Tootfinder

The bump-on-tail instability excited by energetic electrons in helicon plasma
Shi-Jie Zhang, Dong Jing, Lei Chang, Kai-Jun Fu, Chao Wang, Zi-Chen Kan, Ye Tao, Jing-Jing Ma, Ji-Kai Sun, Ding-Zhou Li, Ilya Zadiriev, Elena Kralkina, Shin-Jae You
https://arxiv.org/abs/2506.13321

The bump-on-tail instability excited by energetic electrons in helicon plasma
This work explores for the first time bump-on-tail (BOT) instability excited by energetic electrons in helicon plasma. The Berk-Breizman model that developed for the wave-particle interaction and resulted instability in magnetic fusion is used. Details of the BOT instability are computed referring to typical helicon discharge conditions. Parameter studies are also conducted to reveal the effects of collisionality and energetic drive, to account for high-pressure and high-power senarios respecti…

TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update
Jeongyun Kim, Seunghoon Jeong, Giseop Kim, Myung-Hwan Jeon, Eunji Jun, Ayoung Kim
https://arxiv.org/abs/2507.11069

TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update
Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussian Splatting-based depth reconstruction method for transparent objects. Our key insight lies in separating transparent objects from the background, enabling focused optimization of Gaussians correspon…

Empirical Evaluation of Large Language Models in Automated Program Repair
Jiajun Sun, Fengjie Li, Xinzhu Qi, Hongyu Zhang, Jiajun Jiang
https://arxiv.org/abs/2506.13186

Empirical Evaluation of Large Language Models in Automated Program Repair
The increasing prevalence of software bugs has made automated program repair (APR) a key research focus. Large language models (LLMs) offer new opportunities for APR, but existing studies mostly rely on smaller, earlier-generation models and Java benchmarks. The repair capabilities of modern, large-scale LLMs across diverse languages and scenarios remain underexplored. To address this, we conduct a comprehensive empirical study of four open-source LLMs, CodeLlama, LLaMA, StarCoder, and DeepSeek…

Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
Yichen Han, Xiaoyang Hao, Keming Chen, Weibo Xiong, Jun He, Ruonan Zhang, Junjie Cao, Yue Liu, Bowen Li, Dongrui Zhang, Hui Xia, Huilei Fu, Kai Jia, Kaixuan Guo, Mingli Jin, Qingyun Meng, Ruidong Ma, Ruiqian Fang, Shaotong Guo, Xuhui Li, Yang Xiang, Ying Zhang, Yulong Liu, Yunfeng Li, Yuyi Zhang, Yuze Zhou, Zhen Wang, Zhaowen Chen

Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
Text-to-speech (TTS) synthesis has seen renewed progress under the discrete modeling paradigm. Existing autoregressive approaches often rely on single-codebook representations, which suffer from significant information loss. Even with post-hoc refinement techniques such as flow matching, these methods fail to recover fine-grained details (e.g., prosodic nuances, speaker-specific timbres), especially in challenging scenarios like singing voice or music synthesis. We propose QTTS, a novel TTS fra…

Deceptive Path Planning: A Bayesian Game Approach
Violetta Rostobaya, James Berneburg, Yue Guan, Michael Dorothy, Daigo Shishika
https://arxiv.org/abs/2506.13650

Deceptive Path Planning: A Bayesian Game Approach
This paper investigates how an autonomous agent can transmit information through its motion in an adversarial setting. We consider scenarios where an agent must reach its goal while deceiving an intelligent observer about its destination. We model this interaction as a dynamic Bayesian game between a mobile Attacker with a privately known goal and a Defender who infers the Attacker's intent to allocate defensive resources effectively. We use Perfect Bayesian Nash Equilibrium (PBNE) as our solut…

Fault-Free Analog Computing with Imperfect Hardware
Zhicheng Xu, Jiawei Liu, Sitao Huang, Zefan Li, Shengbo Wang, Bo Wen, Ruibin Mao, Mingrui Jiang, Giacomo Pedretti, Jim Ignowski, Kaibin Huang, Can Li
https://arxiv.org/abs/2507.11134

Fault-Free Analog Computing with Imperfect Hardware
The growing demand for edge computing and AI drives research into analog in-memory computing using memristors, which overcome data movement bottlenecks by computing directly within memory. However, device failures and variations critically limit analog systems' precision and reliability. Existing fault-tolerance techniques, such as redundancy and retraining, are often inadequate for high-precision applications or scenarios requiring fixed matrices and privacy preservation. Here, we introduce an…

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

Variational phase-field modeling of cohesive fracture with flexibly tunable strength surface
Francesco Vicentini, Jonas Heinzmann, Pietro Carrara, Laura De Lorenzis
https://arxiv.org/abs/2506.12188

Variational phase-field modeling of cohesive fracture with flexibly tunable strength surface
Variational phase-field models of brittle fracture are powerful tools for studying Griffith-type crack propagation in complex scenarios. However, as approximations of Griffith's theory-which does not incorporate a strength criterion-these models lack flexibility in prescribing material-specific strength surfaces. Consequently, they struggle to accurately capture crack nucleation under multiaxial stress conditions. In this paper, inspired by Alessi et al. (2014), we propose a variational phase-f…

BUT System for the MLC-SLM Challenge
Alexander Polok, Jiangyu Han, Dominik Klement, Samuele Cornell, Jan \v{C}ernock\'y, Luk\'a\v{s} Burget
https://arxiv.org/abs/2506.13414

BUT System for the MLC-SLM Challenge
We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW -- a diarization-conditioned variant of Whisper -- with DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate both systems in out-of-domain (OOD) multilingual scenarios without any fine-tuning. In this scenario, DiariZen consistently outperforms the baseline Pyannote diarization model, demonstrating strong generalization. Despite being fine-tuned on English-only data for target-speaker …

Compress Any Segment Anything Model (SAM)
Juntong Fan, Zhiwei Hao, Jianqiang Shen, Shang-Ling Jui, Yi Zhang, Jing-Xiao Liao, Feng-Lei Fan
https://arxiv.org/abs/2507.08765

Compress Any Segment Anything Model (SAM)
Due to the excellent performance in yielding high-quality, zero-shot segmentation, Segment Anything Model (SAM) and its variants have been widely applied in diverse scenarios such as healthcare and intelligent manufacturing. Therefore, effectively compressing SAMs has become an increasingly pressing practical need. In this study, we propose Birkhoff, a novel data-free compression algorithm for SAM and its variants. Unlike quantization, pruning, distillation, and other compression methods, Birkh…