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@Techmeme@techhub.social
2026-03-08 05:55:53

Samsung's consumer device chief TM Roh says it was "open to strategic co-operation" with more AI groups, having recently added Perplexity to its mobile OS (Michael Acton/Financial Times)
ft.com/content/3752d058-d3ee-4

Samsung seeks AI deals to challenge Apple’s smartphone lead
Korean giant’s device chief says its future Galaxy devices will host multiple models as users mix and match AI tools

@arXiv_physicsinsdet_bot@mastoxiv.page
2026-02-02 09:14:39

High-bandwidth frequency domain multiplexed readout of transition-edge sensors for neutrinoless double beta decay searches
M. Adami\v{c} (McGill,LBNL), M. Beretta (UCB,INFN), J. Camilleri (LBNL,Virginia Tech), C. Capelli (LBNL,Zurich U.), M. A. Dobbs (McGill), T. Elleflot (LBNL), B. K. Fujikawa (LBNL), Yu. G. Kolomensky (LBNL,UCB), D. Mayer (MIT), J. Montgomery (McGill), V. Novosad (ANL), A. M. Sindhwad (UCB), V. Singh (UCB), G. Smecher (t0.technology), A. Suzuki (LBNL), B. Welliver (UCB)
arxiv.org/abs/2601.23106 arxiv.org/pdf/2601.23106 arxiv.org/html/2601.23106
arXiv:2601.23106v1 Announce Type: new
Abstract: The next-generation of cryogenic neutrinoless double-beta decay experiments require increasingly fast readout in order to improve background discrimination. These experiments, operated as cryogenic calorimeters at $\sim$10 mK, are usually read out by high-impedance neutron transmutation doped (NTD) thermistors, which provide good energy resolution, but are limited by $\sim$1 ms response times. Superconducting detectors, such as transition-edge sensors (TESs) with a time resolution of $\sim$100 $\mu$s, offer superior timing performance over NTD semiconductor bolometers. To make this technology viable for an application to a thousand or more channels, multiplexed readout is necessary in order to minimize the thermal load and radioactive contamination induced by the readout. Frequency-domain multiplexing readout (fMux) for TESs, previously developed at Berkeley Lab and McGill University, is currently in use for mm-wave telescopes with detector sampling rates in the order of 100 Hz. We demonstrate a new readout system, based on the McGill/Berkeley digital fMux readout, to satisfy the higher bandwidth and noise requirements of the next generation of TES-instrumented cryogenic calorimeters. The new readout samples detectors at 156 kHz, three orders of magnitude faster than its cosmology-oriented predecessor. Each multiplexing readout module comprises ten superconducting resonators in the MHz range and a superconducting quantum interference device (SQUID), interfaced to high-bandwidth field programmable gate array (FPGA)-based electronics for digital signal processing and low-latency feedback.
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@arXiv_csLG_bot@mastoxiv.page
2026-02-25 10:38:51

Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Anupam Sharma, Harish Katti, Prajwal Singh, Shanmuganathan Raman, Krishna Miyapuram
arxiv.org/abs/2602.20932 arxiv.org/pdf/2602.20932 arxiv.org/html/2602.20932
arXiv:2602.20932v1 Announce Type: new
Abstract: An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or video stimuli, with limited classes. Because EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the EEG domain for detecting observed text from EEG signals in the PEERS dataset, comprising $931538$ EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring.
We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of EEG decoding and motivates future research in this direction.
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@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:07:47

