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Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
arXiv:2604.02876v1 Announce Type: new Abstract: Operational flood forecasting still relies on high-fidelity two-dimensional hydraulic solvers, but their runtime can be prohibitive for rapid decision support on large urban floodplains. In parallel, AI-based surrogate models have shown strong potential in several areas of computational physics for accelerating otherwise expensive high-fidelity simulations. We address this issue on the lower T\^et River (France), starting from a production-grade Telemac2D model defined on a high-resolution unstructured finite-element mesh with more than $4\times — Valentin Mercier (Toulouse INP, IRIT, EPE UT), Serge Gratton (IRIT, EPE UT, Toulouse INP), Lapeyre Corentin (NVIDIA), Gwena\"el Chevallet
Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees
arXiv:2604.02788v1 Announce Type: new Abstract: The growing number of individual generating units, hybrid resources, and security constraints has significantly increased the computational burden of network-constrained unit commitment (UC), where most solution time is spent exploring branch-and-bound trees over unit-hour binary variables. To reduce this combinatorial burden, recent approaches have explored learning-based guidance to assist commitment decisions. However, directly using tools such as large language models (LLMs) to predict full commitment schedules is unreliable, as infeasible or — Guangwen Wang, Jiaqi Wu, Yang Weng, Baosen Zhang
Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
arXiv:2604.02899v1 Announce Type: new Abstract: Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Traditional anti-money laundering approaches mainly rely on predefined risk-based rules, leading to resource-intensive investigations and high numbers of false positive alerts. In order to restrict operational costs from exploding, while billions of transactions are being processed every day, financial institutions are investing in more sophisticated mechanisms to improve — Haseeb Tariq, Marwan Hassani
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Я потратил месяц на AI-инструменты и удалил половину из них
В пятницу 14 февраля в 23:40 я сидел за ноутом, дожимая дедлайн на проекте за $2300 . Copilot вдруг подсунул мне "оптимизацию", которая так ловко сломала авторизацию сразу в трёх местах. Следующие четыре часа я чинил то, что за 11 секунд превратилось в кашу. Наутро я понял: из моих 14 AI-инструментов реально работали только три. Инструментальная перегрузка Когда я впервые начал работать с AI-инструментами, казалось, что это будет настоящим спасением. Меньше рутинной работы, больше времени на творчество. Но вскоре стало ясно, что эта иллюзия начала трескаться. Каждый инструмент считал своим долгом вмешиваться в код, предлагать "улучшения", которые на деле оборачивались дополнительной работой. К тому же, постоянно переключаться между ними было просто невыносимо. Вроде бы они должны экономить
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