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Scheduling for Offloading Safety-Critical Applications Within Networked Groups of Vehicles

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Scheduling for Offloading Safety-Critical Applications Withi...

Conference on Digital Avionics Systems (DASC)

作者： John B. Mains Thanakorn Khamvilai Eric Feron Georgia Institute of Technology King Abdullah University of Science and Technology Electrical Engineering King Abdullah University of Science and Technology

ISBN: (数字)9781728198255

ISBN: (纸本)9781728180885

This paper presents a combined scheduling and path planning scheme for offloading of safety-critical applications in a group of networked vehicles. Due to the dynamic nature of such systems, a scheduler for applications to be offloaded needs to account for the changes of the network parameters. Such a scheduling scheme based on a mixed-integer program (MIP) is detailed with attention to the time-varying duration of message passing operations. Given knowledge of the scheduling requirements of applications to be offloaded, a path planner for a group of networked vehicles can ensure the resulting path will yield a feasible schedule for offloading. An method for generating such a path is described, utilizing the scheduling scheme and a mixed-integer program for path planning with integral communication capacity constraints. The combined scheme is demonstrated in a case study consisting of a pair of UAS equipped with a line-of-sight inter-vehicle a communication link and onboard processors. We show how the scheme can be used to make individual vehicles of the group fault-tolerant to processor failures.

关键词： Task analysis Processor scheduling Schedules Path planning Channel capacity Fault tolerant systems Fault tolerance

An Offline Assistance Tool for Visually Impaired People Based on Image Captioning

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An Offline Assistance Tool for Visually Impaired People Base...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Guo Yue Chen Yuanyan Xie Xiaojuan Ban Mohammad S. Obaidat School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China King Abdullah II School of Information Technology University of Jordan Amman Jordan Amity University Noida UP India

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Eye defects of visually impaired people bring great inconvenience to daily lives. With the rapid development of computers and artificial intelligence, some assistance tools and wearable devices have provided convenience to a certain extent, but they still possess many shortcomings, such as high prices, limited functionality, poor real-time performance, the inability to be used offline and so on. This paper designs an offline assistance tool specifically for visually impaired people that can help them perceive their surrounding environments by taking photos and reading the descriptions output by an image captioning model. Considering that the photos taken by visually impaired people may be incomplete, an improved image stitching method based on a genetic algorithm is proposed to obtain high-quality stitched images as the inputs of the image captioning model. An improved model pruning algorithm is also designed to compress and accelerate the image captioning model to meet the offline and real-time deployment requirements of portable devices. Experimental results and a practical tool show that the developed system based on an image captioning model and pruning can quickly and accurately describe environments, thus assisting visually impaired people anytime and anywhere.

关键词： Performance evaluation Image coding Wearable computers Biological system modeling Real-time systems Bioinformatics Artificial intelligence

Finite-time simultaneous estimation of aortic blood flow and differentiation order for fractional-order arterial Windkessel model calibration ⁎

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IFAC-PapersOnLine 2021年第15期54卷 538-543页

作者： Mohamed A. Bahloul Marcelo Benencase Zehor Belkhatir Taous-Meriem Laleg Kirati Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) 23955-6900 Thuwal Kingdom of Saudi Arabia School of Engineering and Sustainable Development De Montfort University The Gateway Leicester LE1 9BH United Kingdom

A fractional-order vascular model representation for emulating arterial hemody-namics has been recently presented as an alternative to the well-known integer-order arterial Windkessel. The model uses a fractional-order capacitor (FOC) to describe the complex and frequency-dependent arterial compliance. This paper presents a two-stage algorithm based on modulating functions for finite-time simultaneous estimation of the model’s input and the fractional differentiation order. The proposed approach is validated using in-silico human data. Results show the prominent potential of this method for calibrating arterial models and enhancing cardiovascular mechanics research as well as clinical practice.

关键词： Joint-estimation Modulating functions Windkessel model Fractional derivative

Topological spin-torque diode effect in skyrmion-based magnetic tunnel junctions

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arXiv 2024年

作者： Liu, Shuhui Tomasello, Riccardo Wu, Yuxuan Fang, Bin Chen, Aitian Zheng, Dongxing Zhang, Baoshun Darwin, Emily Hug, Hans J. Carpentieri, Mario Jiang, Wanjun Zhang, Xixiang Finocchio, Giovanni Zeng, Zhongming Nanofabrication facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Jiangsu Suzhou215123 China School of Nano Technology and Nano Bionics University of Science and Technology of China Anhui Hefei230026 China Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina MessinaI-98166 Italy Department of Electrical and Information Engineering Politecnico di Bari BariI-70125 Italy Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955–6900 Saudi Arabia Empa Swiss Federal Laboratories for Materials Science and Technology Ueberlandstrasse 129 Dübendorf8600 Switzerland Department of Physics University of Basel Klingelbergstrasse 82 Basel4056 Switzerland State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China

