arXiv:2602.11947v1 Announce Type: cross Abstract: We present a new mixed-integer programming (MIP) approach for offline multiple change-point detection by casting the problem as a globally optimal piecewise linear (PWL) fitting problem. Our main contribution is a family of strengthened MIP formulations whose linear programming (LP) relaxations admit integral projections onto the segment assignment variables, which encode the segment membership of each data point. This property yields provably tighter relaxations than existing formulations for offline multiple change-point detection. We […]

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arXiv:2603.05642v1 Announce Type: new Abstract: Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surrounding context to guide exploration efficiently. Prior methods either rely on vision-language embeddings similarity, which does not reliably capture task-relevant relational semantics, or large language models (LLMs), which are too slow and costly for real-time deployment. We introduce SCOUT: Scene Graph-Based Exploration with Learned Utility for Open-World Interactive Object Search, a novel method that searches directly over 3D […]

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Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specific demands. In this work, we introduce a novel framework that employs the Gumbel-Softmax trick to enable discrete yet differentiable selection among a predefined set of activation functions during training. Our method dynamically learns the optimal activation function independently of the input, thereby enhancing both predictive accuracy and architectural flexibility. Experiments on synthetic datasets show that our model consistently […]

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arXiv:2604.05011v1 Announce Type: new Abstract: Automatic music genre classification is a major task in music information retrieval; however, most current benchmarks and models have been developed primarily for Western music, leaving culturally specific traditions underrepresented. In this paper, we introduce the Yemeni Music Information Retrieval (YMIR) dataset, which contains 1,475 carefully selected audio clips covering five traditional Yemeni genres: Sanaani, Hadhrami, Lahji, Tihami, and Adeni. The dataset was labeled by five Yemeni music experts following a clear and […]

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arXiv:2603.22318v1 Announce Type: new Abstract: Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are computationally prohibitive. To address this challenge, we present two data-driven chemical mechanism reduction formulations based on graph neural networks (GNNs) with message-passing transformer layers that learn nonlinear dependencies among species and reactions. The first formulation, GNN-SM, employs a pre-trained surrogate model to guide reduction across a broad range […]

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Singapore – Blockman PR –  JANUARY 30, 2026 – Bitget Token (BGB) is now available for trading on Kraken, marking its first major regulated U.S. exchange listing and an important step in expanding global access to the token. The listing brings BGB onto one of the industry’s most established exchanges, improving liquidity and making the asset more accessible across global markets. As onchain finance scales, the way crypto assets are evaluated is changing. Focus is shifting toward tokens […]

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Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the intervention strategies for controlling infectious disease spread and responding to outbreaks in recent years. The potential of RL for assisting public health sectors in preventing and controlling infectious diseases is gradually emerging and being explored by rapidly increasing publications relevant to COVID-19 and […]

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