Application of the generalized double reduction method to the (1+1)-dimensional Kaup–Boussinesq (K–B) system: Exploiting Lie symmetries and conservation laws by Molahlehi Charles Kakuli

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Spectrum-Convertible BVWXC Placement in OFDM-Based Elastic Optical Networks by Mohammad Hadi, Mohammad Reza Pakravan

“…To compare the capability of the introduced architectures, we develop an analytical method for computing average connection request blocking probability in a spectrum-convertible OFDM-based EON in which all, part, or none of the BVWXCs can convert the spectrum. …”
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Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise by Avimita Chatterjee, Subrata Das, Swaroop Ghosh

“…Quantum error correction codes (QECCs) are essential for reliable quantum computing as they protect quantum states against noise and errors. …”
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Production Forecasting of Unruly Geoenergy Extraction Wells Using Gaussian Decline Curve Analysis by Ruud Weijermars

“…Because GPT solutions are physics-based, these can be used for production forecasting as well as in reservoir simulation mode (by computing the spatial and temporal pressure gradients everywhere in the reservoir section drained by either an existing or a planned well). …”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Classification and Recognition of Fish Farming by Extraction New Features to Control the Economic Aquatic Product by Yizhuo Zhang, Fengwei Zhang, Jinxiang Cheng, Huan Zhao

“…Power and performance of computing with the analyzed given data are applied in the proposed DL method within fish farming. …”
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Exponentially Reduced Circuit Depths Using Trotter Error Mitigation by James D. Watson, Jacob Watkins

“…This work provides a rigorous, general analysis of these techniques for computing time-evolved observables, simplifying the interpolation algorithm in the process, and shows that extrapolation generically improves the performance of product formulas for this task. …”
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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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Deployment and Application of On-Board PHM Models Based on Docker Containers for Intelligent Operation and Maintenance by DU Jiwei, WEN Lin, LYU Yu, JIANG Guotao, CHEN Kai, XIONG Yukai

“…Additionally, the parallel computing efficiency of multiple models is improved due to the lack of data interactions among them, facilitating superior portability and scalability.…”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

“…Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.…”
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CYBER-PHYSICAL SOCIAL SYSTEMS: APPLICATION AREAS, EXISTING PROBLEMS AND PERSPECTIVES by Araz Mustafa

“…These systems consist of sensors, actuators, and computing devices that interact with the real world to monitor, analyze, and make decisions based on the obtained data. …”
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Theoretical Basics of Teaching Discrete Mathematics by Y. A. Perminov

“…The paper deals with the research findings concerning the process of mastering the theoretical basics of discrete mathematics by the students of vocational pedagogic profile. …”
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The entropy of radiation for local quenches in higher dimensions by Lorenzo Bianchi, Andrea Mattiello, Jacopo Sisti

“…We conclude with a holographic analysis of the process, computing the time evolution of the holographic entanglement entropy (HEE) as the area of the Ryu-Takayanagi surface in a backreacted geometry. …”
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Variational approach to photonic quantum circuits via the parameter shift rule by Francesco Hoch, Giovanni Rodari, Taira Giordani, Paul Perret, Nicolò Spagnolo, Gonzalo Carvacho, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Fabio Sciarrino

“…In the era of noisy intermediate-scale quantum computers, variational quantum algorithms are promising approaches for solving optimization tasks by training parameterized quantum circuits with the aid of classical routines informed by quantum measurements. …”
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Design and Implementation of a State-Driven Operating System for Highly Reconfigurable Sensor Networks by Tae-Hyung Kim

“…Due to the low-cost and low-power requirement in an individual sensor node, the available computing resources turn out to be very limited like small memory footprint and irreplaceable battery power. …”
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Data Mining Techniques for Wireless Sensor Networks: A Survey by Azhar Mahmood, Ke Shi, Shaheen Khatoon, Mi Xiao

“…Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. …”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Morse Theory and Meron-Mediated Interactions Between Disclination Lines in Nematic Materials by Joseph Pollard, Richard G. Morris

“…We also clarify the notion of the charge of a disclination line, give a simple method for computing it, and apply this to examples drawn from experiment. …”
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Examining trip-level errors in passively collected mobile device data for data quality assurance. by Peiqi Zhang, Kathleen Stewart, Aref Darzi

“…We designed a distributed-computing workflow to quantify the errors by comparing the number of trips on closed road segments during road closures with time periods before and after. …”
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Decision-Making Evaluation and Optimization Strategies for Construction EPC Project Developers Utilizing BIM Technology by Yujie Wu, Xiaoming He, Tianyi Cui, Mengze Wu

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