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Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we developed FB-GNN-MBE by integrating a fragment-based graph neural network (FB-GNN) into the many-body expansion (MBE) theory and demonstrated its capacity to reproduce first-principles potential energy surfaces (PES) for hierarchically structured systems with manageable accuracy, complexity, and interpretability. […]

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This investigation taking place in Florida shows a significant change in how governments are now approaching artificial intelligence, particularly in relation to minor users. The largely theoretical debate about AI ethics has now shifted to legal observation with officials questioning fast moving technology: has it outspaced the safeguards meant to protect vulnerable groups? At the centre of this ongoing investigation are concerns about AI systems which are designed to be helpful and conversational, might unintentionally engage in harmful […]

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The Kimi K2.5 AI model and Claude AI by Anthropic currently stand among the two most popular emerging models for coding and research. The two systems maintain their individual strengths but differ in their methods to tackle programming and scientific tasks. Kimi AI uses multiple input methods to run its agent-based system, while Claude AI uses structured reasoning together with safety features to create efficient operational processes. Kimi K2.5: The Multimodal Specialist The Kimi K2.5 AI model stands […]

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This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynamics or objectives include unknown parameters. The problem is formulated as an inverse optimal control / inverse reinforcement learning task, with the intention treated as a parameter in the objective. A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates. Simulations under varying […]

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We propose a hybrid physics-informed framework for solving families of parametric linear partial differential equations (PDEs) by combining a meta-learned predictor with a least-squares corrector. The predictor, termed textbf{KAPI} (Kernel-Adaptive Physics-Informed meta-learner), is a shallow task-conditioned model that maps query coordinates and PDE parameters to solution values while internally generating an interpretable, task-adaptive Gaussian basis geometry. A lightweight meta-network maps PDE parameters to basis centers, widths, and activity patterns, thereby learning how the approximation space should adapt across […]

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We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regression, low-rank functional TTNs can be efficiently optimized using alternating optimization, this is not directly possible in other problems, such as multinomial logistic regression. We propose a natural Riemannian gradient descent type approach applicable to arbitrary losses which is based on the natural gradient by Amari. In particular, the search direction obtained by the natural gradient […]

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High-Level Synthesis (HLS) compiles C/C++ into RTL, but exploring pragma-driven optimization choices remains expensive because each design point requires time-consuming synthesis. We propose textbf{DiffHLS}, a differential learning framework for HLS Quality-of-Result (QoR) prediction that learns from kernel–design pairs: a kernel baseline and a pragma-inserted design variant. DiffHLS~encodes kernel and design intermediate-representation graphs with dedicated graph neural network (GNN) branches, and augments the delta pathway with code embeddings from a pretrained code large language model (LLM). Instead of regressing […]

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