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arXiv 2024年

作者： Clayton, Connor Leng, Jiaqi Yang, Gengzhi Qiao, Yi-Ling Lin, Ming C. Wu, Xiaodi Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Computer Science University of Maryland United States Department of Mathematics University of Maryland United States Center for Machine Learning University of Maryland United States Department of Mathematics Simons Institute The Theory of Computing UC Berkeley United States

As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significant overhead as problem dimensions grow. In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation. Specifically, we build a quantum-assisted differentiable simulator for efficient gradient estimation that is more accurate and robust than classical methods relying on stochastic approximation. Compared to the classical approaches, our method achieves a super-quadratic speedup. To the best of our knowledge, this is the first end-to-end quantum application to linear control problems with provable quantum advantage. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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File Fragment Classification using Light-Weight Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Ghaleb, Mustafa Saaim, Kunwar Felemban, Muhamad Al-Saleh, Saleh Al-Mulhem, Ahmad The Department of Computer Engineering The Interdisciplainry Research Center for Intelligent Secure Systems at KFUPM Dhahran Saudi Arabia The Department of Computing Science University of Alberta Canada

In digital forensics, file fragment classification is an important step toward completing file carving process. There exist several techniques to identify the type of file fragments without relying on meta-data, such as using features like header/footer and N-gram to identify the fragment type. Recently, convolutional neural network (CNN) models have been used to build classification models to achieve this task. However, the number of parameters in CNNs tends to grow exponentially as the number of layers increases. This results in a dramatic increase in training and inference time. In this paper, we propose lightweight file fragment classification models based on depthwise separable CNNs. The evaluation results show that our proposed models provide faster inference time with comparable accuracy as compared to the state-of-art CNN based models. In particular, our models were able to achieve an accuracy of 79% on the FFT-75 dataset with nearly 100K parameters and 164M FLOPs, which is 4x smaller and 6x faster than the state-of-the-art classifier in the literature. © 2023, CC BY.

关键词： Convolution

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Per-RMAP: Feasibility-Seeking and Superiorization Methods for Floorplanning with I/O Assignment

Per-RMAP: Feasibility-Seeking and Superiorization Methods fo...

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Yu, Shan Censor, Yair Jiang, Ming Luo, Guojie School of Mathematical Sciences Peking University Department of Information and Computational Sciences Beijing China University of Haifa Department of Mathematics Haifa Israel School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing Beijing China Center for Energy-efficient Computing and Applications Peking University Beijing China

ISBN: (纸本)9798350304510

The feasibility-seeking approach provides a systematic scheme to manage and solve complex constraints for continuous problems, and we explore it for the floorplanning problems with increasingly heterogeneous constraints. The classic legality constraints can be formulated as the union of convex sets. However, the convergence of conventional projection-based algorithms is not guaranteed when the constraints sets are non-convex, which is the case with unions of convex sets. In this work, we propose a resetting strategy to greatly eliminate the divergence issue of the projection-based algorithm for the feasibility-seeking formulation. Furthermore, the superiorization methodology (SM), which lies between feasibility-seeking and constrained optimization, is firstly applied to floorplanning. The SM uses perturbations to steer the iterates of a feasibility-seeking algorithm to a feasible solution with shorter total wirelength. The proposed algorithmic flow is extendable to tackle various constraints and variants of floorplanning problems, e.g., floorplanning with I/O assignment problems. We have evaluated the proposed algorithm on the MCNC benchmarks. We can obtain legal floorplans only two times slower than the branch-And-bound method in its current prototype using MATLAB, with only 3 % wirelength inferior to the optimal results. We evaluate the effectiveness of the algorithmic flow by considering the constraints of I/O assignment, and our algorithm achieves 8 % improvement on wirelength. © 2023 IEEE.

