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Monitoring High-Frequency Data Streams in FinTech: FADO Versus <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="***"/></alternatives></inline-formula>-Means

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IEEE INTELLIGENT SYSTEMS 2020年第2期35卷 36-42页

作者： Pelckmans, Kristiaan Uppsala Univ Dept Informat Technol Uppsala Sweden

Modern applications of FinTech are challenged by enormous volumes of financial data. One way to handle these is to adopt a streaming setting where data are only available to the algorithms during a very short time. When a new data point (financial transaction) is generated, it needs to be processed directly, and be forgotten immediately after. Especially, ongoing globalization efforts in FinTech require modern methods of fault detection to be able to work efficiently through more than 10 000 financial transactions per second if they are to be deployed as a first line of defence. This article investigates two algorithms able to perform well in this demanding setting: $K$K-means and FADO. Especially, this article provides supports for the claim that "the use of multiple clusters does not necessarily translate into increased detection performance." To support this claim, results are reported when operating in a quasi-realistic case study of Anti Money Laundering (AML) detection in real-time payment systems. We focus on two prototypical algorithms: the passive aggressive FADO assuming a single cluster, and the well-known $K$K-means algorithm working with $K>1$K>1 clusters. We find-in this case-that the use of $K$K-means with multiple clusters is unfavorable as 1) both tuning for $K$K, as well as the need for additional complexity in the $K$K-means algorithm challenges the computational constraints;2) $K$K-means introduces necessarily added variability (unreliability) in the results;3) it requires dimensionality reduction, compromising interpretability of the detections;4) the prevalence of singleton clusters adds unreliability to the outcome. This makes in the presented case FADO favorable over K-means (with $K>1$K>1).

关键词： Clustering algorithms Intelligent systems Anomaly detection approximation algorithms Reliability Law Fraud detection fintech artificial intelligence filtering

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Finite element beam propagation method for three-dimensional optical waveguide structures

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JOURNAL OF LIGHTWAVE TECHNOLOGY 1997年第9期15卷 1728-1734页

作者： Tsuji, Y Koshiba, M Shiraishi, T HOKKAIDO UNIV DIV ELECT & INFORMAT ENGNSAPPOROHOKKAIDO 060JAPAN

A beam propagation method (BPM) based on the finite element method (FEM) is described for longitudinally varying three-dimensional (3-D) optical waveguides, In order to avoid nonphysical reflections from the computational window edges, the transparent boundary condition is introduced. The present algorithm using the Fade approximation is, to our knowledge, the first wide-angle finite element beam propagation method for 3-D waveguide structures, To show the validity and usefulness of this approach, numerical results are shown for Gaussian-beam excitation of a straight rib waveguide and guided-mode propagation in a Y-branching rib waveguide.

关键词： Optical waveguides Finite element methods Optical propagation Nonlinear optics Optical refraction Optical variables control Equations Optical reflection Boundary conditions approximation algorithms

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On the Least Trimmed Squares Estimator

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ALGORITHMICA 2014年第1期69卷 148-183页

作者： Mount, David M. Netanyahu, Nathan S. Piatko, Christine D. Silverman, Ruth Wu, Angela Y. Univ Maryland Dept Comp Sci College Pk MD 20742 USA Bar Ilan Univ Dept Comp Sci IL-52900 Ramat Gan Israel Univ Maryland Ctr Automat Res College Pk MD 20742 USA Johns Hopkins Univ Appl Phys Lab Laurel MD USA Amer Univ Dept Comp Sci Washington DC 20016 USA

The linear least trimmed squares (LTS) estimator is a statistical technique for fitting a linear model to a set of points. Given a set of n points in a"e (d) and given an integer trimming parameter ha parts per thousand currency signn, LTS involves computing the (d-1)-dimensional hyperplane that minimizes the sum of the smallest h squared residuals. LTS is a robust estimator with a 50 %-breakdown point, which means that the estimator is insensitive to corruption due to outliers, provided that the outliers constitute less than 50 % of the set. LTS is closely related to the well known LMS estimator, in which the objective is to minimize the median squared residual, and LTA, in which the objective is to minimize the sum of the smallest 50 % absolute residuals. LTS has the advantage of being statistically more efficient than LMS. Unfortunately, the computational complexity of LTS is less understood than LMS. In this paper we present new algorithms, both exact and approximate, for computing the LTS estimator. We also present hardness results for exact and approximate LTS. A number of our results apply to the LTA estimator as well.