The growing market and massive use of Internet of Things nodes is placing unprecedented demands of energy efficient hardware for edge computing and microwave devices. In particular, magnetic tunnel junctions (MTJs), as main building blocks of spintronic microwave technology, can offer a path for the development of compact and high-performance microwave detectors. On the other hand, the fascinating field of skyrmionics is bridging together concepts from topology and spintronics. Here, we show the proof-of-concept of the direct electrical excitation and detection of the dynamics of a topological protected magnetic texture, i.e. skyrmion at room temperature and for a wide region of applied fields, including the zero-field case. This topological spin-torque diode is realized with an MTJ on top of a skyrmionic material. Quantitative Magnetic Force Microscopy measurements are employed to confirm the existence of a single skyrmion in the MTJ free layer. Spin-torque diode electrical measurements show the electrical excitation via spin-transfer torque (STT) of a skyrmion resonant mode with frequencies near 4 GHz and a selectivity one order of magnitude smaller than the uniform modes excited in the same device. Micromagnetic simulations identify these dynamics with the excitation of the breathing mode and point out the role of thickness dependent magnetic parameters (magnetic anisotropy field and Dzyaloshinkii–Moriya interaction) in both stabilizing and exciting the magnetic skyrmions. This work marks a milestone for the development of topological spin-torque diodes. © 2024, CC BY.

关键词： Magnetic anisotropy

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches

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computers in Biology and Medicine 2025年 190卷 110044-110044页

作者： Saif, Ahmed Islam, Md. Tarikul Raihan, Md. Obayed Yousefi, Niloofar Rahman, Md. Ajijur Faridi, Hafeez Hasan, Al Riyad Hossain, Mirza Mahfuj Saleem, Rasha Mohammed Albadrani, Ghadeer M. Al-Ghadi, Muath Q. Ahasan Setu, Md. Ali Kamel, Mohamed Abdel-Daim, Mohamed M. Aktaruzzaman, Md. Department of Pharmacy Faculty of Science University of Rajshahi Rajshahi6205 Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Jashore7408 Bangladesh Department of Pharmaceutical Sciences College of Health Sciences and Pharmacy Chicago State University ChicagoIL United States Department of Industrial Engineering and Management Systems University of Central Florida USA OrlandoFL United States Department of Pharmacy Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Laboratory Medicine Faculty of Applied Medical Sciences Al-Baha University Al-Baha65431 Saudi Arabia Department of Biology College of Science Princess Nourah bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Microbiology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Medicine and Infectious Diseases Faculty of Veterinary Medicine Cairo University Giza12211 Egypt Department of Pharmaceutical Sciences Pharmacy Program Batterjee Medical College P.O. Box 6231 Jeddah21442 Saudi Arabia Pharmacology Department Faculty of Veterinary Medicine Suez Canal University Ismailia41522 Egypt

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein. © 2025

关键词： Gene expression

Efficient training of spiking neural networks with temporally-truncated local backpropagation through time

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arXiv 2021年

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia CECS University of California-Irvine IrvineCA92612 United States

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Algebraic Structure of the Weak Stage Order Conditions for Runge-Kutta Methods

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arXiv 2022年

作者： Biswas, Abhijit Ketcheson, David Seibold, Benjamin Shirokoff, David Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Mathematics Temple University 1805 North Broad Street PhiladelphiaPA19122 United States Department of Mathematical Sciences New Jersey Institute of Technology University Heights NewarkNJ07102 United States

Runge-Kutta (RK) methods may exhibit order reduction when applied to stiff problems. For linear problems with time-independent operators, order reduction can be avoided if the method satisfies certain weak stage order (WSO) conditions, which are less restrictive than traditional stage order conditions. This paper outlines the first algebraic theory of WSO, and establishes general order barriers that relate the WSO of a RK scheme to its order and number of stages for both fully-implicit and DIRK schemes. It is shown in several scenarios that the constructed bounds are sharp. The theory characterizes WSO in terms of orthogonal invariant subspaces and associated minimal polynomials. The resulting necessary conditions on the structure of RK methods with WSO are then shown to be of practical use for the construction of such *** Codes 65L04, 65L20, 65M12 © 2022, CC BY.

关键词： Runge Kutta methods

DESIGN OF DIRK SCHEMES WITH HIGH WEAK STAGE ORDER

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arXiv 2022年

Runge-Kutta (RK) methods may exhibit order reduction when applied to certain stiff problems. While fully implicit RK schemes exist that avoid order reduction via high-stage order, DIRK (diagonally implicit Runge-Kutta) schemes are practically important due to their structural simplicity;however, these cannot possess high stage order. The concept of weak stage order (WSO) can also overcome order reduction, and it is compatible with the DIRK structure. DIRK schemes of WSO up to 3 have been proposed in the past, however, based on a simplified framework that cannot be extended beyond WSO 3. In this work a general theory of WSO is employed to overcome the prior WSO barrier and to construct practically useful high-order DIRK schemes with WSO 4 and above. The resulting DIRK schemes are stiffly accurate, L-stable, have optimized error coefficients, and are demonstrated to perform well on a portfolio of relevant ODE and PDE test problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Runge Kutta methods

Quaternion factorization machines: A lightweight solution to intricate feature interaction modelling

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arXiv 2021年

作者： Chen, Tong Yin, Hongzhi Zhang, Xiangliang Huang, Zi Wang, Yang Wang, Meng The School of Information Technology and Electrical Engineering The University of Queensland Australia The Machine Intelligence and Knowledge Engineering Laboratory King Abdullah University of Science and Technology Saudi Arabia The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained IoT and edge devices. In this paper, we move beyond the traditional real space where most deep FM-based models are defined, and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions, but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods. Copyright © 2021, The Authors. All

关键词： Large dataset