Tootfinder

GOP Senators agree to change a provision in the tax bill from a 10-year ban on state AI regulation to a 5-year ban with carve outs for issues such as copyrights (Bloomberg)
https://www.bloomberg.com/news/articles/20…

Leveraging In-Context Learning for Political Bias Testing of LLMs
Patrick Haller, Jannis Vamvas, Rico Sennrich, Lena A. J\"ager
https://arxiv.org/abs/2506.22232

Leveraging In-Context Learning for Political Bias Testing of LLMs
A growing body of work has been querying LLMs with political questions to evaluate their potential biases. However, this probing method has limited stability, making comparisons between models unreliable. In this paper, we argue that LLMs need more context. We propose a new probing task, Questionnaire Modeling (QM), that uses human survey data as in-context examples. We show that QM improves the stability of question-based bias evaluation, and demonstrate that it may be used to compare instruct…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Yuanfang Ren, Esra Adiyeke, Ziyuan Guan, Zhenhong Hu, Mackenzie J Meni, Benjamin Shickel, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
https://arxiv.org/abs…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting postoperative complications and death after major surgery on a large and multicenter dataset, following the previously validated MySurgeryRisk algorithm. This retrospective, longitudinal and multicenter cohort analysis included 508,097 encounters from 366,875 a…

Proposed Cowboys-Jets trade sends projected $145 million superstar to New York https://www.sportingnews.com/us/nfl/new-york-jets/news/proposed-cowboys-jets-trade-micah-parsons-new-york/94cf5614ca51cd915dc…

Efficient Multilingual ASR Finetuning via LoRA Language Experts
Jiahong Li, Yiwen Shao, Jianheng Zhuo, Chenda Li, Liliang Tang, Dong Yu, Yanmin Qian
https://arxiv.org/abs/2506.21555

Efficient Multilingual ASR Finetuning via LoRA Language Experts
Recent advancements in deep learning have significantly enhanced multilingual automatic speech recognition (ASR) due to the development of advanced model architectures and available large-scale multilingual datasets. Despite that, multilingual ASR still suffers from the curse of multilinguality in that different languages tend to interfere with each other, making it difficult for the ASR model to identify multiple languages effectively while sharing model capacity across them. This paper propos…

Cross-lingual Data Selection Using Clip-level Acoustic Similarity for Enhancing Low-resource Automatic Speech Recognition
Shunsuke Mitsumori, Sara Kashiwagi, Keitaro Tanaka, Shigeo Morishima
https://arxiv.org/abs/2506.22194

Cross-lingual Data Selection Using Clip-level Acoustic Similarity for Enhancing Low-resource Automatic Speech Recognition
This paper presents a novel donor data selection method to enhance low-resource automatic speech recognition (ASR). While ASR performs well in high-resource languages, its accuracy declines in low-resource settings due to limited training data. A common solution is to leverage multilingual self-supervised learning (SSL) models with donor languages. However, existing methods rely on language-level similarity, overlooking clip-level variations. To address this limitation, we propose clip-wise aco…

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm
Kosei Tsuji, Ichiro Maruta, Kenji Fujimoto, Tomoyuki Maeda, Yoshihisa Tamase, Tsukasa Shinohara
https://arxiv.org/abs/2505.23138

System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm
Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a…

Jazzaria – Mislaid Mariachi
#acoustic

Speaker Targeting via Self-Speaker Adaptation for Multi-talker ASR
Weiqing Wang, Taejin Park, Ivan Medennikov, Jinhan Wang, Kunal Dhawan, He Huang, Nithin Rao Koluguri, Jagadeesh Balam, Boris Ginsburg
https://arxiv.org/abs/2506.22646

Speaker Targeting via Self-Speaker Adaptation for Multi-talker ASR
We propose a self-speaker adaptation method for streaming multi-talker automatic speech recognition (ASR) that eliminates the need for explicit speaker queries. Unlike conventional approaches requiring target speaker embeddings or enrollment audio, our technique dynamically adapts individual ASR instances through speaker-wise speech activity prediction. The key innovation involves injecting speaker-specific kernels generated via speaker supervision activations into selected ASR encoder layers. …