Tootfinder

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan, Tianle Zhang, Yao Huang, Yanghao Zhang, Yan Wang, Yanshu Li, Xizhong Guo, Yusheng Zhao, Jun Zhang, Zhi Zhang, Xiaojian Lin, Yixiu Zou, Haoxuan Ma, Yuhu Shang, Yuzhi Hu, Keshu Cai, Ruochen Zhang, Boyuan Chen, Yilan Gao, Ziheng Jiao, Yi Qin, Shuangjun Du, Xiao Tong, Zhekun Liu, Yu Chen, Xuankun Rong, Rui Wang, Yejie Zheng, Zhaoxin Fan, Hongyuan Zhang, Pan Zhou, Lei Jin, Hao Z…

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, that pose significant societal risks, including misinformation, inequity, security breaches, physical harm, and eroded public trust. These challenges highlight the urgent need for robust AI governance. We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconne…

Impact of shape coexistence on the symmetric to asymmetric fission mode transition in Th isotopes
Shengyuan Chen, Zeyu Li, Minghui Zhou, Zhipan Li
https://arxiv.org/abs/2508.08616

Impact of shape coexistence on the symmetric to asymmetric fission mode transition in Th isotopes
We study the evolution of fission modes along the Th isotopic chain using a microscopic framework combining the time-dependent generator coordinate method and finite-temperature covariant density functional theory. Theoretical fission fragment charge distributions agree well with experiments, and reveal a rapid symmetric-to-asymmetric transition from $A=222$ to 234. By analyzing the free energy surfaces and time evolution of collective probability density distributions, we demonstrate that this…

LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning
Sandipan Dhar, Mayank Gupta, Preeti Rao
https://arxiv.org/abs/2507.04966

LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning
The field of Singing Voice Synthesis (SVS) has seen significant advancements in recent years due to the rapid progress of diffusion-based approaches. However, capturing vocal style, genre-specific pitch inflections, and language-dependent characteristics remains challenging, particularly in low-resource scenarios. To address this, we propose LAPS-Diff, a diffusion model integrated with language-aware embeddings and a vocal-style guided learning mechanism, specifically designed for Bollywood Hin…

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
Cheonsu Jeong, Seunghyun Lee, Sunny Jeong, Sungsu Kim
https://arxiv.org/abs/2509.00398 ht…

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
This study provides an in_depth analysis of the ethical and trustworthiness challenges emerging alongside the rapid advancement of generative artificial intelligence (AI) technologies and proposes a comprehensive framework for their systematic evaluation. While generative AI, such as ChatGPT, demonstrates remarkable innovative potential, it simultaneously raises ethical and social concerns, including bias, harmfulness, copyright infringement, privacy violations, and hallucination. Current AI ev…

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Zhijin Wang, Senzhen Wu, Yue Hu, Xiufeng Liu
https://arxiv.org/abs/2508.18891 http…

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data. We introduce pyFAST, a research-oriented PyTorch framework that explicitly decouples data processing from model computation, fostering a cleaner separation of concerns and facilitating rapid experimentation. Its data engine is engineered for complex scenarios…

Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions
Tianliang Yao, Bingrui Li, Bo Lu, Zhiqiang Pei, Yixuan Yuan, Peng Qi
https://arxiv.org/abs/2506.21631

Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions
Accurate three-dimensional (3D) reconstruction of guidewire shapes is crucial for precise navigation in robot-assisted endovascular interventions. Conventional 2D Digital Subtraction Angiography (DSA) is limited by the absence of depth information, leading to spatial ambiguities that hinder reliable guidewire shape sensing. This paper introduces a novel multimodal framework for real-time 3D guidewire reconstruction, combining preoperative 3D Computed Tomography Angiography (CTA) with intraopera…

K-PACT: Kernel Planning for Adaptive Context Switching - A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
H. Umut Suluhan, Jiahao Lin, Serhan Gener, Chaitali Chakrabarti, Umit Ogras, Ali Akoglu
https://arxiv.org/abs/2507.19662

K-PACT: Kernel Planning for Adaptive Context Switching - A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
Efficient wideband spectrum sensing requires rapid evaluation and re-evaluation of signal presence and type across multiple subchannels. These tasks involve multiple hypothesis testing, where each hypothesis is implemented as a decision tree workflow containing compute-intensive kernels, including FFT, matrix operations, and signal-specific analyses. Given dynamic nature of the spectrum environment, ability to quickly switch between hypotheses is essential for maintaining low-latency, high-thro…

Learning Steerable Imitation Controllers from Unstructured Animal Motions
Dongho Kang, Jin Cheng, Fatemeh Zargarbashi, Taerim Yoon, Sungjoon Choi, Stelian Coros
https://arxiv.org/abs/2507.00677

Learning Steerable Imitation Controllers from Unstructured Animal Motions
This paper presents a control framework for legged robots that leverages unstructured real-world animal motion data to generate animal-like and user-steerable behaviors. Our framework learns to follow velocity commands while reproducing the diverse gait patterns in the original dataset. To begin with, animal motion data is transformed into a robot-compatible database using constrained inverse kinematics and model predictive control, bridging the morphological and physical gap between the animal…

G-SEED: A Spatio-temporal Encoding Framework for Forest and Grassland Data Based on GeoSOT
Xuan Ouyang, Xinwen Yu, Yan Chen, Guang Deng, Xuanxin Liu
https://arxiv.org/abs/2506.18094

G-SEED: A Spatio-temporal Encoding Framework for Forest and Grassland Data Based on GeoSOT
In recent years, the rapid development of remote sensing, Unmanned Aerial Vehicles, and IoT technologies has led to an explosive growth in spatio-temporal forest and grassland data, which are increasingly multimodal, heterogeneous, and subject to continuous updates. However, existing Geographic Information Systems (GIS)-based systems struggle to integrate and manage of such large-scale and diverse data sources. To address these challenges, this paper proposes G-SEED (GeoSOT-based Scalable Encod…

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Supawich Sitdhipol, Waritwong Sukprasongdee, Ekapol Chuangsuwanich, Rina Tse
https://arxiv.org/abs/2507.19947

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Fusing information from human observations can help robots overcome sensing limitations in collaborative tasks. However, an uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs. This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features and their relationships with spatial relation semantics. The model is trained as a probability estimator to capture a…