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

作者： Shirui Chen Linxing Preston Jiang Rajesh P. N. Rao Eric Shea-Brown Department of Applied Mathematics University of Washington Seattle Paul G. Allen School of Computer Science & Engineering University of Washington Seattle Paul G. Allen School of Computer Science & Engineering and Center for Neurotechnology University of Washington Seattle Department of Applied Mathematics Computational Neuroscience Center University of Washington Seattle

In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for recurrent neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based Bayesian brain models.

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What can real information content tell us about compressing climate model data? 8

What can real information content tell us about compressing ...

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8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-8 2022

作者： Sather, Hayden Pinard, Alexander Baker, Allison H. Hammerling, Dorit M. Colorado School of Mines Department of Computer Science GoldenCO United States Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Computational and Information Systems Lab National Center for Atmospheric Research BoulderCO United States

ISBN: (纸本)9781665463379

The massive data volumes produced by climate simulation models create an urgent need for data reduction. Lossy compression in particular is a solution that can significantly reduce storage requirements, however, a tradeoff must be made between the amount of compression applied and the scientific integrity of the data. Determining how much compression can be applied is therefore vital for applying lossy compression. One particular metric for gauging the quality of compression is the percentage of real information present in the original data that is preserved in the compressed data. We compute bitwise real information content for several climate variables from the popular Community Earth System Model, and we investigate the amount of compression that can be applied to each of these climate variables using two popular compression algorithms designed for floating-point data while preserving 99% of the real information content. The analysis of the real information content of data after lossy compression has been applied shows a helpful visualization of how compression artifacts have been introduced to the data. Finally, we demonstrate how this real information content can be used in a straightforward manner to determine compressor settings for our data. © 2022 IEEE.

关键词： Climate models

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Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning

arXiv

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arXiv 2024年

作者： Shah, Ashka DePavia, Adela Hudson, Nathaniel Foster, Ian Stevens, Rick Department of Computer Science University of Chicago ChicagoIL United States Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us to infer mechanisms as sets of cause and effect relationships in a generalized way-without necessarily tailoring to a specific domain. Causal discovery algorithms search over a structured hypothesis space, defined by the set of directed acyclic graphs, to find the graph that best explains the data. For high-dimensional problems, however, this search becomes intractable and scalable algorithms for causal discovery are needed to bridge the gap. In this paper, we define a novel causal graph partition that allows for divide-and-conquer causal discovery with theoretical guarantees. We leverage the idea of a superstructure-a set of learned or existing candidate hypotheses-to partition the search space. We prove under certain assumptions that learning with a causal graph partition always yields the Markov Equivalence Class of the true causal graph. We show our algorithm achieves comparable accuracy and a faster time to solution for biologically-tuned synthetic networks and networks up to 104 variables. This makes our method applicable to gene regulatory network inference and other domains with high-dimensional structured hypothesis spaces. © 2024, CC BY.

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Original Article: A New Method for Optimal Solution of Intuitionistic Fuzzy Transportation Problems via Generalized Trapezoidal Intuitionistic Fuzzy Numbers

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Fuzzy Information and Engineering 2024年第1期11卷 105-120页

作者： Darunee Hunwisai Poom Kumam Wiyada Kumam Department of Applied Mathematics Faculty of Science and Technology Valaya Alongkorn Rajabhat University under the Royal Patronage Pathumthani Thailand Science Laboratory Building Faculty of Science KMUTT-Fixed Point Theory and Applications Research Group (KMUTT-FPTA) Theoretical and Computational Science Center (TaCS) King Mongkut's University of Technology Thonburi (KMUTT) Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Pathumthani Thailand

In this paper, we introduce the new method for solving the intuitionistic fuzzy transportation problem (IFTP), by using north-west corner method and modified distribution method to find the optimal solution for IFTP.

关键词： Costs Shape Transportation

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Entropy generation analysis of unsteady MHD nanofluid flow in a porous pipe

International Journal of Thermofluids

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International Journal of Thermofluids 2024年 24卷

作者： Zahor, Feda A. Jain, Reema Ali, Ahmada O. Masanja, Verdiana Grace Department of Applied Mathematics and Computational Sciences Nelson Mandela African Institution of Science and Technology P.O. Box 447 Arusha Tanzania United Republic of Department of Computer Systems and Mathematics Ardhi University P.O. Box 35176 Dar-es-Salam Tanzania United Republic of Department of Mathematics and Statistics University Jaipur India

This article presents a numerical investigation into the entropy production of nanofluids flowing through a porous medium in a permeable conduit, focusing on water-based solutions containing Titanium Carbide (TiC) and Silicon Carbide (SiC) nanoparticles. These nanoparticles are selected for their heat transfer properties. The flow system's governing equations are developed and solved using the Runge-Kutta-Fehlberg method. The study examines the influence of key nondimensional parameters, including the solid volume fraction of nanoparticles, radiation parameters, and Reynolds number, on temperature, velocity profiles, and entropy generation. The results show that Silicon Carbide-water nanofluids perform efficiently in terms of heat transfer and entropy minimization. Additionally, Silicon Carbide exhibits low skin friction at the pipe wall, with this effect increasing as the solid volume percentage of nanoparticles rises. The study also indicates that irreversibility due to heat transfer becomes more significant near the pipe wall as the solid volume fraction decreases and increases with higher radiation parameters and Reynolds number. These findings are presented graphically and in tabular form to illustrate the physical significance of the problem. © 2024

关键词： Reynolds number

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Development and Comparative Analysis of Event Relation Extraction Methods 2

Development and Comparative Analysis of Event Relation Extra...

