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Universal distances for extended persistence

Journal of Applied and computational Topology 2024年第3期8卷 475-530页

作者： Bauer, Ulrich Botnan, Magnus Bakke Fluhr, Benedikt Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching 85747 Germany Munich Data Science Institute Technical University of Munich Boltzmannstraße 3 Garching 85747 Germany Department of Mathematics VU University Amsterdam De Boelelaan 1111 Amsterdam 1081 HV Netherlands Faculty of Mathematics Bielefeld University Postfach 100 131 Bielefeld 33501 Germany

The extended persistence diagram is an invariant of piecewise linear functions, which is known to be stable under perturbations of functions with respect to the bottleneck distance as introduced by Cohen–Steiner, Edelsbrunner, and Harer. We address the question of universality, which asks for the largest possible stable distance on extended persistence diagrams, showing that a more discriminative variant of the bottleneck distance is universal. Our result applies more generally to settings where persistence diagrams are considered only up to a certain degree. We achieve our results by establishing a functorial construction and several characteristic properties of relative interlevel set homology, which mirror the classical Eilenberg–Steenrod axioms. Finally, we contrast the bottleneck distance with the interleaving distance of sheaves on the real line by showing that the latter is not intrinsic, let alone universal. This particular result has the further implication that the interleaving distance of Reeb graphs is not intrinsic either. © The Author(s) 2024.

关键词： 55N31 62R40 Bottleneck distance Persistence diagrams Persistent homology Reeb graphs Topological data analysis

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Improving Prediction Quality of Face Image Preference Using Combinatorial Fusion Algorithm 16th

Improving Prediction Quality of Face Image Preference Using...

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16th International Conference on Brain Informatics, BI 2023

作者： Zhang, Zihan Schweikert, Christina Shimojo, Shinsuke Hsu, D. Frank Laboratory of Informatics and Data Mining Department of Computer and Information Science Fordham University YorkNY10023 United States Division of Computer Science Mathematics and Science St. John’s University QueensNY11439 United States Division of Biology and Biological Engineering/Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9783031430749

When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement gaze cascade in the recorded video sequence together with a set of extracted attributes (features). Combinatorial fusion algorithm (CFA) is a new information fusion and machine learning paradigm for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we apply CFA in the analysis of the eye movement gaze cascade sequence. In particular, we characterize each of the attributes and measure the diversity between two attributes using RSC function and CD respectively. The novelty of the combinatorial fusion approach is the new paradigm of incorporating both score function and rank function as well as using both score combination and rank combination. In addition, our results demonstrate that weighted rank combination has some advantage over weighted score combination when the weight is measured using diversity strength. Since diversity strength depends only on cognitive diversity between two attributes but not on any specific sequence in the eye movement gaze cascade, our result facilitates learning and modeling in an unsupervised manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information fusion

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Evolutionary Approach to Security Games with Signaling 31

Evolutionary Approach to Security Games with Signaling

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31st International Joint Conference on Artificial Intelligence, IJCAI 2022

作者： Żychowski, Adam Mańdziuk, Jacek Bondi, Elizabeth Venugopal, Aravind Tambe, Milind Ravindran, Balaraman Faculty of Mathematics and Information Science Warsaw University of Technology Poland Center for Research on Computation and Society Harvard University United States Robert Bosch Centre for Data Science and AI IIT Madras India Department of Computer Science and Engineering IIT Madras India

ISBN: (纸本)9781956792003

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Chromosomes

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Rice phenology classification based on random forest algorithm for data imbalance using Google Earth engine 6

Rice phenology classification based on random forest algorit...

