a ground vehicle capable of sensing its environment via visual images and moving safely with little or no human input. Due to the limitation of the computing power of end side devices, the autonomous driving algorithm should adopt a lightweight model and have high performance. Conditional imitation learning has been proved an efficient and promising policy for autonomous driving and other applications on end side devices due to its high performance and offline characteristics. In driving scenarios, the images captured in different weathers have different styles, which are influenced by various interference factors, such as illumination, raindrops, etc. These interference factors bring challenges to the perception ability of deep models, thus affecting the decision-making process in autonomous driving. The first contribution of this paper is to investigate the performance gap of driving models under different weather conditions. Following the investigation, we utilise StarGAN-V2 to translate images from source domains into the target clear sunset domain. Based on the images translated by StarGAN-V2, we propose Conditional Imitation Learning with ResNet backbone named Star-CILRS. The proposed method is able to convert an image to multiple styles using only one single model, making our method easier to deploy on end side devices. Visualization results show that Star-CILRS can eliminate some environmental interference factors. Our method outperforms other methods and the success rate values in different tasks are 98%, 74%, and 22%, respectively. [ABSTRACT FROM AUTHOR]

The autonomous driving algorithm studied in this paper makes a ground vehicle capable of sensing its environment via visual images and moving safely with little or no human input. Due to the limitation of the computing power of end side devices, the autonomous driving algorithm should adopt a lightweight model and have high performance. Conditional imitation learning has been proved an efficient and promising policy for autonomous driving and other applications on end side devices due to its high performance and offline characteristics. In driving scenarios, the images captured in different weathers have different styles, which are influenced by various interference factors, such as illumination, raindrops, etc. These interference factors bring challenges to the perception ability of deep models, thus affecting the decision-making process in autonomous driving. The first contribution of this paper is to investigate the performance gap of driving models under different weather conditions. Following the investigation, we utilise StarGAN-V2 to translate images from source domains into the target clear sunset domain. Based on the images translated by StarGAN-V2, we propose Conditional Imitation Learning with ResNet backbone named Star-CILRS. The proposed method is able to convert an image to multiple styles using only one single model, making our method easier to deploy on end side devices. Visualization results show that Star-CILRS can eliminate some environmental interference factors. Our method outperforms other methods and the success rate values in different tasks are 98%, 74%, and 22%, respectively. [ABSTRACT FROM AUTHOR]

