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IEEE International Conference on Information Reuse and Integration (IRI)

作者： S. van Delden D. Bracewell F. Gomez Division of Math and Computer Science University of South Carolina Spartanburg SC USA Computer Science Department University of Central Florida Orlando FL USA

We present two algorithms for automatically improving the quality of texts which contain a large number of spelling errors. A supervised algorithm, which automatically corrects unknown words that are generated primarily from typing errors, is presented first. The second algorithm is an unsupervised approach to automatically correcting typing errors, individual words that have been split, multiple words which have been concatenated, and a combination of these errors. The algorithms have been developed and tested on a large source of real-world, human- and machine-generated spelling errors.

关键词： Error correction computer errors Databases Natural languages Information retrieval computer science Concatenated codes Humans Filtering algorithms NASA

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Enhancing prediction understandability for transmembane segments by BoostingFOIL

Enhancing prediction understandability for transmembane segm...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Jieyue He Pingping Chen Dejing Zhao Wei Zhong School of Computer Science and Engineering South-East University Nanjing China Division of Math and Computer Science University of South Carolina Spartanburg SC USA

In recent years, many studies have focused on improving the accuracy of prediction of trans-membrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of the decision process is important for acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. Decision trees provide insightful interpretation, with form of the propositional IF-THEN rules. The decision tree algorithm produces a large number of rules which is not easy to read and difficult to express adequately complex characteristics of biological sequence. First-order rules with variables have outstanding representation capability, and they can be used to reduce the number of rules. Therefore, in this paper, we present an innovative approach to generate rules for understanding prediction of transmembrane segments. This new approach combines the first-order inductive learning (FOIL) with enhancing techniques of Boosting to produce a new algorithm called BoostingFOIL. The experimental results for prediction of transmembrane segments on 165 low-resolution test data set show that not only the comprehensibility of BoostingFOIL is much better than that of decision tree, but also the test accuracy of these rules is higher as well. The most important contribution of our work is that the first-order rules produced by BoostingFOIL can be easily applied to advanced deduction in inductive learning procedure.

关键词： Proteins Decision trees Hidden Markov models Bioinformatics Support vector machines computer science Machine learning Testing Decision making Humans

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Design of storage hierarchy in multithreaded architectures 28

Design of storage hierarchy in multithreaded architectures

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Proceedings of the 28th annual international symposium on Microarchitecture

作者： Lucas Roh Walid A. Najjar Math. & Computer Science Division Argonne National Laboratory Argonne IL Department of Computer Science Colorado State University Fort Collins CO

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Discovering frequent probability pattern in uncertain biological networks by circuit simulation method

Discovering frequent probability pattern in uncertain biolog...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chunyan Wang Kunpu Qiu Wei Zhong Jieyue He School of Computer Science and Engineering Southeast University Nanjing China Division of Math and Computer Science University of South Carolina Upstate Spartanburg SC USA

ISBN: (纸本)9781479913114

In the field of bioinformatics, many types of data can be represented as the topological graph, such as protein-protein interaction network. Milo proposed the concept of biological motif 0 on science, which is referred as a substructure that appears in different parts of a network, and appears significantly more frequently than in a random network. Research shows that the motif recognition is important for many biological studies. As the life process itself is a dynamic process, the motif of the same function may be made up of the subgraphs which may slightly differ in topology, so Berg etc. [2] proposed probability motif mining algorithms in the biological network. And science graph data are obtained with the inevitable experimental error or noise data, and some biological network data carries probability information. Since biological evolution itself is a mutant selection process, the input of biological networks should also be a probabilistic network. Therefore, it is more intuitively and practically significantly to mine probability motif in the probability biological network.

关键词： Probability Algorithm design and analysis Circuit simulation Biological system modeling Data models Educational institutions

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On biased compression for distributed learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 12974-13023页

作者： Aleksandr Beznosikov Samuel Horváth Peter Richtárik Mher Safaryan Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA and Skolkovo Institute of Science and Technology Moscow Russia and School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $O\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where δ ≥1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.

关键词： compression operators biased compressors distributed learning linear convergence error feedback

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Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control

Tuning the Interpolation Basis in a Multigrid Decomposition ...

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IEEE International Conference on Big Data

作者： Nicolas Vidal Qian Gong Viktor Reshniak Rick Archibald Scott Klasky Computer Science and Math. Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global metrics on the dataset. To improve on these two aspects, we propose a method that aims to divide the original dataset into blocks based on their smoothness and refactors each block separately with the most relevant interpolation order. We show the relevance of such a method on tailored datasets and the benefits and challenges when applying it to large scientific data.

