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Deep learning enables accurate sound redistribution via nonlocal metasurfaces

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Research Square

Research Square 2021年

作者： Ding, Hua Fang, Xinsheng Jia, Bin Wang, Nengyin Cheng, Qian Li, Yong Institute of Acoustics School of Physics Science and Engineering Tongji University Shanghai200092 China

Conventional acoustic metasurfaces are constructed with gradiently "local" phase shift profiles provided by subunits. The local strategy implies the ignorance of the mutual coupling between subunits, which limits the efficiency of targeted sound manipulation, especially in complex environments. By taking into account the "nonlocal" interaction among subunits, nonlocal metasurface offers an opportunity for accurate control of sound propagation, but the requirement of the consideration of gathering coupling among all subunits, not just the nearest-neighbor coupling, greatly increases the complexity of the system and therefore hinders the explorations of functionalities of nonlocal metasurfaces. In this work, empowered by deep learning algorithms, the complex gathering coupling can be learned efficiently from the preset dataset so that the functionalities of nonlocal metasurfaces can be significantly uncovered. As an example, we demonstrate that nonlocal metasurfaces, which can redirect an incident wave into multi-channel reflections with arbitrary energy ratios, can be accurately predicted by deep learning algorithms. Compared to the theory, the relative error of the energy ratios is less than 1%. Furthermore, experiments witness three-channel reflection with three types of energy ratios of (1, 0, 0), (1/2, 0, 1/2), and (1/3, 1/3, 1/3), proving the validity of the deep learning enabled nonlocal metasurfaces. Our work might blaze a new trail in the design of acoustic functional devices, especially for the cases containing complex wave-matter interactions. © 2021, CC BY.

关键词： learning algorithms

RLBayes: a Bayesian Network Structure learning Algorithm via Reinforcement learning-Based Search Strategy

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arXiv 2025年

作者： Wang, Mingcan Xin, Junchang Qu, Luxuan Chen, Qi Wang, Zhiqiong School of Computer Science and Engineering Northeastern University China Northeastern University China Ara Institute of Canterbury International Engineering College Shenyang Jianzhu University China College of Medicine and Biological Information Engineering Northeastern University China

The score-based structure learning of Bayesian network (BN) is an effective way to learn BN models, which are regarded as some of the most compelling probabilistic graphical models in the field of representation and reasoning under uncertainty. However, the search space of structure learning grows super-exponentially as the number of variables increases, which makes BN structure learning an NP-hard problem, as well as a combination optimization problem (COP). Despite the successes of many heuristic methods on it, the results of the structure learning of BN are usually unsatisfactory. Inspired by Q-learning, in this paper, a Bayesian network structure learning algorithm via reinforcement learning-based (RL-based) search strategy is proposed, namely RLBayes. The method borrows the idea of RL and tends to record and guide the learning process by a dynamically maintained Qtable. By creating and maintaining the dynamic Q-table, RLBayes achieve storing the unlimited search space within limited space, thereby achieving the structure learning of BN via Q-learning. Not only is it theoretically proved that RLBayes can converge to the global optimal BN structure, but also it is experimentally proved that RLBayes has a better effect than almost all other heuristic search algorithms. Codes available at Appendix. Copyright © 2025, The Authors. All rights reserved.

关键词： learning algorithms

A machine learning approach to address robust bi-objective gate assignment under aircraft's taxi delay uncertainties

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Expert Systems with Applications 2025年 285卷

作者： Shadman, Majid Kordani, Ali Abdi Akbaripour, Hossein Imam Khomeini International University Faculty of Technical and Engineering Qazvin Iran Amirkabir University of Technology Faculty of Industrial Engineering and Management Systems Tehran Iran

Although gate assignment may appear to be a relatively straightforward process, it represents a significant challenge due to the inherent uncertainties. Despite the extensive research conducted on the gate assignment, its uncertainty aspects have not been subjected to more realistic robustness approach and the utilization of modern forecasting methods. Indeed, the main problem of this study, find the method to solve gate assignment problem with a taxi time prediction approach with considering uncertain parameters. This research aims to optimize gate assignment by considering uncertainties and deploying machine learning approach, with the goal of increasing operational efficiency and improving passenger experiences. Despite the absence of concerted efforts to integrate machine learning methodologies for taxi time prediction with taxi delay-based gate assignment, this research endeavored to combine the above with robust optimization using a polyhedral approach, which represents a significant innovation over previous work. A mathematical multiobjective model comprising uncertain parameters was constructed and three machine learning algorithms were employed to estimate taxi times. Subsequently, a set-induced robust counterpart with polyhedral approach was created. With the case study that is performed on the Phoenix airport, strong correlation was observed between the actual and predicted taxi times/lengths based on statistical analysis. The results obtained from the mathematical programming output demonstrated favorable outcomes in the context of uncertainty, thereby facilitating the optimization of gate assignment with respect to taxi delay while maintaining a conservative approach. The results exhibit implementation of robust optimization enables airport authorities to effectively manage gate assignment within acceptable confidence interval. © 2025

关键词： learning algorithms

Comparative Synthesis: learning Near-Optimal Network Designs by Query

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arXiv 2021年

作者： Wang, Yanjun Li, Zixuan Jiang, Chuan Qiu, Xiaokang Rao, Sanjay G. Purdue University West LafayetteIN47907 United States

When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Propose and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach. Copyright © 2021, The Authors. All rights reserved.

