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Communication-Efficient Distributed Cooperative Learning With Compressed Beliefs

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2022年第3期9卷 1215-1226页

作者： Toghani, Mohammad Taha Uribe, Cesar A. Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

In this article, we study the problem of distributed cooperative learning, where a group of agents seeks to agree on a set of hypotheses that best describes a sequence of private observations. In the scenario where the set of hypotheses is large, we propose a belief up-date rule where agents share compressed (either sparse or quantized) beliefs with an arbitrary positive compression rate. Our algorithm leverages a unified communication rule that enables agents to access wide-ranging compression operators as black-box modules. We prove the almost sure asymptotic convergence of beliefs on the set of optimal hypotheses. Additionally, we show a nonasymptotic, explicit, and linear concentration rate in probability of the beliefs on the optimal hypothesis set. We provide numerical experiments to illustrate the communication benefits of our method. The simulation results show that the number of transmitted bits can be reduced to 5%-10% of the noncompressed method in the studied scenarios.

关键词： algorithm design and analysis Bayesian update compressed communication distributed algorithms

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Multi-Objective Material Generation algorithm (MOMGA) for Optimization Purposes

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IEEE ACCESS 2022年 10卷 107095-107115页

作者： Nouhi, Behnaz Khodadadi, Nima Azizi, Mahdi Talatahari, Siamak Gandomi, Amir H. Univ Tabriz Dept Math Sci Tabriz 51666 Iran Florida Int Univ Coll Engn & Comp Dept Civil & Environm Engn Miami FL 33199 USA Univ Tabriz Dept Civil Engn Tabriz 51666 Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary RMIT Univ Sch Engn Melbourne Vic 3001 Australia

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the Material Generation algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on Evolutionary Computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.

关键词： Metaheuristics Evolutionary computation Chemical reactions Chemical compounds Heuristic algorithms algorithm design and analysis Machine learning algorithms Material generation algorithm multi-objective optimization real-world engineering problems competitions on evolutionary computation

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Local Community Detection: A Survey

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IEEE ACCESS 2022年 10卷 110701-110726页

作者： Baltsou, Georgia Christopoulos, Konstantinos Tsichlas, Konstantinos Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54636 Greece Univ Patras Dept Comp Engn & Informat Patras 26504 Greece

Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods.

关键词： Liquid crystal displays Social networking (online) Image edge detection Taxonomy Symbols Proteins Market research algorithm design and analysis Distance measurement Community networks algorithms community detection local survey

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Solution Density and Search Strategy in Narrative Generation

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IEEE TRANSACTIONS ON GAMES 2022年第4期14卷 715-724页

作者： Siler, Cory Ware, Stephen G. Univ Kentucky Narrat Intelligence Lab Lexington KY 40506 USA

Intelligent interactive narrative systems coordinate a cast of nonplayer characters to make the overall story experience meaningful for the player. Narrative generation involves a tradeoff between plot-structure requirements and quality of character behavior, as well as computational efficiency. We study this tradeoff using the example of benchmark problems for narrative planning algorithms. A typical narrative planning problem calls for a sequence of actions that leads to an overall plot goal being met, while also requiring each action to respect constraints that create the appearance of character autonomy. We consider simplified solution definitions that enforce only plot requirements or only character requirements, and we measure how often each of these definitions leads to a solution that happens to meet both types of requirements-i.e., the density with which narrative plans occur among plot- or character-requirement-satisfying sequences. We then investigate whether solution densities can guide the selection of narrative planning algorithms. We compare the performance of two search strategies: one that satisfies plot requirements first and checks character requirements afterward, and one that continuously verifies character requirements. Our results show that comparing solution densities does not by itself predict which of these search strategies will be more efficient in terms of search nodes visited, suggesting that other important factors exist. We discuss what some of these factors could be. Our work opens further investigation into characterizing narrative planning algorithms and how they interact with specific domains. The results also highlight the diversity and difficulty of solving narrative planning problems.

关键词： Intelligent systems algorithm design and analysis algorithms artificial intelligence computational and artificial intelligence computer applications computers and information processing mathematics modeling systems engineering and theory

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Fast spatial autocorrelation

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第4期64卷 919-941页

作者： Amgalan, Anar Mujica-Parodi, L. R. Skiena, Steven S. SUNY Stony Brook Dept Phys & Astron Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's I and Geary's C are widely used to measure spatial autocorrelation, they are slow: All popular methods run in Omega (n(2)) time, rendering them unusable for large datasets, or long time-courses with moderate numbers of points. We propose a new S-A statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate S-A for a variable with an input agglomeration order (available at https://***/aamgalan/spatial_autocorrelalion) . For a typical dataset of n approximate to 63, 000 points, our S-A autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's I and Geary's C. Through simulation studies, we demonstrate that SA identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's I or Geary's C. Finally, we prove several theoretical properties of S-A: namely that it behaves as a true correlation statistic and is invariant under addition or multiplication by a constant.

