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arXiv 2022年

作者： Zohar, Inbar Haylock, Ben Romach, Yoav Arshad, Muhammad Junaid Halay, Nir Drucker, Niv Stöhr, Rainer Denisenko, Andrej Cohen, Yonatan Bonato, Cristian Finkler, Amit Department of Chemical and Biological Physics Weizmann Institute of Science Rehovot7610001 Israel Institute of Photonics and Quantum Sciences SUPA Heriot-Watt University EdinburghEH14 4AS United Kingdom Quantum Machines Tel-Aviv6744332 Israel Third Institute of Physics University of Stuttgart Stuttgart70569 Germany

Quantum sensors can potentially achieve the Heisenberg limit of sensitivity over a large dynamic range using quantum algorithms. The adaptive phase estimation algorithm (PEA) is one example that was proven to achieve such high sensitivities with single-shot readout (SSR) sensors. However, using the adaptive PEA on a non-SSR sensor is not trivial due to the low contrast nature of the measurement. The standard approach to account for the averaged nature of the measurement in this PEA algorithm is to use a method based on ‘majority voting’. Although it is easy to implement, this method is more prone to mistakes due to noise in the measurement. To reduce these mistakes, a binomial distribution technique from a batch selection was recently shown theoretically to be superior, as all ranges of outcomes from an averaged measurement are considered. Here we apply, for the first time, real-time non-adaptive PEA on a non-SSR sensor with the binomial distribution approach. We compare the mean square error of the binomial distribution method to the majority-voting approach using the nitrogen-vacancy center in diamond at ambient conditions as a non-SSR sensor. Our results suggest that the binomial distribution approach achieves better accuracy with the same sensing times. To further shorten the sensing time, we propose an adaptive algorithm that controls the readout phase and, therefore, the measurement basis set. We show by numerical simulation that adding the adaptive protocol can further improve the accuracy in a future real-time experiment. © 2022, CC BY.

关键词： adaptive algorithms

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adaptive Bayesian Inference of Markov Transition Rates

arXiv

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arXiv 2022年

作者： Barendregt, Nicholas W. Webb, Emily G. Kilpatrick, Zachary P. Department of Applied Mathematics University of Colorado Boulder 1111 Engineering Center ECOT 225 526 UCB BoulderCO80309 United States Applied Physics Laboratory Johns Hopkins University 11100 Johns Hopkins Road LaurelMD20723 United States

Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past observations, adaptive approaches adjust sampling constraints online as model parameter estimates are refined, continually maximizing expected information gained or variance reduced. We apply adaptive Bayesian inference to estimate transition rates of Markov chains, a common class of models for stochastic processes in nature. Unlike most previous studies, our sequential Bayesian optimal design is updated with each observation, and can be simply extended beyond two-state models to birth-death processes and multistate models. By iteratively finding the best time to obtain each sample, our adaptive algorithm maximally reduces variance, resulting in lower overall error in ground truth parameter estimates across a wide range of Markov chain parameterizations and conformations. © 2022, CC BY.

关键词： adaptive algorithms

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adaptive Data Depth via Multi-Armed Bandits

arXiv

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arXiv 2022年

作者： Baharav, Tavor Z. Lai, Tze Leung Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Statistics Stanford University StanfordCA94305 United States

Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of nd operations to exactly compute the depth of a single point within a data set of n points in d-dimensional space. Often however, we are not directly interested in the absolute depths of the points, but rather in their relative ordering. For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth). With this observation, we develop a novel and instance-adaptive algorithm for adaptive data depth computation by reducing the problem of exactly computing n depths to an n-armed stochastic multiarmed bandit problem which we can efficiently solve. We focus our exposition on simplicial depth, developed by Liu (1990), which has emerged as a promising notion of depth due to its interpretability and asymptotic properties. We provide general instance-dependent theoretical guarantees for our proposed algorithms, which readily extend to many other common measures of data depth including majority depth, Oja depth, and likelihood depth. When specialized to the case where the gaps in the data follow a power law distribution with parameter α d) to Õ(nd−(d−1)α/2), where Õ suppresses logarithmic factors. We corroborate our theoretical results with numerical experiments on synthetic data, showing the practical utility of our proposed methods. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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Fast adaptive Federated Bilevel Optimization

