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arXiv 2024年

作者： Song, Ziying Zhang, Guoxin Xie, Jun Liu, Lin Jia, Caiyan Xu, Shaoqing Wang, Zhepeng School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang050018 China Lenovo Research Beijing100085 China State Key Laboratory of Internet of Things for Smart City Department of Electrome chanical Engineering University of Macau 999078 China

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies

Alleviating Barren Plateaus in Parameterized Quantum Machine...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Muhammad Kashif Muhammad Rashid Saif Al-Kuwari Muhammad Shafique Division of Engineering New York University eBrain Lab Abu Dhabi Abu Dhabi UAE Center for Quantum and Topological Systems NYUAD Research Institute New York University Abu Dhabi UAE Department of Computer Engineering Umm Al Qura University Makkah Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad bin Khalifa University Qatar

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, characterized by vanishing gradients with an increasing number of qubits, hinder optimization in quantum algorithms. In this paper, we analyze the impact of state-of-the-art parameter initialization strategies from classical machine learning in random PQCs from the aspect of BP phenomenon. Our investigation encompasses a spectrum of initialization techniques, including random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal methods. Empirical assessment reveals a pronounced reduction in variance decay of gradients across all these methodologies compared to the randomly initialized PQCs. Specifically, the Xavier initialization technique outperforms the rest, showing a 62% improvement in variance decay compared to the random initialization. The He, Lecun, and orthogonal methods also display improvements, with respective enhancements of 32 %, 28 %, and 26 %. This compellingly suggests that the adoption of these existing initialization techniques holds the potential to significantly amplify the training efficacy of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this effect, we employ the identified techniques to train QNNs for learning the identity function, effectively mitigating the adverse effects of BPs. The training performance, ranked from the best to the worst, aligns with the variance decay enhancement as outlined above. This paper underscores the role of tailored parameter initialization in mitigating the BP problem and eventually enhancing the training dynamics of QNNs.

关键词： Training Quantum algorithm Neural networks Qubit Machine learning Quantum circuit Optimization

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EasyControl: Transfer ControlNet to Video Diffusion for Controllable Video Generation and Interpolation

arXiv

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arXiv 2024年

作者： Wang, Cong Gu, Jiaxi Hu, Panwen Zhao, Haoyu Guo, Yuanfan Han, Jianhua Xu, Hang Liang, Xiaodan The School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China The School of Computer Science and Technology Fudan University Shanghai China Huawei Noah'ark Lab Shanghai201206 China DarkMatter AI Research Guangzhou511458 China Pengcheng Lab Shenzhen518000 China

Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. However, relying solely on text guidance for video generation has serious limitations, as videos contain much richer content than images, especially in terms of motion. This information can hardly be adequately described with plain text. Fortunately, in computer vision, various visual representations can serve as additional control signals to guide generation. With the help of these signals, video generation can be controlled in finer detail, allowing for greater flexibility for different applications. Integrating various controls, however, is nontrivial. In this paper, we propose a universal framework called EasyControl. By propagating and injecting condition features through condition adapters, our method enables users to control video generation with a single condition map. With our framework, various conditions including raw pixels, depth, HED, etc., can be integrated into different Unet-based pre-trained video diffusion models at a low practical cost. We conduct comprehensive experiments on public datasets, and both quantitative and qualitative results indicate that our method outperforms state-of-the-art methods. EasyControl significantly improves various evaluation metrics across multiple validation datasets compared to previous works. Specifically, for the sketch-to-video generation task, EasyControl achieves an improvement of 152.0 on FVD and 19.9 on IS, respectively, in UCF101 compared with VideoComposer. For fidelity, our model demonstrates powerful image retention ability, resulting in high FVD and IS in UCF101 and MSR-VTT compared to other image-to-video models. © 2024, CC BY.

关键词： Video analysis

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Efficient Methods for Approximating the Shapley Value for Asset Sharing in Energy Communities

arXiv

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arXiv 2022年

作者： Cremers, Sho Robu, Valentin Zhang, Peter Andoni, Merlinda Norbu, Sonam Flynn, David Amsterdam Netherlands Delft Netherlands Systems Power & Energy Research Group University of Glasgow Glasgow United Kingdom Smart System Research Group Heriot-Watt University Edinburgh United Kingdom Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O’Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower. © 2022, CC BY.

关键词： Digital storage

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A New Hybrid Cipher based on Prime Numbers Generation Complexity: Application in Securing 5G Networks

A New Hybrid Cipher based on Prime Numbers Generation Comple...

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ACS/IEEE International Conference on computer systems and Applications

作者： Adda Boualem Abdelkader Berrouachedi Marwane Ayaida Hisham Kholidy Elhadj Benkhelifa Department of Computer Science Ibn Khaldoun University Tiaret Algeria Paragraphe Lab University of Paris 8 Paris France Polytechnique Hauts-de-France CNRS Univ. Lille UMR 8520 - IEMN France CReSTIC EA 3804 Univ. Reims Champagne Ardenne France Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica New Yourk USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ UK

Today’s cellular networks (known as 2G, 3G, and 4G) provide a solid foundation for connecting things. The Internet of Things (IoT) is helping people live and work smarter and take full control of their lives. In addition to providing smart devices for home automation, IoT is also critical for businesses. The security of classical cryptosystems is characterized by their simple arithmetic complexity. This type uses; shifts, arrangements, permutations, and substitutions of letters, words, or phrases to encrypt a given message. Moreover, this kind of cryptosystem is easy to break, because the complexity of their scheme is convergent. On the other hand, the security of modern cryptosystems is characterized by complex arithmetic, using the concept of keys; private keys to encrypt and public keys to decrypt. This type is hard to break because of the complexity of their scheme being divergent. This paper aims to secure the 5G networks with a new variant of the classical Polybius Checkerboard Cipher (HPCC). The system of letter substitution is based on the complexity and the divergence of private key generation. Filling a magic square with primes is an NP-hard task, which shows the complexity and divergence of the strategy adopted in this variant.

