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39th International Symposium on computational Geometry, SoCG 2023

作者： Bauer, Ulrich Lenzen, Fabian Lesnick, Michael Department of Mathematics TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Germany Department of Mathematics TUM School of Computation Information and Technology Technical University of Munich Germany Department of Mathematics SUNY AlbanyNY United States

ISBN: (纸本)9783959772730

Clearing is a simple but effective optimization for the standard algorithm of persistent homology (ph), which dramatically improves the speed and scalability of ph computations for Vietoris-Rips filtrations. Due to the quick growth of the boundary matrices of a Vietoris-Rips filtration with increasing dimension, clearing is only effective when used in conjunction with a dual (cohomological) variant of the standard algorithm. This approach has not previously been applied successfully to the computation of two-parameter ph. We introduce a cohomological algorithm for computing minimal free resolutions of two-parameter ph that allows for clearing. To derive our algorithm, we extend the duality principles which underlie the one-parameter approach to the two-parameter setting. We provide an implementation and report experimental run times for function-Rips filtrations. Our method is faster than the current state-of-the-art by a factor of up to 20. © Ulrich Bauer, Fabian Lenzen, and Michael Lesnick;licensed under Creative Commons License CC-BY 4.0.

关键词： Parameter estimation

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The Impact of Synchronized Visual and Auditory Attention on Human Perception 16th

The Impact of Synchronized Visual and Auditory Attention o...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Jiang, Lichuan Zhong, Jiani Jian, Muqing Liu, Xuanzhuo Cai, Siqi Li, Haizhou School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China TUM School of Computation Information and Technology Department of Electrical Engineering Technical University of Munich Munich Germany University of Bremen Bremen Germany

ISBN: (纸本)9789819611508

The cocktail party problem shows the remarkable human ability to selectively attend to and recognize one source of auditory input in a noisy environment. However, individuals may struggle to identify a speaker’s voice when they are unfamiliar with the speakers and don’t have a clear visual focus, which results in less visual information. This raises the question: How can visual information aid in extracting information from a speaker’s voice? This study explores how synchronized visual and auditory attention impact human perception in scenarios involving two speakers. Using Tobii Glasses 3 to track participants’ eye movements and pupil diameters, combined with questionnaire responses, we explore how these factors influence speech comprehension. Our results demonstrate that participants achieve higher accuracy in speech comprehension when they focus their gaze on the speaker they are listening to, compared to scenarios where visual attention is divided between speakers or where they rely solely on auditory cues. These findings highlight the effectiveness of synchronizing visual and auditory attention in improving the acquisition and processing of information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eye movements

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Finite Element Method for HJB in Option Pricing with Stock Borrowing Fees

arXiv

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arXiv 2025年

作者： Kazbek, Rakhymzhan Abdukarimova, Aidana Astana IT University Department of Computation and Data Science Mangilik El C1 Astana Kazakhstan

In mathematical finance, many derivatives from markets with frictions can be formulated as optimal control problems in the HJB framework. Analytical optimal control can result in highly nonlinear PDEs, which might yield unstable numerical results. Accurate and convergent numerical schemes are essential to leverage the benefits of the hedging process. In this study, we apply a finite element approach with a non-uniform mesh for the task of option pricing with stock borrowing fees, leading to an HJB equation that bypasses analytical optimal control in favor of direct PDE discretization. The time integration employs the theta-scheme, with initial modifications following Rannacher‘s procedure. A Newton-type algorithm is applied to address the penalty-like term at each time step. Numerical experiments are conducted, demonstrating consistency with a benchmark problem and showing a strong match. The CPU time needed to reach the desired results favors P2-FEM over FDM and linear P1-FEM, with P2-FEM displaying superior convergence. This paper presents an efficient alternative framework for the HJB problem and contributes to the literature by introducing a finite element method (FEM)-based solution for HJB applications in mathematical finance. © 2025, CC BY.

