Tootfinder

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar
https://arxiv.org/abs/2506.09272

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often struggle, either failing to generalize beyond historical data or, when using Large Language Models (LLMs), suffering from inaccuracies and poor empirical alignment. We introduce G-Sim, a hybrid framework that automates simulator construction by synergizing LLM-driven structural design with rigorous empirical calibration…

STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Kainan Wang, Chengyi Yang, Chengting Yu, Yee Sin Ang, Bo Wang, Aili Wang
https://arxiv.org/abs/2506.08842

STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Brain-inspired Spiking Neural Networks (SNNs) have attracted attention for their event-driven characteristics and high energy efficiency. However, the temporal dependency and irregularity of spikes present significant challenges for hardware parallel processing and data reuse, leading to some existing accelerators falling short in processing latency and energy efficiency. To overcome these challenges, we introduce the STI-SNN accelerator, designed for resource-constrained applications with high…

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
Xinyi Wu, Haohong Wang, Aggelos K. Katsaggelos
https://arxiv.org/abs/2506.07076

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
With the popularity of video-based user-generated content (UGC) on social media, harmony, as dictated by human perceptual principles, is critical in assessing the rhythmic consistency of audio-visual UGCs for better user engagement. In this work, we propose a novel harmony-aware GAN framework, following a specifically designed harmony evaluation strategy to enhance rhythmic synchronization in the automatic music-to-motion synthesis using a UGC dance dataset. This harmony strategy utilizes refin…

The might of the USA versus one man
#uspol
Trump’s Running Tab in the Abrego Garcia Case
Rather than admit a mistake, the Trump administration is trying to build a criminal case against Kilmar Abrego Garcia by cutting deals with felons, driving out federal prosecutors, and threatening to deport him again, @NickMiroff writes.

Inside the Negotiations Over Abrego Garcia’s Release
The administration is cutting deals with felons, driving out federal prosecutors, and threatening to abandon its criminal case—all to avoid admitting error.

DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification
Boris Kriuk, Logic Ng, Zarif Al Hossain
https://arxiv.org/abs/2507.01971

DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification
Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper presents DeepSupp, a new deep learning approach for detect…

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Ina K. Schieferdecker
https://arxiv.org/abs/2506.14640

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening…

This https://arxiv.org/abs/2501.16423 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Theory of neutrino slow flavor evolution. Part II. Space-time evolution of linear instabilities
Slow flavor evolution (defined as driven by neutrino masses and not necessarily ``slow'') is receiving fresh attention in the context of compact astrophysical environments. In Part~I of this series, we have studied the slow-mode dispersion relation following our recently developed analogy to plasma waves. The concept of resonance between flavor waves in the linear regime and propagating neutrinos is the defining feature of this approach. It is best motivated for weak instabilities, which probab…

Automatic computation of the glycemic index: data driven analysis of the glucose standard
Fabio Credali, Maria Teresa Venuti, Daniele Boffi, Paola Rossi
https://arxiv.org/abs/2506.15471

Automatic computation of the glycemic index: data driven analysis of the glucose standard
The Glycemic Index (GI) is a tool for classifying carbohydrates based on their impact on postprandial glycemia, useful for diabetes prevention and management. This study applies a mathematical model for a data driven simulation of the glycemic response following glucose ingestion. The analysis reveals a direct correlation between glucose response profiles and parameters describing glucose absorption, enabling the classification of subjects into three groups based on the timing of their glycemic…

Controllable Video-to-Music Generation with Multiple Time-Varying Conditions
Junxian Wu, Weitao You, Heda Zuo, Dengming Zhang, Pei Chen, Lingyun Sun
https://arxiv.org/abs/2507.20627

Controllable Video-to-Music Generation with Multiple Time-Varying Conditions
Music enhances video narratives and emotions, driving demand for automatic video-to-music (V2M) generation. However, existing V2M methods relying solely on visual features or supplementary textual inputs generate music in a black-box manner, often failing to meet user expectations. To address this challenge, we propose a novel multi-condition guided V2M generation framework that incorporates multiple time-varying conditions for enhanced control over music generation. Our method uses a two-stage…