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6th Information Systems International Conference, ISICO 2021

作者： Suryono, Hady Kuswanto, Heri Iriawan, Nur Department of Statistics Faculty of Mathematics Computation and Data Science Institut Teknologi Sepuluh Nopember East Java Surabaya60111 Indonesia

The agricultural sector is one of the important sectors in the world and has a very significant contribution to the achievement of the Sustainable Development Goals (SDGs) program. In the SDGs, attention to food security is focused on the second key indicator namely zero hunger (SDG 2). Availability of accurate rice production data is required to measure the level of food security. This can be done by monitoring the growth phase of a food plant which is called rice phenology classification. We used Random Forest (RF) algorithm on the Google Earth Engine (GEE) platform in Lamongan Regency, East Java in 2019 to classify rice phenology from Landsat-8 satellite imagery, which has the characteristics of imbalance case. To deal with the imbalanced issue, an oversampling technique was used for sampling minority classes. Reference data for the classification model training were collected from the Area Sampling Framework survey published by Statistics Indonesia in 2019. The results showed that the overall accuracy (OA) using RF algorithm by modifying the dataset using oversampling was 81.46% and the kappa statistic (κ) was 0.76, outperforming the RF technique without oversampling. © 2021 The Authors. Published by Elsevier B.V.

关键词： Sustainable development

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods

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SN Computer science 2023年第6期4卷 1-15页

作者： Mazumdar, Bitopan Sarma, Pankaj Kumar Deva Mahanta, Hridoy Jyoti Department of Computer Science Assam University Silchar India Advanced Computation and Data Sciences Division CSIR-North East Institute of Science and Technology Jorhat India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India

Renal toxicity prediction plays a vital role in drug discovery and clinical practice, as it helps to identify potentially harmful compounds and mitigate adverse effects on the renal system. Compound with inherent renal-toxic potential is one of the major concerns for drug development as it leads to failure in drug discovery. Predicting nephrotoxic probabilities of a compound at an early stage can be effective for reducing the drug failure rate. Hence, it is crucial to develop a mechanism to analyze the renal toxicity of a drug-candidate optimally and quickly. To mitigate the risks associated with renal toxicity, predictive models leveraging machine learning and deep learning techniques have gained significant attention. In this study, 287 human renal-toxic drugs and 278 non-renal-toxic drugs were collected to develop a deep learning model and 27 machine learning models using 8 kinds of fingerprints and Rdkit descriptors. The deep neural network model shows better generalization scores on five-fold cross-validation and Extra-tree model shows better performance score on test data. Structural alerts, specific chemical substructures associated with renal toxicity, offer a valuable tool for early toxicity assessment. Therefore, the substructures of renal toxic compounds were studied by applying association rule mining technique based on frequent itemset patterns. A method has been proposed for generating structural alerts and 10 structural alerts have been generated using the method.

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Rice phenology classification based on random forest algorithm for data imbalance using Google Earth engine

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Procedia Computer science 2022年 197卷 668-676页

作者： Hady Suryono Heri Kuswanto Nur Iriawan Department of Statistics Faculty of Mathematics Computation and Data Science Institut Teknologi Sepuluh Nopember Surabaya 60111 East Java Indonesia

关键词： Sustainable development goals rice phenology classification google earth engine random forest data imbalance

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Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning

arXiv

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arXiv 2025年

作者： Wang, Ziming Huang, Changwu Tang, Ke Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Data Science Lingnan University Hong Kong

Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness. This paper proposes a novel method to achieve procedural fairness during the model training phase. The effectiveness of the proposed method is validated through experiments conducted on one synthetic and six real-world datasets. Additionally, this work studies the relationship between procedural fairness and distributive fairness in ML models. On one hand, the impact of dataset bias and the procedural fairness of ML model on its distributive fairness is examined. The results highlight a significant influence of both dataset bias and procedural fairness on distributive fairness. On the other hand, the distinctions between optimizing procedural and distributive fairness metrics are analyzed. Experimental results demonstrate that optimizing procedural fairness metrics mitigates biases introduced or amplified by the decision-making process, thereby ensuring fairness in the decision-making process itself, as well as improving distributive fairness. In contrast, optimizing distributive fairness metrics encourages the ML model’s decision-making process to favor disadvantaged groups, counterbalancing the Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Improving Zero-Shot Coordination with Diversely Rewarded Partner Agents

Improving Zero-Shot Coordination with Diversely Rewarded Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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Congress on Evolutionary computation

作者： Zhenhua Yang Muyao Zhong Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration.

关键词： Adaptation models Fluctuations Heuristic algorithms computational modeling Scalability Manuals data models

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