Tootfinder

NeuCODEX: Edge-Cloud Co-Inference with Spike-Driven Compression and Dynamic Early-Exit
Maurf Hassan, Steven Davy, Muhammad Zawish, Owais Bin Zuber, Nouman Ashraf
https://arxiv.org/abs/2509.19156

Flow-dependent tagging of $^{214}$Pb decays in the LZ dark matter detector
J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder, C. S. Amarasinghe, A. Ames, T. J. Anderson, N. Angelides, H. M. Ara\'ujo, J. E. Armstrong, M. Arthurs, A. Baker, S. Balashov, J. Bang, J. W. Bargemann, E. E. Barillier, K. Beattie, T. Benson, A. Bhatti, T. P. Biesiadzinski, H. J. Birch, E. Bishop, G. M. Blockinger, B. Boxer, C. A. J. Brew, P. Br\'as, S. Burdin, M. C. Carmona-Benitez, M. Carter, A. Cha…

Flow-dependent tagging of $^{214}$Pb decays in the LZ dark matter detector
The LUX-ZEPLIN (LZ) experiment is searching for dark matter interactions in a liquid xenon time projection chamber (LXe-TPC). This article demonstrates how control of the flow state in the LXe-TPC enables the identification of pairs of sequential alpha-decays, which are used to map fluid flow and ion drift in the liquid target. The resulting transport model is used to tag $^{214}$Pb beta-decays, a leading background to dark matter signals in LZ. Temporally evolving volume selections, at a cost …

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Maximilian Kromer, Panagiotis Agrafiotis, Beg\"um Demir
https://arxiv.org/abs/2508.07760

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions such as wave induced patterns, scattering and sunglint, introduced by the dynamic water surface, the water column properties, and solar illumination. In this work, we introduce Sea-Undistort, a comprehensive synthetic dataset of 1200 paired 512x512 through-water scenes rendered in Blender. Each pair comprises a distortion-free and a distorted view, featuring realistic water effe…

Extended AI Interactions Shape Sycophancy and Perspective Mimesis
Shomik Jain, Charlotte Park, Matheus Mesquita Viana, Ashia Wilson, Dana Calacci
https://arxiv.org/abs/2509.12517

Extended AI Interactions Shape Sycophancy and Perspective Mimesis
We investigate whether long-context interactions between users and LLMs lead to AI mirroring behaviors. We focus on two forms of mirroring: (1) sycophancy -- the tendency of models to be overly agreeable with users, and (2) perspective mimesis -- the extent to which models reflect a user's perspective. Using two weeks of interaction context collected from 38 users, we compare model responses with and without long-context for two tasks: political explanations and personal advice. Our results dem…

A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
https://arxiv.org/abs/2510.05687

A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
We utilize a large sample of $\sim$113,000 galaxies ($z < 0.3$) from the Sloan Digital Sky Survey with high-quality data to compare star formation rates (SFRs) across multiple diagnostic methods and examine their connection to Active Galactic Nuclei (AGNs) strength, indicated by Eddington ratio. Our sample encompassed star-forming (SF), composite, Seyfert, and LINER galaxies. Our analysis utilizes various SFRs indicators, including observed infrared flux ($\rm SFR_{FIR}$) from AKARI/Herschel ($…

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Sara Makenali, Babak Rokh, Ali Azarpeyvand
https://arxiv.org/abs/2509.04244 https://

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to the large number of layers and parameters, which result in considerable computational and memory demands. To address this issue, pruning and quantization are two widely used compression techniques, commonly applied individually in most studies to reduce model size and enhance processing speed. Nevertheless, combining…

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Humza Ashraf, Syed Muhammad Danish, Aris Leivadeas, Yazan Otoum, Zeeshan Sattar
https://arxiv.org/abs/2508.08332

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Large Language Models (LLMs) are widely used for code generation. However, commercial models like ChatGPT require significant computing power, which leads to high energy use and carbon emissions. This has raised concerns about their environmental impact. In this study, we evaluate open-source Small Language Models (SLMs) trained explicitly for code generation and compare their performance and energy efficiency against large LLMs and efficient human-written Python code. The goal is to investigat…

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
Riccardo Mereu, Aidan Scannell, Yuxin Hou, Yi Zhao, Aditya Jitta, Antonio Dominguez, Luigi Acerbi, Amos Storkey, Paul Chang
https://arxiv.org/abs/2510.07092

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compression, focused on predicting future discrete latent codes. For the sampling track, we adapt the video generation foundation model Wan-2.2 TI2V-…

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Ostonya Thomas, Muhaimin Bin Munir, Jean-Michel Tine, Mizanur Rahman, Yuchen Cai, Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan, Trayce Hockstad, Mashrur Chowdhury
https://arxiv.org/abs/2508.025…

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Technological advancements have revolutionized numerous industries, including transportation. While digitalization, automation, and connectivity have enhanced safety and efficiency, they have also introduced new vulnerabilities. With 95% of data breaches attributed to human error, promoting cybersecurity awareness in transportation is increasingly critical. Despite numerous cyberattacks on transportation systems worldwide, comprehensive and centralized records of these incidents remain scarce. …

Low-energy nuclear recoil calibration of the LUX-ZEPLIN experiment with a photoneutron source
J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder, C. S. Amarasinghe, A. Ames, T. J. Anderson, N. Angelides, H. M. Ara\'ujo, J. E. Armstrong, M. Arthurs, A. Baker, S. Balashov, J. Bang, J. W. Bargemann, E. E. Barillier, K. Beattie, T. Benson, A. Bhatti, T. P. Biesiadzinski, H. J. Birch, E. Bishop, G. M. Blockinger, B. Boxer, C. A. J. Brew, P. Br\'as, S. Burdin, M. C. Carmona-Benitez…

Low-energy nuclear recoil calibration of the LUX-ZEPLIN experiment with a photoneutron source
The LZ experiment is a liquid xenon time-projection chamber (TPC) searching for evidence of particle dark matter interactions. In the simplest assumption of elastic scattering, many dark matter models predict an energy spectrum which rises quasi-exponentially with decreasing energy transfer to a target atom. LZ expects to detect coherent neutrino-nucleus scattering of $^{8}$B solar neutrinos, the signal from which is very similar to a dark matter particle with mass of about 5.5 GeV/$c^{2}$, whi…