arXiv:2504.11349v3 Announce Type: replace-cross Abstract: The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D image reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) has emerged as a promising approach for improving reconstruction accuracy while reducing acquisition and processing time, thereby minimizing patient radiation exposure and discomfort […]
Multi-Source Neural Activity Indices for EEG/MEG Localization: A Two-Stage Spatial Filtering Framework and Extension to MNE-Python
arXiv:2509.14118v2 Announce Type: replace-cross Abstract: Accurate electroencephalography (EEG) and magnetoencephalography (MEG) source localization and reconstruction are essential for understanding brain function, yet remain challenging because the underlying EEG/MEG inverse problem is inherently ill-posed. Spatial filtering (beamforming) approaches, such as linearly constrained minimum variance (LCMV) spatial filters, are widely used and well supported by existing analysis […]
SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition
arXiv:2603.17729v3 Announce Type: replace-cross Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to tackle this challenge, but both are constrained by two […]
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
arXiv:2604.10359v2 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE techniques often rely on large models and multi-stage training, limiting practicality for edge deployment. Moreover, their dependence on a single color space introduces instability and visible exposure or color […]
WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention
arXiv:2604.22824v2 Announce Type: replace-cross Abstract: WeatherSeg, an advanced semi-supervised segmentation framework, addresses autonomous driving’s environmental perception challenges in adverse weather while reducing annotation costs. This framework integrates a Dual Teacher-Student Weight-Sharing Model (DTSWSM) that enables knowledge distillation from weather-affected images, and a Classifier Weight Updating Attention Mechanism (CWUAM) that dynamically adjusts classifier weights based on […]
Simultaneous Fragment Docking for Geometrically Linkable Pose Pairs
arXiv:2604.24773v1 Announce Type: new Abstract: Computational molecular design requires binding arrangements that are not only energetically favorable but also chemically realizable. However, computational methods remain limited in directly recovering fragment pose pairs that can later be connected into a single molecule. To address this problem, we formulated the simultaneous placement of two fragments as a […]
CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation
arXiv:2604.25774v1 Announce Type: cross Abstract: Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient terminology and highly variable quantity expressions. We systematically evaluate models spanning a wide range of representational capacity, from lexical matching methods (TF-IDF with Ridge Regression), to deep semantic encoders (DeBERTa-v3), […]
Equation Learning for multiscale models of infectious diseases
arXiv:2604.25038v1 Announce Type: new Abstract: Tuberculosis (TB) is an airborne disease caused by the pathogen Mycobacterium tuberculosis. In 2023, according to the World Health Organization, it ”probably” replaced COVID-19 as the leading cause of death from an infectious agent globally; in the nineteenth century, one in seven of all humans deaths were as a result […]
G-Loss: Graph-Guided Fine-Tuning of Language Models
arXiv:2604.25853v1 Announce Type: cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure. We present G-Loss, a graph-guided loss function that incorporates semi-supervised label propagation to use structural […]
Schema Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding
arXiv:2604.14862v2 Announce Type: replace-cross Abstract: Constrained decoding is widely used to make large language models produce structured outputs that satisfy schemas such as JSON. Existing work mainly treats schemas as structural constraints, overlooking that schema-key tokens also enter the autoregressive context and may guide generation. To the best of our knowledge, we present the first […]
spectroxide: A code package for computing cosmic microwave background spectral distortions
arXiv:2604.24838v1 Announce Type: cross Abstract: We present spectroxide, a code package for computing cosmic microwave background spectral distortions in which all $sim14,500$ lines of Rust code, Python interface, and $sim400$ automated tests were written by an AI assistant (Claude Code) under human physicist supervision. The solver evolves the photon Boltzmann equation under Compton scattering, double […]
Leverage Laws: A Per-Task Framework for Human-Agent Collaboration
arXiv:2604.25040v1 Announce Type: new Abstract: We propose a per-task leverage ratio for human-agent collaboration: human work displaced by an agent, divided by the human time required to specify the task, resolve mid-run interrupts, and review the result. The denominator decomposes into three channels through which a conserved per-task information requirement must flow, each with its […]