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Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： V. I. Djigan Department of Integrated Circuits Design Methodology Institute for Design Problems in Microelectronics of RAS Moscow Russia

ISBN: (数字)9781665470643

ISBN: (纸本)9781665470650

The adaptive array with a rectangular aperture, whose antennas are placed in the nodes of the rectangular grid, is considered in the paper. The partial adaptability is provided via the base-band combination of the signals in the rows and in the columns of the array. Opposite to the hybrid arrays, based on the subarray output signals combination, the arrays based on the row and the column signal combination, do not suffer on the grating lobes phenomenon, which restricts the ability of the cancellation of the interferences, whose source spatial locations are coincided with those of the grating lobes. A version of the Matrix Inversion Lemma Recursive Least Squares adaptive algorithm for the partially adaptive narrow-band array weight calculation is presented. The Householder transform or QR decomposition adaptive algorithms can be used as well. The algorithm arithmetic complexity does not depend on the number of the array antennas, but depends on the array columns number and the rows number. The simulation results demonstrate that the steady-state performance of the adaptive array with the partial adaptation is the same as that of the adaptive array with the full adaptation if the number of the received signal sources does not exceed the number of the partially adaptive array weights. The dynamic behavior of the both arrays in the terms of the radiation pattern values in the directions of the received signal sources is differed only at the initial stage of the transient response. The equivalent weights of the full array used for the radiation pattern calculations are reconstructed via the combination of the weights of the crossed rows and the crossed columns of the partially adaptive array. The considered partial adaptation can be used in the large rectangular antenna arrays.

关键词： Transient response Array signal processing adaptive arrays adaptive algorithms Transforms Complexity theory Telecommunications

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AMD-DBSCAN: An adaptive Multi-density DBSCAN for datasets of extremely variable density

arXiv

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arXiv 2022年

作者： Wang, Ziqing Ye, Zhirong Du, Yuyang Mao, Yi Liu, Yanying Wu, Ziling Wang, Jun School of Microelectronics Science and Technology Sun Yat-sen University China School of Microelectronics Science and Technology University of Nottingham United Kingdom School of Microelectronics Science and Technology Zhuhai Jieli Technology Co.Ltd. China

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and MinPts), which are the key parameters to determine the clustering results and performance, therefore allowing the model to be applied to Multi-density datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid the complicated repetitive initialization operations. Furthermore, the variance of the number of neighbors (VNN) is proposed to measure the difference in density between each cluster. The experimental results show that our AMD-DBSCAN reduces execution time by an average of 75% due to lower algorithm complexity compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN improves accuracy by 24.7% on average over the state-of-the-art design on Multi-density datasets of extremely variable density, while having no performance loss in Single-density scenarios. Our code and datasets are available at https://***/AlexandreWANG915/AMD-DBSCAN. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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BadPrompt: Backdoor Attacks on Continuous Prompts

arXiv

引用

arXiv 2022年

作者： Cai, Xiangrui Xu, Haidong Xu, Sihan Zhang, Ying Yuan, Xiaojie TKLNDST TMCC College of Computer Science Nankai University China TKLNDST College of Cyber Science CHNkai University China TKLNDST TMCC College of Cyber Science Nankai University China TMCC College of Computer Science Nankai University China

The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available 1 Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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Robust Pareto Set Identification With Contaminated Bandit Feedback

arXiv

引用

arXiv 2022年

作者： Korkmaz, İlter Onat Ceyani, Efe Eren Bozgan, Kerem Tekin, Cem Department of Electrical and Electronics Engineering Bilkent University Turkey Department of Computer Science Virginia Polytechnic Institute State University United States

We consider the Pareto set identification (PSI) problem in multi-objective multi-armed bandits (MO-MAB) with contaminated reward observations. At each arm pull, with some fixed probability, the true reward samples are replaced with the samples from an arbitrary contamination distribution chosen by an adversary. We consider (α, δ)-PAC PSI and propose a sample median-based multi-objective adaptive elimination algorithm that returns an (α, δ)PAC Pareto set upon termination with a sample complexity bound that depends on the contamination probability. As the contamination probability decreases, we recover the well-known sample complexity results in MO-MAB. We compare the proposed algorithm with a mean-based method from MO-MAB literature, as well as an extended version that uses median estimators, on several PSI problems under adversarial corruptions, including review bombing and diabetes management. Our numerical results support our theoretical findings and demonstrate that robust algorithm design is crucial for accurate PSI under contaminated reward observations. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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A DISTRIBUTED adaptive ALGORITHM FOR NODE-SPECIFIC SIGNAL FUSION PROBLEMS IN WIRELESS SENSOR NETWORKS

arXiv

引用

arXiv 2022年

作者： Musluoglu, Cem Ates Bertrand, Alexander Stadius Center for Dynamical Systems Signal Processing and Data Analytics Kasteelpark Arenberg 10 box 2446 Leuven3001 Belgium

Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centralized way would lead to high energy and communication costs. The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve these signal fusion problems in a distributed fashion, which reduces the communication and energy costs in the network. The DASF framework assumes that there is a common goal across the nodes, i.e., the optimal filter is shared across the network. However, many applications require a node-specific objective, while all these node-specific objectives are still related via a common latent data model. In this work, we propose the DANSF algorithm which builds upon the DASF framework, and extends it to allow for node-specific spatial filtering problems. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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LCSM: A Lightweight Complex Spectral Mapping Framework for Stereophonic Acoustic Echo Cancellation

arXiv

引用

arXiv 2022年

作者： Zhang, Chenggang Liu, Jinjiang Zhang, Xueliang Department of Computer Science Inner Mongolia University China

