arXiv:2603.13406v2 Announce Type: replace-cross Abstract: Emotion recognition in videos is a pivotal task in affective computing, where identifying subtle psychological states such as Ambivalence and Hesitancy holds significant value for behavioral intervention and digital health. Ambivalence and Hesitancy states often manifest through cross-modal inconsistencies such as discrepancies between facial expressions, vocal tones, and textual semantics, […]
Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences
arXiv:2603.21508v1 Announce Type: cross Abstract: Machine learning models are widely integrated into modern mobile apps to analyze user behaviors and deliver personalized services. Ensuring low-latency on-device model execution is critical for maintaining high-quality user experiences. While prior research has primarily focused on accelerating model inference with given input features, we identify an overlooked bottleneck in […]
DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers
arXiv:2603.21608v1 Announce Type: cross Abstract: Recent advances in generative models, such as diffusion and flow matching, have shown strong performance in audio tasks. However, speech enhancement (SE) models are typically trained on limited datasets and evaluated under narrow conditions, limiting real-world applicability. To address this, we propose DiT-Flow, a flow matching-based SE framework built on […]
Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration
arXiv:2603.21760v1 Announce Type: cross Abstract: Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing long-range anatomical correspondence and maintaining deformation consistency. In this work, we present a […]
SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
arXiv:2603.21904v1 Announce Type: cross Abstract: Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo-label validation that disregards global anatomical constraints, thus failing to prevent the formation of globally implausible […]
Mamba-VMR: Multimodal Query Augmentation via Generated Videos for Precise Temporal Grounding
arXiv:2603.22121v1 Announce Type: cross Abstract: Text-driven video moment retrieval (VMR) remains challenging due to limited capture of hidden temporal dynamics in untrimmed videos, leading to imprecise grounding in long sequences. Traditional methods rely on natural language queries (NLQs) or static image augmentations, overlooking motion sequences and suffering from high computational costs in Transformer-based architectures. Existing […]
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
arXiv:2603.22281v1 Announce Type: cross Abstract: Recent progress in latent world models (e.g., V-JEPA2) has shown promising capability in forecasting future world states from video observations. Nevertheless, dense prediction from a short observation window limits temporal context and can bias predictors toward local, low-level extrapolation, making it difficult to capture long-horizon semantics and reducing downstream utility. […]
Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests
arXiv:2508.14936v2 Announce Type: replace Abstract: Synthetic data holds substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure […]
Social Comparison without Explicit Inference of Others’ Reward Values: A Constructive Approach Using a Probabilistic Generative Model
arXiv:2512.18687v4 Announce Type: replace Abstract: Social comparison$unicodex2014$the process of evaluating one’s rewards relative to others$unicodex2014$is an essential feature of social emotions such as envy and plays a fundamental role in primate social cognition. However, it remains unknown how information about others’ rewards affects one’s own reward valuation. This study examines whether monkeys merely recognize objective […]
Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts
arXiv:2603.13239v2 Announce Type: replace Abstract: Smart contracts play a central role in blockchain systems by encoding financial and operational logic. Still, their susceptibility to subtle security flaws poses significant risks of financial loss and erosion of trust. LLMs create new opportunities for automating vulnerability detection, yet the effectiveness of different prompting strategies and model choices […]
Imaging foundation model for universal enhancement of non-ideal measurement CT
arXiv:2410.01591v3 Announce Type: replace-cross Abstract: Non-ideal measurement computed tomography (NICT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degrade image quality, limiting clinical acceptability. Although deep learning methods have been used to enhance NICT images, their reliance on large training datasets and limited generalizability across diverse settings hinder practical use. We […]
Must Read: A Comprehensive Survey of Computational Persuasion
arXiv:2505.07775v2 Announce Type: replace-cross Abstract: Persuasion is a fundamental aspect of communication, influencing decision-making across diverse contexts, from everyday conversations to high-stakes scenarios such as politics, marketing, and law. The rise of conversational AI systems has significantly expanded the scope of persuasion, introducing both opportunities and risks. AI-driven persuasion can be leveraged for beneficial applications, […]