关键词： learning algorithms

learning highly recursive input grammars

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arXiv 2021年

作者： Kulkarni, Neil Lemieux, Caroline Sen, Koushik University of California Berkeley United States

This paper presents Arvada, an algorithm for learning context-free grammars from a set of positive examples and a Boolean-valued oracle. Arvada learns a context-free grammar by building parse trees from the positive examples. Starting from initially flat trees, Arvada builds structure to these trees with a key operation: it bubbles sequences of sibling nodes in the trees into a new node, adding a layer of indirection to the tree. Bubbling operations enable recursive generalization in the learned grammar. We evaluate Arvada against GLADE and find it achieves on average increases of 4.98x in recall and 3.13x in F1 score, while incurring only a 1.27x slowdown and requiring only 0.87x as many calls to the oracle. Arvada has a particularly marked improvement over GLADE on grammars with highly recursive structure, like those of programming languages. Copyright © 2021, The Authors. All rights reserved.

关键词： learning algorithms

Adaptive learning algorithm and its convergence analysis with complex-valued error loss network

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Neural Networks 2025年 190卷 107677页

作者： Qian, Guobing Lin, Bingqing Mei, Jiaojiao Qian, Junhui Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Cogenda Pte Ltd Jiangsu 215028 China School of Microelectronic and Communication Engineering Chongqing University Chongqing400030 China

In machine learning, the initial task is to construct a model that is capable of predicting the outcomes of new samples with the help of training samples. The loss function plays a key role in this task, as it acts as an important indicator to evaluate the overall model prediction performance. Building upon the work of Chen et al., this study introduces a novel model named as the Complex Error Loss Network (CELN). The CELN is designed to address the scenarios involving complex-valued signals and parameters within the context of supervised learning. Leveraging the contraction mapping theorem, this study investigates the convergence of the corresponding adaptive learning algorithm, underscoring the inherent capability of CELN to consistently approach and potentially reach the global minimum or optimal solution through iterative methods. CELN reduces the error by at least 4.1% compared to benchmark methods while maintaining stability in non-Gaussian noise scenarios. © 2025

关键词： learning algorithms

Causal Machine learning in IoT-based Engineering Problems: A Tool Comparison in the Case of Household Energy Consumption

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arXiv 2025年

作者： Kosioris, N.-L. Nikoletseas, S. Filios, G. Panagiotou, S. Department of Computer Engineering and Informatics University of Patras Greece Connect Line S.A. Athens Greece

The rapid increase in computing power and the ability to store Big Data in the infrastructure has enabled predictions in a large variety of domains by Machine learning. However, in many cases, existing Machine learning tools are considered insufficient or incorrect since they exploit only probabilistic dependencies rather than inference logic. Causal Machine learning methods seem to close this gap. In this paper, two prevalent tools based on Causal Machine learning methods are compared, as well as their mathematical underpinning background. The operation of the tools is demonstrated by examining their response to 18 queries, based on the IDEAL Household Energy Dataset, published by the University of Edinburgh. First, it was important to evaluate the causal relations assumption that allowed the use of this approach;this was based on the preexisting scientific knowledge of the domain and was implemented by use of the in-built validation tools. Results were encouraging and may easily be extended to other domains. © 2025, CC BY-NC-SA.

关键词： learning algorithms

Safety and Optimality in learning-Based Control At Low Computational Cost

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IEEE Transactions on Automatic Control 2025年

作者： Baumann, Dominik Kowalczyk, Krzysztof Rojas, Cristian R. Tiels, Koen Wachel, Pawel Aalto University Cyber-physical Systems Group Espoo Finland Uppsala University Department of Information Technology Uppsala Sweden Wroc Law University of Science and Technology Department of Control Systems and Mechatronics Wroclaw Poland Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Eindhoven University of Technology Department of Mechanical Engineering Eindhoven Netherlands

Applying machine learning methods to physical systems that are supposed to act in the real world requires providing safety guarantees. However, methods that include such guarantees often come at a high computational cost, making them inapplicable to large datasets and embedded devices with low computational power. In this paper, we propose Colsafe, a computationally lightweight safe learning algorithm whose computational complexity grows sublinearly with the number of data points. We derive both safety and optimality guarantees and showcase the effectiveness of our algorithm on a seven-degrees-of-freedom robot arm. © 1963-2012 IEEE.

关键词： learning algorithms

ADAM: A sandbox for implementing language learning

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arXiv 2021年

作者： Gabbard, Ryan Beser, Deniz Lichtefeld, Jacob Cecil, Joe Marcus, Mitch Payne, Sarah Yang, Charles Freedman, Marjorie Information Sciences Institute University of Southern California Department of Computer and Information Science University of Pennsylvania Department of Linguistics University of Pennsylvania

We present ADAM, a software system for designing and running child language learning experiments in Python. The system uses a virtual world to simulate a grounded language acquisition process in which the language learner utilizes cognitively plausible learning algorithms to form perceptual and linguistic representations of the observed world. The modular nature of ADAM makes it easy to design and test different language learning curricula as well as learning algorithms. In this report, we describe the architecture of the ADAM system in detail, and illustrate its components with examples. We provide our code.1. © 2021, CC BY.

关键词： learning algorithms