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
arxiv.org/abs/2505.19698 mastoxiv.page/@arXiv_csLG_bot/
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
arxiv.org/abs/2506.08660 mastoxiv.page/@arXiv_csLG_bot/
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
arxiv.org/abs/2506.10167 mastoxiv.page/@arXiv_csLG_bot/
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
arxiv.org/abs/2506.10914 mastoxiv.page/@arXiv_csLG_bot/
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
arxiv.org/abs/2506.18481 mastoxiv.page/@arXiv_csLG_bot/
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
arxiv.org/abs/2506.21220 mastoxiv.page/@arXiv_csLG_bot/
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
arxiv.org/abs/2507.04448 mastoxiv.page/@arXiv_csLG_bot/
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
arxiv.org/abs/2508.01115 mastoxiv.page/@arXiv_csLG_bot/
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
arxiv.org/abs/2508.13904 mastoxiv.page/@arXiv_csLG_bot/
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
arxiv.org/abs/2508.16815 mastoxiv.page/@arXiv_csLG_bot/
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
arxiv.org/abs/2508.21785 mastoxiv.page/@arXiv_csLG_bot/
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
arxiv.org/abs/2509.15796 mastoxiv.page/@arXiv_csLG_bot/
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
arxiv.org/abs/2509.19975 mastoxiv.page/@arXiv_csLG_bot/
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
arxiv.org/abs/2509.21895 mastoxiv.page/@arXiv_csLG_bot/
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
arxiv.org/abs/2509.22566 mastoxiv.page/@arXiv_csLG_bot/
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
arxiv.org/abs/2509.23115 mastoxiv.page/@arXiv_csLG_bot/
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
arxiv.org/abs/2509.25424 mastoxiv.page/@arXiv_csLG_bot/
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
arxiv.org/abs/2509.26626 mastoxiv.page/@arXiv_csLG_bot/
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
arxiv.org/abs/2510.12402 mastoxiv.page/@arXiv_csLG_bot/
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
arxiv.org/abs/2510.15425 mastoxiv.page/@arXiv_csLG_bot/
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
arxiv.org/abs/2510.18114 mastoxiv.page/@arXiv_csLG_bot/
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
arxiv.org/abs/2510.22293 mastoxiv.page/@arXiv_csLG_bot/
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@arXiv_csLG_bot@mastoxiv.page
2025-12-22 11:50:31

Crosslisted article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
arxiv.org/abs/2512.17341 mastoxiv.page/@arXiv_statML_bo
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
arxiv.org/abs/2512.17381 mastoxiv.page/@arXiv_csNI_bot/
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
arxiv.org/abs/2512.17419 mastoxiv.page/@arXiv_csSE_bot/
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
arxiv.org/abs/2512.17426 mastoxiv.page/@arXiv_statML_bo
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
arxiv.org/abs/2512.17450 mastoxiv.page/@arXiv_csCV_bot/
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
arxiv.org/abs/2512.17460 mastoxiv.page/@arXiv_csSE_bot/
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
arxiv.org/abs/2512.17462 mastoxiv.page/@arXiv_csIR_bot/
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
arxiv.org/abs/2512.17466 mastoxiv.page/@arXiv_eessSY_bo
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
arxiv.org/abs/2512.17470 mastoxiv.page/@arXiv_csAI_bot/
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
arxiv.org/abs/2512.17473 mastoxiv.page/@arXiv_eessSP_bo
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
arxiv.org/abs/2512.17488 mastoxiv.page/@arXiv_csCV_bot/
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
arxiv.org/abs/2512.17515 mastoxiv.page/@arXiv_eessIV_bo
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
arxiv.org/abs/2512.17517 mastoxiv.page/@arXiv_csCV_bot/
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
arxiv.org/abs/2512.17534 mastoxiv.page/@arXiv_physicsfl
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
arxiv.org/abs/2512.17562 mastoxiv.page/@arXiv_csSD_bot/
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
arxiv.org/abs/2512.17574 mastoxiv.page/@arXiv_csDC_bot/
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
arxiv.org/abs/2512.17585 mastoxiv.page/@arXiv_eessIV_bo
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
arxiv.org/abs/2512.17594 mastoxiv.page/@arXiv_csCR_bot/
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
arxiv.org/abs/2512.17630 mastoxiv.page/@arXiv_csCL_bot/
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
arxiv.org/abs/2512.17659 mastoxiv.page/@arXiv_statML_bo
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