Tootfinder

VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones
Tan Bui, Yan Naing Tun, Thanh Phuc Nguyen, Yindu Su, Ferdian Thung, Yikun Li, Han Wei Ang, Yide Yin, Frank Liauw, Lwin Khin Shar, Eng Lieh Ouh, Ting Zhang, David Lo
https://arxiv.org/abs/2507.16661

VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones
Code reuse is common in modern software development, but it can also spread vulnerabilities when developers unknowingly copy risky code. The code fragments that preserve the logic of known vulnerabilities are known as vulnerable code clones (VCCs). Detecting those VCCs is a critical but challenging task. Existing VCC detection tools often rely on syntactic similarity or produce coarse vulnerability predictions without clear explanations, limiting their practical utility. In this paper, we propo…

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…

This really bums me out. JD Twitch’s mixtapes are legendary. Much respect and admiration for him donating all the proceeds to a local food bank. https://www.theguardian.com/music/2025/jul/03/jd-twitch-of-scottish-…

Multi-modal brain MRI synthesis based on SwinUNETR
Haowen Pang, Weiyan Guo, Chuyang Ye
https://arxiv.org/abs/2506.02467 https://arxiv…

Multi-modal brain MRI synthesis based on SwinUNETR
Multi-modal brain magnetic resonance imaging (MRI) plays a crucial role in clinical diagnostics by providing complementary information across different imaging modalities. However, a common challenge in clinical practice is missing MRI modalities. In this paper, we apply SwinUNETR to the synthesize of missing modalities in brain MRI. SwinUNETR is a novel neural network architecture designed for medical image analysis, integrating the strengths of Swin Transformer and convolutional neural networ…

LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures
Seungoh Han, Jaehoon Jang, Hyunsu Kim, Jaeheung Surh, Junhyung Kwak, Hyowon Ha, Kyungdon Joo
https://arxiv.org/abs/2507.06109

LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures
Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time novel view synthesis (NVS) with impressive quality in indoor scenes. However, achieving high-fidelity rendering requires meticulously captured images covering the entire scene, limiting accessibility for general users. We aim to develop a practical 3DGS-based NVS framework using simple panorama-style motion with a handheld camera (e.g., mobile device). While convenient, this rotation-dominant motion and narrow baseline make …

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…