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An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

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Research Square

Research Square 2022年

作者： Salehi, Tahereh Zomorodi, Mariam Plawiak, Pawel Abbaszade, Mina Salari, Vahid Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Bilbao Spain Department of Physics and Astronomy Howard University WashingtonDC20059 United States

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are

关键词： Big data

Reservoir computing based on mutually injected phase modulated semiconductor lasers as a monolithic integrated hardware accelerator

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arXiv 2021年

作者： Sozos, Kostas Mesaritakis, Charis Bogris, Adonis The Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece The Department of Information and Communication Systems Engineering University of the Aegean 83200 Karlovasi Samos Greece

In this paper we propose and numerically study a neuromorphic computing scheme that applies delay-based reservoir computing in a laser system consisting of two mutually coupled phase modulated lasers. The scheme can be monolithic integrated in a straightforward manner and alleviates the need for external optical injection, as the data can be directly applied on the on-chip phase modulator placed between the two lasers. The scheme also offers the benefit of increasing the nodes compared to a reservoir computing system using either one laser under feedback or laser under feedback and optical injection. Numerical simulations assess the performance of the integrated reservoir computing system in dispersion compensation tasks in short-reach optical communication systems. We numerically demonstrate that the proposed platform can recover severely distorted 25 Gbaud PAM-4 signals for transmission distances exceeding 50 km and outperform other competing delay-based reservoir computing systems relying on optical feedback. The proposed scheme, thanks to its compactness and simplicity, can play the role of a monolithic integrated hardware accelerator in a wide range of application requiring high-speed real-time processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Phase modulation

On the Effectiveness of Binary Emulation in Malware Classification

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arXiv 2022年

作者： Vouvoutsis, Vasilis Casino, Fran Patsakis, Constantinos Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Spain Information Management Systems Institute Athena Research Centre Greece

Malware authors are continuously evolving their code base to include counter-analysis methods that can significantly hinder their detection and blocking. While the execution of malware in a sandboxed environment may provide a lot of insightful feedback about what the malware actually does in a machine, anti-virtualisation and hooking evasion methods may allow malware to bypass such detection methods. The main objective of this work is to complement sandbox execution with the use of binary emulation frameworks. The core idea is to exploit the fact that binary emulation frameworks may quickly test samples quicker than a sandbox environment as they do not need to open a whole new virtual machine to execute the binary. While with this approach, we lose the granularity of the data that can be collected through a sandbox, due to scalability issues, one may need to simply determine whether a file is malicious or to which malware family it belongs. To this end, we record the API calls that are performed and use them to explore the efficacy of using them as features for binary and multiclass classification. Our extensive experiments with real-world malware illustrate that this approach is very accurate, achieving state-of-the art outcomes with a statistically robust set of classification experiments while simultaneously having a relatively low computational overhead compared to traditional sandbox approaches. In fact, we compare the binary analysis results with a commercial sandbox, and our classification outperforms it at the expense of the fine-grained results that a sandbox provides. Copyright © 2022, The Authors. All rights reserved.

关键词： Malware

The Challenges of Music Deep Learning for Traditional Music

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The Challenges of Music Deep Learning for Traditional Music

International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Lazaros Moysis Lazaros Alexios Iliadis Sotirios P. Sotiroudis Kostas Kokkinidis Panagiotis Sarigiannidis Spiridon Nikolaidis Christos Volos Achilles D. Boursianis Dimitrios Babas Maria S. Papadopoulou Sotirios K. Goudos Laboratory of Nonlinear Systems - Circuits & Complexity School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Information and Electronic Engineering International Hellenic University Sindos Greece

Numerous applications for music listeners, educators, DJs, and musicians have been created over the past decade as the field of Music Deep Learning has expanded. Evidently, the majority of works rely on training their architectures using well-established databases, which consist primarily of Western popular music genres. This creates a deficiency in applications focusing on traditional and regional music, which are vital to preserving culture. As an increasing number of research groups begin to develop works centered on traditional music, several practical challenges for applying deep learning arise. This work discusses these issues and the future of deep learning in the study of traditional music.

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Current and future directions in network biology

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

3d Point Cloud Representation Learning Via Dual Curve-Graph Feature Fusion

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SSRN

SSRN 2024年

作者： Putra, Oddy Virgantara Priyadi, Ardyono Ogata, Kohichi Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Teknik Kimia Street East Java Surabaya60111 Indonesia Department of Informatics Universitas Darussalam Gontor Jalan Raya Siman East Java Ponorogo63471 Indonesia Faculty of Advanced Science and Technology Kumamoto University 2 Chome Chuo-ku Kurokami Kumamoto-ken Kumamoto860-0862 Japan Department of Computer Engineering Institut Teknologi Sepuluh Nopember Teknik Kimia Street East Java Surabaya60111 Indonesia

