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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andreas Anastasiou Angelos Zacharia Savvas Papaioannou Panayiotis Kolios Christos G. Panayiotou Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence (KIOS CoE) University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work introduces a cooperative inspection system designed to efficiently control and coordinate a team of distributed heterogeneous UAV agents for the inspection of 3D structures in cluttered, unknown spaces. Our proposed approach employs a two-stage innovative methodology. Initially, it leverages the complementary sensing capabilities of the robots to cooperatively map the unknown environment. It then generates optimized, collision-free inspection paths, thereby ensuring comprehensive coverage of the structure's surface area. The effectiveness of our system is demonstrated through qualitative and quantitative results from extensive Gazebo-based simulations that closely replicate real-world inspection scenarios, highlighting its ability to thoroughly inspect real-world-like 3D structures.

关键词： Solid modeling Three-dimensional displays Robot kinematics Inspection Robot sensing systems Autonomous aerial vehicles Control systems

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Aggregation-induced dual-color luminogen with enhanced chiroptical property

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Science China Chemistry 2022年第9期65卷 1679-1680页

作者： Wen Su Fanglong Yuan CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China Department of Physics Chemistryand Biology(IFM)Linkoping UniversityLinkoping 58183Sweden Department of Electrical and Computer Engineering University of TorontoTorontoM5S 2E8 OntarioCanada

Aggregation-induced emission(AIE),which was first proposed by Tang et al.[1]in 2001,describes a photophysical phenomenon that organic molecules can show much stronger light emission in aggregated state than that of si... 详细信息

关键词： state property diluted

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Toward Improving Binary Program Comprehension via Embodied Immersion: A Survey

arXiv

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arXiv 2024年

作者： Brown, Dennis Mulder, Emily Mulder, Samuel Auburn University Department of Computer Science and Software Engineering 1301 Shelby Center AuburnAL36849 United States

Binary program comprehension is critical for many use cases but is difficult, suffering from compounded uncertainty and lack of full automation. We seek methods to improve the effectiveness of the human-machine joint cognitive system performing binary PC. We survey three research areas to perform an indirect cognitive task analysis: cognitive models of the PC process, related elements of cognitive theory, and applicable affordances of virtual reality. Based on common elements in these areas, we identify three overarching themes: enhancing abductive iteration, augmenting working memory, and supporting information organization. These themes spotlight several affordances of VR to exploit in future studies of immersive tools for binary PC. Copyright © 2024, The Authors. All rights reserved.

关键词： Reverse engineering

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models

arXiv

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arXiv 2024年

作者： Shi, Luohe Yao, Yao Li, Zuchao Zhang, Lefei Zhao, Hai National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without changing model parameters. However, this approach involves trade-offs, such as slower inference speed and increased space occupancy. PEFT assists the model in adapting to tasks through minimal parameter modifications, but the training process still demands high hardware requirements, even with a small number of parameters involved. To address these challenges, we propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning, maintaining low inference costs. RTD constructs a reference datastore from the provided training examples and optimizes the LLM's final vocabulary distribution by flexibly selecting suitable references based on the input, resulting in more trustable responses and enabling the model to adapt to downstream tasks at a low cost. Experimental evaluations on various LLMs using different benchmarks demonstrate that RTD establishes a new paradigm for augmenting models to downstream tasks. Furthermore, our method exhibits strong orthogonality with traditional methods, allowing for concurrent usage. Our code can be found at https://***/ShiLuohe/ReferenceTrustableDecoding. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems Science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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Approximate k-Nearest Neighbor Query over Spatial Data Federation 28th

Approximate k-Nearest Neighbor Query over Spatial Data Fede...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Kaining Tong, Yongxin Shi, Yexuan Zeng, Yuxiang Xu, Yi Chen, Lei Zhou, Zimu Xu, Ke Lv, Weifeng Zheng, Zhiming State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering and Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou China City University of Hong Kong Hong Kong

ISBN: (纸本)9783031306365

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Dynamically Reconfigurable Broadband SP3T Switch Powered by WPT for Antenna Switching Applications 52

Dynamically Reconfigurable Broadband SP3T Switch Powered by ...

