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GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

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arXiv 2023年

作者： Feng, Ke Liu, Dahai Liu, Yongxin Liu, Hong Song, Houbing University of Maryland Baltimore CountyMD21250 United States Embry-Riddle Aeronautical University FL32114 United States

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic, ideal for emergencies. The proposed approach constructs the airspace as a constraints-embedded graph, compresses its dimensions, and applies a spectral clustering-enabled adaptive algorithm to generate collaborative airport groups and evenly distribute workloads among them. Under various traffic conditions, our experiments demonstrate a 50% reduction in workload imbalances. This research could ultimately form the basis for a recommendation system for optimized airspace configuration. Code available at https://***/KeFenge2022/***. © 2023, CC0.

关键词： adaptive algorithms

An adaptive Noise Cancellation Based for a BFSK Demodulation in a Narrow Band Communication 9

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An Adaptive Noise Cancellation Based for a BFSK Demodulation...

9th International Electrical Engineering Congress, iEECON 2021

作者： Inban, Prayuth Punchalard, Rachu Loedwassana, Wuthiporn Rajabhat Rajanagarindra University Faculty of Industrial Technology Department of Electrical Engineering Chachoengsao Thailand Bangkok Thailand Samuthprakan Thailand

ISBN: (纸本)9781728195841

Another scheme of a binary frequency phase shift keying (BFSK) signal demodulation has been presented in this paper. The proposed demodulated scheme utilizes an ANC-based adaptive signal processing. The adaptive algorithm which adapts the coefficients of the ANC system will be constrained in adaptation sequence corresponding to each symbol interval of the received BFSK signal. By using this scheme, the system will provide demodulated behavior similar to an integrated and dump correlator of an optimum receiver. The analytical and simulation results have shown that the proposed system can demodulate the received BFSK signal in AWGN channel. Furthermore, the system provides the same performance as the optimum receiver. © 2021 IEEE.

关键词： adaptive algorithms

Using adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

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arXiv 2023年

作者： Kumar, Harsh Li, Tong Shi, Jiakai Musabirov, Ilya Kornfield, Rachel Meyerhoff, Jonah Bhattacharjee, Ananya Karr, Chris Nguyen, Theresa Mohr, David Rafferty, Anna Villar, Sofia Deliu, Nina Williams, Joseph Jay Department of Computer Science University of Toronto Canada Department of Statistics University of Toronto Canada Center for Behavioral Intervention Technologies Northwestern University United States Audacious Software United States Mental Health America United States Carleton College United States MRC - Biostatistics Unit University of Cambridge United Kingdom MEMOTEF Department Sapienza University of Rome Italy

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health nonprofit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains. Copyright © 2023, The Authors. All rights reserved.

关键词： adaptive algorithms

Low-complexity subspace-descent over symmetric positive definite manifold

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arXiv 2023年

作者： Darmwal, Yogesh Rajawat, Ketan Dept. of Electrical Engineering Indian Institute of Technology Uttar Pradesh Kanpur208016 India

This work puts forth low-complexity Riemannian subspace descent algorithms for the minimization of functions over the symmetric positive definite (SPD) manifold. Different from the existing Riemannian gradient descent variants, the proposed approach utilizes carefully chosen subspaces that allow the update to be written as a product of the Cholesky factor of the iterate and a sparse matrix. The resulting updates avoid the costly matrix operations like matrix exponentiation and dense matrix multiplication, which are generally required in almost all other Riemannian optimization algorithms on SPD manifold. We further identify a broad class of functions, arising in diverse applications, such as kernel matrix learning, covariance estimation of Gaussian distributions, maximum likelihood parameter estimation of elliptically contoured distributions, and parameter estimation in Gaussian mixture model problems, over which the Riemannian gradients can be calculated efficiently. The proposed uni-directional and multi-directional Riemannian subspace descent variants incur per-iteration complexities of O(n) and O(n2) respectively, as compared to the O(n3) or higher complexity incurred by all existing Riemannian gradient descent variants. The superior runtime and low per-iteration complexity of the proposed algorithms is also demonstrated via numerical tests on large-scale covariance estimation and matrix square root problems. MATLAB code implementation is publicly available on GitHub: https://***/yogeshd-iitk/subspace descent over SPD manifold © 2023, CC BY.

关键词： adaptive algorithms

A p-step-ahead sequential adaptive algorithm for D-optimal nonlinear regression design

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arXiv 2023年

作者： Freise, Fritjof Gaffke, Norbert Schwabe, Rainer University of Veterinary Medicine Hannover Germany University of Magdeburg Germany

Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage, the current design is augmented by adding p design points where p is the dimension of the parameter of the model. The augmenting p points are such that, at the current parameter estimate, they constitute the locally D-optimal design within the set of all saturated designs. Two relevant subclasses of nonlinear regression models are focused on, which were considered in previous work of the authors on the adaptive Wynn algorithm: firstly, regression models satisfying the ‘saturated identifiability condition’ and, secondly, generalized linear models. adaptive least squares estimators and adaptive maximum likelihood estimators in the algorithm are shown to be strongly consistent and asymptotically normal, under appropriate assumptions. For both model classes, if a condition of ‘saturated D-optimality’ is satisfied, the almost sure asymptotic D-optimality of the generated design sequence is implied by the strong consistency of the adaptive estimators employed by the algorithm. The condition states that there is a saturated design which is locally D-optimal at the true parameter point (in the class of all designs).MSC Codes 62L05 (Primary), 62F12, 62J12 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： adaptive algorithms

