2982 papers
This paper introduces Elemental Variational Expanse (EVE), a neuron-level variational unit that replaces deterministic hidden activations with input-dependent sampled latent states to improve probabilistic prediction and provide measurable diagnostics while maintaining compatibility with standard neural architectures.
This paper proposes GeoDualNet, the first end-to-end neural codec that jointly learns disparity vector prediction and depth map coding through a unified geometric latent space featuring five novel components, achieving significant bitrate savings and improved synthesized view quality over traditional 3D-HEVC and existing learned multiview codecs.
This paper introduces Modality-Conditional Conformal Prediction (MC²), a method that restores valid, informative coverage guarantees for multimodal medical diagnosis models under missing modality inputs by stratifying calibration according to modality-availability patterns without requiring model retraining.
This paper presents the Enhanced Passive Reader-Writer (EPRW) lock, a userspace adaptation of the original PRW lock that eliminates the incompatible heartbeat requirement for poll-mode datapaths while preserving scalability and adding critical features like lock upgrade, downgrade, and non-blocking acquisition.
This paper proposes ICATS, an Issue-Centric Autonomous Thinking Scheduling framework that enables robots in dynamic environments to prioritize internal cognitive processing by treating diverse factors like unfinished tasks and emerging risks as dynamic issues, thereby outperforming traditional fixed-baseline methods in weighted reward and status detection.
This paper presents an interactive decision support dashboard for the UAC Care System, developed using Python, Streamlit, SQLite, and Power BI, which integrates real-time monitoring, predictive analytics, and scenario planning to enhance operational visibility and evidence-based decision-making for public-sector care managers.
This paper proposes RL-Trust RPL, a Reinforcement Learning-based adaptive trust framework that mitigates sinkhole attacks in Multicast RPL networks by monitoring node behaviors like packet delivery and energy consumption to detect and isolate malicious nodes, thereby enhancing routing security and network performance.
This paper proposes a hybrid Genetic Algorithm and Deep Learning framework that integrates a lightweight ResNet model with a custom GA-driven optimization scheme to enable rapid, energy-efficient cluster head selection in Rechargeable Wireless Sensor Networks, thereby significantly delaying the First Node Death and achieving near real-time performance without the iterative overhead of traditional heuristic methods.
This study proposes a scalable, big data-driven framework utilizing a Spatio-Temporal Parallel Transformer (STPT) and clustering techniques to accurately predict traffic accidents and fatalities in Bangkok, thereby enabling evidence-based policy interventions to enhance urban road safety.
This paper proposes PHUE, a progressive hardness-aware undersampling ensemble method that dynamically selects critical majority-class samples near the decision boundary and employs multi-metric weighted integration to significantly outperform existing algorithms in imbalanced data classification.