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arXiv 2024年

作者： Jaberipur, Ghassem Nadimi, Bardia Kazemi, R. Lee, Jeong-A The Brain Pool Program for Chosun University Gwangju Korea Republic of The University of South Florida TampaFL United States Computer Engineering The Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran The Department of Computer Engineering Chosun University Korea Republic of

Modulo-(2q + 2q−1 ± 1) adders have recently been implemented using the regular parallel prefix (RPP) architecture, matching the speed of the widely used modulo-(2q ± 1) RPP adders. Consequently, we introduce a new moduli set τ+ = {22q+1, 2q + 2q−1 ± 1}, with over (2q+2)× dynamic range and adder speeds comparable to the conventional τ = {2q, 2q ± 1} set. However, to fully leverage τ+ in residue number system applications, a complete set of circuitries is necessary. This work focuses on the design and implementation of the forward and reverse converters for τ+. These converters consist of four and seven levels of carry-save addition units, culminating in a final modulo-(2q + 2q−1 ± 1) and modulo-(22q+1 + 22q−2 − 1) adder, respectively. Through analytical evaluations and circuit simulations, we demonstrate that the overall performance of a sequence of operations-including residue generation, k additions, and reverse conversion-using τ+ surpasses that of τ when k exceeds a certain practical threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Adders

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

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Monte Carlo Tree Search for Behavior Planning in Autonomous Driving

Monte Carlo Tree Search for Behavior Planning in Autonomous ...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Qianfeng Wen Zhongyi Gong Lifeng Zhou Zhongshun Zhang Department of Computer Science University of Toronto Canada Technique Expert at Bosch (China) Investment Ltd. Shanghai China Department of Electrical and Computer Engineering Drexel University Philadelphia Staff Engineer at Horizon Robotics Shanghai China

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

The integration of autonomous vehicles into urban and highway environments necessitates the development of robust and adaptable systems for behavior planning and decision-making. This study introduces a Monte Carlo Tree Search (MCTS)-based algorithm designed to navigate the challenges of autonomous driving. Our core objective is to exploit the balance between exploration and exploitation offered by MCTS to enable intelligent driving decisions in complex scenarios. We present a Monte Carlo Tree Search (MCTS)-based algorithm specifically designed for the nuanced requirements of autonomous driving. This approach incorporates meticulously devised cost functions that address safety, comfort, and efficiency, seamlessly integrated into the MCTS framework. Our algorithm demonstrates its effectiveness in guiding autonomous vehicles through real-world derived scenarios with traffic con-gestion, such as navigating roundabouts, intricate intersections, dynamic merge/splits, executing unprotected left turns, and managing cut-ins and ramps. Qualitative examples highlight the algorithm's proficiency in making diverse driving decisions, including lane changes, acceleration, and deceleration. Furthermore, the quantitative analysis underscores the efficiency of our approach, with the ability to deliver decision-making results (including longitudinal and lateral decisions) within less than one second, showcasing its potential for real-time, online planning. The success rate across various scenarios further attests to the algorithm's robustness, showcasing its ability to handle complex driving decisions and reinforcing its effectiveness and reliability.

关键词： Monte Carlo methods Navigation Heuristic algorithms Decision making Search problems Planning Safety Security Vehicle dynamics Autonomous vehicles

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Woosung Kang Jinkyu Lee Youngmoon Lee Sangeun Oh Kilho Lee Hoon Sung Chwa Dept. of Electrical Engineering and Computer Science DGIST Republic of Korea Dept. of Computer Science and Engineering Sungkyunkwan University (SKKU) Republic of Korea Dept. of Robotics Hanyang University Republic of Korea Department of Software & Computer Engineering Ajou University Republic of Korea School of AI Convergence Soongsil University Republic of Korea

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.

关键词： Scheduling algorithms Memory management Graphics processing units Artificial neural networks Machine learning Drives Real-time systems

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Robot Person Following Under Partial Occlusion

Robot Person Following Under Partial Occlusion

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hanjing Ye Jieting Zhao Yaling Pan Weinan Cherr Li He Hong Zhang Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology (SUSTech) SUSTech Biomimetic and Intelligent Robotics Lab Guangdong University of Technology

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of hislher joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning

arXiv

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arXiv 2023年

作者： Kalaria, Dvij Lin, Qin Dolan, John M. The Robotics Institute Carnegie Mellon University United States The Electrical Engineering and Computer Science Department Cleveland State University United States

Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also actively looking for strategies to overtake/stay ahead of the opponent. In this work we propose a head-to-head racing environment for reinforcement learning which accurately models vehicle dynamics. Some previous works have tried learning a policy directly in the complex vehicle dynamics environment but have failed to learn an optimal policy. In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy. We also propose a control barrier function-based safe reinforcement learning algorithm to enforce the safety of the agent in a more effective way while not compromising on optimality. © 2023, CC BY.

关键词： Reinforcement learning

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Computing optimal nash equilibria in multiplayer games 23

Computing optimal nash equilibria in multiplayer games

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Youzhi Zhang Bo An V. S. Subrahmanian Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences School of Computer Science and Engineering Nanyang Technological University Singnapore Department of Computer Science Northwestern University Evanston

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. For example, when there is a team of players independently playing against an adversary in a game (e.g., several groups in a forest trying to interdict illegal loggers in green security games), these team members may need to find an NE minimizing the adversary's utility. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we first propose a general framework for this formulation based on a set of correlation plans. We then develop a novel algorithm called CRM based on this framework, which uses Correlation plans with their Relations to restrict the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to reduce the number of bilinear terms. We show that our techniques can significantly reduce the time complexity, and CRM can be several orders of magnitude faster than the state-of-the-art baseline.

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PhageBox: An Open Source Digital Microfluidic Extension with Applications for Phage Discovery

TechRxiv

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TechRxiv 2023年

作者： Albin, Dreycey Buecherl, Lukas Kochavi, Eitan Niehaus, Elise Novack, Sasha Uragoda, Shenali Myers, Chris J. Alistar, Mirela Department of Computer Science CU Boulder BoulderCO United States Biomedical Engineering Program CU Boulder United States Department of Electrical Computer and Energy Engineering and Biomedical Engineering Program CU Boulder United States ATLAS Institute The Department of Computer Science CU Boulder BoulderCO United States

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox— an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox’s efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ±0.2◦C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating X174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document. (GitHub: https://***/Dreycey/PhageBox) © 2023, CC BY-SA.

关键词： Digital microfluidics

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

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Procedia computer science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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