Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative among different parties. Compared traditional centralized that requires collecting data from each party, in federated learning, only the locally trained models or computed gradients are exchanged, without exposing any information. As a result, it is able protect privacy some extent. In recent years, become more and prevalent there have been many surveys for summarizing related methods this hot...

Abstract Background Cross-sectional and longitudinal studies have found that problematic mobile phone use, bedtime procrastination, sleep quality, depressive symptoms are strongly associated. However, inconsistent regarding whether use predicts or vice versa, factors been infrequently focused on in this regard. In addition, few examined the associations directions of effects between these factors. Therefore, study aims to explore relationship among college students. Methods Overall, 1181...

Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative among different parties. Compared traditional centralized that requires collecting data from each party, in federated learning, only the locally trained models or computed gradients are exchanged, without exposing any information. As a result, it is able protect privacy some extent. In recent years, become more and prevalent there have been many surveys for summarizing related methods this hot...

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise the remarkable performance of GNNs relies complete and trustworthy initial descriptions (i.e., node features structure), which is often not satisfied since real-world graphs are incomplete due to unavoidable factors. In particular, face greater challenges when both structure at same time. The existing methods either focus feature completion or completion. They...

The small conductance mechanosensitive ion channel (MscS)-like (MSL) proteins in plants are evolutionarily conserved homologs of the bacterial channels. As sole member Arabidopsis MSL family localized mitochondrial inner membrane, MSL1 is essential to maintain normal membrane potential mitochondria. Here, we report a cryoelectron microscopy (cryo-EM) structure thaliana (AtMSL1) at 3.3 Å. overall architecture AtMSL1 similar MscS. However, transmembrane domain larger. Structural differences...

The transient receptor potential channel subfamily A member 1 (TRPA1) ion is an evolutionary conserved polymodal sensor responding to noxious temperature or chemical stimuli. Notably, the thermosensitivity of TRPA1 varies among different species even isoforms in same species. However, underlying molecular basis its thermo-gating remains largely unknown. Here, we determine structures a heat-sensitive isoform Drosophila melanogaster two distinct conformations with cryo-samples prepared at 8...

The increasing interest in international travel has raised the demand of retrieving point interests (POIs) multiple languages. This is even superior to find local venues such as restaurants and scenic spots unfamiliar languages when traveling abroad. Multilingual POI retrieval, enabling users desired POIs a demanded language using queries numerous languages, become an indispensable feature today's global map applications Baidu Maps. task non-trivial because two key challenges: (1) visiting...

Scholar clustering has garnered increasing attention due to the explosive growth of scholar data. Although researchers have proposed many algorithms cluster scholars, they typically focus on scholars from intrinsic view (scholars' contents). These may lead inaccurate and biased results because ignore extrinsic (scholar's specialty) changeability scholars' interest in each view. In this paper, we propose a multi-view topic model (MSCT), which integrates complementary information both views...

The edge computing-based 5G networks have the advantages in efficiently offloading large-scale Internet traffic, which is considered to be a promising architecture alleviate conflict between transmission performance and quality of experience (QoE). However, due unreliability service providers mutual interference wireless channels networks, it still difficult for existing solutions provide satisfactory multimedia services mobile users. In response these crucial challenges, this paper proposes...

In this paper, we propose a novel distributed scalable federated graph neural network (FGNN) to solve the cross-graph node classification problem. existing methods, source and target need share their data label, for nodes in are same semantic space. However, graphs cannot label without encryption due regulations interests. order satisfy privacy of all parties, universal rules learned. We add PATE mechanism into domain adversarial (DANN) construct cross-network model, extract effective...

Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches drawing long-range incongruity patterns. However, constructed structure might be prone to errors (e.g., noisy or incomplete) not optimal task. Errors produced during construction step cannot remedied may accrue following stages, resulting poor...

In this paper, we introduce L.IVE: an online interactive video-based learning environment with alternative design and architecture that integrates three major interface components: video, comment threads, assessments. This is in contrast the approach of existing interfaces which visually separate these components. Our study, compares L.IVE popular environments, suggests advantages integrated as compared to separated learning.

Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal query consists of finding journeys from source destination in networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation generally continent-sized. This characteristic highlights the need for parallel computing accelerate queries. Meanwhile, often fragmented distributively stored on different...

During the training process of federated learning models, domain information target test data on server can differ greatly from each client, leading to a decrease in performance model. Additionally, due privacy protection during training, clients cannot see data, and distribution be used. This poses new challenge for learning. Domain generalization techniques are often used centralized frameworks resolve such problems. In recent years, method based feature decorrelation has enabled models...

User preferences behind users' decision-making processes are highly diverse and may range from lower-level concepts with more specific intentions higher-level general intentions. In this case, user tend to be expressed hierarchically. However, learning such different levels behaviors is challenging, remains largely neglected by the existing literature. Meanwhile, behavior data tends sparse because of limited response vast combinations users items, which results in cold-start problems unclear...

Abstract Human action understanding (HAU) is a broad topic that involves specific tasks, such as localisation, recognition, and assessment. However, most popular HAU datasets are bound to one task based on particular actions. Combining different but relevant tasks establish unified system challenging due the disparate actions across datasets. A large‐scale comprehensive benchmark, namely SkatingVerse constructed for segmentation, proposal, focus fine‐grained sport action, hence figure...