Point cloud data in three dimensions is extensively utilized in various domains like computer vision, augmented reality, and construction. Despite its accuracy and comprehensive detail, the unstructured nature of this data presents challenges for analysis. To tackle these issue, we introduces a novel method incorporates graph-based and curve-based representations to enhance representation learning for 3D point clouds. The proposed method integrates multiple specialized modules, including Curve Grouping , Curve Aggregation, Local Point-Feature Aggregation , and Geometry Disentanglement Attention, into a unified framework. This dual approach allows the model to capture local and global geometric features effectively. The model transforms point cloud data into graphs and curves, distilling sharp and gentle features, and then aggregating these features for advanced tasks like classification and segmentation. The efficacy of the proposed model was demonstrated through experiments on benchmark datasets for point cloud classification and segmentation. In point cloud classification using the ModelNet40 dataset, the DCGRL model achieved the highest accuracy of 92.4%, outperforming other well-known models. Similarly, in part segmentation, the DCGRL model obtained the highest mean Intersection over Union (mIoU) score of 86, indicating superior performance compared to other state-of-the-art methods. These results highlight the significant improvements the DCGRL approach offers in capturing intricate geometric details and enhancing the overall accuracy and robustness of point cloud processing tasks. © 2024, The Authors. All rights reserved.

关键词： Augmented reality

Sensitivity analysis of an AC microgrid turning in DC microgrid _UCY case study 12

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Sensitivity analysis of an AC microgrid turning in DC microg...

12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Charalambous, Chrysanthos Papadimitriou, Christina N. Polycarpou, Alexis Efthymiou, Venizelos University of Cyprus FOSS Research Centre for Sustainable Energy Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9781839535635

During the last decade a continual rise in global average temperature has been recorded and a gradual deterioration of the climate. Therefore, significant steps must be taken to address these phenomena. An important measure in the electricity sector is the modernization of power systems for a more efficient distribution of electricity. With the increasing availability of DC consumer appliances as well as the increasing penetration of direct current (DC) Distributed Energy Resources (DERs) such as photovoltaics (PV) and Energy Storage Systems (ESS), the concept of DC and hybrid AC/DC distribution systems and microgrids attract more and more attention. The present paper presents the most important advantages of DC and hybrid AC/DC distribution systems and microgrids based on literature review. In addition, the FOSS nanogrid of University of Cyprus was used as a case study for the power flow simulations in order to examine and compare the efficiency gains from the use of hybrid nanogrids instead of AC nanogrids. Furthermore, a draft economic analysis has been done. The main outcome of this study is that the cost of the hybrid AC/DC nanogrid is lower than the AC nanogrid, and also the use of the hybrid nanogrid can bring significant gains in terms of nanogrid performance and energy exchange with the main grid. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Energy resources

PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection

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arXiv 2023年

作者： Seesawad, Narongrid Ittichaiwong, Piyalitt Sudhawiyangkul, Thapanun Sawangjai, Phattarapong Thuwajit, Peti Boonsakan, Paisarn Sripodok, Supasan Veerakanjana, Kanyakorn Luenam, Phoomraphee Charngkaew, Komgrid Pongpaibul, Ananya Angkathunyakul, Napat Hnoohom, Narit Yuenyong, Sumeth Thuwajit, Chanitra Wilaiprasitporn, Theerawit Rayong Thailand The Siriraj Informatics and Data Innovation Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand The Department of Immunology Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand The Department of Pathology Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand The Department of Pathology Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand The Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand

Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address this, we propose PseudoCell, an object detection framework to automate centroblast detection in WSI (source code is available at https://***/IoBT-VISTEC/*** [Note: The code will be public after the manuscript is accepted]). This framework incorporates centroblast labels from pathologists and combines them with pseudo-negative labels obtained from undersampled false-positive predictions using the cell’s morphological features. By employing PseudoCell, pathologists’ workload can be reduced as it accurately narrows down the areas requiring their attention during examining tissue. Depending on the confidence threshold, PseudoCell can eliminate 58.18–99.35% of non-centroblasts tissue areas on WSI. This study presents a practical centroblast prescreening method that does not require pathologists’ refined labels for improvement. Detailed guidance on the practical implementation of PseudoCell is provided in the discussion section. © 2023, CC BY-NC-ND.

关键词： Tissue

A Survey on the Communication of UAVs with Charging and Control Stations

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Recent Advances in Computer Science and Communications

Recent Advances in computer Science and Communications 2025年

作者： Chalkiadaki, Aikaterini Milidonis, Athanasios Mourgelas, Christos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Str.Athens Egaleo12243 Greece

Unmanned Aerial Vehicles (UAVs) have a history of over a century of deployment, but in recent decades, they have progressed at a staggering rate. Nowadays, UAVs are used by a large number of civil and military applications. The communication functionality of a UAV with external systems for control and charging is strongly connected with evolving technologies and services. This leads to an increased number of alternatives when designing UAV communications. This review presents the information needed for choosing an efficient communication system between UAVs and two important elements, the Ground Control Station (GCS) and the Charging Station (CS). GCS is responsible for monitoring and controlling the UAV’s units, while CS is used for the formal charging of the UAV. This study aimed at collecting, classifying, and evaluating all of the necessary information in order to obtain the final decision about the kind of communication that is most efficient for a target UAV application. The features of the telemetry open-source protocols are presented for the UAV-GCS communication and evaluated according to the needs of the most significant application domains. Communication between UAVs and CSs is classified depending on the existence of an intermediate server and analyzed considering telemetry protocols and application domains. Communication algorithms are evaluated in terms of time and energy efficiency. Lastly, for the most significant application domains, the most suitable algorithms are matched. © 2024 Bentham Science Publishers.

关键词： Signaling