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Planning Cyclic Queuing and Forwarding for Time-sensitive Networking via Heuristic and Neurodynamic Optimization

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IEEE Transactions on Network science and Engineering 2025年

作者： Wang, Jiasen Wang, Jun Purple Mountain Laboratories Future Network Research Center Nanjing211111 China City University of Hong Kong Departments of Computer Science and Data Science Hong Kong

Time-sensitive networking (TSN) is a potential means to provide high-quality communication for cyber-physical systems in industrial automation and manufacturing. IEEE 802.1Qch cyclic queuing and forwarding (CQF) is a mechanism for transmitting time-sensitive packets in TSN systems. This paper addresses the planning of periodic time-sensitive flows for CQF in TSN systems. An integer programming problem is formulated to plan the periodic time-sensitive flows. In view of the NP-hardness of the integer programming problem, a polynomial-time heuristic algorithm is developed for CQF planning based on flow efficiency sorting and packet swapping to compute time offsets of multi-period flows. The heuristic algorithm is proven to converge to a CQF plan with a constant worst-case performance ratio with respect to the global optimal plan under given assumptions and derived sufficient conditions. Based on the heuristic solution, neurodynamic optimization is used to search for global optimal plans. Experimental and simulation results on open-loop network flow planning and closed-loop networked model predictive control are elaborated to substantiate the efficacy of the planning algorithms. © 2013 IEEE.

关键词： collaborative neurodynamic optimization Cyclic queuing and forwarding heuristic planning time-sensitive networking

data Selection for ERMs

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arXiv 2025年

作者： Hanneke, Steve Moran, Shay Shlimovich, Alexander Yehudayoff, Amir Department of Computer Science Purdue University United States Departments of Mathematics Computer Science and Data and Decision Sciences Technion and Google Research Department of Mathematics Technion Department of Computer Science Copenhagen University Denmark

Learning theory has traditionally followed a model-centric approach, focusing on designing optimal algorithms for a fixed natural learning task (e.g., linear classification or regression). In this paper, we adopt a complementary data-centric perspective, whereby we fix a natural learning rule and focus on optimizing the training data. Specifically, we study the following question: given a learning rule A and a data selection budget n, how well can A perform when trained on at most n data points selected from a population of N points? We investigate when it is possible to select n N points and achieve performance comparable to training on the entire population. We address this question across a variety of empirical risk minimizers. Our results include optimal data-selection bounds for mean estimation, linear classification, and linear regression. Additionally, we establish two general results: a taxonomy of error rates in binary classification and in stochastic convex optimization. Finally, we propose several open questions and directions for future research. © 2025, CC BY.

关键词： Active learning

Unlocking the Past: The Potential of Large Language Models to Revolutionize Transcription of Natural History Collections

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data Intelligence 2025年第2期7卷 237-264页

作者： Riley Herbst Daniel Stille Gilbert F.Gwilliam Ⅲ Matt von Konrat Thomas Campbell Wyatt Gaswick Felix Grewe Kimberly Hansen Francisco Iacobelli Karen Jellema Maria Lucia Kawasaki Danny Kreider Richard H.Ree Yarency Rodriguez Alexander Wolpert Gantz Family Collections Center Field Museum Department of Computer Science Roosevelt University Department of Computer Science/Department of Biology Northeastern Illinois University Grainger Bioinformatics Center Field Museum Departments of Health Informatics and Data Science and Surgery Loyola University

Globally, natural history collections(NHCs) house nearly three billion scientific collections offering an unparalleled resource that contributes to both science and society. The first two decades of the twentyfirst century have seen a rapid rise in the mobilization of digital biodiversity data. Yet, the transcription of important scientific information from NHCs remains a challenge presenting a critical bottleneck in the digitization process. This experimental study investigates the application of Large Language Models(LLMs) in automating the transcription of herbarium specimen labels. Various LLMs were tested, including Open AI's GPT models and Anthropic's Claude, to evaluate their accuracy and efficiency in transcribing label data. Algorithms have been developed to automate the workflow, leveraging the image processing capabilities of LLMs to bypass traditional Optical Character Recognition(OCR) tools. Prompt engineering strategies were explored to enhance transcription accuracy, focusing on field-specific instructions and formatting. The study demonstrates the potential for LLMs to streamline digitization workflows in natural history collections but underscores several challenges, such as transcription errors, logic errors and inconsistency of results. Promising directions that emerge include improving prompt engineering techniques, exploring the integration of LLMs into workflows and model selection to achieve higher accuracy and reliability.

关键词： OCR Digitization LLM prompt engineering Biodiversity data Herbarium vouchers

Design and Assessment of Representative Hybrid Clinical Trials using Health Recommender System 6

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Design and Assessment of Representative Hybrid Clinical Tria...

6th International Workshop on Health Recommender Systems, HealthRecSys 2024

作者： Neehal, Nafis Anand, Vibha Bennett, Kristin P. Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Biomedical AI-Healthcare and Life Sciences IBM T.J. Watson Research Center CambridgeMA02141 United States Institute of Data Exploration and Applications Mathematical Sciences and Computer Science Departments Rensselaer Polytechnic Institute TroyNY12180 United States

Incorporating real-world data (RWD) into clinical trials can enhance trial efficiency, diversity, and generalizability. This paper introduces the Framework for Research in Synthetic Control Arms (FRESCA), which uses a novel Recommender System combined with Equity Adjustment strategies to design and evaluate Representative Hybrid Clinical Trials (HCTs). FRESCA employs a novel matching algorithm through its recommendation system to select suitable patients from RWD while ensuring that the selected population is representative of the target demographic. This dual approach improves both patient selection and trial outcomes by balancing statistical appropriateness and equity. Simulations based on data from two existing randomized clinical trials (RCTs) show that using FRESCA to recommend patients from RWD and apply equity adjustments enhances internal validity and generalizability. Our analysis indicates that combining matching and equity adjustments yields more accurate treatment effect estimates and fair population representation, even with reduced RCT control group sizes. In contrast, using either method alone may result in biased outcomes. The flexibility of FRESCA to simulate various HCT scenarios makes it a valuable tool for advancing equitable and efficient clinical trial designs. © 2024 Copyright for this paper by its authors.