The traditional adaptive algorithms will face the non-uniqueness problem when dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we first propose an efficient multi-input and multi-output (MIMO) scheme based on deep learning to filter out echoes from all microphone signals at once. Then, we employ a lightweight complex spectral mapping framework (LCSM) for end-to-end SAEC without decorrelation preprocessing to the loudspeaker signals. Inplace convolution and channel-wise spatial modeling are utilized to ensure the near-end signal information is preserved. Finally, a cross-domain loss function is designed for better generalization capability. Experiments are evaluated on a variety of untrained conditions and results demonstrate that the LCSM significantly outperforms previous methods. Moreover, the proposed causal framework only has 0.55 million parameters, much less than the similar deep learning-based methods, which is important for the resource-limited devices. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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A Cost-Efficient Space-Time adaptive Algorithm for Coupled Flow and Transport

arXiv

引用

arXiv 2022年

作者： Bruchhäuser, Marius Paul Bause, Markus Helmut Schmidt University University of the German Federal Armed Forces Hamburg Faculty of Mechanical and Civil Engineering Numerical Mathematics Holstenhofweg 85 Hamburg22043 Germany

In this work, a cost-efficient space-time adaptive algorithm based on the Dual Weighted Residual (DWR) method is developed and studied for a coupled model problem of flow and convection-dominated transport. Key ingredients are a multirate approach adapted to varying dynamics in time of the subproblems, weighted and non-weighted error indicators for the transport and flow problem, respectively, and the concept of space-time slabs based on tensor product spaces for the data structure. In numerical examples the performance of the underlying algorithm is studied for benchmark problems and applications of practical interest. Moreover, the interaction of stabilization and goal-oriented adaptivity is investigated for strongly convection-dominated transport. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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Verification and search algorithms for causal DAGs

arXiv

引用

arXiv 2022年

作者： Choo, Davin Shiragur, Kirankumar Bhattacharyya, Arnab National University of Singapore Singapore Stanford University United States

We study two problems related to recovering causal graphs from interventional data: (i) verification, where the task is to check if a purported causal graph is correct, and (ii) search, where the task is to recover the correct causal graph. For both, we wish to minimize the number of interventions performed. For the first problem, we give a characterization of a minimal sized set of atomic interventions that is necessary and sufficient to check the correctness of a claimed causal graph. Our characterization uses the notion of covered edges, which enables us to obtain simple proofs and also easily reason about earlier known results. We also generalize our results to the settings of bounded size interventions and node-dependent interventional costs. For all the above settings, we provide the first known provable algorithms for efficiently computing (near)-optimal verifying sets on general graphs. For the second problem, we give a simple adaptive algorithm based on graph separators that produces an atomic intervention set which fully orients any essential graph while using O(log n) times the optimal number of interventions needed to verify (verifying size) the underlying DAG on n vertices. This approximation is tight as any search algorithm on an essential line graph has worst case approximation ratio of Ω(log n) with respect to the verifying size. With bounded size interventions, each of size ≤ k, our algorithm gives an O(log n · log k) factor approximation. Our result is the first known algorithm that gives a non-trivial approximation guarantee to the verifying size on general unweighted graphs and with bounded size interventions. Copyright © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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Speaking to Remember: Model-Based adaptive Vocabulary Learning Using Automatic Speech Recognition

SSRN

引用

SSRN 2022年

作者： Wilschut, Thomas Sense, Florian van Rijn, Hedderik Department of Experimental Psychology University of Groningen Grote Kruisstraat 2/1 Groningen9712 TS Netherlands Infinite Tactics LLC 1430 Oak Ct Ste 203 BeavercreekOH45430 United States

Memorising vocabulary is a crucial aspect of learning a new language. Although personalised learning systems have successfully improved the process of vocabulary learning by tailoring learning procedures to the needs of individual learners, the majority of such systems are limited to typing-based learning and do not allow for speech practise. Here, we aim to compare the e ciency of typing- and speech based learning. Furthermore, we explore the possibilities of improving such speech-based learning using an adaptive algorithm based on a cognitive model of memory retrieval. We combined a response time–based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using a model-based, adaptive scheduling algorithm improves recall performance relative to traditional learning in both modalities, both immediately after learning and on follow-up tests. These results can inform the development of language learning systems that—unlike traditional systems—focus on one of the most important aspects of learning a language: speaking. © 2022, The Authors. All rights reserved.

关键词： adaptive algorithms

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Performance Comparison between Simple and Adam—Eve-Like Genetic algorithms in Optimal PMU Placement Problem

Performance Comparison between Simple and Adam—Eve-Like Gen...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Mohammad Shahraeini Reza Soltanifar Department of Electrical Engineering Golestan University Gorgan Iran

ISBN: (纸本)9781665476249

In this research, the Adam-Eve algorithm is investigated, and its performance is compared with the simple genetic algorithm. The simple genetic algorithm is the simplest type of genetic algorithm in which the population size is constant, and the probability of mutation and crossover in it does not change over different generations. Adam-Eve algorithm is one of the adaptive algorithms that the number of population and rates of mutation and crossover in it are variable. In this research, both algorithms are implemented, and they are used to solve the phasor measurement unit placement problem in the power system. The simulation results show that the probability of achieving global optimal in the Adam-Eve algorithm is higher, although the convergence speed of this algorithm decreases.

关键词： Simulation Sociology Signal processing algorithms adaptive algorithms Signal processing Phasor measurement units Power systems

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