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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks Programming Predictive models Quantum circuit Physics Long short term memory

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DruSiLa: an integrated, in-silico disease similarity-based approach for drug repurposing

DruSiLa: an integrated, in-silico disease similarity-based a...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Amatya, Pratuat Stolfi, Paola Lombardi, Flavio Tieri, Paolo Sapienza University of Rome Data Science Master Program Rome Italy IAC Institute for Applied Computing CNR National Research Council of Italy Rome Italy

ISBN: (纸本)9781665468190

The importance of faster drug development has never been more evident than in present time when the whole world is struggling to cope up with the COVID-19 pandemic. At times when timely development of effective drugs and treatment plans could potentially save millions of lives, drug repurposing is one area of medicine that has garnered much of research interest. Apart from experimental drug repurposing studies that happen within wet labs, lot many new quantitative methods have been proposed in the literature. In this paper, one such quantitative methods for drug repurposing is implemented and evaluated. DruSiLa (DRUg in-SIlico LAboratory) is an in-silico drug repurposing method that leverages disease similarity measures to quantitatively rank existing drugs for their potential therapeutic efficacy against novel diseases. The proposed method makes use of available, manually curated, and open datasets on diseases, their genetic origins, and disease-related patho-phenotypes. DruSiLa evaluates pairwise disease similarity scores of any given target disease to each known disease in our dataset. Such similarity scores are then propagated through disease-drug associations, and aggregated at drug nodes to rank them for their predicted effectiveness against the target disease. © 2022 IEEE.

关键词： Bioinformatics

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Achieving compelling student comprehension of complex information structures for both on-site and on-line courses

Achieving compelling student comprehension of complex inform...

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2008 ASEE Annual Conference and Exposition

作者： Uhlig, Ronald Farahani, Alireza Viswanathan, Shekar Evans, Howard Sotelo, Mark National University Department of Computer Science and Information Systems School of Engineering and Technology National University Wireless Communications for the Master ot Science in Wireless Communications Program International Telecommunications Academy School of Engineering and Technology Department of Applied Engineering Lead Faculty for Engineering Management and Homeland Security and Safety Engineering Academic Computing for National University

Teaching engineering and technology subjects involves conveying understanding of abstract information structures and processes such as complex telecommunications protocols, complex mathematical concepts, and data structures. These are multidimensional and can be difficult to grasp quickly. Supported by a Hewlett-Packard (HP) Technology for Teaching grant, the School of Engineering and Technology at National University has embarked on a project to enable students to grasp these complex concepts more quickly and easily, using continuous dialog among students and instructors as the structures are first introduced and then examined from multiple perspectives through real-time interaction among students, small groups, and instructors. HP Wireless Tablet PCs are used to discuss and experiment with diagrams and processes in realtime. This allows combining lectures and problem-solving sessions into a single class session. Our hypothesis is that: 1) an in-depth learning of theory is accomplished, and 2) student engagement is enhanced. Presentation of theory by the instructor is integrated with application while the theory is still in the student's short-term memory. Students no longer have to wait for a separate session such as a recitation session to apply the concepts. This approach often takes less time. In addition to accelerating the learning process, expert instructors find this approach more rewarding as students grasp new concepts more quickly. Analysis of data captured from both students and instructors are presented to support our hypotheses, and our results are compared with similar research carried out by other universities. In addition, the level of interaction enabled by the use of HP Tablets in on-line classes is discussed. The best teaching tools available in existing on-line teaching platforms are compared with the additional tools available in on-site courses when every student has a wireless Tablet PC and specific recommendations are made to on-line teachi

关键词： Students

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DruSiLa: an integrated, in-silico disease similarity-based approach for drug repurposing

DruSiLa: an integrated, in-silico disease similarity-based a...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pratuat Amatya Paola Stolfi Flavio Lombardi Paolo Tieri Data Science Master Program Sapienza University of Rome Rome Italy IAC Institute for Applied Computing CNR National Research Council of Italy Rome Italy

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

关键词： Drugs COVID-19 Pandemics Biomedical measurement Prediction algorithms Genetics Breast cancer

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on Deep Learning of Convolutional Neural Network for Action Recognition of Intelligent Terminals in the Big Data Environment and its Intelligent Software Application

Research on Deep Learning of Convolutional Neural Network fo...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Jiahan Sun Shengyuan Zhang Jieting Lian Lei Fu Zijie Zhou Yuxin Fan Kai Xu Shenyang Jianzhu University Shenyang China Master of Professional Science in Applied Statistics- Data Science College of Computing and Information Science Cornell University New York America Master of Science in Database Technology School of Professional Studies New York University New York America Master of Science in Computer Software Engineering College of Engineering Northeastern University Boston Massachusetts USA University of Pennsylvania Philadelphia Pennsylvania USA PipeChina Zhejiang Pipeline Network Co. LTD. Hangzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

