GoMA-DTA: A Gene Ontology-Guided Multimodal Attention Fusion Model for Drug-Target Affinity Prediction [0.03%]
一种基于Gene Ontology的多模态注意力融合模型用于药物靶点亲和力预测
An Xiong,Zheyu Zhou,Yazi Li et al.
An Xiong et al.
Accurate prediction of drug-target affinity (DTA) is essential for accelerating drug discovery. Although pretrained protein language models have achieved significant progress, existing methods predominantly focus on bottom-up sequence patte...
Multiway Autoregressive Network: A Dynamic Graph Representation Framework for Temporal Link Prediction [0.03%]
多路自回归网络：时空链预测的动态图表示框架
Ping He,Xiaohua Xu
Ping He
Understanding how links form and predicting future link states is of great importance in social, traffic, and many other complex temporal networks. These temporal networks are typically governed by multiple evolutionary mechanisms. However,...
Useok Choi,Seunjoong Lee,MyeongAh Cho
Useok Choi
Diffusion models have demonstrated remarkable performance across a wide range of generative tasks; however, their high sampling cost remains a critical bottleneck. To address this, consistency distillation (CD) was proposed, offering a redu...
Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network With Domain-Adversarial Training [0.03%]
基于注意力的BiLSTM网络在抑郁症检测中的多模态领域泛化研究：采用域对抗训练方法
Ali Tabaraei,Federico Simonetta,Stavros Ntalampiras
Ali Tabaraei
Automatic depression detection with deep learning has shown promise, but often suffers from limited generalization due to domain shift arising from interspeaker variability. To address this critical issue, we present the first patient-indep...
Tree-IS: Efficient Index Selection and Optimization Model for Dynamic Workloads [0.03%]
树-IS：动态工作负载下的高效索引选择与优化模型
Shaojie Qiao,Lei Yang,Rongmin Tang et al.
Shaojie Qiao et al.
Index selection is a crucial component in database query optimization. Traditional database index selection is inefficient when handling large-scale and complex structured query language (SQL) queries, and existing methods often overlook in...
Causality-Aware Spatiotemporal Adversarial Learning for Knowledge-Data Fault Diagnosis in Large-Scale Industrial Processes [0.03%]
用于大规模工业过程知识数据故障诊断的因果关系感知时空对抗学习方法
Chi Zhang,Tengxuan Sun,Kaixiang Peng et al.
Chi Zhang et al.
Effective process monitoring and fault diagnosis (PMFD) are essential for safe and efficient operation in large-scale industrial processes. However, many existing methods still suffer from limited interpretability, insufficient exploitation...
Generative Incomplete Multiview Representation Learning With Learnable Graph [0.03%]
基于可学习图的生成性多视图不完备表示学习
Ying Zou,Zihan Fang,Shide Du et al.
Ying Zou et al.
Incomplete and partially observed multiview data pose a fundamental challenge to representation learning, as missing views and highly complex cross-view inconsistencies hinder effective feature integration and alignment. While recent deep g...
Chameleon: Backdoor Attacks With Restoration-Based Triggers Using Diffusion Models [0.03%]
变色龙：使用扩散模型进行恢复型触发的后门攻击
Boyang Zhou,Yixin He,Xiaofu Chen et al.
Boyang Zhou et al.
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where the backdoored models behave normally on benign samples but misclassify trigger-carrying samples. However, when triggers are introduced as external cues inconsistent with...
Toward Robust Weakly Supervised Text Classification: Weak Supervision Generation and Correlation-Aware Supervision Propagation [0.03%]
迈向鲁棒的弱监督文本分类：弱监督生成和关联感知监督传播
Ximing Li,Yiming Wang,Chenglong Hu et al.
Ximing Li et al.
Multilabel text classification (MLTC) methods require enormous labeled training samples to ensure the model's performance, which involves significant manual labor costs. An alternative to conducting MLTC is to only employ predefined represe...
VCF-CLIP: Visual Context-Driven Fine-Grained Prompt Learning for Zero-Shot Anomaly Detection [0.03%]
基于视觉上下文的细粒度提示学习的零样本异常检测方法(VCF-CLIP)
Kaiwen Fu,Fei Qi,Chengyuan Chang et al.
Kaiwen Fu et al.
Benefiting from recent advances in vision-language models (VLMs), numerous CLIP-based zero-shot anomaly detection (ZSAD) methods have been proposed to address the cold-start problem. Despite their impressive performance, these methods still...