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of traffic data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of traffic data, and the problem is more difficult for long-term forecast. In this paper, we propose an Attention based Spatial-Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self-attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive fields that is beneficial for long-term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self-attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels. However, most existing unsupervised approaches suffer from the problem of anomaly contamination, which results in models that are unable to learn the normal pattern well and further deteriorate the performance of detection methods. To this end, a novel unsupervised method, called Self-adversarial Variational Autoencoder with Spectral Residual (SaVAE-SR), is introduced for time series anomaly detection in this paper. The SaVAE-SR first produces labels for unlabeled training data using the spectral residual technique to identify the most critical anomalies. A VAE model with a modified loss that can leverage label information to remove the influence of anomalous points is then trained in a self-adversarial manner, enabling the model to self-evaluate the learning of complex data distribution and improve itself accordingly. Specifically, the encoder acts as an encoder to approximate the posterior of latent variables and as a discriminator to evaluate the generative ability of the generator and improve itself accordingly. The generator is trained to capture the underlying data distribution and attempts to produce real samples to deceive the discriminator. The encoder and generator of the model compete with each other just like the behavior of GANs but work together under the theoretical framework of VAEs. As a result, the SaVAE-SR model combines the respective strengths of the VAE and adversarial training but does not require an additional discriminator, which makes the whole model very compact. Extensive experiments on five datasets demonstrate the superiority of the proposed method over the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Summary: Bug repair is time consuming and tedious, which hampers software maintenance. To alleviate the burden, automated program repair (APR) is proposed and has been fruitful in the last decade. Evolutionary repair is the seminal work of this field and proliferated a family of approaches. The performance of evolutionary repair approaches is affected by two main factors: (1) search space, which defines all possible patches, and (2) search algorithms, which navigate the space. Although recent approaches have achieved remarkable progress, the main challenges of the two factors still remain. On one hand, the different kinds of search space are very coarse for containing correct patches. On the other hand, the search process guided by genetic algorithms is inefficient in finding the correct patches in an appropriate time budget. In this paper, we propose MicroRepair, a new evolutionary repair approach to address the two challenges. Rather than finding statement‐level patches like existing genetic repair approaches, MicroRepair enlarges the search space by breaking the statements into finer‐granularity ingredients that consist of AST leaves. As the search space grows exponentially, the former search algorithms may become inefficient in navigating the larger space. We utilize the best multiobjective search algorithm selected from our empirical comparison of a set of search algorithms. At last, we find redundancies search in the existing genetic process, and we further design a history‐aware search strategy to accelerate the process. We evaluated MicroRepair on 224 bugs of real‐world from the benchmark Defects4J and compared it with several state‐of‐the‐art repair approaches. The evaluation results show that MicroRepair correctly repaired 26 bugs with a precision of 62%, which significantly outperforms the state‐of‐the‐art evolutionary APR approaches in terms of precision. Moreover, the history‐aware search boosts the repair execution speed by 4% on average. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITS), deploying fine-grained sensors to continuously collect spatial–temporal traffic data is important but impractical due to the expensive cost. Fortunately, spatial–temporal kriging methods bring advanced solutions for interpolating traffic data for locations without sensors, but they still have the following two drawbacks: (1) The widely adopted predefined and adaptive graphs are either inflexible or limited to transductive learning. (2) The sampling strategies to support inductive learning on new graphs result in losing important partial information. To overcome the above issues, in this paper, we propose an Inductive and Adaptive Graph Convolution Networks (IAGCN) for spatial–temporal traffic data kriging in an inductive manner. Specifically, we propose an adaptive graph constructor to model the hidden spatial relation of nodes and learn the spatial dependency that the predefined graph structure cannot capture. It can work inductively and does not require retraining when introducing new nodes. Additionally, we design a framework that integrates inductive graph convolution and temporal convolution to simultaneously capture the complex spatial–temporal dependencies of traffic data. Finally, to address the information loss issue caused by random sampling, we design a predicted-based constraint task that perceives and utilizes the history information of all observed sensors to predict the current data, as well as approach the result of the interpolation and prediction. Experiments on four real-world datasets show that IAGCN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

• Method achieved a good result of 94.4% accuracy for apnea detection in segment. • Firstly employ deep residual network for apnea detectionbased on low-sample-rate data. • Weights of network can be fine-tuned on clinical. • Strong adaptivity. Model also works well using ECG-derived respiration (EDR) signal. Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to the complex and time-consuming polysomnography (PSG) diagnosis method. Effective and precise diagnosis support system using electrocardiograph (ECG) is required. In this paper, we propose an approach using residual network to detect apnea based on RR intervals (intervals between R-peaks of ECG signal). In our model, we apply residual network to represent information carried by RR intervals. Moreover, we proposed a novel perspective, called dynamic autoregressive representation, to provide interpretation of representing the RR intervals by convolutional layers. This approach is tested for per-segment apnea detection using publicly available dataset on Physionet. 30 overnight recordings are used for training and 5 for testing. We achieve a good result of 94.4% accuracy, 93.0% sensitivity and 94.9% specificity. This result outperform other prevalent methods based on RR intervals. This model also shows its good adaptivity while using ECG-derived respiration signal (EDR) in experiments. Its extensiveness is evaluated and compared in experiments. The proposed model is also compared with deep neural networks using original ECG signals for apnea detection, and it achieves better result using fewer input samples. We develop a deep residual network to detect apnea on low-sample-rate RR intervals. The result suggests a possibility of representing RR intervals by neural network. The model showed strong adaptivity when using EDR input. [ABSTRACT FROM AUTHOR]