Influence Maximization based on Simplicial Contagion Models in Hypergraphs

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arXiv 2023年

作者： Zhang, Renquan Wei, Ting Sun, Yifan Pei, Sen School of Mathematical Sciences Dalian University of Technology China Center for Applied Statistics School of Statistics Remin University of China Beijing China Department of Environmental Health Sciences Mailman School of Public Health Columbia University United States

In recent years, the exploration of node centrality has received significant attention and extensive investigation, primarily fuelled by its applications in diverse domains such as product recommendations, opinion propagation, disease spread, and other scenarios requiring the maximization of node influence. Despite various perspectives emphasizing the indispensability of higher-order networks, research specifically delving into node centrality within the realm of hypergraphs has been relatively constrained. This paper focuses on the problem of influence maximization on the Simplicial Contagion Model (SCM), using the susceptible-infected-recovered (SIR) model as an example. To find practical solutions to this optimization problem, we have developed a theoretical framework based on message passing process and conducted stability analysis of equilibrium solutions for the self-consistent equations. Furthermore, we introduce a metric called collective influence and propose an adaptive algorithm, known as the Collective Influence adaptive (CIA), to identify influential propagators in the spreading process. Notably, our algorithm distinguishes itself by prioritizing collective influence over individual influence, resulting in demonstrably superior performance, a characteristic substantiated by a comprehensive array of experiments. © 2023, CC BY.

关键词： adaptive algorithms

Success history based adaptive multi-objective differential evolution variants with an interval scheme for solving simultaneous topology, shape and sizing truss reliability optimisation

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KNOWLEDGE-BASED SYSTEMS 2022年 253卷

作者： Anosri, Siwakorn Panagant, Natee Bureerat, Sujin Pholdee, Nantiwat Khon Kaen Univ Sustainable Infrastructure Res & Dev Ctr Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

A general approach based on the most probable point (MPP) method for solving reliability truss optimisation with simultaneous topology, shape and sizing (TSS) design variables is developed. The design problems are solved using double loop optimisation where the inner loop is for reliability index and the probability of failure approximation is solved by Harris Hawk Optimisation (HHO). The outer loop, the main TSS truss optimisation loop, is solved by two newly developed algorithms, namely interval success history based adaptive multi-objective differential evolution (iSHAMODE) and its hybrid variant with the whale optimisation algorithm (iSHAMODE-WO). Six TSS truss optimisation problems are evaluated. The results from the proposed method for reliability approximation and First Order Second Moment (FOSM) are compared and validated with Monte Carlo Simulation (MCS). The proposed method shows more consistent and accurate results compared to FOSM. Furthermore, the ef-ficiency of the proposed optimisation algorithms (iSHAMODE and iSHAMODE-WO) is proved;they can outperform their predecessors and several state-of-the-art algorithms including multi-objective multi-verse optimisation algorithm (MOMVO), multi-objective grasshopper optimisation algorithm (MOGOA), multi-objective dragonfly optimisation (MODA), multi-objective salp swarm algorithm (MSSA), hybridi-sation of real-code population-based incremental learning and differential evolution (RPBILDE), and multi-objective meta-heuristic with iterative parameter distribution estimation (MMIPDE). (C) 2022 Elsevier B.V. All rights reserved.

关键词： Reliability optimisation Truss optimisation Metaheuristics Most probable point adaptive algorithms

Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systems

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CHAOS SOLITONS & FRACTALS 2022年 163卷

作者： Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Kiani, Adiqa Kausar Raja, Muhammad Asif Zahoor Chaudhary, Iqra Ishtiaq Pinto, Carla M. A. Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Int Islamic Univ Dept Elect Engn Islamabad Pakistan Technol Univ Dublin FOCAS Res Inst Dublin Ireland Univ Porto Polytech Porto Porto Portugal Univ Porto Ctr Math Porto Portugal

A new avenue of fractional calculus applications has emerged that investigates the design of fractional gradient based novel iterative methods for analyzing fractals and nonlinear dynamics in solving engineering and applied sciences problems. The most discussed algorithm in this regard is fractional least mean square (FLMS) algorithm. This study presents an auxiliary model based normalized variable initial value FLMS (AM-NVIV-FLMS) algorithm for input nonlinear output error (INOE) system identification. First, NVIV-FLMS is presented to automatically tune the learning rate parameter of VIV-FLMS and then the AM-NVIV-FLMS is introduced by incorporating the auxiliary model idea that replaces the unknown values of the information vector with the output of auxiliary model. The proposed AM-NVIV-FLMS scheme is accurate, convergent, robust and reliable for INOE system identification. Simulation results validate the significance and efficacy of the proposed scheme.

关键词： Nonlinear systems Auxiliary model Fractional Calculus adaptive algorithms

A Communication-Efficient adaptive Algorithm for Federated Learning under Cumulative Regret

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arXiv 2023年

作者： Salgia, Sudeep Gabay, Tamir Zhao, Qing Cohen, Kobi The School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States The School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

We consider the problem of online stochastic optimization in a distributed setting with M clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that separately tackles the communication frequency and the number of bits in each communication round. © 2023, CC BY.

关键词： adaptive algorithms