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arXiv 2024年

作者： Wang, Xue Zhou, Tian Zhu, Jianqing Liu, Jialin Yuan, Kun Yao, Tao Yin, Wotao Jin, Rong Cai, Han Qin Alibaba Group BellevueWA98004 United States Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Statistics and Data Science University of Central Florida OrlandoFL32816 United States Center for Machine Learning Research Peking University Beijing100871 China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China Meta Menlo ParkCA94025 United States Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S3Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S3Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S3Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S3Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Unboxing the black-box of deep learning based reconstruction of undersampled MRIs 4

Unboxing the black-box of deep learning based reconstruction...

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4th Italian Workshop on Explainable Artificial Intelligence, XAI.it 2023

作者： Chatterjee, Soumick Das, Arnab Khatun, Rupali Nürnberger, Andreas Genomics Research Centre Human Technopole Milan Italy Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany German Research Centre for Artificial Intelligence Kaiserslautern Germany Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Comprehensive Cancer Centre Erlangen-EMN Erlangen Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Deep learning has emerged as a very important area of research and has shown immense potential in solving different kinds of problem, including in the medical field. For tasks like undersampled MRI reconstruction - the process of speeding up MRI acquisition with the help of undersampling, deep learning has shown its dominance over the years. But one of the major problems with deep learning is trust: Complex reasonings done by these models appear black-box to the users. Therefore, to build trust and better acceptability, it is important to open up this black-box nature of these models. For classification models, several approaches have been proposed. Nevertheless, for models dealing with inverse problems, like the reconstruction of the undersampled MRIs, it is more challenging as the output of the model has the same number of output pixels as the input, making the interpretability of such models more complex. This research explores different methods to understand the working mechanism of a deep learning model for the task of undersampled MRI reconstruction. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Inverse problems

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On Aggregation in Ensembles of Multilabel Classifiers 23rd

On Aggregation in Ensembles of Multilabel Classifiers

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23rd International Conference on Discovery science, DS 2020

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Loza Mencía, Eneldo Fürnkranz, Johannes Heinz Nixdorf Institute and Department of Computer Science Paderborn University Paderborn Germany Knowledge Engineering Group TU Darmstadt Darmstadt Germany Computational Data Analytics Group JKU Linz Linz Austria

ISBN: (纸本)9783030615260

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning

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Measurement: Sensors 2022年 24卷

作者： Naidu, K Bapayya Ravi Prasad, B. Hassen, Samar Mansour Kaur, Chamandeep Al Ansari, Mohammed Saleh Vinod, R. Nivetha, M. Kiran Bala, B. Surampalem Jawaharlal Nehru Technological University Kakinada India CSE Dept Marri Laxman Reddy Institute of Technology and Management Hyderabad500043 India Computer Science JazanUnivertsity Jazan Saudi Arabia Jazan University Saudi Arabia Jazan Saudi Arabia College of Engineering Department of Chemical Engineering University of Bahrain Bahrain School of Contemporary Knowledge Systems Chinmaya Vishwavidyapeeth Cochin Kerala India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy India Head of the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Log files are a significant piece of records that once properly analyzed, can yield helpful information. The usage of cloud services for log analysis is almost inevitable due to the greater rate of production and quantity of hardware and sensors that produce logs. The idea is to adopt machine learning mechanisms to "understand" what such a test suite's "anticipated" behavior is. This implies the system needs to be able to learn the difference among a Hadoop log file that contains a threat that does not really, based on past patterns. Many programs that really can effectively monitor the Hadoop cluster have been developed. The bulk of these tools collect relevant information from every cluster node and send it to be processed. The majority of these diagnostic tools were post-execution techniques commonly. This paper gives an experimental evaluation of the various log analysts used in Hadoop for error monitoring and detection. MapReduce is configured using these files. The algorithms were tested on a parser set of labelled log files, and their efficiency was assessed by looking for abnormal events in the Hadoop log files and executing an experimentation using the methods. The algorithms used were Term Frequency Inverse Document Frequency, Random Forest, and Local Outlier Factor. Later, the clustering with K-Means and Principal component analysis is utilized to extract certain relevant details from the data, monitor groups of bits of data to find aberrant events. The Term Frequency Inverse Document Frequency strategy surpassed the other two techniques in predicting threat occurrences in data, according to the findings. The findings would help developers in locating abnormal occurrences without having to go through the Hadoop log file row by row individually. The framework generates events that behave in a different way than the rest of such events in the log and leads the mechanism to fail to work. © 2022 The Authors

关键词： Principal component analysis

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Sequential Solutions in Machine Scheduling Games 16th

Sequential Solutions in Machine Scheduling Games

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16th International Conference on Web and Internet Economics, WINE 2020

作者： Chen, Cong Giessler, Paul Mamageishvili, Akaki Mihalák, Matúš Penna, Paolo School of Business Administration South China University of Technology Guangzhou China Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Management Technology and Economics ETH Zurich Zurich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland

ISBN: (纸本)9783030649456

We consider the classical machine scheduling, where n jobs need to be scheduled on m machines, and where job j scheduled on machine i contributes pi,j∈ R to the load of machine i, with the goal of minimizing the makespan, i.e., the maximum load of any machine in the schedule. We study inefficiency of schedules that are obtained when jobs arrive sequentially one by one, and the jobs choose themselves the machine on which they will be scheduled, aiming at being scheduled on a machine with small load. We measure the inefficiency of a schedule as the ratio of the makespan obtained in the worst-case equilibrium schedule, and of the optimum makespan. This ratio is known as the sequential price of anarchy (SPoA). We also introduce two alternative inefficiency measures, which allow for a favorable choice of the order in which the jobs make their decisions. As our first result, we disprove the conjecture of [22] claiming that the sequential price of anarchy for m= 2 machines is at most 3. We show that the sequential price of anarchy grows at least linearly with the number n of players, assuming arbitrary tie-breaking rules. That is, we show SPoA∈ Ω(n). Complementing this result, we show that SPoA∈ O(n), reducing previously known exponential bound for 2 machines. Furthermore, we show that there exists an order of the jobs, resulting in makespan that is at most linearly larger than the optimum makespan. To the end, we show that if an authority can change the order of the jobs adaptively to the decisions made by the jobs so far (but cannot influence the decisions of the jobs), then there exists an adaptive ordering in which the jobs end up in an optimum schedule. © 2020, Springer Nature Switzerland AG.

关键词： Scheduling

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RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

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arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

arXiv

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arXiv 2023年

作者： Chatterjee, Soumick Gaidzik, Franziska Sciarra, Alessandro Mattern, Hendrik Janiga, Gábor Speck, Oliver Nürnberger, Andreas Pathiraja, Sahani Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Laboratory of Fluid Dynamics and Technical Flows Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Magdeburg Germany Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany School of Mathematics & Statistics UNSW Sydney UNSW Data Science Hub Australia

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. We propose the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. We analyse our method for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments are characterised by class-imbalanced, low signal to noise ratio data sets with highly ambiguous features. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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