Tootfinder

Exact distribution of discrete-time D-BMAP/G/\inf queueing model
Tonglin Liao, Youming Li
https://arxiv.org/abs/2505.23178 https://ar…

Exact distribution of discrete-time D-BMAP/G/\inf queueing model
In this paper, we consider discrete-time D-BMAP/G/\inf queueing model. We construct effective discrete-time Markovian dynamics for this model and utilize it to derive exact time-dependent distribution of customer number and the corresponding moments for the original queueing model. Numerical simulations are used to verify our results. Using our result, we provide analytical distribution for discrete-time M/M/\inf, and then compare it with the distribution of continuous-time M/M/\inf.

Measuring topological invariants of even-dimensional non-Hermitian systems through quench dynamics
Xiao-Dong Lin, Long Zhang
https://arxiv.org/abs/2505.23633

Measuring topological invariants of even-dimensional non-Hermitian systems through quench dynamics
The accurate determination of non-Hermitian (NH) topological invariants plays a central role in the study of NH topological phases. In this work, we propose a general framework for directly measuring NH topological invariants in even-dimensional systems through quench dynamics. Our approach hinges on constructing an auxiliary Hermitian matrix topologically equivalent to the original NH Hamiltonian, enabling topological characterization via reduced-dimensional momentum subspaces called band inve…

Last morning in Qaanaaq, time to go home, but first a massive clean up is due! Lots of notes to write up, still some data to download, need to go and repair a cable too. May have time to update and round off the full #FieldDiary later.
But first a peaceful shot from last night, when the narwhals were in the bay again...

we [[hack]]
tonight
https://www.youtube.com/watch?v=QQnc-hM80UQ

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Tuan Tuan, Yue Liu, Yuhao Qing, Dong Huang, Xinyi He, Qian Liu, Zejun Ma, See-kiong Ng
https://arxiv.org/abs/2505.23387

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features
Qianchao Wang, Leena Heistrene, Yoash Levron, Yuxuan Ding, Yaping Du
https://arxiv.org/abs/2505.23097

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features
In practical resource-constrained environments, efficiently extracting the potential high-frequency fault-critical information is an inherent problem. To overcome this problem, this work suggests leveraging a bi-residual neural network named Bi-ResNet to extract the inner spatial-temporal high-frequency features using embedded spatial-temporal convolution blocks and intra-link layers. It can be considered as embedding a high-frequency extractor into networks without adding any parameters, helpi…

Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM
Zhaokai Sun, Li Zhang, Qing Wang, Pan Zhou, Lei Xie
https://arxiv.org/abs/2505.23207 …

Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM
Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation, a critical challenge in multi-party speech processing. This work proposes a speaker-aware progressive OSD model that leverages a progressive training strategy to enhance the correlation between subtasks such as voice activity detection (VAD) and overlap detection. To improve acoustic representation, we explore the effectiveness of state-of-the-art self-supervised learning (SSL) models,…

This https://arxiv.org/abs/2505.08944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, load skew across experts often leads to suboptimal device utilization and excessive synchronization overheads. This paper introduces Asynchronous Expert Parallelism (AEP), a new paradigm that decouples layer execution from barrier-style synchronization. By dynamically queuing tokens at each layer (referred to as $μ$-qu…

SWE-bench Goes Live!
Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
https://arxiv.org/abs/2505.23419

SWE-bench Goes Live!
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability a…

Universal principles for sudden-quench quantum Otto engines
R. S. Watson, K. V. Kheruntsyan
https://arxiv.org/abs/2505.21933 https://…

Universal principles for sudden-quench quantum Otto engines
We apply a simple sudden quench approximation for the unitary work strokes of a quantum Otto engine in order to provide a general analysis of its performance, applicable to arbitrary quantum models with two-body interactions. This work extends recent results for an interaction-driven Otto cycle to generic many-body interacting quantum models, providing universal bounds on their operation efficiency. From this, we demonstrate that the net work of such an engine cycle is determined entirely by in…