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks. [ABSTRACT FROM AUTHOR]

Multiscale entropy analysis has become a prevalent complexity measurement and been successfully applied in various fields. However, it only takes into account the information of mean values (first moment) in coarse-graining procedure. Then generalized multiscale entropy (MSE n ) considering higher moments to coarse-grain a time series was proposed and MSE σ 2 has been implemented. However, the MSE σ 2 sometimes may yield an imprecise estimation of entropy or undefined entropy, and reduce statistical reliability of sample entropy estimation as scale factor increases. For this purpose, we developed the refined model, RMSE σ 2 , to improve MSE σ 2 . Simulations on both white noise and 1 ∕ f noise show that RMSE σ 2 provides higher entropy reliability and reduces the occurrence of undefined entropy, especially suitable for short time series. Besides, we discuss the effect on RMSE σ 2 analysis from outliers, data loss and other concepts in signal processing. We apply the proposed model to evaluate the complexity of heartbeat interval time series derived from healthy young and elderly subjects, patients with congestive heart failure and patients with atrial fibrillation respectively, compared to several popular complexity metrics. The results demonstrate that RMSE σ 2 measured complexity (a) decreases with aging and diseases, and (b) gives significant discrimination between different physiological/pathological states, which may facilitate clinical application. [ABSTRACT FROM AUTHOR]

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai–Alanis–Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors. [ABSTRACT FROM AUTHOR]

In vehicle re-identification, identifying a specific vehicle from a large image dataset is challenging due to occlusion and complex backgrounds. Deep models struggle to identify vehicles accurately when critical details are occluded or the background is distracting. To mitigate the impact of these noisy factors, we propose Identity-guided Spatial Attention (ISA) to extract more beneficial details for vehicle re-identification. Our approach begins by visualizing the high activation regions of a strong baseline method and identifying noisy objects involved during training. ISA generates an attention map to mask most discriminative areas, without the need for manual annotation. Finally, the ISA map refines the embedding feature in an end-to-end manner to improve vehicle re-identification accuracy. Visualization experiments demonstrate ISA's ability to capture nearly all vehicle details, while results on three vehicle re-identification datasets show that our method outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract: The detection of overlapping community structure in networks can give insight into the structures and functions of many complex systems. In this paper, we propose a simple but efficient overlapping community detection method for very large real-world networks. Taking a high-quality, non-overlapping partition generated by existing, efficient, non-overlapping community detection methods as input, our method identifies overlapping nodes between each pair of connected non-overlapping communities in turn. Through our analysis on modularity, we deduce that, to become an overlapping node without demolishing modularity, nodes should satisfy a specific condition presented in this paper. The proposed algorithm outputs high quality overlapping communities by efficiently identifying overlapping nodes that satisfy the above condition. Experiments on synthetic and real-world networks show that in most cases our method is better than other algorithms either in the quality of results or the computational performance. In some cases, our method is the only one that can produce overlapping communities in the very large real-world networks used in the experiments. [Copyright &y& Elsevier]

Complex physiological signals carry critical information about the underlying dynamics of the complex system that produced them. By analyzing and exploring these signals, we can identify which state the system is in and understand the underlying dynamics of that system. Numerous linear and nonlinear methods have been developed for analyzing physiological signals, but they either rely on certain assumptions or are easily affected by various factors, which limits their applications. In this paper, we propose a novel dissimilarity measure between two signals, which is based on the ordinal pattern representation of signals. Therefore, it naturally inherits the advantages of ordinal pattern analysis, namely simplicity, robustness, and low complexity in computation without further prior assumptions. Finally, we apply this new measure to analyze several physiological signals derived from different physiological and pathological conditions to assess the effectiveness of the proposed measure. Experimental results from heart rate analysis, EEG signals analysis of healthy subjects and patients with epilepsy, and analysis of EEG signals at different sleep stages in healthy subjects demonstrate that the new measure is capable of distinguishing signals from different conditions and is therefore a useful tool for analyzing various physiological signals. • A new dissimilarity measure based on ordinal pattern representation is proposed. • The new measure is robust to noise and without further model assumptions. • The new measure is capable of distinguishing signals from different states. • Three case studies demonstrate the effectiveness of the new dissimilarity measure. [ABSTRACT FROM AUTHOR]

