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Extracting GHZ states from linear cluster states

Physical Review Research 2024年第1期6卷 013330-013330页

作者： J. de Jong F. Hahn N. Tcholtchev M. Hauswirth A. Pappa Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Fraunhofer Institute for Open Communication Systems - FOKUS 10589 Berlin Germany

Quantum information processing architectures typically only allow for nearest-neighbor entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to n=19 qubits.

关键词： Measurement-based quantum computing Quantum communication Quantum entanglement Quantum networks Superconducting qubits

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

arXiv

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arXiv 2023年

作者： Brandt, Bryan Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States Computer Science Department University of Wisconsin-Whitewater WI United States

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same *** Codes 68T07 © 2023, CC BY.

关键词： computer games

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Multi-Antenna Coded Caching Analysis in Finite SNR and Finite Subpacketization 25

Multi-Antenna Coded Caching Analysis in Finite SNR and Finit...

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25th International ITG Workshop on Smart Antennas, WSA 2021

作者： Zhao, Hui Lampiris, Eleftherios Caire, Giuseppe Elia, Petros Communication Systems Department EURECOM Sophia Antipolis 06410 France Electrical Engineering and Computer Science Department Technical University of Berlin Berlin10587 Germany

ISBN: (纸本)9783800756889

We investigate the performance of multi-antenna coded caching delivery algorithms operating under practical constraints. Specifically, under the constraints of finite subpacketization and finite signal-to-noise ratio (SNR) we compare two popular algorithms, one exhibiting low subpacketization while the other transmits multicast messages, hence requiring fewer symbols for communication and resulting in increased power per transmitted symbol. Our work is motivated by the fact that, while many multi-antenna coded caching algorithms exhibit the same asymptotic performance, when factoring practical limitations such as finite subpacketization and finite SNR, the real-world performances differ significantly. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signal to noise ratio

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Machine learning algorithms for predicting the risk of chronic kidney disease in type 1 diabetes patients: a retrospective longitudinal study

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Neural Computing and Applications 2024年第26期36卷 16545-16565页

作者： Chowdhury, Md Nakib Hayat Reaz, Mamun Bin Ibne Ali, Sawal Hamid Md Crespo, María Liz Cicuttin, Andrés Ahmad, Shamim Haque, Fahmida Bakar, Ahmad Ashrif A. Razak, Mohd Ibrahim Bin Shapiai Abd Bhuiyan, Mohammad Arif Sobhan Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Saidpur Cantonment Saidpur Nilphamari5310 Bangladesh Department of Electrical and Electronic Engineering Independent University Bangladesh Dhaka Bashundhara Bangladesh Department of Electrical Electronic and Systems Engineering Centre of Advanced Electronic and Communication Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Trieste34151 Italy Department of Computer Science and Engineering University of Rajshahi Rajshahi6205 Bangladesh Artificial Intelligence Resource Molecular Imaging Branch National Cancer Institute BethesdaMD United States Department of Electrical and Electronics Engineering Xiamen University Malaysia Selangor Bandar Sunsuria Sepang43900 Malaysia

Chronic kidney disease (CKD) is a significant concern for individuals with type 1 diabetes (T1D), impacting their quality of life and healthcare costs. Identifying T1D patients at greater risk of developing CKD is crucial for preventive measures. However, it is challenging due to the asymptomatic progression of CKD and limited nephrologist availability in many countries. This study explores machine learning algorithms to predict CKD risk in T1D patients using ten years of retrospective data from the Epidemiology of Diabetes Interventions and Complications clinical trial. Eleven machine learning algorithms were applied to twenty-two readily available features from T1D patients’ routine check-ups and self-assessments to develop 10-year CKD risk prediction models. In addition, we also proposed a heterogeneous ensemble model (STK) using a stacking generalization approach. The models’ performance was evaluated using different evaluation metrics and repeated stratified k-fold cross-validation. Several predictive models showed reliable performance in CKD risk prediction, with the proposed ensemble model being the best performing with an average accuracy of 0.97, specificity of 0.98, sensitivity/recall of 0.96, precision of 0.98, F1 score of 0.97, Kappa and MCC score of 0.94, AUROC of 0.99, and Precision-Recall curve of 0.99. The proposed machine learning approach could be applicable for CKD risk prediction in T1D patients to ensure the necessary precautions to overcome the risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Machine learning

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High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation

High-Level Metrics for Service Level Objective-aware Autosca...

引用

IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Nicolò Bartelucci Paolo Bellavista Thomas Pusztai Andrea Morichetta Schahram Dustdar Department of Computer Science and Engineering University of Bologna Bologna Italy Distributed Systems Group Vienna University of Technology Vienna Austria

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes 4

Seamless Decision-Making in the Big Data Era: A Modular Appr...