关键词： algorithm design and analysis Computational efficiency Autocorrelation Biomedical informatics Magnetic resonance Clustering algorithms

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Cryptographic Standards in the Post-Quantum Era

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IEEE SECURITY & PRIVACY 2022年第6期20卷 66-72页

作者： Moody, Dustin Robinson, Angela NIST Comp Secur Div Gaithersburg MD 20877 USA NIST Postquantum Cryptog Project Gaithersburg MD 20877 USA NIST Postquantum Cryptog Standardizat effort Gaithersburg MD 20877 USA

If large-scale quantum computers are ever built, they will compromise the security of many commonly used cryptographic algorithms. In response, the National Institute of Standards and Technology is in the process of s... 详细信息

关键词： Cryptography Standards algorithm design and analysis

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Robust and Adaptive Sequential Submodular Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 89-104页

作者： Tzoumas, Vasileios Jadbabaie, Ali Pappas, George J. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA MIT Inst Data Syst & Soc Cambridge MA 02139 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

Emerging applications of control, estimation, and machine learning, from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used across time. Therefore, many researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. By exploiting notions of discrete convexity, such as submodularity, the researchers have been able to provide scalable algorithms with provable suboptimality bounds. In this article, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem robust sequential submodular maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. However, in this article, we propose robust and adaptive maximization (RAM), the first scalable algorithm. RAM runs in an online fashion, adapting in every step to the history of failures. Also, it guarantees a near-optimal performance, even against any number of failures among the used elements. Particularly, RAM has both provable per-instance a priori bounds and tight and/or optimal a posteriori bounds. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.

关键词： Sensors Random access memory Optimization History Target tracking Greedy algorithms Actuators algorithm design and analysis approximation algorithms autonomous systems combinatorial mathematics control estimation fault-tolerant systems mobile robots multirobot systems

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On the analysis of Inexact Augmented Lagrangian Schemes for Misspecified Conic Convex Programs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 3981-3996页

作者： Aybat, Necdet Serhat Ahmadi, Hesam Shanbhag, Uday V. Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA Optym Gainesville FL 32607 USA

In this article, we consider the misspecified optimization problem of minimizing a convex function f(x;theta*) in x over a conic constraint set represented by h(x;theta*) is an element of K, where theta* is an unknown (or misspecified) vector of parameters, K is a closed convex cone, and h is affine in x. Suppose that theta* is unavailable but may be learnt by a separate process that generates a sequence of estimators theta(k), each of which is an increasingly accurate approximation of theta*. We develop a first-order inexact augmented Lagrangian (AL) scheme for computing an optimal solution x* corresponding to theta* while simultaneously learning theta*. In particular, we derive rate statements for such schemes when the penalty parameter sequence is either constant or increasing and derive bounds on the overall complexity in terms of proximal gradient steps when AL subproblems are inexactly solved via an accelerated proximal gradient scheme. Numerical results for a portfolio optimization problem with a misspecified covariance matrix suggest that these schemes perform well in practice, while naive sequential schemes may perform poorly in comparison.

关键词： algorithm design and analysis augmented lagrangian methods computational complexity misspecified optimization optimization methods

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Resilient Active Information Acquisition With Teams of Robots

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IEEE TRANSACTIONS ON ROBOTICS 2022年第1期38卷 244-261页

作者： Schlotfeldt, Brent Tzoumas, Vasileios Pappas, George J. Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

Emerging applications of collaborative autonomy, such as multitarget tracking, unknown map exploration, and persistent surveillance, require robots plan paths to navigate an environment while maximizing the information collected via on-board sensors. In this article, we consider such information acquisition tasks but in adversarial environments, where attacks may temporarily disable the robots' sensors. We propose the first receding horizon algorithm, aiming for robust and adaptive multirobot planning against any number of attacks, which we call Resilient Active Information acquisitioN (RAIN). RAIN calls, in an online fashion, a robust trajectory planning (RTP) subroutine that plans attack-robust control inputs over a look-ahead planning horizon. We quantify RTP's performance by bounding its suboptimality. We base our theoretical analysis on notions of curvature introduced in combinatorial optimization. We evaluate RAIN in three information acquisition scenarios: multitarget tracking, occupancy grid mapping, and persistent surveillance. The scenarios are simulated in C++ and a unity-based simulator. In all simulations, RAIN runs in real time, and exhibits superior performance against a state-of-the-art baseline information acquisition algorithm, even in the presence of a high number of attacks. We also demonstrate RAIN's robustness and effectiveness against varying models of attacks (worst case and random), as well as varying replanning rates.

关键词： Robots Task analysis Robot sensing systems Planning Sensors Rain Optimization algorithm design and analysis autonomous robots combinatorial mathematics multiagent systems reactive-sensor-based mobile planning robotics in hazardous fields

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Comprehensive analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第4期26卷 1847-1860页

作者： McLane, Ian Lauwers, Eline Stas, Toon Busch-Vishniac, Ilene Ides, Kris Verhulst, Stijn Steckel, Jan Sonavi Labs Baltimore MD 21230 USA Univ Antwerp Lab Expt Med & Pediat B-2610 Antwerp Belgium Univ Antwerp Fac Appl Engn CoSys Res Lab B-2020 Antwerp Belgium Flanders Make Strateg Res Ctr B-3920 Lommel Belgium Antwerp Univ Hosp Dept Pediat B-2650 Edegem Belgium

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.

关键词： Lung Motion artifacts Noise reduction Annotations Deep learning Training Noise measurement algorithm design and analysis biomedical coustics machine learning medicine signal processing

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