Tootfinder

Constraining the Extragalactic Magnetic Field: Auger Data Meet UHECR Propagation Modeling
Ala'a AL-Zetoun, Arjen van Vliet, Andrew M. Taylor, Walter Winter
https://arxiv.org/abs/2506.16169

Constraining the Extragalactic Magnetic Field: Auger Data Meet UHECR Propagation Modeling
Recent analyses from the Pierre Auger Collaboration suggest correlations between the arrival directions of Ultra-High-Energy Cosmic Rays (UHECRs) and catalogs of starburst galaxies (SGBs) and jetted active galactic nuclei (AGNs). We revisit these analyses using the same methodology as \auger , but explicitly incorporating UHECR deflections in turbulent extragalactic magnetic fields (EGMFs). We demonstrate that while for SBGs the same sources as for the generic \auger\ analysis dominate the cata…

Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASR
Zhong-Qiu Wang, Ruizhe Pang
https://arxiv.org/abs/2507.15229

Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASR
In multi-channel speech enhancement and robust automatic speech recognition (ASR), beamforming can typically improve the signal-to-noise ratio (SNR) of the target speaker and produce reliable enhancement with little distortion to target speech. With this observation, we propose to leverage beamformed mixture, which has a higher SNR of the target speaker than the input mixture, as a weak supervision to train deep neural networks (DNNs) to enhance the input mixture. This way, we can train enhance…

Control-Based Online Distributed Optimization
Wouter J. A. van Weerelt, Nicola Bastianello
https://arxiv.org/abs/2508.15498 https://arxiv.org/pdf/2508.1549…

Control-Based Online Distributed Optimization
In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming to know an internal model of their variation. In this set-up, we formulate the algorithm design as a robust control problem, showing that it yields a fully distributed algorithm. We also provide a distributed routine to acquire the internal model. We show that the algorithm converges exactly to the sequence of optima…

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

Enhanced Sensitivity and Noise Resilience in Two-Qubit Quantum Magnetometers
S. Nohekhan Shishavan, K. Aghayar Gharehbagh, H. Sedgi Gamichi
https://arxiv.org/abs/2508.13400 http…

Enhanced Sensitivity and Noise Resilience in Two-Qubit Quantum Magnetometers
We present a novel two-qubit quantum magnetometer Hamiltonian optimized for enhanced sensitivity and noise resilience. Compared to existing models, our formulation offers advantages in accuracy, robustness against noise, and entanglement dynamics. Using analytical methods, we derive the Quantum Fisher Information (QFI) and the Signal-to-Noise Ratio (SNR), highlighting its practical viability for magnetic field sensing. Our approach bridges theoretical insights with real-world applicability. We …

Adapting Whisper for Streaming Speech Recognition via Two-Pass Decoding
Haoran Zhou, Xingchen Song, Brendan Fahy, Qiaochu Song, Binbin Zhang, Zhendong Peng, Anshul Wadhawan, Denglin Jiang, Apurv Verma, Vinay Ramesh, Srivas Prasad, Michele M. Franceschini
https://arxiv.org/abs/2506.12154 …

Adapting Whisper for Streaming Speech Recognition via Two-Pass Decoding
OpenAI Whisper is a family of robust Automatic Speech Recognition (ASR) models trained on 680,000 hours of audio. However, its encoder-decoder architecture, trained with a sequence-to-sequence objective, lacks native support for streaming ASR. In this paper, we fine-tune Whisper for streaming ASR using the WeNet toolkit by adopting a Unified Two-pass (U2) structure. We introduce an additional Connectionist Temporal Classification (CTC) decoder trained with causal attention masks to generate str…

LLM Robustness Leaderboard v1 --Technical report
Pierre Peign\'e - Lefebvre, Quentin Feuillade-Montixi, Tom David, Nicolas Miailhe
https://arxiv.org/abs/2508.06296 https://

LLM Robustness Leaderboard v1 --Technical report
This technical report accompanies the LLM robustness leaderboard published by PRISM Eval for the Paris AI Action Summit. We introduce PRISM Eval Behavior Elicitation Tool (BET), an AI system performing automated red-teaming through Dynamic Adversarial Optimization that achieves 100% Attack Success Rate (ASR) against 37 of 41 state-of-the-art LLMs. Beyond binary success metrics, we propose a fine-grained robustness metric estimating the average number of attempts required to elicit harmful behav…

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
Xiaoxiao Li, An Zhu, Youhai Jiang, Fengjie Zhu
https://arxiv.org/abs/2508.14916 https://

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
This paper presents the architecture and performance of a novel Multilingual Automatic Speech Recognition (ASR) system developed by the Transsion Speech Team for Track 1 of the MLC-SLM 2025 Challenge. The proposed system comprises three key components: 1) a frozen Whisper-large-v3 based speech encoder, leveraging large-scale pretraining to ensure robust acoustic feature extraction; 2) a trainable adaptor module using Linear-ReLU-Linear transformation mechanisms to effectively align speech and t…

Revisiting $\Lambda$CDM extensions in light of re-analyzed CMB data
Jacobo Asorey, Javier de Cruz P\'erez
https://arxiv.org/abs/2506.13537 https://

Revisiting $Λ$CDM extensions in light of re-analyzed CMB data
In the last years with the increasing precision in cosmological observations we have been able to establish an standard model of cosmology, the so-called $Λ$CDM, but also find some tensions between cosmological probes that are difficult to explain within the context of this model. We tested several phenomenological extensions of the $Λ$CDM with the newest datasets from the chain CMB+BAO+SNIa, to see whether they are able to alleviate the aforementioned tensions. We find that when the updated …

Event reconstruction with the Radio detector of the Pierre Auger Observatory
Simon Str\"ahnz (for the Pierre Auger collaboration)
https://arxiv.org/abs/2507.08556

Event reconstruction with the Radio detector of the Pierre Auger Observatory
The surface detector of the Pierre Auger Observatory has recently been upgraded with the addition of radio antennas, forming the radio detector (RD). This contribution outlines the standard methods for reconstructing extensive air showers using the RD, along with recent developments. The reconstruction pipeline is based on a robust understanding of the detector itself. The entire instrument, including the antenna pattern and analog chain, has been meticulously characterized within the Offline s…