关键词： Measurement Interpolation Codes Production Big Data Benchmark testing Compressors Entropy Error correction Tuning

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A model for usage policy-based resource allocation in grids

A model for usage policy-based resource allocation in grids

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International Workshop on Policies for Distributed Systems and Networks

作者： C.L. Dumitrescu M. Wilde I. Foster Department of Computer Science University of Chicago USA Math and CS Division Argonne National Laboratory USA

Challenging usage policy issues can arise within virtual organizations (VOs) that integrate participants and resources spanning multiple physical institutions. Participants may wish to delegate to one or more VOs the right to use certain resources subject to local policy and service level agreements; each VO then wishes to use those resources subject to VO policy. How are such local and VO policies to be expressed, discovered, interpreted, and enforced? As a first step to addressing these questions, we develop and evaluate policy management solutions within a specialized context, namely scientific data grids within which the resources to be shared are computers and storage. We propose an architecture and recursive policy model, and define roles and functions, for scheduling resources in grid environments while satisfying resource owner and VO policies.

关键词： Resource management Collaborative work Laboratories Grid computing computer science computer architecture Processor scheduling Outsourcing Collaboration Monitoring

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A Framework for Compressing Unstructured Scientific Data via Serialization

A Framework for Compressing Unstructured Scientific Data via...

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IEEE International Conference on Big Data

作者： Viktor Reshniak Qian Gong Rick Archibald Scott Klasky Norbert Podhorszki Computer Science and Math. Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.

关键词： Correlation Codes Pipelines Big Data Data processing Throughput Compressors Topology Finite element analysis Compression algorithms

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A time division multiplexing (TDM) logic mapping method for computational applications

A time division multiplexing (TDM) logic mapping method for ...

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International Conference on Computational science and Its Applications (ICCSA 2007)

作者： Jeong, Taikyeong Kang, Jinsuk John, Youngjun Choi, Inhwa Choi, Sungsoo Yang, Hyosik Park, Gyngleen Yoo, Sehwan Dept. of Communications Eng. Myongji University South Korea Dept. of Computer Science and Eng. University of Incheon College of Information and Media Seoul Woman's University Fusion Technology Division Korea Electrotechnology Research Institute Dept. of Information and Telecommunication Eng. Sejong University Dept. of Computer and Statistics Cheju National University Dept. of Math. and Computer Science University of Maryland Eastern Shore United States

ISBN: (纸本)9783540744689

This paper discusses a large number of logic circuit mapping methods for complex systems, focusing on network hardware system designs. This logic mapping technique enables significant logic simulation time savings by mapping identical logic processor modules. Under the logic mapping method which is called the time division multiplexing (TDM) logic mapping method, the speed of the required to simulate it is significantly reduced, compared with conventional mapping methods, when folding the identical modules into a single module copy is done at the hardware description language (HDL) level. In principle, this method can be applied to any type of a network design platform, e.g., communication data stream through physical channel (fiber optic line), video signal transfer logic display environment, etc. In this paper, we demonstrate this method using several configurations of the IBM Serial Link architecture.

关键词： Logic circuits

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Towards On-Chip Bayesian Neuromorphic Learning

arXiv

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arXiv 2020年

作者： Wycoff, Nathan Balaprakash, Prasanna Xia, Fangfang Department of Statistics Virginia Tech United States Math and Computer Science Division Argonne National Laboratory Data Science and Learning Division Argonne National Laboratory

If edge devices are to be deployed to critical applications where their decisions could have serious financial, political, or public-health consequences, they will need a way to signal when they are not sure how to react to their environment . For instance, a lost delivery drone could make its way back to a distribution center or contact the client if it is confused about how exactly to make its delivery, rather than taking the action which is "most likely" correct. This issue is compounded for health care or military applications. However, the brain-realistic temporal credit assignment problem neuromorphic computing algorithms have to solve is difficult. The double role weights play in backpropagation-based-learning, dictating how the network reacts to both input and feedback, needs to be decoupled. e-prop 1 is a promising learning algorithm that tackles this with Broadcast Alignment (a technique where network weights are replaced with random weights during feedback) and accumulated local information. We investigate under what conditions the Bayesian loss term can be expressed in a similar fashion, proposing an algorithm that can be computed with only local information as well and which is thus no more difficult to implement on hardware. This algorithm is exhibited on a store-recall problem, which suggests that it can learn good uncertainty on decisions to be made over time. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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