引用

52nd European Microwave Conference, EuMC 2022

作者： Bilal, Asif Quddious, Abdul Rezaeieh, Sasan Ahdi Iezekiel, Stavros Antoniades, Marco A. Emphasis Research Centre University of Cyprus Cyprus University of Cyprus Department of Electrical and Computer Engineering Cyprus Kios Research Center for Excellence University of Cyprus Cyprus School of Information Technology and Electrical Engineering The University of Queensland Australia Ryerson University Department of Electrical Computer & Biomedical Engineering Canada

ISBN: (数字)9782874870699

ISBN: (纸本)9782874870699

In this work, a broadband single pole triple throw (SP3T) switch that operates between a frequency range of 0.02 - 6 GHz is reconfigured by means of wireless power transfer (WPT) through an energy harvesting (EH) circuit. The WPT is achieved by designing three rectennas working at 2.3 GHz, 2.45 GHz, and 2.6 GHz. Each rectenna consists of a low-power RF rectifier circuit associated with a distinct antenna element and a commercially available e-peas AEM30940 power management unit (PMU) that is able to provide an output of 1.8 V - 4.1 V with cold start-up from 380 mV and 3 μW input power. The PMU also consists of a storage capacitor where the energy from the low power received signal is stored. The three input pins of the SP3T switch require a driving voltage of 1.8 V to actuate its RF port. This voltage is provided by the designed rectennas through the PMU, and we can actuate the switch with a very low power (3 μW) received signal. The rectennas are designed to provide the 380 mV between -12 dBm to -10 dBm received power with a conversion efficiency of approx. 40 %for each rectenna. The proposed method to wirelessly enable the SP3T switch with a low-power RF signal can provide a breakthrough to reconfigure microwave devices and antennas for a broad range of applications, and also paves the way not only for the generation of clean energy, but also for its use as an alternative to batteries. © 2022 European Microwave Association (EuMA).

关键词： Energy harvesting

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Keep the Cost Down: A Review on Methods to Optimize LLM’s KV Cache Consumption

arXiv

引用

arXiv 2024年

作者： Luohe, Shi Hongyi, Zhang Yao, Yao Zuchao, Li Hai, Zhao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Large Language Models (LLMs), epitomized by ChatGPT’s release in late 2022, have revolutionized various industries with their advanced language comprehension. However, their efficiency is challenged by the Transformer architecture’s struggle with handling long texts. KV Cache has emerged as a pivotal solution to this issue, converting the time complexity of token generation from quadratic to linear, albeit with increased GPU memory overhead proportional to conversation length. With the development of the LLM community and academia, various KV Cache compression methods have been proposed. In this review, we dissect the various properties of KV Cache and elaborate on various methods currently used to optimize the KV Cache space usage of LLMs. These methods span the pre-training phase, deployment phase, and inference phase, and we summarize the commonalities and differences among these methods. Additionally, we list some metrics for evaluating the long-text capabilities of large language models, from both efficiency and capability perspectives. Our review thus sheds light on the evolving landscape of LLM optimization, offering insights into future advancements in this dynamic field. Links to the papers mentioned in this review can be found in our Github Repo https://***/zcli-charlie/Awesome-KV-Cache. Copyright © 2024, The Authors. All rights reserved.

关键词： Cache memory

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Not All Fabrics Are Created Equal: Exploring eFPGA Parameters for IP Redaction

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第10期31卷 1459-1471页

作者： Bhandari, Jitendra Moosa, Abdul Khader Thalakkattu Tan, Benjamin Pilato, Christian Gore, Ganesh Tang, Xifan Temple, Scott Gaillardon, Pierre-Emmanuel Karri, Ramesh New York University Center for Cybersecurity New YorkNY11201 United States University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada Politecnico di Milano Informazione e Bioingegneria Dipartimento di Elettronica Milan20133 Italy The University of Utah Laboratory for NanoIntegrated Systems Electrical and Computer Engineering Department Salt Lake CityUT84112 United States

Semiconductor design houses rely on third-party foundries to manufacture their integrated circuits (ICs). While this trend allows them to tackle fabrication costs, it introduces security concerns as external (and potentially malicious) parties can access critical parts of the designs and steal or modify the intellectual property (IP). Embedded field-programmable gate array (eFPGA) redaction is a promising technique to protect critical IPs of an ASIC by redacting (i.e., removing) critical parts and mapping them onto a custom reconfigurable fabric. Only trusted parties will receive the correct bitstream to restore the redacted functionality. While previous studies imply that using an eFPGA is a sufficient condition to provide security against IP threats like reverse-engineering, whether this truly holds for all eFPGA architectures is unclear, thus motivating the study in this article. We examine the security of eFPGA fabrics generated by varying different FPGA design parameters. We characterize the power, performance, and area (PPA) characteristics and evaluate each fabric's resistance to Boolean satisfiability (SAT)-based bitstream recovery. Our results encourage designers to work with custom eFPGA fabrics rather than off-The-shelf commercial FPGAs and reveals that only considering a redaction fabric's bitstream size is inadequate for gauging security. © 1993-2012 IEEE.

关键词： Fabrication

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Reference trustable decoding: a training-free augmentation paradigm for large language models 24

Reference trustable decoding: a training-free augmentation p...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Luohe Shi Yao Yao Zuchao Li Lefei Zhang Hai Zhao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan P. R. China Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9798331314385

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