关键词： Branch and bound method

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Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors

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Photodiagnosis and Photodynamic Therapy 2025年 53卷 104614-104614页

作者： Huang, Shaofen Luo, Man Ye, Guitong Yang, Zhenlan Liao, Lingling Wu, Nina Zhu, Yingting Liu, Xing Zhuo, Yehong Xiao, Hui State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangdong Provincial Clinical Research Center for Ocular Diseases Guangzhou China Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China

Background: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors. Methods: Patients with OHT with at least two follow-up visits within 3 years were categorized into non-progressive and progressive groups based on optic nerve morphology and/or functional changes during follow-up. Data were split into training and testing sets (8:2 ratio). Least absolute shrinkage and selection operator regression and logistic regression were used to select predictors. Machine learning models were constructed using the selected predictors as input and evaluated using area under the receiver operating characteristic (AUC) and precision-recall (AP) curve values. The optimal model was further evaluated using 10-fold cross-validation and a validation set. The Shapley additive explanations method was applied to interpret the predictors. Results: Overall, 395 eyes from 395 patients were included (non-progressive: n = 295;progressive: n = 100). The random forest model outperformed all others, achieving AUC values of 0.881 (95 % confidence interval [CI]: 0.835–0.926) in the training set and 0.937 (95 % CI: 0.884–0.991) in the testing set. In the independent validation set (n = 82), the AUC and AP values were 0.865 (95 % CI: 0.782–0.947) and 0.707, respectively. Key predictors were baseline intraocular pressure, rim area, ganglion cell-inner plexiform layer (GCIPL) inferior-temporal thickness difference, and GCIPL superior-temporal thickness difference. Family history and male sex also contributed. Conclusions: The developed model could effectively predict the risk for OHT progression to early glaucoma and may aid devising individualized treatment plans and follow-up protocols. © 2025 The Authors

关键词： Early glaucoma OCT Ocular hypertension Predictive model Risk stratification

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Semi-supervised Federated Learning for Misbehavior Detection of BSMs in Vehicular Networks

Semi-supervised Federated Learning for Misbehavior Detection...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiaqi Huang Yili Jiang Sohan Gyawali Zhiguo Zhou Fangtian Zhong School of Computer Science and Cybersecurity University of Central Missouri MO USA Department of Computer Science Georgia State University GA USA Department of Technology Systems East Carolina University NC USA Department of Biostatistics & Data Science University of Kansas Medical Center and Cancer Center KS USA Gianforte School of Computing Montana State University MT USA

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Basic Safety Messages (BSMs) exchanged among vehicles and roadside units through vehicular communications can significantly enhance road safety and improve traffic efficiency. Protecting the integrity of BSMs, which are transmitted wirelessly in plaintext, is critical for the proper operation of vehicular networks. As a result, various machine learning-based misbehavior detection systems have been proposed to identify corrupted BSMs. Recent studies have applied federated learning methods to further preserve user privacy while facilitating detection model updates. However, supervised federated learning cannot be directly applied since BSMs received by vehicles are unlabeled. In this paper, we propose a semi-supervised federated learning framework that enables the federated training process between vehicles and the server without transmitting any datasets. Our experimental results show that the performance of the proposed semi-supervised framework is very close to the centralized method, while preserving user privacy and reducing communication costs.

关键词： Training Privacy Vehicular and wireless technologies Costs Federated learning Road safety Safety Servers

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Realistic electric field characterization of clinically used deformable large TMS coils in a large cohort

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Brain stimulation 2025年第4期18卷 1174-1183页

作者： Torge Worbs Bianka Rumi Kristoffer H Madsen Axel Thielscher Section for Magnetic Resonance DTU Health Tech Technical University of Denmark Kgs Lyngby Denmark Danish Research Centre for Magnetic Resonance Department of Radiology and Nuclear Medicine Copenhagen University Hospital Amager and Hvidovre Copenhagen Denmark. Danish Research Centre for Magnetic Resonance Department of Radiology and Nuclear Medicine Copenhagen University Hospital Amager and Hvidovre Copenhagen Denmark Section for Visual Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China. Section for Visual Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark. Electronic address: khma@dtu.dk.

