检索结果-内蒙古大学图书馆

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic programming

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dvorkin, Vladimir Fioretto, Ferdinando Van Hentenryck, Pascal Pinson, Pierre Kazempour, Jalal Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48103 United States Department of Computer Science University of Virginia CharlottesvilleVA22903 United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Wind and Energy Systems Technical University of Denmark Lyngby2800 Denmark

Convex optimization finds many real-life applications, where – optimized on real data – optimization results may expose private data attributes (e.g., individual health records, commercial information), thus leading to privacy breaches. To avoid these breaches and formally guarantee privacy to optimization data owners, we develop a new privacy-preserving perturbation strategy for convex optimization programs by combining stochastic (chance-constrained) programming and differential privacy. Unlike standard noise-additive strategies, which perturb either optimization data or optimization results, we express the optimization variables as functions of the random perturbation using linear decision rules;we then optimize these rules to accommodate the perturbation within the problem’s feasible region by enforcing chance constraints. This way, the perturbation is feasible and makes different, yet adjacent in the sense of a given distance function, optimization datasets statistically similar in randomized optimization results, thereby enabling probabilistic differential privacy guarantees. The chance-constrained optimization additionally internalizes the conditional value-at-risk measure to model the tolerance towards the worst-case realizations of the optimality loss w.r.t. the non-private solution. We demonstrate the privacy properties of our perturbation strategy analytically and through optimization and machine learning applications. © 2022, CC BY.

关键词： Convex optimization

Streaming quantum state purification

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Childs, Andrew M. Fu, Honghao Leung, Debbie Li, Zhi Ozols, Maris Vyas, Vedang Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Perimeter Institute for Theoretical Physics 31 Caroline St. N. WaterlooONN2L 2Y5 Canada QuSoft University of Amsterdam Science Park 123 Amsterdam1098 XG Netherlands Concordia Institute of Information Systems Engineering Concordia University 1455 Blvd. De Maisonneuve Ouest MontrealQCH3G 1M8 Canada

Quantum state purification is the task of recovering a nearly pure copy of an unknown pure quantum state using multiple noisy copies of the state. This basic task has applications to quantum communication over noisy channels and quantum computation with imperfect devices, but has primarily been studied previously for the case of qubits. We derive an efficient purification procedure based on the swap test for qudits of any dimension, starting with any initial error parameter. Treating the initial error parameter and the dimension as constants, we show that our procedure has sample complexity asymptotically optimal in the final error parameter. Our protocol has a simple recursive structure that can be applied when the states are provided one at a time in a streaming fashion, requiring only a small quantum memory to implement. © 2023, CC BY.

关键词： Quantum communication

Performance Analysis of Model Predictive Intersection Control for Autonomous Vehicles ⁎

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2021年第2期54卷 240-245页

作者： András Mihály Zsófia Farkas Bede Zsuzsanna Péter Gáspár Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 H-1111 Budapest Hungary

The paper focuses on the control challenge of intersections related to the appearance of autonomous vehicles on the roads, which established mixed traffic situations with human-driven vehicles or scenarios with only autonomous vehicles. The goal of the research is to control autonomous vehicles by Model Predictive Control method to guarantee the collision-free passage at the intersection. Generally the outcome of a traffic situation can be varied by human-driven vehicles and fully automated vehicles. Therefore the results of the proposed coordination method used for a given intersection scenario is compared to solution of human-driven vehicles. For the comparison the simulation examples were made in VISSIM and CarSim simulation environments.

关键词： intersection control optimization model predictive control autonomous vehicle control

Features for the 0-1 knapsack problem based on inclusionwise maximal solutions

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jooken, Jorik Leyman, Pieter De Causmaecker, Patrick Department of Computer Science KU Leuven Kulak Etienne Sabbelaan 53 Kortrijk8500 Belgium Department of Industrial Systems Engineering and Production Design Technologiepark Zwijnaarde 46 Zwijnaarde9052 Belgium Industrial Systems Engineering Flanders Make@UGent Sint-Martens-Latemlaan 2B Kortrijk8500 Belgium Department of Engineering Management University of Antwerp Prinsstraat 13 Antwerp2000 Belgium

Decades of research on the 0-1 knapsack problem led to very efficient algorithms that are able to quickly solve large problem instances to optimality. This prompted researchers to also investigate whether relatively small problem instances exist that are hard for existing solvers and investigate which features characterize their hardness. Previously the authors proposed a new class of hard 0-1 knapsack problem instances and demonstrated that the properties of so-called inclusionwise maximal solutions (IMSs) can be important hardness indicators for this class. In the current paper, we formulate several new computationally challenging problems related to the IMSs of arbitrary 0-1 knapsack problem instances. Based on generalizations of previous work and new structural results about IMSs, we formulate polynomial and pseudopolynomial time algorithms for solving these problems. From this we derive a set of 14 computationally expensive features, which we calculate for two large datasets on a supercomputer in approximately 540 CPU-hours. We show that the proposed features contain important information related to the empirical hardness of a problem instance that was missing in earlier features from the literature by training machine learning models that can accurately predict the empirical hardness of a wide variety of 0-1 knapsack problem instances. Using the instance space analysis methodology, we also show that hard 0-1 knapsack problem instances are clustered together around a relatively dense region of the instance space and several features behave differently in the easy and hard parts of the instance space. Copyright © 2022, The Authors. All rights reserved.

