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A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditionally served as the basis for quantile regression; however, its fixed skewness limits flexibility and renders it sensitive to outliers. In contrast, the generalized asymmetric Laplace (GAL) distribution enables more flexible modeling of skewness and heavy-tailed behavior, yet it remains vulnerable to extreme observati…

MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG
Taehwan Park, Geonho Lee, Min-Soo Kim
https://arxiv.org/abs/2507.01079 h…

MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG
Retrieval-Augmented Generation (RAG) has proven effective on server infrastructures, but its application on mobile devices is still underexplored due to limited memory and power resources. Existing vector search and RAG solutions largely assume abundant computation resources, making them impractical for on-device scenarios. In this paper, we propose MobileRAG, a fully on-device pipeline that overcomes these limitations by combining a mobile-friendly vector search algorithm, \textit{EcoVector}, …

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
Zhongming Zhang, Samuel Murphy, Michael Aspinall
https://arxiv.org/abs/2506.00675

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
With the development of fast neutron reactors and nuclear fusion reactors, it is necessary to find new radiation-hardened high-flux core neutron detectors. The use of perovskite Caesium Lead Bromide (CsPbBr$_3$) for neutron radiation detection is a new research direction. However, at high temperatures, the effects of neutron radiation, especially the Primary Knock-out Atom (PKA) and Displacement Per Atom (DPA), and the defect distribution at the molecular level have not been reported. This stud…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
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Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

Enhancing Noisy Quantum Sensing by GHZ State Partitioning
Allen Zang, Tian-Xing Zheng, Peter C. Maurer, Frederic T. Chong, Martin Suchara, Tian Zhong
https://arxiv.org/abs/2507.02829

Enhancing Noisy Quantum Sensing by GHZ State Partitioning
Presence of harmful noise is inevitable in entanglement-enhanced sensing systems, requiring careful allocation of resources to optimize sensing performance in practical scenarios. We advocate a simple but effective strategy to improve sensing performance in the presence of noise. Given a fixed number of quantum sensors, we partition the preparation of GHZ states by preparing smaller, independent sub-ensembles of GHZ states instead of a GHZ state across all sensors. We perform extensive analytic…

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng, Chengxu Zhou
https://arxiv.org/abs/2506.01563

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behavio…

Flexible Mixed Precision Quantization for Learned Image Compression
Md Adnan Faisal Hossain, Zhihao Duan, Fengqing Zhu
https://arxiv.org/abs/2506.01221 htt…

Flexible Mixed Precision Quantization for Learned Image Compression
Despite its improvements in coding performance compared to traditional codecs, Learned Image Compression (LIC) suffers from large computational costs for storage and deployment. Model quantization offers an effective solution to reduce the computational complexity of LIC models. However, most existing works perform fixed-precision quantization which suffers from sub-optimal utilization of resources due to the varying sensitivity to quantization of different layers of a neural network. In this p…

Differentially Private Synthetic Data Release for Topics API Outputs
Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andres Munoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
https://arxiv.org/abs/2506.23855

Differentially Private Synthetic Data Release for Topics API Outputs
The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite this interest, the empirical study of these methods is hindered by the lack of publicly available data. Reliable empirical analysis of the privacy properties of an API, in fact, requires access to a dataset consisting of realistic API outputs; however, privacy concerns prevent the general release of such data to the publ…

SINDy on slow manifolds
Diemen Delgado-Cano, Erick Kracht, Urban Fasel, Benjamin Herrmann
https://arxiv.org/abs/2507.00747 https://ar…

SINDy on slow manifolds
The sparse identification of nonlinear dynamics (SINDy) has been established as an effective method to learn interpretable models of dynamical systems from data. However, for high-dimensional slow-fast dynamical systems, the regression problem becomes simultaneously computationally intractable and ill-conditioned. Although, in principle, modeling only the dynamics evolving on the underlying slow manifold addresses both of these challenges, the truncated fast variables have to be compensated by …