arXiv

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arXiv 2022年

作者： Huang, Feihu College of Computer Science and Technique College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning and policy optimization. Although many bilevel optimization algorithms recently have been developed, few adaptive algorithm focuses on the bilevel optimization under the distributed setting. It is well known that the adaptive gradient methods show superior performances on both distributed and non-distributed optimization. In the paper, thus, we propose a novel adaptive federated bilevel optimization algorithm (i.e.,AdaFBiO) to solve the distributed bilevel optimization problems, where the objective function of Upper-Level (UL) problem is possibly nonconvex, and that of Lower-Level (LL) problem is strongly convex. Specifically, our AdaFBiO algorithm builds on the momentum-based variance reduced technique and local-SGD to obtain the best known sample and communication complexities simultaneously. In particular, our AdaFBiO algorithm uses the unified adaptive matrices to flexibly incorporate various adaptive learning rates to update variables in both UL and LL problems. Moreover, we provide a convergence analysis framework for our AdaFBiO algorithm, and prove it needs the sample complexity of Õ(ǫ−3) with communication complexity of Õ(ǫ−2) to obtain an ǫ-stationary point. Experimental results on federated hyper-representation learning and federated data hyper-cleaning tasks verify efficiency of our algorithm. © 2022, CC BY-NC-SA.

关键词： adaptive algorithms

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Distributed Active Noise Control based on Inverse Tangent Robust Least Mean Logarithmic Square

Distributed Active Noise Control based on Inverse Tangent Ro...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Rajapantula Kranthi Vasundhara Vasundhara Department of ECE National Institute of Technology Warangal Telangana India

ISBN: (纸本)9798350399233

In a dispersed scenario, active noise reduction in the presence of outliers and burst noises presents a significant challenge. Conventional distributed active noise control (DANC) systems perform poorly in the presence of outliers and impulse-like noises. The robustness against non-Gaussian/impulsive noises is improved by the saturation features of the error non-linearities. The saturation characteristics of the inverse tangent function are used in this technique. The proposed inverse tangent robust least mean logarithmic square incremental DANC (IRL-DANC) method converges faster and the noise is mitigated effectively in these scenarios. The simulation outcomes validate the proposed distributed active noise system effectiveness in contrast to the existing methodologies. The results show that the proposed strategy achieves faster convergence and a 1–5 dB improvement in noise cancellation for various noise environments.

关键词： Active noise reduction adaptive algorithms Acoustic sensors Control systems Noise cancellation Cost function Robustness

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An Optimized Successive Cancellation List Decoder for Polar Codes Combined with Critical Set

An Optimized Successive Cancellation List Decoder for Polar ...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xiuqi Hu Huiling Hou Xinglong Jiang Siyue Sun Guang LIANG Shuai Han Shanghaitech university Innovation Academy for Microsatellites of CAS Shanghai China Innovation Academy for Microsatellites of CAS Shanghai China Communication Research Center Harbin Indtitute of Technology Harbin China

Successive cancellation list (SCL) decoder has high decoding performance but also require great resource consumption. In this paper, we combine the SCL decoder and the critical set to form a new decoder, which can stably reduce the number of operations by 65% $\sim$ 70% and maintain similar decoding performance to SCL decoder. The reduction in resource consumption depends only on the arrangement of unfrozen bit sequences, not on the quality of the channel. We also propose to combine this new decoder with adaptive algorithms. In this way, the decoding performance of the decoder can be improved beyond SCL decoders without increasing resource consumption.

关键词： Wireless communication Error analysis adaptive algorithms Decoding Polar codes Satellite communication Optimization

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ERT image reconstruction based on DPSO-ATikhonov regularization algorithm

ERT image reconstruction based on DPSO-ATikhonov regularizat...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xingkun Dong Hongwei Ren Qian Lu Zheng Zhuang Xin Cheng Lei Qin School of Electrical Engineering University of Jinan Jinan China School of Civil Engineering and Architecture University of Jinan Jinan China

ISBN: (纸本)9781665465373

Tikhonov regularization algorithm is obviously influenced by prior information, and the selection of prior information determines the imaging quality of Tikhonov regularization algorithm. In this paper, a spatial adaptive tikhonov regularization algorithm processed by double-population PSO algorithm(DPSO-ATikhonov) is proposed, which can automatically find the initial regularization coefficient and contraction factor required by the spatial adaptive algorithm, solve the problem of prior information of the regularization algorithm, and further improve the image quality. Compared with the traditional Tikhonov regularization algorithm, the experimental results show that the algorithm can solve the prior information confusion and obtain better imaging results.