关键词： Robust estimation Linear estimation Least trimmed squares estimator approximation algorithms Lower bounds

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A LINEAR-TIME ALGORITHM FOR GENERALIZED TRUST REGION SUBPROBLEMS

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SIAM JOURNAL ON OPTIMIZATION 2020年第1期30卷 915-932页

作者： Jiang, Rujun Li, Duan Fudan Univ Sch Data Sci Shanghai Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

In this paper, we provide the first provable linear-time (in terms of the number of nonzero entries of the input) algorithm for approximately solving the generalized trust region subproblem (GTRS) of minimizing a quadratic function over a quadratic constraint under some regularity condition. Our algorithm is motivated by and extends a recent linear-time algorithm for the trust region subproblem by Hazan and Koren [Math. Program., 158 (2016), pp. 363-381]. However, due to the nonconvexity and noncompactness of the feasible region, such an extension is nontrivial. Our main contribution is to demonstrate that under some regularity condition, the optimal solution is in a compact and convex set and lower and upper bounds of the optimal value can be computed in linear time. Using these properties, we develop a linear-time algorithm for the GTRS.

关键词： generalized trust region subproblem semidefinite programming linear-time complexity approximation algorithms

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Macroscopic Analysis of Vector Approximate Message Passing in a Model-Mismatched Setting

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第8期68卷 5579-5600页

作者： Takahashi, Takashi Kabashima, Yoshiyuki Univ Tokyo Grad Sch Sci Inst Phys Intelligence Dept Phys Bunkyo City Tokyo 1130033 Japan

In this study, macroscopic properties of the vector approximate message passing (VAMP) algorithm for inference of generalized linear models are investigated using a non-rigorous heuristic method of statistical mechanics when the true posterior cannot be used and the measurement matrix is a sample from rotation-invariant random matrix ensembles. The focus is on the correspondence between the non-rigorous replica analysis of statistical mechanics and the performance assessment of VAMP in the model-mismatched setting. The correspondence of this kind is well-known when the measurement matrix has independent and identically distributed entries. However, when the measurement matrix follows a general rotation-invariant matrix ensemble, the correspondence has been validated only under limited cases, such as the Bayes optimal inference or the convex empirical risk minimization. The result presented in this paper is to extend the scope of such correspondence. Herein, we heuristically derive the explicit formula of state-evolution equations, which macroscopically describe VAMP dynamics for the current model-mismatched case, and show that their fixed point is generally consistent with the replica symmetric solution obtained by the replica method of statistical mechanics. We also show that the fixed point of VAMP can exhibit a microscopic instability, which indicates that message variables continue to move by VAMP while their macroscopically summarized quantities converge to fixed values. The critical condition the for microscopic instability agrees with that for breaking the replica symmetry that is derived within the non-rigorous replica analysis. The results of the numerical experiments cross-check our findings.

关键词： Mathematical models Analytical models approximation algorithms Message passing Heuristic algorithms Inference algorithms Rotation measurement Message passing algorithms inference algorithms random matrices statistical mechanics phase transitions compressive sensing

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Data-Driven Near Optimization for Fast Sampling Singularly Perturbed Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第7期69卷 4689-4694页

作者： Shen, Hao Peng, Chuanjun Yan, Huaicheng Xu, Shengyuan Anhui Univ Technol Anhui Prov Key Lab Power Elect & Mot Control Maanshan 243002 Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

The optimal control issue of fast sampling singularly perturbed systems is discussed throughout this article. As a new attempt, without using complete system dynamics, the composite controller is designed by using controller of the different subsystems along with the usage of the subsystem decomposition technique by means of singular perturbation theory. Compared with the existing policy iteration and value iteration algorithms, there is no enforced requirement for a stabilizing control strategy and the increase in convergence speed is achieved by the proposed hybrid iteration algorithm. For the purpose of considering that the fast and slow subsystems have different characteristics, two hybrid iteration algorithms that can be applied in different situations are developed. Meanwhile, the difference between the given composite controller and the optimal controller is analyzed in detail. Finally, the validity of the proposed controller design method is demonstrated by an example.

关键词： Heuristic algorithms Optimal control Design methodology approximation algorithms Perturbation methods Performance analysis Optimization Fast sampling singularly perturbed systems hybrid iteration learning optimal control subsystem decomposition

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S-VIT: Stereo Visual-Inertial Tracking of Lower Limb for Physiotherapy Rehabilitation in Context of Comprehensive Evaluation of SLAM Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第4期18卷 1550-1562页

作者： Hussain, Abid Memon, Azam Rafique Wang, Hesheng Wang, Yong Miao, Yanzi Zhang, Xiufeng Shanghai Jiao Tong Univ Inst Med Robot Dept Automat Key Lab Syst Control & Informat ProcMinist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Marine Intelligent Equipment & Syst Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Marine Intelligent Equipment & Syst Dept Automat Inst Med RobotKey Lab Syst Control Shanghai 200240 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Beijing Inst Control Engn Beijing 100811 Peoples R China China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China Natl Res Ctr Rehabil Tech Aids Beijing 100081 Peoples R China