关键词： Hardness

3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Lu, Dening Xie, Qian Xu, Linlin Li, Jonathan The Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada The the Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom

Although accurate and fast point cloud classification is a fundamental task in 3D applications, it is difficult to achieve this purpose due to the irregularity and disorder of point clouds that make it challenging to achieve effective and efficient global discriminative feature learning. Lately, 3D Transformers have been adopted to improve point cloud processing. Nevertheless, massive Transformer layers tend to incur huge computational and memory costs. This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer. Our method has two main modules operating on the downsampling point sets, and each module consists of a multi-scale local feature aggregating (LFA) block and a global feature learning (GFL) block, which are implemented by using Graph Convolution and Transformer respectively. We also conduct a detailed investigation on a series of Transformer variants to explore better performance for our network. Various experiments on ModelNet40 demonstrate that our method achieves state-of-the-art classification performance, in terms of both accuracy and efficiency. Our code will be publicly available. © 2022, CC BY-NC-ND.

关键词： Convolution

Dynamics of social balance on networks: The emergence of multipolar societies

学校读者我要写书评

暂无评论

Physical Review E 2021年第3期104卷 034303-034303页

作者： Pouya Manshour Afshin Montakhab Physics Department Persian Gulf University Bushehr 75169 Iran Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 2 182 07 Prague 8 Czech Republic Physics Department Shiraz University Shiraz 71454 Iran

Within the context of social balance theory, much attention has been paid to the attainment and stability of unipolar or bipolar societies. However, multipolar societies are commonplace in the real world, despite the fact that the mechanism of their emergence is much less explored. Here, we investigate the evolution of a society of interacting agents with friendly (positive) and enmity (negative) relations into a final stable multipolar state. Triads are assigned energy according to the degree of tension they impose on the network. Agents update their connections to decrease the total energy (tension) of the system, on average. Our approach is to consider a variable energy ε∈[0,1] for triads which are entirely made of negative relations. We show that the final state of the system depends on the initial density of the friendly links ρ0. For initial densities greater than an ε-dependent threshold ρ0c(ε), a unipolar (paradise) state is reached. However, for ρ0≤ρ0c(ε), multipolar and bipolar states can emerge. We observe that the number of stable final poles increases with decreasing ε where the first transition from bipolar to multipolar society occurs at ε*≈0.67. We end the paper by providing a mean-field calculation that provides an estimate for the critical (ε dependent) initial positive link density, which is consistent with our simulations.

关键词： Collective behavior in networks Evolving networks Phase transitions Social networks

Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies

学校读者我要写书评

暂无评论

Procedia computer science 2021年 192卷 3922-3931页

作者： Beata Zielosko Urszula Stańczyk University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms.

关键词： Discretisation Decision rule Rule length Support Attribute Rough Sets

COMPUTATIONAL COMPLEXITY OF DECOMPOSING A SYMMETRIC MATRIX AS A SUM OF POSITIVE SEMIDEFINITE AND DIAGONAL MATRICES

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tunçel, Levent Vavasis, Stephen A. Xu, Jingye Department of Combinatorics and Optimization Faculty of Mathematics University of Waterloo WaterlooONN2L 3G1 Canada School of Industrial and Systems Engineering Georgia Institute of Technology United States David R. Cheriton School of Computer Science The Department of Statistics and Actuarial Science Faculty of Mathematics University of Waterloo WaterlooONN2L 3G1 Canada

We study several variants of decomposing a symmetric matrix into a sum of a low-rank positive semidefinite matrix and a diagonal matrix. Such decompositions have applications in factor analysis and they have been studied for many decades. On the one hand, we prove that when the rank of the positive semidefinite matrix in the decomposition is bounded above by an absolute constant, the problem can be solved in polynomial time. On the other hand, we prove that, in general, these problems as well as their certain approximation versions are all NP-hard. Finally, we prove that many of these low-rank decomposition problems are complete in the first-order theory of the reals;i.e., given any system of polynomial equations, we can write down a low-rank decomposition problem in polynomial time so that the original system has a solution iff our corresponding decomposition problem has a feasible solution of certain (lowest) *** Codes 65F55, 68Q17, 62H25 Copyright © 2022, The Authors. All rights reserved.

关键词： Polynomial approximation