Tootfinder

Classification of kinetic-related injury in hospital triage data using NLP
Midhun Shyam, Jim Basilakis, Kieran Luken, Steven Thomas, John Crozier, Paul M. Middleton, X. Rosalind Wang
https://arxiv.org/abs/2509.04969

Classification of kinetic-related injury in hospital triage data using NLP
Triage notes, created at the start of a patient's hospital visit, contain a wealth of information that can help medical staff and researchers understand Emergency Department patient epidemiology and the degree of time-dependent illness or injury. Unfortunately, applying modern Natural Language Processing and Machine Learning techniques to analyse triage data faces some challenges: Firstly, hospital data contains highly sensitive information that is subject to privacy regulation thus need to be …

Dynamically encircling an exceptional point through phase-tracked closed-loop control
Sen Zhang, Yangyu Huang, Lei Yu, Kaixuan He, Ning Zhou, Dingbang Xiao, Xuezhong Wu, Franco Nori, Hui Jing, Xin Zhou
https://arxiv.org/abs/2509.04940

Dynamically encircling an exceptional point through phase-tracked closed-loop control
The intricate complex eigenvalues of non-Hermitian Hamiltonians manifest as Riemann surfaces in control parameter spaces. At the exceptional points (EPs), the degeneracy of both eigenvalues and eigenvectors introduces noteworthy topological features, particularly during the encirclement of the EPs. Traditional methods for probing the state information on the Riemann surfaces involve static measurements; however, realizing continuous encircling remains a formidable challenge due to non-adiabatic…

Probing lepton flavor violating dark matter scenarios via astrophysical photons and positrons
Jin-Han Liang, Yi Liao, Xiao-Dong Ma
https://arxiv.org/abs/2508.05121 https://

Probing lepton flavor violating dark matter scenarios via astrophysical photons and positrons
In this Letter we explore, for the first time, the constraints on lepton flavor violating (LFV) dark matter (DM) scenarios via the astrophysical photons and positrons, including both the annihilation and decay modes, ${\tt DM(+DM)}\to e^\pm μ^\mp, e^\pm τ^\mp, μ^\pm τ^\mp$. Given the presence of LFV interactions in various DM models and the challenge of probing such interactions at terrestrial facilities, such as DM direct detection and collider experiments, indirect detection offers a uniq…

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Qingyuan Li, Bo Zhang, Hui Kang, Tianhao Xu, Yulei Qian, Yuchen Xie, Lin Ma
https://arxiv.org/abs/2508.03760

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Nowadays, communication bottlenecks have emerged as a critical challenge in the distributed training and deployment of large language models (LLMs). This paper introduces FlashCommunication V2, a novel communication paradigm enabling efficient cross-GPU transmission at arbitrary bit widths. Its core innovations lie in the proposed bit splitting and spike reserving techniques, which address the challenges of low-bit quantization. Bit splitting decomposes irregular bit widths into basic units, en…

ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Ting Dang
https://arxiv.org/abs/2508.04529 https://…

ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
Recent advances in audio generation systems have enabled the creation of highly realistic and immersive soundscapes, which are increasingly used in film and virtual reality. However, these audio generators also raise concerns about potential misuse, such as generating deceptive audio content for fake videos and spreading misleading information. Existing datasets for environmental sound deepfake detection (ESDD) are limited in scale and audio types. To address this gap, we have proposed EnvSDD, …

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Saptarshi Banerjee, Himadri Nath Saha, Utsho Banerjee, Rajarshi Karmakar, Jon Turdiev
https://arxiv.org/abs/2510.05123

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Neuro-oncological prognostics are now vital in modern clinical neuroscience because brain tumors pose significant challenges in detection and management. To tackle this issue, we propose a cognitive digital twin framework that combines real-time EEG signals from a wearable skullcap with structural MRI data for dynamic and personalized tumor monitoring. At the heart of this framework is an Enhanced Vision Transformer (ViT++) that includes innovative components like Patch-Level Attention Regulari…

Frozen states in shear-induced coagulation: When history matters
Mingliang Xie
https://arxiv.org/abs/2510.05319 https://arxiv.org/pdf/2510.05319

Frozen states in shear-induced coagulation: When history matters
We discover a frozen state in decaying turbulent coagulation where the moment ratio M_C stabilizes at 4.5, defying the theoretical prediction of 2 for homogeneous systems. This persistent state emerges from historical memory effects that freeze spatial correlations, challenging the reduction of spatially extended systems to zero-dimensional models. Our findings fundamentally reshape the understanding of long-term coagulation dynamics with immediate implications for aerosol science and industria…

Heat Reveals What Clouds Conceal: Global Carbon & Longitudinally Asymmetric Chemistry on LTT 9779 b
Reza Ashtari, Sean Collins, Jared Splinter, Kevin B. Stevenson, Vivien Parmentier, Jonathan Brande, Suman Saha, Sarah Stamer, Ian J. M. Crossfield, James S. Jenkins, K. Angelique Kahle, Joshua D. Lothringer, Nishil Mehta, Nicolas B. Cowan, Diana Dragomir, Laura Kreidberg, Thomas M. Evans-Soma, Tansu Daylan, Olivia Venot, Xi Zhang

Heat Reveals What Clouds Conceal: Global Carbon & Longitudinally Asymmetric Chemistry on LTT 9779 b
LTT-9779 b is an ultra-hot Neptune (Rp ~ 4.7 Re, Mp ~ 29 Me) orbiting its Sun-like host star in just 19 hours, placing it deep within the "hot Neptune desert," where Neptunian planets are seldom found. We present new JWST NIRSpec G395H phase-curve observations that probe its atmospheric composition in unprecedented detail. At near-infrared wavelengths, which penetrate the high-altitude clouds inferred from previous NIRISS/SOSS spectra, thermal emission reveals a carbon-rich atmosphere with opac…

D\'esentrelacement Fr\'equentiel Doux pour les Codecs Audio Neuronaux
Beno\^it Gini\`es, Xiaoyu Bie, Olivier Fercoq, Ga\"el Richard
https://arxiv.org/abs/2510.03741

Désentrelacement Fréquentiel Doux pour les Codecs Audio Neuronaux
While neural-based models have led to significant advancements in audio feature extraction, the interpretability of the learned representations remains a critical challenge. To address this, disentanglement techniques have been integrated into discrete neural audio codecs to impose structure on the extracted tokens. However, these approaches often exhibit strong dependencies on specific datasets or task formulations. In this work, we propose a disentangled neural audio codec that leverages spec…

CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
Justin Luong, Hao Xue, Flora D. Salim
https://arxiv.org/abs/2508.03764 https://

CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
Physicians routinely assess respiratory sounds during the diagnostic process, providing insight into the condition of a patient's airways. In recent years, AI-based diagnostic systems operating on respiratory sounds, have demonstrated success in respiratory disease detection. These systems represent a crucial advancement in early and accessible diagnosis which is essential for timely treatment. However, label and data scarcity remain key challenges, especially for conditions beyond COVID-19, li…