Estimation of the motion of an agent and its environment concurrently is done by simultaneous localization and mapping (SLAM). In the recent past, SLAM has made rapid and exciting progress and is used in different fields such as unmanned aerial vehicle (UAV), medical surgeries, and endoscopic procedures. The aim of this article is to devise a more accurate physiotherapy exercise monitoring device on the basis of analysis from eight different SLAM algorithms with criteria including power and memory consumption, CPU heat, and CPU utilization. This article provides a comprehensive evaluation on an embedded platform and is first of its kind, especially providing that SLAM systems ego-motion estimation has never been done so explicitly before. Based on the results of the prior analysis, we proposed a stereo visual-inertial tracking (S-VIT) for lower limb tracking in physiotherapy applications. Our proposed algorithm has significantly improved results compared with the state-of-the-art algorithms. Data sets of various physiotherapy rehabilitation exercises for leg are also collected for detailed validations where the ground truth is acquired with a state-of-the-art motion tracking system, Vicon. Note to Practitioners-Accurate real-time tracking in an unknown environment is a challenging task, especially if high accuracy is needed. In physiotherapy, analyzing the daily recorded data (data acquired from a patient's body movement) will be beneficial in the process of rehabilitation. However, keeping the daily record of patient body motion during exercise is a difficult task in most of the circumstances, which is due to the nonavailability of precise portable devices for accurate motion tracking. In this article, a solution for tracking the patient's motion (during physiotherapy) using a small hand-held device is presented. For this purpose, simultaneous localization and mapping (SLAM) is used, which, according to the best of our knowledge, is not used in the field of physiot

关键词： Simultaneous localization and mapping Tracking Feature extraction approximation algorithms Estimation Cameras Real-time systems Ego-motion estimation EMoVI-simultaneous localization and mapping (SLAM) lower limb tracking physiotherapy rehabilitation SLAM stereo visual-inertial SLAM visual-inertial sensor fusion

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Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 13985-14001页

作者： Hu, Zijian Gao, Xiaoguang Wan, Kaifang Wang, Qianglong Zhai, Yiwei Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely used in military warfare, and realizing safely autonomous motion control (AMC) in complex unknown environments is a challenge to face. In this article, we formulate the AMC problem as a Markov decision process (MDP) and propose an advanced deep reinforcement learning (DRL) method that allows UAVs to execute complex tasks in different environments. Aiming to overcome the limitations of the prioritized experience replay (PER), the proposed asynchronous curriculum experience replay (ACER) uses multithreads to asynchronously update the priorities and assigns the true priorities to increase the diversity of experiences. It also applies a temporary pool to enhance learning fromnew experiences and changes the fashion of experience pool to first-in-useless-out (FIUO) to make better use of old experiences. In addition, combined with curriculum learning (CL), a more reasonable training paradigm is designed for ACER to train UAV agents smoothly. By training in a large-scale dynamic environment constructed based on the parameters of a real UAV, ACER improves the convergence speed by 24.66% and the convergence result by 5.59% compared to the twin delayed deep deterministic policy gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities further demonstrate the strong robustness and generalization ability of the ACER agents.

关键词： Autonomous aerial vehicles Training Convergence Task analysis approximation algorithms Deep learning Complexity theory Autonomous motion control curriculum learning deep reinforcement learning experience replay UAV

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Relay node placement under budget constraint

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PERVASIVE AND MOBILE COMPUTING 2019年 53卷 1-12页

作者： Zhou, Chenyang Mazumder, Anisha Das, Arun Basu, Kaustav Matin-Moghaddam, Navid Mehrani, Saharnaz Sen, Arunabha Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85287 USA

The relay node placement problem in the wireless sensor network domain has been studied extensively. But under a fixed budget, it may be impossible to procure the minimum number of relay nodes needed to design a connected network of sensor and relay nodes. Nevertheless, one would still like to design a network with high level of connectedness, or low disconnectedness. In this paper, we introduce the notion of a measure of the "connectedness" of a disconnected graph. We study a family of problems whose goal is to design a network with "maximal connectedness" subject to a fixed budget constraint. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Maximal connectedness Disconnectivity NP-complete approximation algorithms Inapproximability

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Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2019年第12期30卷 2677-2691页

作者： Xia, Qiufen Xu, Zichuan Liang, Weifa Yu, Shui Guo, Song Zomaya, Albert Y. Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Int Sch Informat Sci & Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Univ Technol Sydney Sch Software Ultimo NSW 2007 Australia Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Univ Sydney Sch Comp Sci Camperdown NSW 2006 Australia

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promisi

关键词： Big Data Query processing Delays approximation algorithms Quality of service Distributed databases Software Data replication and placement big data analytics approximate query evaluation approximation algorithms algorithm analysis

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