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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Czygrinow, Andrzej Hanćkowiak, Michal Witkowski, Marcin School of Mathematical and Statistical Sciences Arizona State University TempeAZ United States Faculty of Mathematics and Computer Science Adam Mickiewicz University Poznań Poland

ISBN: (纸本)9783959772143

We give a distributed algorithm which given ϵ > 0 finds a (1 - ϵ)-factor approximation of a maximum f-matching in graphs G = (V, E) of sub-logarithmic expansion. Using a similar approach we also give a distributed approximation of a maximum b-matching in the same class of graphs provided the function b : V → Z+ is L-Lipschitz for some constant L. Both algorithms run in O(log∗ n) rounds in the LOCAL model, which is optimal. © Andrzej Czygrinow, MichalHanćkowiak, and Marcin Witkowski.

关键词： Expansion

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PBMC Cell Classification from Single Cell mRNA Expression by Artificial Neural Networks, Profiles, Gene Markers, and Protein Markers

PBMC Cell Classification from Single Cell mRNA Expression by...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Lyu, Minjie Zhang, Yihan Yang, Luning Lin, Xin Li, Yilin Jin, Huan Bellotti, Anthony G. Mitic, Nenad Brusic, Vladimir University of Nottingham School of Computer Science Ningbo China University of Nottingham School of Mathematical Sciences Ningbo China University of Belgrade Faculty of Mathematics Belgrade Serbia

ISBN: (纸本)9781665401265

We performed classification of healthy Peripheral Blood Mononuclear Cells cell types using four methods Artificial Neural Network (ANN), Profiles, Protein Markers (PMs), and RNA markers (RNAMs). Profiles represent patterns of gene expressions characteristic of the subtypes of cells. PMs are protein found exclusively in certain types or subtypes of cells, or represent particular cell states, RNAMs are genes which demonstrate significant differential expressions between cell types. A total of 109 datasets from four different sources containing sim 120,000 single cells gene expression were used to train and test prediction models. We combined the methods which perform prediction using the whole set of gene features (ANN and Profiles), and those that used specific gene features (PMs and RNAMs) to predict the cell type. The overall classification accuracy was 94.8% for ANN, 94.5% for Profiles, 90.7% for PMs, 67.9% for RNAMs. The combination of four methods showed accuracy of 90.9% with high confidence of positive predictions. The combination of four methods allowed identification of mislabeled cell types in test data sets. © 2021 IEEE.

关键词： Neural networks

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An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations

arXiv

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arXiv 2024年

作者： Taheri, Tayebeh Aghaei, Alireza Afzal Parand, Kourosh Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Cognitive Modeling Institute for Cognitive and Brain Sciences Shahid Beheshti University Tehran Iran Department of Statistics and Actuarial Science University of Waterloo Waterloo Canada

The recent introduction of the Least-Squares Support Vector Regression (LS-SVR) algorithm for solving differential and integral equations has sparked interest. In this study, we expand the application of this algorithm to address systems of differential-algebraic equations (DAEs). Our work presents a novel approach to solving general DAEs in an operator format by establishing connections between the LS-SVR machine learning model, weighted residual methods, and Legendre orthogonal polynomials. To assess the effectiveness of our proposed method, we conduct simulations involving various DAE scenarios, such as nonlinear systems, fractional-order derivatives, integro-differential, and partial DAEs. Finally, we carry out comparisons between our proposed method and currently established state-of-the-art approaches, demonstrating its reliability and effectiveness. © 2024, CC BY.

关键词： Integral equations

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Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results

Pattern Discovery in an EEG Database of Depression Patients:...

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International Conference on Measurement

作者： Kateřina Hlaváčková-Schindler Christina Pacher Claudia Plant Mykola Lazarenko Milan Paluš Jaroslav Hlinka Aditi Kathpalia Martin Brunovský Data Mining and Machine Learning Research Group Faculty of Computer Science University of Vienna Vienna Austria Department of Complex Systems Institute of Computer Science Czech Academy of Sciences Prague Czechia Clinical Research Programme National Institute of Mental Health Klecany Czechia

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient’s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants" ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic.

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Preparation and application of superhydrophobic surfaces: from fluorine to nature

Preparation and application of superhydrophobic surfaces: fr...

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2022 International Conference on Optoelectronic Information and Functional Materials, OIFM 2022

作者： He, Yijia Yin, Junyi Chen, Shiyu Wang, Weize Faculty of Engineering Computer and Mathematical Sciences The University of Adelaide Hohhot China Department of Bioengineering University of California Los Angeles United States School of Polymer Science and Engineering Sichuan University Chengdu China Beijing No.80 High School Beijing China

ISBN: (数字)9781510655126

ISBN: (纸本)9781510655119

The worldwide industries and scientific communities have raised various research works to develop surfaces with special wettability for a variety of applications. Fluorinated synthesis materials as currently widespread liquid repellent products proved to adversely affect human health and environment. As eco-friendly and non-toxic alternatives, artificial superhydrophobic surfaces inspired by nature non-wettable biological structures have received both academic and practical attention. The recent development on fluorine-free superhydrophobic surface design and the state-of-the-art techniques have widely applied in marine, medical, textiles and other fields. The manufacture and negative environmental impact of fluorinated materials have been discussed, as well as the classical natural superhydrophobic surfaces and theoretical models. © 2022 SPIE.

关键词： Fluorine

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Effect Analysis of Image Translation by Controlling Size of Dataset and Adjusting Parameters for CycleGAN 3

Effect Analysis of Image Translation by Controlling Size of ...