BACKGROUND:Transcranial Magnetic Stimulation (TMS) therapies use both focal and unfocal coil designs. Unfocal designs often employ bendable windings and moveable parts, making realistic simulations of their electric fields in inter-individually varying head sizes and shapes challenging. This hampers comparisons of the various coil designs and prevents systematic evaluations of their dose-response relationships. OBJECTIVE:Introduce and validate a novel method for optimizing the position and shape of flexible coils taking individual head anatomies into account. Evaluate the impact of realistic modeling of flexible coils on the electric field simulated in the brain. METHODS:Accurate models of four coils (Brainsway H1, H4, H7; MagVenture MST-Twin) were derived from computed tomography data and mechanical measurements. A generic representation of coil deformations by concatenated linear transformations was introduced and validated. This served as basis for a principled approach to optimize the coil positions and shapes, and to optionally maximize the electric field strength in a region of interest (ROI). RESULTS:For all four coil models, the new method achieved configurations that followed the scalp anatomy while robustly preventing coil-scalp intersections on 1100 head models. In contrast, setting only the coil center positions without shape deformation regularly led to physically impossible configurations. This also affected the electric field calculated in the cortex, with a median peak difference of ∼16%. In addition, the new method outperformed grid search-based optimization for maximizing the electric field of a standard figure 8 coil in a ROI with a comparable computational complexity. CONCLUSION:Our approach alleviates practical hurdles that so far hampered accurate simulations of bendable coils. This enables systematic comparison of dose-response relationships across the various coil designs employed in therapy.

关键词： Brainsway H-coils MagVenture MST-twin coil TMS electric field calculations personalized dosing

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A brief overview of programmed instructions for quantum software education

arXiv

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arXiv 2023年

作者： Wolf, Richard A. Araiba, Sho Department of Computer Science University of Galway Irish Center for High-End Computing Galway Ireland Psychology Department University of Hawaii Honolulu United States

In this paper we provide an overview of the programmed instructions approach for the purpose of quantum software education. The article presents the programmed instructions method and recent successes in STEM fields before describing its operating mode. Elements tackled include the core components of programmed instructions, its behavioural roots and early use as well as adaptation to complex STEM material. In addition, we offer recommendations for its use in the specific context of quantum software education and provide one example of PI-based instruction for the notion of entanglement. The aim of this work is to provide high-level guidelines for incorporating programmed instructions in quantum education with the goal of disseminating quantum skills and notions more efficiently to a wider audience. © 2023, CC BY-NC-SA.

关键词： Quantum entanglement

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Operation Optimization of Laser-Powered Aerial Data Harvesting for Passive IoT Networks

Operation Optimization of Laser-Powered Aerial Data Harvesti...

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IEEE Conference on Wireless Communications and Networking

作者： Amr M. Abdelhady Abdulkadir Çelik Carles Diaz-Vilor Hamid Jafarkhani Ahmed M. Eltawil Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah Province Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Center for Pervasive Communications and Computing University of California Irvine CA USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

关键词： Wireless communication Heuristic algorithms Power lasers Data collection Autonomous aerial vehicles Trajectory Internet of Things

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Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

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computers, Materials & Continua 2023年第2期74卷 2889-2903页

作者： Mahmoud Ragab Mohammed W.Al-Rabia Sami Saeed Binyamin Ahmed A.Aldarmahi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Medical Microbiology and Parasitology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Health Promotion Center King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah 21589Saudi Arabia Basic Science Department College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia King Abdullah International Medical Research Center Ministry of National Guard-Health AffairsJeddah 21423Saudi Arabia

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of *** important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional *** accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition *** study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest *** attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature *** addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 ***,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of *** ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive *** experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

关键词： COVID-19 artificial intelligence intelligent systems deep learning decision making

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Quantifying OpenMP: Statistical Insights into Usage and Adoption

Quantifying OpenMP: Statistical Insights into Usage and Adop...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Tal Kadosh Niranjan Hasabnis Timothy Mattson Yuval Pinter Gal Oren Department of Computer Science Ben-Gurion University Israel Israel Atomic Energy Commission Intel Labs United States Scientific Computing Center Nuclear Research Center - Negev Israel Department of Computer Science Technion - Israel Institute of Technology Israel

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong “common core” of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://***/Scientific-computing-Lab-NRCN/HP

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