Item co-occurrence is an important pattern in recommendation. Due to the difference in correlation, the matching degrees between the target and historical items vary. The higher the matching degree, the greater probability they co-occur. Recently, the recommendation performance has been greatly improved by leveraging item relations. As an important bond imposed by relations, these connected items should have a strong correlation in the calculation of certain measures. This kind of correlation can be the biased knowledge that benefits parameter training. Specifically, we focus on tuples containing the target item and latest relational items that have relations such as complement or substitute with the target item in user's behavior sequence. Such close relations mean the matching degrees between relational items and historical items should be highly affected by that of the target item and historical items. For example, given a relational item having relation complement with the target item, if the target item has high matching degrees with some items in user's behavior sequence, this complementary item should behave similarly for the co-occurrence of complementary items. Under guidance of the above thought, in this work, we propose a target-relation regulated mechanism which converts the biased knowledge of high correlation of matching degrees into a regulation. It integrates the target item and relational items in history as a whole to characterize the matching score between the target item and historical items. Experiments conducted on three real-world datasets demonstrate that our model can significantly outperform a set of state-of-the-art models. [ABSTRACT FROM AUTHOR]

Causal analysis based on non-uniform embedding schemes is an important way to detect the underlying interactions between dynamic systems. However, there are still some obstacles to estimating high-dimensional conditional mutual information and forming optimal mixed embedding vector in traditional non-uniform embedding schemes. In this study, we present a new non-uniform embedding method framed in information theory to detect causality for multivariate time series, named LM-PMIME, which integrates the low-dimensional approximation of conditional mutual information and the mixed search strategy for the construction of the mixed embedding vector. We apply the proposed method to simulations of linear stochastic, nonlinear stochastic, and chaotic systems, demonstrating its superiority over partial conditional mutual information from mixed embedding (PMIME) method. Moreover, the proposed method works well for multivariate time series with weak coupling strengths, especially for chaotic systems. In the actual application, we show its applicability to epilepsy multichannel electrocorticographic recordings. [ABSTRACT FROM AUTHOR]

Light Field (LF) cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of LF cameras. In this paper, an end-to-end learning-based method is proposed to simultaneously reconstruct all view images in LFs with higher spatial resolution. Based on the epipolar geometry, view images in one LF are first grouped into several image stacks and fed into different network branches to learn sub-pixel details for each view image. Since LFs have dense sampling in angular domain, sub-pixel details in multiple spatial directions are learned from corresponding angular directions in multiple branches, respectively. Then, sub-pixel details from different directions are further integrated to generate global high-frequency residual details. Combined with the spatially upsampled LF, the final LF with high spatial resolution is obtained. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods in both visual and numerical evaluations. We also implement the proposed method on LFs with different angular resolution and experiments show that the proposed method achieves superior results than others, especially for LFs with small angular resolution. Furthermore, since the epipolar geometry is fully considered, the proposed network shows good performances in preserving the inherent epipolar property in LF images. [ABSTRACT FROM AUTHOR]