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3rd IEEE International Conference on Communications, Information System and computer Engineering, CISCE 2021

作者： Xu, Keang Zhou, Ziyu Wei, Lesi School of Computing and Information System University of Melbourne Wuhan China School of Computer Science and Engineering University of New South Wales Shantou China Faculty of Engineering Computer and Mathematical Sciences University of Adelaide Adelaide Australia

ISBN: (纸本)9780738112152

Image translation can be achieved effectively by CycleGAN. However, it is not clear whether the dataset size and parameter adjustment would influence the performance of CycleGAN. Therefore, this paper uses the different kinds of dataset and different dataset sizes to evaluate the performance of the model. Furthermore, parameters are adjusted in CycleGAN to further evaluate the performance of the CycleGAN-based method for image translation. The experimental results show that with the increases of the size of the dataset, except for some special situations, the loss of the model will decrease when different loss shows different descending amplitude which means small size of dataset would lead to overfitting. In addition, this cycle GAN model is more suitable for the 'horse to zebra' dataset, because this dataset has the least loss. Cycle consistency or its weight, hyper parameters adjusting can achieve the better unpaired image resolution than the original image, which shows this model can do a fairly good translation. Our research is of great significance to explore the best parameter settings and dataset size of image translation optimization based on CycleGAN. © 2021 IEEE.

关键词： Image resolution

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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IEEE International Conference on Big Data

作者： Jia-Hao Syu Jerry Chun-Wei Lin Dariusz Mrozek Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9781665480468

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems.

关键词： Q-learning Remotely guided vehicles Big Data Benchmark testing Multitasking Energy management systems Anomaly detection

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The properties of semi-simple splicing system over alternating group, A3 2

The properties of semi-simple splicing system over alternati...

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2nd International Conference on mathematical sciences, ICMS 2020

作者： Selvarajoo, Mathuri Heng, Fong Wan Sarmin, Nor Haniza Turaev, Sherzod Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah AlamSelangor40450 Malaysia Department of Mathematical Science Faculty of Science Universiti Teknologi Malaysia Johor BharuJohor Malaysia Faculty of Engineering and Natural Sciences International University of Sarajevo Hrasnicka cesta 15 llidza Sarajevo71210 Bosnia and Herzegovina

Splicing system was introduced by Head in 1987 in order to explore the recombinant behaviour of deoxyribonucleic acid (DNA) strands in the presence of restriction enzymes and ligases. Restriction enzymes cut the DNA strands into a left - pattern and right-pattern while the ligases recombine the left-pattern of the first string with the right-pattern of the second string and vice-versa. Semi-simple splicing system is a variant of splicing system where the splicing of DNA strands will be at two distinct sites. Splicing languages generated from the splicing system are classified based on their computational power, according to the Chomsky Hierarchy. Previous researchers found that the splicing system of finite set of strings and rules generates only regular languages which has the lowest computational power. Hence, some restriction has been introduced to increase the computational power of splicing languages generated. In this research, an element of alternating group of order three is associated with the initial strings of semi-simple splicing system to generate non-regular languages which has higher computational power. Some lemmas and theorems are proven to show that associating the alternating group to the initial strings could increase the computational power of the splicing languages. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： DNA

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Variational Inference for Sparse Poisson Regression

arXiv

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arXiv 2023年

作者： Kharabati, Mitra Amini, Morteza Arashi, Mohammad Department of Statistics School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran Department of Statistics Faculty of Mathematical Sciences Ferdowsi University of Mashhad Razavi Khorasan Mashhad*** Iran

We have utilized the non-conjugate VB method for the problem of the sparse Poisson regression model. To provide an approximated conjugacy in the model, the likelihood is approximated by a quadratic function, which provides the conjugacy of the approximation component with the Gaussian prior to the regression coefficient. Three sparsity-enforcing priors are used for this problem. The proposed models are compared with each other and two frequentist sparse Poisson methods (LASSO and SCAD) to evaluate the estimation, prediction and the sparsing performance of the proposed methods. Throughout a simulated data example, the accuracy of the VB methods is computed compared to the corresponding benchmark MCMC methods. It can be observed that the proposed VB methods have provided a good approximation to the posterior distribution of the parameters, while the VB methods are much faster than the MCMC ones. Using several benchmark count response data sets, the prediction performance of the proposed methods is evaluated in real-world applications. © 2023, CC BY.

关键词： Prediction models

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A Thorough Comparison of the Variable-Sample-Size Weighted-Loss CUSUM and ABS-SPRT Control Charts

A Thorough Comparison of the Variable-Sample-Size Weighted-L...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Jing Wei Teoh Wei Lin Teoh Laila El-Ghandour Zhi Lin Chong Sin Yin Teh School of Mathematical and Computer Sciences Heriot-Watt University Malaysia Putrajaya Malaysia School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Selangor Malaysia School of Management Universiti Sains Malaysia Penang Malaysia

It is frequently documented that concurrent shifts in the mean and dispersion of a process quality characteristic carry serious consequences to a manufacturing enterprise. It is, therefore, crucial to consider the detection ability of a control scheme when monitoring simultaneous changes in the process mean and dispersion. In this article, we conduct a thorough study on the detection performances of two dynamic control schemes, i.e., the variable-sample-size weighted-loss cumulative sum (VSS WLC) chart and the absolute-value sequential probability ratio test (ABS-SPRT) chart. This article reveals that the optimal ABS-SPRT chart outperforms the optimal VSS WLC chart in terms of the average extra quadratic loss and the average time to signal over a range of shift sizes. For small process shifts, the optimal VSS WLC chart is a slightly better performer than the optimal ABSSPRT chart in terms of the average number of observations to signal. However, for moderate and large process shifts, the optimal ABS-SPRT chart remains the most powerful scheme in all aspects. The ABS-SPRT chart is also favored due to its smaller long-run expected sample size compared to the VSS WLC chart, making it extremely promising in many industrial applications.

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