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arXiv 2023年

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D’Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent’s behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deep Learning Models for Intelligent Healthcare: Implementation and Challenges 7th

Deep Learning Models for Intelligent Healthcare: Implementat...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Rehman, Sadaqat ur Tu, Shanshan Shah, Zubair Ahmad, Jawad Waqas, Muhammad Rehman, Obaid ur Kouba, Anis Abbasi, Qammer H. Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Computer Science and Engineering Hammad Bin Khalifa University Doha Qatar School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering Sarhad University of Science and IT Peshawar25000 Pakistan Department of Computer Science Prince Sultan University Riyadh Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783030786083

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service;for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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An Exact Algorithm for Finite Metric Space Embedding into a Euclidean Space When the Dimension of the Space Is Not Known 21st

An Exact Algorithm for Finite Metric Space Embedding into a ...

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21st International Conference on Computational science, ICCS 2021

作者： Skubalska-Rafajlowicz, Ewa Rafajlowicz, Wojciech Department of Computer Engineering Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50 370 Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50 370 Poland

ISBN: (纸本)9783030779603

We present a O(n3) algorithm for solving the Distance Geometry Problem for a complete graph (a simple undirected graph in which every pair of distinct vertices is connected by a unique edge) consisting of n+ 1 vertices and non-negatively weighted edges. It is known that when the solution of the problem exists, the dimension of the Euclidean embedding is at most n. The algorithm provides the smallest possible dimension of the Euclidean space for which the exact embedding of the graph exists. Alternatively, when the distance matrix under consideration is non-Euclidean, the algorithm determines a subset of graph vertices whose mutual distances form the Euclidean matrix. The proposed algorithm is an exact algorithm. If the distance matrix is a Euclidean matrix, the algorithm provides a geometrically unambiguous solution for the location of the graph vertices. The presented embedding method was illustrated using examples of the metric traveling salesman problem that allowed us in some cases to obtain high dimensional partial immersions. © 2021, Springer Nature Switzerland AG.

关键词： Undirected graphs

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Jia-Xiang Li Yu-Chuan Tsai Wei-Ming Huang Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan

ISBN: (纸本)9781665480468

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets.

关键词： Measurement Industries Costs Itemsets Databases Decision making Big Data

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Incremental Fuzzy Utility Mining with Tree Structure

Incremental Fuzzy Utility Mining with Tree Structure

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Wei-Teng Hung Wei-Ming Huang Yu-Chuan Tsai Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781665480468

High-utility-itemset mining, extended from frequent-itemset mining, considers external utilities of items in quantitative databases to obtain the itemsets with high utility. To easily understand those patterns with high utility values, fuzzy utility mining adopts fuzzy sets to increase the readability of the derived utility itemsets. However, real-world databases are usually dynamic. New transactions may be intermittently added, and the corresponding mined patterns must be updated to keep correct knowledge. In this paper, we propose an incremental method based on our previously proposed batch-processing fuzzy utility tree-based mining algorithm. The proposed approach adopts the fast-update (FUP) strategy. It considers newly coming data to readjust the head table and the tree structure, which generate desired itemsets in two phases. The experimental results reveal that the proposed algorithm outperforms the tree-based batch mining method.

关键词： Fuzzy sets Databases Itemsets Heuristic algorithms Big Data Data mining

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Truthful Matching with Online Items and Offline Agents

arXiv

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arXiv 2022年

作者： Feldman, Michal Fusco, Federico Leonardi, Stefano Mauras, Simon Reiffenhäuser, Rebecca Blavatnik School of Computer Science Tel Aviv University Israel Microsoft Research Israel Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy

We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multi-parameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. Unlike most online matching settings, where agents arrive online, in our setting the items arrive online in an adversarial order while the buyers are present for the entire duration of the process. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds. We provide an almost full picture of the competitive ratios in different scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments, and private vs. public desired sets. Among other results, we identify the frontier for which the celebrated e/(e−1) competitive ratio for the vertex-weighted online matching of Karp, Vazirani and Vazirani extends to truthful agents and online items. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Experimental Validation of Sensitivity-Aware Trajectory Planning for a Quadrotor UAV Under Parametric Uncertainty

Experimental Validation of Sensitivity-Aware Trajectory Plan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ali Srour Salvatore Marcellini Tommaso Belvedere Marco Cognetti Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Rennes Cedex France Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France LAAS-CNRS Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this work, we provide an experimental vali-dation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4 1 1 https://***/ controller. Our objective is to experimentally assess how the optimization of the reference trajectory w.r.t. these sensitivity metrics can improve the closed-loop system performance against model uncertainties commonly affecting the quadrotor systems. To accomplish this, we present a series of experiments designed to validate our optimization approach on two distinct trajectories, with the primary aim of assessing its precision in guiding the quadrotor through the center of a window at relatively high speeds. This approach provides some interesting insights for increasing the closed-loop robustness of the robot state and inputs against physical parametric uncertainties that may degrade the system's performance.

关键词： Uncertainty Sensitivity Target tracking Trajectory planning Perturbation methods Autonomous aerial vehicles Robustness

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