arXiv:2603.10845v3 Announce Type: replace-cross Abstract: Human Presence Detection (HPD) is key to enable intelligent power management and security features in everyday devices. In this paper we propose the first HPD solution that leverages monostatic Wi-Fi sensing and detects user position using only the built-in Wi-Fi hardware of a device, with no need for external devices, […]
VARIANT: Web Server for Decoding and Analyzing Viral Mutations at Genome and Protein Levels
arXiv:2604.20942v1 Announce Type: new Abstract: A comprehensive analysis of viral mutations is essential for understanding viral evolution, disease epidemiology, diagnosis, drug resistance, etc. However, challenges remain in capturing complex mutation patterns and supporting diverse viral families with varying genome architectures. To address these needs, we present VARIANT, an web server for mutational analysis of RNA […]
Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach
arXiv:2604.21651v1 Announce Type: cross Abstract: Denoising of periodic signals and accurate waveform estimation are core tasks across many signal processing domains, including speech, music, medical diagnostics, radio, and sonar. Although deep learning methods have recently shown performance improvements over classical approaches, they require substantial computational resources and are usually trained separately for each signal observation. […]
Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos
arXiv:2604.21814v1 Announce Type: cross Abstract: Capsule endoscopy (CE) enables non-invasive gastrointestinal screening, but current CE research remains largely limited to frame-level classification and detection, leaving video-level analysis underexplored. To bridge this gap, we introduce and formally define a new task, diagnosis-driven CE video summarization, which requires extracting key evidence frames that covers clinically meaningful findings […]
C-SHAP for time series: An approach to high-level temporal explanations
arXiv:2504.11159v2 Announce Type: replace Abstract: In high-stakes domains, such as healthcare and industry, the explainability of AI-based decision-making has become crucial. Without insight into model reasoning, the reliability of these models cannot be ensured. Applications often rely on the time series data type which, unlike the image data type, is underexplored with respect to the […]
On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models
arXiv:2603.27406v2 Announce Type: replace Abstract: In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also called structural causal models, is studied. Structural causal models are deterministic models, based on structural equations or functions, that can be provided with uncertainty by adding independent, unobserved random variables to the models, […]
Federated Co-tuning Framework for Large and Small Language Models
arXiv:2411.11707v3 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server’s LLM and the downstream clients’ Small Language Models (SLMs). To address this, we propose FedCoLLM, […]
Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning
arXiv:2509.23744v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-modal reasoning remains underexplored, with conflicting reports on whether added modalities help or harm performance. These inconsistencies stem from a lack of controlled evaluation frameworks and analysis of models’ internals to […]
Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores
arXiv:2511.20697v4 Announce Type: replace-cross Abstract: Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision–Language Models to interpret full musical notation remains insufficiently examined. We introduce Musical Score Understanding Benchmark (MSU-Bench), a human-curated benchmark for score-level musical understanding across textual (ABC notation) […]
Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra
arXiv:2602.03875v4 Announce Type: replace-cross Abstract: We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so the forward map and its inverse are available by construction. We train the model […]
Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
arXiv:2603.18740v2 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, ranging from interactive assistants to autonomous agents in CI/CD pipelines. In this paper, we study how LLM-based vulnerability detection in ACR is affected by the framing effect: the tendency to let the presentation […]
Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech
arXiv:2604.11417v2 Announce Type: replace-cross Abstract: Co-speech gestures increase engagement and improve speech understanding. Most data-driven robot systems generate rhythmic beat-like motion, yet few integrate semantic emphasis. To address this, we propose a lightweight transformer that derives iconic gesture placement and intensity from text and emotion alone, requiring no audio input at inference time. The model […]