To enhance the accuracy and reliability of big data network traffic intrusion detection, an innovative detection model based on the two-branch convolution and gated recurrent unit was constructed, and its application effectiveness in the field of software engineering was thoroughly explored. This model first performs preprocessing operations on the original network traffic data, and then imports it as input information into the core architecture system jointly composed of the two-branch convolution and gated recurrent unit. Among them, the two-branch convolution focuses on quickly and accurately extracting the multi-dimensional features of the network traffic data, while the gated recurrent unit emphasizes on modeling and analyzing the long-term correlation patterns in the feature sequence. Through the organic combination of the two, the potential features of the data are deeply mined. Subsequently, the obtained feature output is directly transmitted to the classifier, so as to achieve accurate discrimination of various types of network traffic such as normal traffic, DoS attack traffic, DDoS attack traffic, port scanning traffic, and SQL injection attack traffic. The comparison experiment data shows that for network traffic data from different sources and with diverse patterns, the detection accuracy rate of this model is as high as 84.3%. This result strongly verifies its high efficiency and practicality in the field of big data network traffic intrusion detection, provides a solid technical guarantee foundation for the network security protection link in software engineering, has important theoretical and practical values, and is expected to promote the further development and application expansion of network security technologies.

关键词： Accuracy Convolution Telecommunication traffic Big Data Logic gates Feature extraction Data models Software Convolutional neural networks Software engineering

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An analysis of replication and retrieval of medical image data using a database management system and a distributed file system

An analysis of replication and retrieval of medical image da...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Elias Amaral Santos Eros Comunello Douglas D.J. de Macedo Miriam A.M. Capretz Thiago Coelho Prado M.A.R. Dantas Master of Science in Applied Computing University of Itajai Valley (UNIVALI) Itajaí SC Brazil Informatics and Statistic Department Federal University of Santa Catarina (UFSC) Florianopolis SC Brazil Department of Electrical and Computer Engineering Western University London ON Canada

This paper presents a research study consisting of a comparison analysis of replication and retrieval of medical image data which use the Digital Imaging and Communications in Medicine (DICOM) standard. These data are stored in a relational database management system (RDBMS) and in the Hierarchical Data Format (HDF) using a distributed file system as a data backend. The importance of this work was measured by verification of elapsed-time reduction for medical image data replication and retrieval in a real telemedicine environment.

关键词： Arrays Angiography DICOM Indexes US Department of Defense

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Molecular dynamics simulation approach to explore atomistic molecular mechanism of peroxidase activity of apoptotic cytochrome c mutants

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Informatics in Medicine Unlocked 2018年 11卷 51-60页

作者： Muneeswaran, Gurusamy Pandiaraj, Manickam Kartheeswaran, Subramanian Sankaralingam, Muniyandi Muthukumar, Kaliappan Karunakaran, Chandran Biomedical Research Lab Department of Chemistry VHNSN College (Autonomous) Virudhunagar Tamilnadu India Department of Chemistry School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil 626 126 Tamilnadu India Electrodics and Electrocatalysis Division CSIR-Central Electrochemical Research Institute Karaikudi 630003 India Department of Master of Computer Applications School of Computing Kalasalingam Academy of Research and Education Krishnankoil 626 126 Tamilnadu India Department of Chemistry and Nano Science Ewha Womans University Seoul 03760 South Korea Applied Materials Sunnyvale 94085 CA United States

Mutations in cytochrome c (Cyt c) have been reported in tuning peroxidase activity, which in-turn cause Cyt c release from mitochondria and early apoptosis. However, the molecular tuning mechanism underlying this activity remains elusive. Herein, multiple 20 ns molecular dynamics (MD) simulations of wild type (WT), Y67F and K72W mutated Cyt c in aqueous solutions have been carried out to study how the changes in structural features alters the peroxidase activity of the protein. MD simulation results indicate that Y67F mutation caused, (i) increased distances between critical electron-transfer residues, (ii) higher fluctuations in omega loops, and (iii) weakening of intraprotein hydrogen bonds result in open conformation at heme crevice loop in Cyt c leading to an enhanced peroxidase activity. Interestingly, the aforementioned structural features are strengthened in K72W compared to WT and Y67F, which triggers K72W mutated Cyt c into a poor peroxidase. Essential dynamics results unveil that first two eigenvectors are responsible for overall motions of WT, Y67F and K72W mutated Cyt c. This study thus provides atomic level insight into molecular mechanism of peroxidase activity of Cyt c, which will help in designing novel Cyt c structures that is more desirable than natural Cyt c for biomedical and industrial processes. © 2018

关键词： Apoptosis Cytochrome c Molecular dynamics Mutations Peroxidase activity

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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