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arXiv 2023年

作者： Atallah, Mostafa Velmurugan, Haemanth Sharma, Rohan Midha, Siddhant Mamun, Shamim Al Botelho, Ludmila Glos, Adam Salehi, Özlem Department of Physics Faculty of Science Cairo University Giza12613 Egypt Department of Computer Science and Engineering NIT Tiruchirappalli India Department of Applied Geophysics IIT Dhanbad India Department of Electrical Engineering IIT Bombay India Department of Electrical and Electronics Engineering Islamic University of Technology Dhaka Gazipur1704 Bangladesh Joint Doctoral School Silesian University of Technology Akademicka 2A Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland QWorld Association Tallinn Estonia

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor’s breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

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Multi-Scale Terahertz Image Reconstruction ⋆,⋆⋆

SSRN

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SSRN 2024年

作者： Su, Zhipeng Zhang, Yixiong Zhou, Jianyang Shi, Jianghong Qi, Feng Zhang, Xiao-Ping Ji, Chunlin School of Informatics Xiamen University National Demonstrative Software School Xiamen University China School of Electronic Science and Engineering National Model Microelectronics College Xiamen University China Shenyang Institute of Automation Chinese Academy of Sciences China Department of Electrical Computer & Biomedical Engineering Finance Department The Ted Rogers School of Management Toronto Metropolitan University Canada Kuang-Chi Institute of Advanced Technology State Key Laboratory of Meta-RF Electromagnetic Modulation Technology Shen Zhen China

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function

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An Axiomatic Viewpoint on the Rogers–Veraart and Suzuki–Elsinger Models of Systemic Risk

arXiv

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arXiv 2022年

作者： Kabanov, Yuri Sidorenko, Arthur Lomonosov Moscow State University Institute of Informatics Problems Federal Research Center "Computer Science and Control" the Russian Academy of Sciences Moscow Russia Laboratoire de Mathématiques Université de Franche-Comté 16 Route de Gray cedex Besançon25030 France Lomonosov Moscow State University Moscow Russia

We study a model of clearing in an interbank network with cross-holdings and default charges. Following the Eisenberg–Noe approach, we define the model via a set of natural financial regulations including those related with eventual default charges and derive a finite family of fixpoint problems. These problems are parameterized by vectors of binary variables. Our model combines features of the Ararat–Meimanjanov, Rogers–Veraart, and Suzuki–Elsinger networks. We develop methods of computing the maximal and minimal clearing pairs using the mixed integer-linear programming and a Gaussian elimination *** Codes 90B10, 90B50 © 2022, CC0.

关键词： Integer programming

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Intelligent System for Recommendation of Mobile Services to Consumers

Intelligent System for Recommendation of Mobile Services to ...

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Radio science Meeting (AT-AP-RASC), URSI Atlantic and Asia Pacific

作者： Ivan Ganchev Zhanlin Ji Má irtí n O’ Droma Department of Computer Systems University of Plovdiv "Paisii Hilendarski" Plovdiv Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences (IMI-BAS) Sofia Bulgaria Telecommunications Research Centre (TRC) University of Limerick (UL) Limerick Ireland College of Artificial Intelligence North China University of Science and Technology P. R. China

This paper presents an intelligent system for recommendation of services to mobile users (consumers) by considering the current context. The system builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices for getting the best ‘cost / quality of experience (QoE)’ ratio when utilizing different mobile services.

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Designing a Low-cost High-end Android-based Wireless Board for the EMULSION IoT Platform

Designing a Low-cost High-end Android-based Wireless Board f...

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Radio science Meeting (AT-AP-RASC), URSI Atlantic and Asia Pacific

This paper proposes an innovative generic wireless low-cost high-end Android-based board for use by the EMULSION IoT platform, which is being elaborated as IoT-service and IoT-system prototyping ready. Based on the AllWinner A33 quad core Cortex-A7 CPU with a Mali400MP2 GPU and 512MB RAM, the board supports a 5MP CMOS sensor camera, 1080p video, MIDP display, and Hi-Fi audio. The paper reveals the basics of the design of this proprietary board.

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Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning

Single Image 3D Shape Retrieval via Cross-Modal Instance and...

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International Conference on computer Vision (ICCV)

作者： Ming-Xian Lin Jie Yang He Wang Yu-Kun Lai Rongfei Jia Binqiang Zhao Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Center on Frontiers of Computing Studies Peking University School of Computer Science and Informatics Cardiff University Alibaba Group Zhejiang Lab