关键词： Finite element method

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Unveiling Medical Insights: Advanced Topic Extraction from Scientific Articles

Unveiling Medical Insights: Advanced Topic Extraction from S...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Bitaraf, Ehsan Jafarpour, Maryam Shool, Sina Saboori Amleshi, Reza Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Department of Algorithms and Computation School of Engineering Science College of Engineering University of Tehran Tehran Iran Center for Medical Data Science Medical University of Vienna Vienna Austria

ISBN: (纸本)9781643685335

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results. © 2024 The Authors.

关键词： Drug discovery

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Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes 38th

Efficient GPU Offloading with OpenMP for a Hyperbolic Fin...

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38th International Conference on High Performance Computing, ISC High Performance 2023

作者： Wille, Mario Weinzierl, Tobias Brito Gadeschi, Gonzalo Bader, Michael TUM School of Computation Information and Technology Technical University of Munich Garching Germany Department of Computer Science Institute for Data Science—Large-scale Computing Durham University Durham United Kingdom NVIDIA Munich Germany

ISBN: (纸本)9783031320408

We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP’s map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude. © 2023, The Author(s).

关键词： Graphics processing unit

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A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction 23

A Framework based on Deep Neural Network for Ranking-oriente...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Dai, Jiapeng Yang, Xiaoxing Huang, Bingding Lu, Xiaofen Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350319583

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models. © 2023 IEEE.

关键词： Deep neural networks

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms 13

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time. © 2024 IEEE.

关键词： Optimization algorithms

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A consistent estimator for confounding strength

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Mathematical Statistics and Learning 2024年第3-4期7卷 189-220页

作者： Rendsburg, Luca Vankadara, Leena Chennuru Ghoshdastidar, Debarghya von Luxburg, Ulrike Department of Computer Science and Tübingen AI Center University of Tübingen Maria-von-Linden-Straße 6 Baden-Württemberg Tübingen 72076 Germany School of Computation Information and Technology and Munich Data Science Institute Technical University of Munich Boltzmannstraße 3 Bavaria Garching 85748 Germany

Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the independence of causal mechanisms, which relies on concentration phenomena in high dimensions. While high dimensions enable the estimation of confounding strength, they also necessitate adapted estimators. In this paper, we derive the asymptotic behavior of the confounding strength estimator by Janzing and Schölkopf (2018) and show that it is generally not consistent. We then use tools from random matrix theory to derive an adapted, consistent estimator. © 2024 European Mathematical Society.

关键词： confounding high-dimensional linear regression observational data random matrix theory

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Reinforcement Learning Based Vertical Scaling for Hybrid Deployment in Cloud Computing 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Cao, Jianqi Li, Guiying Yang, Peng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

To maximize the CPU utilization of the server, offline tasks are usually deployed to the same server where the online service is running. Considering the necessity to ensure the service quality of online services, it is common practice to isolate the resources of online services. How to set the resource quota for online services not only affects the service quality of online services, but also affects the number and the stability of offline tasks that can be run on the server. Traditional rule-based methods or prediction-based methods will cause over-provision and fail to consider the stability of offline tasks, which often cannot achieve stability and efficiency. In this paper, reinforcement learning is proposed for the first time to solve the hybrid deployment of online services and offline tasks and dynamically adjust the resource quota of online services more effectively. Compared with the original state of the server, our proposed method reduces CPU idleness rate by 35.32% and increases CPU resource utilization rate by 3.84%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Large Language Models as Evolutionary Optimizers 13

Large Language Models as Evolutionary Optimizers

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13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Liu, Shengcai Chen, Caishun Qu, Xinghua Tang, Ke Ong, Yew-Soon Southern University of Science and Technology Centre for Frontier Ai Research A∗star Department of Computer Science and Engineering China Centre for Frontier Ai Research A∗star Singapore Tianqiao & Chrissy Chen Institute China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering China Nanyang Technological University Centre for Frontier Ai Research A∗star College of Computing and Data Science Singapore

ISBN: (纸本)9798350308365

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges. © 2024 IEEE.

关键词： Traveling salesman problem

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