Distantly supervised relation extraction aims to obtain relational facts from unstructured texts. Although distant supervision can automatically generate labeled training instances, it inevitably suffers from the wrong-label problem. Most of the current work is based on the bag-level for solving the noise problem, where a bag is composed of multiple sentences containing mentions of the same entity pair. However, previous studies mostly represent sentences from a single perspective, wherein insufficient modeling of global information restricts the effectiveness of denoising. In this study, we propose a novel distantly supervised relation extraction approach that incorporates the global contextual information of sentences to guide the denoising process and generate an effective bag-level representation. Simultaneously, knowledge-aware word embeddings were generated to enrich sentence-level representations by introducing both structured knowledge from external knowledge graphs and semantic knowledge from the corpus. The experimental results demonstrate that our proposed approach outperforms state-of-the-art methods on both versions of the large-scale benchmark New York Times dataset. In addition, the differences between the two versions of the dataset were investigated through further comparative experiments. [ABSTRACT FROM AUTHOR]

With the increasing accumulation of spatial-temporal trajectory data, location-based data mining has recently been extensively studied. A fundamental research topic in this field is learning the embedding vectors of locations through self-supervised pre-training. Pre-trained embedding vectors can utilize the highly available unlabeled trajectory data, and benefit downstream tasks in multiple aspects. However, most existing methods ignore the temporal information hidden in the visited time of locations in trajectories. Considering that human activities are highly regulated by specific periods of a day, temporal information can reflect some intrinsic characteristics of locations, so it is necessary to fuse them into location embedding vectors. In this paper, we propose a Time-Aware Location Embedding (TALE) pre-training method based on the CBOW framework, which is able to incorporate temporal information into the learned embedding vectors of locations. A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. In order to verify the effectiveness of TALE, we apply the learned embedding vectors into three downstream location-based prediction tasks, i.e., location classification, location visitor flow prediction and user next location prediction. Experiments are conducted on four real-world user trajectory datasets, and the experimental results demonstrate that our TALE model can obviously help downstream tasks gain better performance. [ABSTRACT FROM AUTHOR]

Providing both point estimation and uncertainty quantification for traffic forecasting is crucial for supporting accurate and reliable services in intelligent transportation systems. However, the majority of existing traffic forecasting works mainly focus on point estimation without quantifying the uncertainty of predictions. Meanwhile, existing uncertainty quantification (UQ) methods fail to capture the inherent static characteristics of traffic uncertainty along both the spatial and temporal dimensions. Directly equipping the traffic forecasting works with uncertainty quantification techniques may even damage the prediction accuracy. In this paper, we propose a novel traffic forecasting model aiming at providing point estimation and uncertainty quantification simultaneously, called STUP. Compared to the traditional graph convolution networks (GCNs), our framework is able to incorporate uncertainty quantification into traffic forecasting to further improve forecasting performance. Specifically, we first develop an adaptive strategy to initialize uncertainty distribution. Then a kind of spatial–temporal uncertainty layer is carefully designed to model the evolution process of both the traffic state and its corresponding uncertainty, along with a gated adjusting unit to avoid error information propagation. Finally, we propose a novel constraint loss to further help improve the forecasting accuracy and to alleviate the training difficulty caused by the lack of uncertainty labels. Experiments on five real-world traffic datasets demonstrate that STUP outperforms the state-of-the-art baselines on both the traffic prediction task and uncertainty quantification task. [Display omitted] • A spatial–temporal traffic forecasting method considering uncertainty is proposed. • Discovering the relationship between traffic data and factors of uncertainty. • Balancing the accuracy and reliability on traffic forecasting. [ABSTRACT FROM AUTHOR]