ISBN: (纸本)9781665428132

In this work, we tackle the problem of single image-based 3D shape retrieval (IBSR), where we seek to find the most matched shape of a given single 2D image from a shape repository. Most of the existing works learn to embed 2D images and 3D shapes into a common feature space and perform metric learning using a triplet loss. Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning. We note that adopting such cross-modal contrastive learning between 2D images and 3D shapes into IBSR tasks is non-trivial and challenging: contrastive learning requires very strong data augmentation in constructed positive pairs to learn the feature invariance, whereas traditional metric learning works do not have this requirement. Moreover, object shape and appearance are entangled in 2D query images, thus making the learning task more difficult than contrasting single-modal data. To mitigate the challenges, we propose to use multi-view grayscale rendered images from the 3D shapes as a shape representation. We then introduce a strong data augmentation technique based on color transfer, which can significantly but naturally change the appearance of the query image, effectively satisfying the need for contrastive learning. Finally, we propose to incorporate a novel category-level contrastive loss that helps distinguish similar objects from different categories, in addition to classic instance-level contrastive loss. Our experiments demonstrate that our approach achieves the best performance on all the three popular IBSR benchmarks, including Pix3D, Stanford Cars, and Comp Cars, outperforming the previous state-of-the-art from 4% - 15% on retrieval accuracy.

关键词： Representation learning Deep learning Three-dimensional displays Shape Image color analysis Pipelines Gray-scale

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Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-b...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Supantha Das Arnab Hazra Soumen Kumar Pati Soumadip Ghosh Saurav Mallik Suharta Banerjee Ayan Mukherji Aimin Li Zhongming Zhao Department of Information Technology Academy of Technology Hooghly West Bengal India Department of Computer Science and Engineering Future Institute of Technology Kolkata West Bengal India Department of Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Nadia West Bengal India Department of Computer Science and Engineering Sister Nivedita University New Town Kolkata West Bengal India Department of Environmental Health Harvard T H Chan School of Public Health Boston MA USA Department of Computer Science and Engineering Meghnad Saha of Institute of Technology Kolkata West Bengal India Department of Computer Science and Engineering Pailan College of Management & Technology Kolkata West Bengal India School of Computer Science & Engineering Xi’an University of Technology Xi’an Shaanxi China Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center Houston TX USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.

关键词： Support vector machines Chronic kidney disease Bioinformatics Regression tree analysis Blood Logistics Cancer

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Self-Timed Multiply-add-subtract Unit Alternates

Self-Timed Multiply-add-subtract Unit Alternates

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Yury A. Stepchenkov Yury G. Diachenko Yury V. Rogdestvenski Denis Y. Diachenko Yury I. Shikunov Istitute of Informatics Problems Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

Paper presents the results of a study of the self-timed fused multiply-add-subtract unit (FMAS) alternates. All FMAS alternates comply with the IEEE 754 standard and use the modified Booth algorithm to multiply two input 64-bit operands, followed by the addition and subtraction of the third operand. They differ from each other by internal signals self-timed coding: dual-rail, redundant ternary, or redundant quaternary code. The paper analyzes and compares their features, offers their optimized pipeline implementations and recommendations for their use. FMAS alternates have approximately the same performance but different hardware costs and layout sizes.

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Meaningful Data Reuse in Research Communities 20th

Meaningful Data Reuse in Research Communities

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20th International Conference Data Analytics and Management in Data-Intensive Domains, DAMDID/RCDL 2018

作者： Skvortsov, Nikolay A. Institute of Informatics Problems Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030235833

FAIR data principles declare data interoperability and reuse according to machine and human readable shared specifications. Adherence to this set of principles brings some implications for data infrastructures and research communities. Meaningful data exchange and reuse by humans and machines require formal specifications of research domains accompanying data and allowing automatic reasoning. Development of formal conceptual specifications in research communities can be stimulated by a necessity to reach semantic interoperability of data collections and components, and reuse of data resources. Usage of formal domain specifications reduces data heterogeneity costs. Formal reasoning allows meaningful search and verified reuse of data, methods, and processes from collections. These means can make research lifecycle in communities more efficient. A lifecycle includes collecting domain knowledge specifications, classifying all data, methods, and processes according to such specifications, reusing relevant data and methods, and collecting and sharing results for reuse. © 2019, Springer Nature Switzerland AG.

关键词： Electronic data interchange

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Iterator Component Development for Data Redundancy Solution in Data-Flow Architecture

Iterator Component Development for Data Redundancy Solution ...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Yury A. Stepchenkov Dmitry V. Khilko Yury I. Shikunov Georgii A. Orlov Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Institute of Informatics Problems Moscow Russia

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

The hardware sample of multi-core data-flow recurrent architecture has been developed and tested on the digital signal processing domain. An analysis of the iterative algorithms execution results made it possible to propose a number of mechanisms to improve one of the components of the architecture - the Iterator. A significant problem in architecture programming is a high program redundancy produced by a significant number of special operands that are designed to control its internal resources. The Iterator component is designed to solve this issue, but its capabilities were not enough. The article presents the development results of the Iterator component. A description of the developed mechanisms to control the Iterator is provided. We demonstrate the results of the Iterator improvements using an example of the Viterbi algorithm for searching at hidden Markov models. The developed tools made it possible to nearly halve the volume of special operands and optimize the software implementation of the algorithm.

关键词： computer architecture Programming Prototypes Hardware Task analysis Process control Redundancy

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