引用

4th International Conference on Electrical, communication and computer Engineering, ICECCE 2023

作者： Zemmouri, Mohamed Laalam, Fatma Zohra Kazar, Okba Himeur, Yassine Oulefki, Adel Toumi, Chahrazad Mansoor, Wathiq Attala, Shadi Artificial intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla30000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Mohamed Khider University Department of Computer Science LINFI Laboratory Biskra07000 Algeria United Arab Emirate University Department of Information Systems and Security College of Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369694

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source. © 2023 IEEE.

关键词： Big data

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IoT and Image Processing based Smart Door Locking System 1

IoT and Image Processing based Smart Door Locking System

引用

1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Jayaram, B. Subhahan, D. Abdus Sakthivel, B. Mohanaprakash, T.A. Joshi, Sunita Kumar, M. Jogendra Department of Computer Science and Business Systems RMK Engineering College Tamil Nadu Kavaraipettai601206 India Department of Computer Science Engineering B V Raju Institute of Technology Telengana Narsapur502313 India Department of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Elayampalayam Namakkal Tamil Nadu Tiruchengode637205 India Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai600123 India Department of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad121004 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665460842

An effective home security system improves the standard of living by making the home a safer place to live in general. In recent years, smart door locks have become increasingly popular as an integral component of home security systems. Locking and unlocking a door with a smart door lock system requires authentication. Unfortunately, several break-in attempts have been made when the invader tried to bypass the locks. In this work, Internet of Things (IoT) and image processing can be used to strengthen the safety of Smart Door Lock systems. The camera is triggered after the motion detector has determined that a human has entered the area. The camera picks up a clear picture of the visitor standing by the entrance. Using image processing methods, the image is delivered to the microcontroller and compared with the registered users. A door will open automatically if the person's face is in the database. Otherwise, a notification and a photo of the person will be sent to the user's mobile app via IoT. In the designed mobile app, the user may view the individual's face and issue commands to the system, such as opening and closing doors. Using the developed methodology, the safety of the house will be increased, and unauthorized visitors will be prevented. © 2022 IEEE

关键词： Internet of things

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A Cloud-Based UV Monitoring System for Remote Real-Time UV Exposure Tracking 4

A Cloud-Based UV Monitoring System for Remote Real-Time UV E...

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4th International Conference on Smart Electronics and communication, ICOSEC 2023

作者： Dankan Gowda, V. Prasad, V. Nuthan Prasad, K.D.V. Prasad, V.K. Satya Mahajan, Yogesh Suneetha, Sampathirao Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India M S R i T Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Pune India Symbiosis International University Symbiosis Centre for Management and Human Resource Development Maharashtra Pune India Andhra University College of Engineering Andhra University Department of Computer Science and Systems Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350300888

Due to the widespread nature of sun-related health risks, sophisticated UV monitoring systems are urgently needed. This research study introduces a cloud-based UV monitoring system with the intent of remotely tracking and analyzing UV exposure in real time. The system incorporates a network of UV sensors deployed in various locations, continuously collecting UV radiation data. These sensors are linked to a cloud system, which allows for easy sharing and processing of collected data. As a result, we're working to perfect a cutting-edge device that can monitor vitamin D absorption, measure cumulative UV exposure, and warn of impending danger. Extensive studies have demonstrated the benefits of UV exposure for both general health and protection against the newly discovered coronavirus. However, finding a happy medium is important because too much sun exposure can cause cancer, lupus flares, and cataracts. The proposed model gives users the ability to set their own daily UV exposure limits, taking into account clinically validated thresholds for various skin tones. The developed sensor module is able to measure the UV index with high precision even in bright sunlight because it closely mimics the skin erythema spectrum. © 2023 IEEE.

关键词： Ultraviolet radiation

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Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks

arXiv

引用

arXiv 2024年

作者： Lhamo, Osel Doan, Tung V. Tasdemir, Elif Attawna, Mahdi Nguyen, Giang T. Seeling, Patrick Reisslein, Martin Fitzek, Frank H.P. TU Dresden Germany Haptic Communication Systems TU Dresden Germany Germany Department of Computer Science Central Michigan University United States School of Electrical Computer and Energy Engineering Arizona State University United States

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC. © 2024, CC BY.

关键词： Packet loss

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FileDAG: A Multi-Version Decentralized Storage Network Built on DAG-based Blockchain

arXiv

引用

arXiv 2022年

作者： Guo, Hechuan Xu, Minghui Zhang, Jiahao Liu, Chunchi Yu, Dongxiao Dustdar, Schahram Cheng, Xiuzhen The School of Computer and Science and Technology Shandong University China The Department of Computer Science The George Washington University United States The Research Division of Distributed Systems TU Wien Austria

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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