关键词： Image quality Automation Imaging adaptive algorithms Image reconstruction

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adaptive central-upwind scheme on triangular grids for the shallow water model with variable density

arXiv

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arXiv 2022年

作者： Nguyen, Thuong Department of Mathematics The University of Utah Salt Lake CityUT84112 United States

In this paper, we construct a robust adaptive central-upwind scheme on unstructured triangular grids for two-dimensional shallow water equations with variable density. The method is wellbalanced, positivity-preserving, and oscillation free at the curve where two types of fluid merge. The proposed approach is an extension of the adaptive well-balanced, positivity-preserving scheme developed in Epshteyn and Nguyen (arXiv preprint arXiv:2011.06143, 2020). In particular, to preserve "lake-at-rest" steady states, we utilize the Riemann Solver with appropriately rotated coordinates to obtain the point values in neighborhood of the fluid interface. In addition, to improve the efficiency of an adaptive method in the multifluid flow, the curve of density discontinuity is reconstructed by using the level set method and volume fraction method. To demonstrate the accuracy, high-resolution, and efficiency of the new adaptive central-upwind scheme, several challenging tests for Shallow water models with variable density are performed. © 2022, CC BY.

关键词： adaptive algorithms

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Cyclical Kernel adaptive Metropolis

arXiv

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arXiv 2022年

作者： Li, Jianan Canal Zeng, Yimeng Guo, Wentao Department of Computer Science China Biological and Environmental Engineering Cornell University United States

We propose cKAM, cyclical Kernel adaptive Metropolis, which incorporates a cyclical stepsize scheme to allow control for exploration and sampling. We show that on a crafted bimodal distribution, existing adaptive Metropolis type algorithms would fail to converge to the true posterior distribution. We point out that this is because adaptive samplers estimates the local/global covariance structure using past history of the chain, which will lead to adaptive algorithms be trapped in a local mode. We demonstrate that cKAM encourages exploration of the posterior distribution and allows the sampler to escape from a local mode, while maintaining the high performance of adaptive methods. © 2022, CC BY.

关键词： adaptive algorithms

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A Locally adaptive Algorithm for Multiple Testing with Network Structure

arXiv

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arXiv 2022年

作者： Liang, Ziyi Cai, T. Tony Sun, Wenguang Xia, Yin Department of Statistics University of California Irvine IrvineCA United States Department of Statistics and Data Science University of Pennsylvania PhiladelphiaPA United States School of Management Center for Data Science Zhejiang University Zhejiang China Department of Statistics and Data Science Fudan University Shanghai China

Incorporating auxiliary information alongside primary data can significantly enhance the accuracy of simultaneous inference. However, existing multiple testing methods face challenges in efficiently incorporating complex side information, especially when it differs in dimension or structure from the primary data, such as network side information. This paper introduces a locally adaptive structure learning algorithm (LASLA), a flexible framework designed to integrate a broad range of auxiliary information into the inference process. Although LASLA is specifically motivated by the challenges posed by network-structured data, it also proves highly effective with other types of side information, such as spatial locations and multiple auxiliary sequences. LASLA employs a p-value weighting approach, leveraging structural insights to derive data-driven weights that prioritize the importance of different hypotheses. Our theoretical analysis demonstrates that LASLA asymptotically controls the false discovery rate (FDR) under independent or weakly dependent p-values, and achieves enhanced power in scenarios where the auxiliary data provides valuable side information. Simulation studies are conducted to evaluate LASLA’s numerical performance, and its efficacy is further illustrated through two real-world applications. © 2022, CC BY-NC-ND.

关键词： adaptive algorithms

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