Tootfinder

Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi, Haoran Wang, Xiwen Zhang, Chen Chen, Cheng Ouyang, Chengliang Dai, Yuanhan Mo, Chenchen Dai, Xutong Kuang, Ruizhe Li, Xin Chen, Xiuzheng Yue, Song Tian, Alejandro Mora-Rubio, Kumaradevan Punithakumar, Shizhan Gong, Qi Dou, Sina Amirrajab, Yasmina Al Khalil, Cian M. Scannell, Lexiaozi Fan, Huili Yang, Xiaowu Sun, Rob van der Geest, Tewodros Weldebirhan Arega, Fabric…

Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In clinical practice, CMR acquisitions are frequently degraded by respiratory motion, yet the robustness of deep learning models against such artifacts remains an underexplored problem. To promote research in this domain, we organized the MICCAI CMRxMotion chall…

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu
https://arxiv.org/abs/2506.14437

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of 'value' labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for perso…

AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments
Chenglong Qian, Yang Xu, Xiufang Shi, Jiming Chen, Liang Li
https://arxiv.org/abs/2507.18317

AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments
In robotic navigation, maintaining precise pose estimation and navigation in complex and dynamic environments is crucial. However, environmental challenges such as smoke, tunnels, and adverse weather can significantly degrade the performance of single-sensor systems like LiDAR or GPS, compromising the overall stability and safety of autonomous robots. To address these challenges, we propose AF-RLIO: an adaptive fusion approach that integrates 4D millimeter-wave radar, LiDAR, inertial measuremen…

Metallicities from High-Resolution TRES Spectra with The Payne and uberMS: Performance Benchmarks and Literature Comparison
Emily K. Pass, Phillip A. Cargile, Victoria DiTomasso, Romy Rodr\'iguez Mart\'inez, David Charbonneau, David W. Latham, Andrew Vanderburg, Allyson Bieryla, Samuel N. Quinn, Lars A. Buchhave
https://…

Metallicities from High-Resolution TRES Spectra with The Payne and uberMS: Performance Benchmarks and Literature Comparison
As the field of exoplanetary astronomy has matured, there has been growing demand for precise stellar abundances to probe subtle correlations between stellar compositions and planetary demographics. However, drawing population-level conclusions from the disparate measurements in the literature is challenging, with various groups measuring metallicities using bespoke codes with differing line lists, radiative transfer models, and other assumptions. Here we use the neural-net framework uberMS to …

V-CASS: Vision-context-aware Expressive Speech Synthesis for Enhancing User Understanding of Videos
Qixin Wang, Songtao Zhou, Zeyu Jin, Chenglin Guo, Shikun Sun, Xiaoyu Qin
https://arxiv.org/abs/2506.16716

V-CASS: Vision-context-aware Expressive Speech Synthesis for Enhancing User Understanding of Videos
Automatic video commentary systems are widely used on multimedia social media platforms to extract factual information about video content. However, current systems may overlook essential para-linguistic cues, including emotion and attitude, which are critical for fully conveying the meaning of visual content. The absence of these cues can limit user understanding or, in some cases, distort the video's original intent. Expressive speech effectively conveys these cues and enhances the user's com…

Quantum Error Mitigation by Global Randomized Error Cancellation for Adiabatic Evolution in the Schwinger Model
Oleg Kaikov, Theo Saporiti, Vasily Sazonov, Mohamed Tamaazousti
https://arxiv.org/abs/2507.06601

Quantum Error Mitigation by Global Randomized Error Cancellation for Adiabatic Evolution in the Schwinger Model
We extend the global randomized error cancellation (GREC) method for quantum error mitigation (QEM) in an application to adiabatic evolution of states on a noisy quantum device. We apply the adiabatic GREC method to the evolution of eigenstates in the lattice Schwinger model on a simulated quantum device with custom noise. Our results suggest that the corresponding QEM learned in one parameter regime of the model successfully transfers to a different parameter regime. In particular, our finding…

Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
Jiahui Liu, Shuo Cao, Yanzhou Wang, Zheyong Fan, Guocai Lv, Ping Qian, Yanjing Su
https://arxiv.org/abs/2507.12388

Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
The effect of chemical short-range order (CSRO) on primary radiation damage in MoNbTaVW high-entropy alloys is investigated using hybrid Monte Carlo/molecular dynamics simulations with a machine-learned potential. We show that CSRO enhances radiation tolerance by promoting interstitial diffusion while suppressing vacancy migration, thereby increasing defect recombination efficiency during recovery stage. However, CSRO is rapidly degraded under cumulative irradiation, with Warren-Cowley paramete…

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li, Yin Liu, Shanshan Shan, Peng Wu, Pengfei Rong, Feng Liu, G. Bruce Pike, Alan H. Wilman, Hongfu Sun, Yang Gao
https://arxiv.org/abs/2506.13293

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sources (i.e., paramagnetic and diamagnetic), may coexist in a single voxel, and cancel each other out in net QSM images. Susceptibility source separation techniques enable the extraction of sub-voxel information from QSM maps. This study proposes a novel SUSEP-Net for susceptibility source separation by training a dual-br…

QCD phase transition at finite temperature and chemical potential with the non-extensive statistics
Zhi-Ying Qin, Jia-Hao Shi, Jin-Peng Zhang, Jian Cao, Bo Feng, Wen-Chao Zhang, Hua Zheng, Shi-Jun Mao
https://arxiv.org/abs/2507.00731

QCD phase transition at finite temperature and chemical potential with the non-extensive statistics
With the application of the non-extensive correction to the equation of state (EoS) in the parton (hadron resonance) gas at high (low) temperature and a smooth interpolation of these two equation of states, a non-extensive version of EoS is constructed to investigate the properties of quantum chromodynamics (QCD) phase transition at finite temperature ($T$) and chemical potential ($μ$). It is based on non-extensive statistics and characterized by a non-extensivity parameter $q$, which measures…

This https://arxiv.org/abs/2409.17458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking
The rapid progress of Large Language Models (LLMs) has opened up new opportunities across various domains and applications; yet it also presents challenges related to potential misuse. To mitigate such risks, red teaming has been employed as a proactive security measure to probe language models for harmful outputs via jailbreak attacks. However, current jailbreak attack approaches are single-turn with explicit malicious queries that do not fully capture the complexity of real-world interactions…