Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam
arXiv:2604.24768v1 Announce Type: new
Abstract: In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally robust Physics-Informed Functional Link Constrained Framework with Domain Mapping (DFL-TFC) method. Ou...
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
arXiv:2604.24767v1 Announce Type: new
Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due t...
arXiv:2604.21991v1 Announce Type: new
Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that...
Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics
arXiv:2604.21953v1 Announce Type: new
Abstract: Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by short detection windows for many prohibited substances. These constraints leave large portions of athl...
Architecture of an AI-Based Automated Course of Action Generation System for Military Operations
arXiv:2604.20862v1 Announce Type: new
Abstract: The automation system for Course of Action (CoA) planning is an essential element in future warfare. As maneuver speeds increase, surveillance ranges extend, and weapon ranges grow, the operational area expands, making traditional manned-based CoA pla...
Algorithm Selection with Zero Domain Knowledge via Text Embeddings
arXiv:2604.19753v1 Announce Type: new
Abstract: We propose a feature-free approach to algorithm selection that replaces hand-crafted instance features with pretrained text embeddings. Our method, ZeroFolio, proceeds in three steps: it reads the raw instance file as plain text, embeds it with a pret...
The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?
arXiv:2604.19749v1 Announce Type: new
Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we first reveal this phen...
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
arXiv:2604.19800v1 Announce Type: new
Abstract: This paper presents a detailed study of how graph neural networks can be used on edge intelligent meters in a microgrid to forecast photovoltaic power generation. The problem background and the adopted technologies are introduced, including ONNX and O...
Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models
arXiv:2604.19767v1 Announce Type: new
Abstract: We evaluate speculative decoding with EAGLE3 as an inference-time optimization for PayPal's Commerce Agent, powered by a fine-tuned llama3.1-nemotron-nano-8B-v1 model. Building on prior work (NEMO-4-PAYPAL) that reduced latency and cost through domain...
On Solving the Multiple Variable Gapped Longest Common Subsequence Problem
arXiv:2604.18645v1 Announce Type: new
Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions' characters. The problem arises in molecular sequence c...
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
arXiv:2604.18728v1 Announce Type: new
Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints, for simulating the activations. Recent works convexly relax the integ...
Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Learning Representations...
Preventing overfitting in deep learning using differential privacy
arXiv:2604.16334v1 Announce Type: new
Abstract: The use of Deep Neural Network based systems in the real world is growing. They have achieved state-of-the-art performance on many image, speech and text datasets. They have been shown to be powerful systems that are capable of learning detailed relat...
Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning
arXiv:2604.16332v1 Announce Type: new
Abstract: We find that LoRA fine-tuning exhibits un-learning on contested examples: items with high annotator disagreement show increasing loss during training, a qualitatively distinct pattern largely absent under full fine-tuning and consistent across all six...
Support Sufficiency as Consequence-Sensitive Compression in Belief Arbitration
arXiv:2604.16434v1 Announce Type: new
Abstract: When a system commits to a hypothesis, much of the evidential structure behind that commitment is lost to compression. Standard accounts assume that selected content and scalar confidence suffice for downstream control. This paper argues that they do ...
Doug Burger, sustainability expert Amy Luers, and optimization researcher Ishai Menache examine the global emissions implications of datacenter operations, efficiency gains, and AI's potential across electrification, materials, and food systems.
The post Can we AI our way to a more sustainable world...
M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention
arXiv:2604.15377v1 Announce Type: new
Abstract: Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multi...
Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures
arXiv:2604.15351v1 Announce Type: new
Abstract: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoRA adapters uniformly to all transformer layers regardless of their relevance to the downstream task. ...