arXiv:2512.17988v1 Announce Type: new Abstract: Microplate-based ‘omic studies of large clinical cohorts can massively accelerate biomedical research, but experimental power and veracity may be negatively impacted when plate positional effects confound clinical variables of interest. Plate designs must therefore deconvolve this technical and biological variation, and several computational approaches now exist to achieve this. However, […]
Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
arXiv:2512.18947v1 Announce Type: new Abstract: Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary algorithms lack adaptability to dynamic changes. To address above challenge, this paper […]
MEGState: Phoneme Decoding from Magnetoencephalography Signals
arXiv:2512.17978v1 Announce Type: new Abstract: Decoding linguistically meaningful representations from non-invasive neural recordings remains a central challenge in neural speech decoding. Among available neuroimaging modalities, magnetoencephalography (MEG) provides a safe and repeatable means of mapping speech-related cortical dynamics, yet its low signal-to-noise ratio and high temporal dimensionality continue to hinder robust decoding. In this work, […]
ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management
arXiv:2512.19001v1 Announce Type: new Abstract: As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how to effectively reconcile AI’s adaptive perception with OR’s structural rigor. To bridge this gap, we propose a novel OR-Guided “Pretrain-then-Reinforce” framework. To provide structured guidance, […]
FOODER: Real-time Facial Authentication and Expression Recognition
arXiv:2512.18057v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection is essential for the safe deployment of neural networks, as it enables the identification of samples outside the training domain. We present FOODER, a real-time, privacy-preserving radar-based framework that integrates OOD-based facial authentication with facial expression recognition. FOODER operates using low-cost frequency-modulated continuous-wave (FMCW) radar and exploits […]
When Does Learning Renormalize? Sufficient Conditions for Power Law Spectral Dynamics
arXiv:2512.18209v1 Announce Type: cross Abstract: Empirical power–law scaling has been widely observed across modern deep learning systems, yet its theoretical origins and scope of validity remain incompletely understood. The Generalized Resolution–Shell Dynamics (GRSD) framework models learning as spectral energy transport across logarithmic resolution shells, providing a coarse–grained dynamical description of training. Within GRSD, power–law scaling […]
ICP-4D: Bridging Iterative Closest Point and LiDAR Panoptic Segmentation
arXiv:2512.18991v1 Announce Type: cross Abstract: Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds or design dedicated modules for instance association. However, these approaches perform redundant point processing and consequently become computationally expensive, yet still overlook the rich geometric priors inherently provided by raw […]
Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation
arXiv:2512.18495v1 Announce Type: cross Abstract: Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable (PE) malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM (LGBM) malware detector by integrating Neural Networks (NN), PriorNet, and Neural Network Ensembles, […]
The Interaction Bottleneck of Deep Neural Networks: Discovery, Proof, and Modulation
arXiv:2512.18607v1 Announce Type: cross Abstract: Understanding what kinds of cooperative structures deep neural networks (DNNs) can represent remains a fundamental yet insufficiently understood problem. In this work, we treat interactions as the fundamental units of such structure and investigate a largely unexplored question: how DNNs encode interactions under different levels of contextual complexity, and how […]
Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
arXiv:2512.18092v1 Announce Type: new Abstract: Neuron identification is a popular tool in mechanistic interpretability, aiming to uncover the human-interpretable concepts represented by individual neurons in deep networks. While algorithms such as Network Dissection and CLIP-Dissect achieve great empirical success, a rigorous theoretical foundation remains absent, which is crucial to enable trustworthy and reliable explanations. In […]