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Biomimetic Frontend for Differentiable Audio Processing

arXiv

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arXiv 2024年

作者： Famularo, Ruolan Leslie Zotkin, Dmitry N. Shamma, Shihab A. Duraiswami, Ramani Perceptual Interfaces and Reality Lab. Computer Science & UMIACS University of Maryland College Park United States Department of Linguistics University of Maryland College Park United States Program in Neuroscience & Cognitive Science University of Maryland College Park United States Institute of Systems Research & Electrical and Computer Engineering University of Maryland College Park United States

While models in audio and speech processing are becoming deeper and more end-to-end, they as a consequence need expensive training on large data, and are often brittle. We build on a classical model of human hearing and make it differentiable, so that we can combine traditional explainable biomimetic signal processing approaches with deep-learning frameworks. This allows us to arrive at an expressive and explainable model that is easily trained on modest amounts of data. We apply this model to audio processing tasks, including classification and enhancement. Results show that our differentiable model surpasses black-box approaches in terms of computational efficiency and robustness, even with little training data. We also discuss other potential applications. © 2024, CC BY-NC-ND.

关键词： Deep learning

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A Novel Non-Terrestrial Networks Architecture: All Optical LEO Constellations with High-Altitude Ground Stations

arXiv

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arXiv 2024年

作者： Madoery, Pablo G. Fraire, Juan A. Finochietto, Jorge M. Yanikomeroglu, Halim Kurt, Gunes Karabulut Non-Terrestrial Networks Lab Department of Systems and Computer Engineering Carleton University Ottawa Canada Inria INSA Lyon CITI UR3720 Villeurbanne69621 France CONICET - Universidad Nacional de Cordoba Cordoba Argentina Poly-Grames Research Center Department of Electrical Engineering Polytechnique Montréal Montréal Canada

The emergence of low Earth orbit (LEO) satellite mega-constellations is dynamically transforming the space sector. While free-space optical (FSO) links efficiently facilitate inter-satellite data forwarding, they suffer from atmospheric/weather conditions in the space-to-ground link. This study delves into utilizing high-altitude platform stations (HAPS) as elevated relay stations strategically positioned above terrestrial ground stations. We introduce the concept of high-altitude ground stations (HAGS), an innovative approach to enabling the development of all optical LEO satellite constellations. The first contribution is an analysis of the HAGS-based network architecture where the LEO spacecraft only hosts FSO transceivers. Secondly, we execute an extensive simulation campaign to determine the gain of HAGS, including a new equivalency model with the traditional ground station approach. Finally, we examine the research challenges of implementing HAGS-based, all optical LEO mega-constellations. © 2024, CC BY.

关键词： Network architecture

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Generalized Coverage Criteria for Combinatorial Sequence Testing

arXiv

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arXiv 2022年

作者： Elyasaf, Achiya Farchi, Eitan Margalit, Oded Weiss, Gera Weiss, Yeshayahu Software and Information Systems Engineering Department Ben-Gurion University of the Negev Israel Computer Science Department Ben-Gurion University of the Negev Israel IBM Haifa Research Lab

We present a new model-based approach for testing systems that use sequences of actions and assertions as test vectors. Our solution includes a method for quantifying testing quality, a tool for generating high-quality test suites based on the coverage criteria we propose, and a framework for assessing risks. For testing quality, we propose a method that specifies generalized coverage criteria over sequences of actions, which extends previous approaches. Our publicly available tool demonstrates how to extract effective test suites from test plans based on these criteria. We also present a Bayesian approach for measuring the probabilities of bugs or risks, and show how this quantification can help achieve an informed balance between exploitation and exploration in testing. Finally, we provide an empirical evaluation demonstrating the effectiveness of our tool in finding bugs, assessing risks, and achieving coverage. © 2022, CC BY.

关键词： Risk assessment

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning

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Big Data Mining and Analytics 2024年第4期7卷 1065-1083页

作者： Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083Chinaand also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland AdministrationBeijing 100083China Advanced Institute of Natural Sciences Beijing Normal UniversityZhuhai 519087Chinaand also with the Guangdong Key Lab of AI and Multi-Modal Data ProcessingBNU-HKBU United International CollegeZhuhai 519087China Department of Computer Information Systems Texas A&M University-Central TexasKilleenTX 76549USA

Edge Computing(EC)pushes computational capability to the Terrestrial Devices(TDs),providing more efficient and faster computing *** Aerial Vehicles(UAVs)equipped with EC servers can be flexibly deployed,even in complex terrains,to provide mobile computing services at all ***,UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication ***,the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees,leading to sub-optimal data transmission *** surmount this issue,Reconfigurable Intelligent Surfaces(RISs)emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and *** this paper,we consider the RISs-assisted multi-UAVs collaborative edge Computing Network(RUCN)in urban environment,in which we study the computational offloading *** goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs,and the phase shifts of *** is worth noting that this problem has been formally established as ***,we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm(UTRP-DDPG)to solve this complex optimization *** results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter ***,the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2%compared to DQN algorithm.

关键词： edge computing computing offloading Unmanned Aerial Vehicle(UAV) Reconfigurable Intelligent Surface(RIS) deep reinforcement learning

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