关键词： Clinical research

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Spherical dimension

arXiv 2025年

作者： Chornomaz, Bogdan Moran, Shay Waknine, Tom Department of Mathematics Technion Departments of Mathematics Computer Science and Data and Decision Sciences Technion Google Research United States

We introduce and study the spherical dimension, a natural topological relaxation of the VC dimension that unifies several results in learning theory where topology plays a key role in the proofs. The spherical dimension is defined by extending the set of realizable datasets (used to define the VC dimension) to the continuous space of realizable distributions. In this space, a shattered set of size d (in the VC sense) is completed into a continuous object, specifically a d-dimensional sphere of realizable distributions. The spherical dimension is then defined as the dimension of the largest sphere in this space. Thus, the spherical dimension is at least the VC dimension. The spherical dimension serves as a common foundation for leveraging the Borsuk-Ulam theorem and related topological tools. We demonstrate the utility of the spherical dimension in diverse applications, including disambiguations of partial concept classes, reductions from classification to stochastic convex optimization, stability and replicability, and sample compression schemes. Perhaps surprisingly, we show that the open question posed by Alon, Hanneke, Holzman, and Moran (FOCS 2021) of whether there exist non-trivial disambiguations for halfspaces with margin is equivalent to the basic open question of whether the VC and spherical dimensions are finite together. © 2025, CC BY-NC-SA.

关键词： Spheres

TWIGMA: a dataset of ai-generated images with metadata from twitter 23

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TWIGMA: a dataset of ai-generated images with metadata from ...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yiqun T. Chen James Zou Department of Biomedical Data Science Stanford University Stanford CA Departments of Biomedical Data Science Electrical Engineering and Computer Science Stanford University Stanford CA

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with Metadata), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://***/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.

关键词：

INTUITIVE NORMS ARE EUCLIDEAN

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arXiv 2025年

作者： Moran, Shay Shlimovich, Alexander Yehudayoff, Amir Departments of Mathematics Computer Science Data and Decision Sciences Technion-IIT Google Research United States Department of Mathematics Technion-IIT Department of Computer Science The University of Copenhagen Denmark

We call a norm on Rn intuitive if for every points p1, . . ., pm in Rn, one of the geometric medians of the points over the norm is in their convex hull. We characterize all intuitive *** Codes 52A41 © 2025, CC BY.

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Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes

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arXiv 2023年

作者： Tran, Ba-Hien Shahbaba, Babak Mandt, Stephan Filippone, Maurizio Department of Data Science EURECOM France Departments of Statistics and Computer Science University of California Irvine United States

Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the learned representations are i.i.d. and fail to capture the correlations between the data samples. To address this issue, we propose a novel Sparse Gaussian Process Bayesian Autoencoder (SGP-BAE) model in which we impose fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder. We perform posterior estimation for this model via stochastic gradient Hamiltonian Monte Carlo. We evaluate our approach qualitatively and quantitatively on a wide range of representation learning and generative modeling tasks and show that our approach consistently outperforms multiple alternatives relying on Variational Autoencoders. © 2023, CC BY.

关键词： Gaussian distribution

How do you want your greedy: simultaneous or repeated?

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 3174-3260页

作者： Moran Feldman Christopher Harshaw Amin Karbasi Department of Computer Science University of Haifa Haifa Israel Simons Institute Univeristy of California Berkeley Berkeley CA Departments of Electrical Engeering Computer Science Statsitics & Data Science Yale University New Haven CT

We present SimultaneousGreedys, a deterministic algorithm for constrained submodular maximization. At a high level, the algorithm maintains ℓ solutions and greedily updates them in a simultaneous fashion. SimultaneousGreedys achieves the tightest known approximation guarantees for both k-extendible systems and the more general k-systems, which are $(k+1)^2/k = k + \mathcal{O}(1)$ and $(1 + \sqrt{k+2})^2 = k + \mathcal{O}(\sqrt{k})$, respectively. We also improve the analysis of RepeatedGreedy, showing that it achieves an approximation ratio of k+O(√k) for k-systems when allowed to run for O(√k) iterations, an improvement in both the runtime and approximation over previous analyses. We demonstrate that both algorithms may be modified to run in nearly linear time with an arbitrarily small loss in the *** SimultaneousGreedys and RepeatedGreedy are flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees: both algorithms yield an approximation guarantee of roughly $k + 2m + \mathcal{O}(\sqrt{k+m})$ for k-systems and SimultaneousGreedys enjoys an improved approximation guarantee of k + 2m + O(√m) for k-extendible systems. To complement our algorithmic contributions, we prove that no algorithm making polynomially many oracle queries can achieve an approximation better than k + 1/2 - ε. We also present ***, a Julia package which implements these algorithms. Finally, we test these algorithms on real datasets.

关键词： submodular maximization k-systems k-extendible systems approximation algorithms