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a widely used diagnostic tool for arrhythmia assessment in clinical practice. However, current arrhythmia detection algorithms rely heavily on signal-based data, while cardiologists often use image-based data. This discrepancy, combined with individual differences in physiological signals, poses challenges for accurate arrhythmia detection. To address these challenges and improve arrhythmia detection performance, we propose a homologous and heterogeneous multi-view inter-patient adaptive network. We designed a multi-view representation learning module to capture dynamic and morphological characteristics from ECG signals and electrocardiographic images. Expert knowledge was also elicited to gain internally-invariant characteristics of each category. Finally, we designed a new loss function that aligns the embedding of the source and target domains in the feature space to minimize the negative effects of individual differences. Experiments on the MIT-BIH arrhythmia database demonstrate that our proposed method outperforms state-of-the-art baselines in terms of accuracy, positive predictive value, sensitivity and F1-score. These results indicate the effectiveness of our method in accurately detecting arrhythmias. Our homologous and heterogeneous multi-view inter-patient adaptive network successfully addresses the challenges of utilizing both ECG signal and electrocardiographic image data for arrhythmia detection and overcoming individual differences in physiological signals. Our proposed method has the potential to improve early diagnosis and treatment outcomes of arrhythmias in clinical practice. • A homologous and heterogeneous multi-view adaptive network is proposed. • The network captures dynamic and morphological characteristics of ECG. • The network elicits expert knowledge to model internally-invariant characteristics. • The network is able to overcome individual differences. • The proposed network outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Traditional collaborative filtering based recommender systems generally suffer from the interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate the issue and improve recommendation performance by introducing social information. Existing social recommendation studies primarily focus on the direct connections between users, such as friendship or users' correlation. Unfortunately, there often is a severe data sparsity issue in above social data as well, which limits the performance of these models. In contrast, user-group relationships, another valuable social information that is formed by users joining the groups they are interested in, have received insufficient attention. In this paper, we focus on this relationship, demonstrate its excellent effectiveness in alleviating the problem of data sparsity, and integrate it into our recommendation model IGRec (I ntegrating user- G roup relationships for social Rec ommendation) in a reasonable way. Specifically, to address the problem that existing group-information-enhanced methods have not modeled users' collaborative interests and social influence in depth, we reformulate the available data into two bipartite graphs: user-item graph and user-group graph. And then employ more robust high-order GCN-based model combining a multi-layer attention mechanism to learn user and item representation from two graphs. Furthermore, we notice that due to the high complexity of user-group networks, the interests of some users in the same group may be far different, especially in those large-scale groups. The indiscriminate use of high-order neighbors' information in user-group graph may result in the introduction of negative information during the embedding propagation. Thus, to obtain a more precise representation for user and item, we propose to constrain the graph convolution operations at the social side inside subgraphs composed of users with similar interests and the groups they have joined in our model. Finally, experimental results on three real-world datasets clearly show the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (, ) motifs (, ) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (, ) motifs in DNA sequences. In conjunction with our method, we have adopted a simple ChIP-enriched sampling strategy for finding these motifs in large-scale ChIP-enriched regions. Empirical studies on synthetic samples and applications using several ChIP data sets including 16 TF (transcription factor) ChIP-seq data sets and five TF ChIP-exo data sets have demonstrated that our proposed method is capable of finding these motifs with high efficiency and accuracy. The source code for FMotif is available at http://211.71.76.45/FMotif/. [ABSTRACT FROM AUTHOR]

Correlated time series refer to multiple time series which are recorded simultaneously to monitor the changing of multiple observations in a whole system. Correlated time series prediction plays a significant role in many real-world applications to help people make reasonable decisions. Yet it is very challenging, because different from single time series, correlated time series show both intra-sequence temporal dependencies and inter-sequence spatial dependencies. In addition, correlated time series are also affected by external factors in actual scenarios. Although RNNs have been proved to be effective on sequential data modeling, existing related works only focus on sequential patterns in a single time series, failing to comprehensively consider the inter-dependencies among multiple time series, which is essential for correlated time series prediction. In this paper, we propose a novel variant of LSTM, named CTS-LSTM, to collectively forecast correlated time series. Specifically, spatial and temporal correlations are explicitly modeled and respectively maintained in cells to capture the complex non-linear patterns in correlated time series. A general interface for handling external factors is further designed to enhance forecasting performance of the model. Experiments are conducted on two types of real-world datasets, viz., civil aviation passenger demand data and air quality data. And our CTS-LSTM achieves at least 9.0%, 16.5% and 21.3% lower RMSE, MAE and MAPE compared to the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]