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2nd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Ni, Shangyuan Ng, Taishing Xue, Ling Zhang, Jiawen Data Science New York University Shanghai Shanghai China Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States School of Mathematics and Statistics Henan University of Science and Technology Luoyang China Applied & Computational Mathematical Sciences University of Washington Seattle United States

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Event relation extraction is an important research direction in the field of information extraction. Compared with named entity recognition, entity relation extraction, and event extraction. Event relation extraction can extract common-sense knowledge that is more in line with human intuition from unstructured texts and can be organized together in the form of logical interaction. In this paper, the current event relation extraction works are reviewed in detail. This paper is organized according to different types of event relations. In addition, this paper also introduces two kinds of event relation data sets. Finally, we discuss the challenges and prospects of event relation extraction. © 2022 IEEE.

关键词： Deep learning

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Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation 39

Harnessing Multimodal Large Language Models for Multimodal S...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Yuyang Zheng, Zhi Shen, Yishan Wang, Tianshu Zhang, Hengruo Zhu, Peijun Yu, Runlong Zhang, Kai Xiong, Hui Department of Management Science and Information Systems Rutgers University United States School of Data Science University of Science and Technology of China China Department of Applied Mathematics and Computational Science University of Pennsylvania United States Bytedance Inc. China School of Computer Science Georgia Institute of Technology United States Department of Computer Science University of Pittsburgh United States China

ISBN: (纸本)157735897X

Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text modality information. Moreover, although Multimodal Large Language Models (MLLMs) capable of processing multi-modal inputs have emerged, how to equip MLLMs with multi-modal recommendation capabilities remains largely unexplored. To this end, in this paper, we propose the Multimodal Large Language Model-enhanced Sequential Multimodal Recommendation (MLLM-MSR) model. To capture the dynamic user preference, we design a two-stage user preference summarization method. Specifically, we first utilize an MLLM-based item-summarizer to extract image feature given an item and convert the image into text. Then, we employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences based on an LLM-based user-summarizer. Finally, to enable the MLLM for multi-modal recommendation task, we propose to fine-tune a MLLM-based recommender using Supervised Fine-Tuning (SFT) techniques. Extensive evaluations across various datasets validate the effectiveness of MLLM-MSR, showcasing its superior ability to capture and adapt to the evolving dynamics of user preferences. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics City University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important class of processes, which frequently appear in applications such as mathematical finance, biology and opinion dynamics. Typically the stationary distribution is unknown and indeed one cannot simulate such processes exactly. As a result one commonly requires a time-discretization scheme which results in a discretization bias and a bias from not being able to simulate the associated stationary distribution. To overcome this bias, we present a new unbiased estimator taking motivation from the literature on unbiased Monte Carlo. We prove the unbiasedness of our estimator, under assumptions. In order to prove this we require developing ergodicity results of various discrete time processes, through an appropriate discretization scheme, towards the invariant measure. Numerous numerical experiments are provided, on a range of MVSDEs, to demonstrate the effectiveness of our unbiased estimator. Such examples include the Currie-Weiss model, a 3D neuroscience model and a parameter estimation problem. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

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Antithetic Multilevel Particle Filters

arXiv

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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps 23rd

Artificial Immune Systems Approach for Surface Reconstructi...

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23rd International Conference on computational science, ICCS 2023

作者： Gálvez, Akemi Fister, Iztok You, Lihua Fister, Iztok Iglesias, Andrés Department of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain Faculty of Pharmaceutical Sciences Toho University 2-2-1 Miyama Funabashi274-8510 Japan Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia National Center for Computer Animation Faculty of Media and Communication Bournemouth University PooleBH12 5BB United Kingdom

ISBN: (纸本)9783031360268

Reverse engineering is one of the classical approaches for quailty assessment in industrial manufacturing. A key technology in reverse engineering is surface reconstruction, which aims at obtaining a digital model of a physical object from a cloud of 3D data points obtained by scanning the object. In this paper we address the surface reconstruction problem for surfaces that can exhibit large smooth bumps. To account for this type of features, our approach is based on using exponentials of polynomial functions in two variables as the approximating functions. In particular, we consider three different models, given by bivariate distributions obtained by combining a normal univariate distribution with a normal, Gamma, and Weibull distribution, respectively. The resulting surfaces depend on some parameters whose values have to be optimized. This yields a difficult nonlinear continuous optimization problem solved through an artificial immune systems approach based on the clonal selection theory. The performance of the method is discussed through its application to a benchmark comprised of three examples of point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Weibull distribution

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