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Online semiparametric regression via sequential Monte Carlo

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS 2025年第0期

作者： Menictas, Marianne Oates, Chris J. Wand, Matt P. Grubhub Inc 111 West Washington St Chicago IL 60602 USA Newcastle Univ Sch Math Stat & Phys Newcastle Upon Tyne NE1 7RU England Univ Technol Sydney Sch Math & Phys Sci POB 123 Broadway NSW 2007 Australia

We develop and describe online algorithms for performing online semiparametric regression analyses. Earlier work on this topic is by Luts, Broderick and Wand (2014), Journal of Computational and Graphical Statististics, 23, 589-615, where online mean-field variational Bayes (MFVB) was employed. In this article we instead develop sequential Monte Carlo approaches to circumvent well-known inaccuracies inherent in variational approaches. For Gaussian response semiparametric regression models, our new algorithms share the online MFVB property of only requiring updating and storage of sufficient statistics quantities of streaming data. In the non-Gaussian case, accurate online semiparametric regression requires the full data to be kept in storage. The new algorithms allow for new options concerning accuracy-speed trade-offs for online semiparametric regression.

关键词： generalised additive models generalised linear mixed models real-time algorithms penalised splines

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A Comparative Analysis of Deadlock Avoidance and Prevention algorithms for Resource Provisioning in Intelligent Autonomous Transport Systems Over 6G Infrastructure

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第7期24卷 7444-7461页

作者： Ugwuanyi, Emeka E. Iqbal, Muddesar Dagiuklas, Tasos London South Bank Univ Div Comp Sci & Informat London SE1 0AA England

6G is the future of intelligent connectivity artefacts with Artificial Intelligence (AI) at its backbone. The Multi-Access Edge Computing (MEC) based 6G enabled infrastructure helps in achieving the required zero latency for autonomous Intelligent Transport Systems (ITS) with features of low power consumption, lower end-to-end latency, minimal processing/transmission overheads, higher throughput and reliability. MEC are prone to a deadlock due to the limited amount of available computational resources, resulting in fatal delays in Vehicle-to-Vehicle (V2V), Vehicles to Road Side Unit (RSU) and RSU to ITS communication. The unresolved deadlock may entail higher energy consumption that can adversely affect the Quality of Service (QoS) in terms of safety and reliability with potential threats of causing fatal accidents. Therefore, it is almost imperative to resolve the deadlocks from MEC to comply with the QoS parameters of MEC based autonomous vehicles. The asserted goals can be achieved by employing an intelligent and adaptive deadlock resolution strategy. In this paper, a deadlock-aware, and collaborative edge decision algorithm has been proposed for facilitating the seamless communication of autonomous vehicles over MEC. Additionally, deadlock avoidance and prevention schemes have been evaluated using Bankers resource request avoidance algorithm, wound wait algorithm and wait-die algorithms for resource provisioning in collaborative MEC. Furthermore, the effectiveness of deadlock avoidance and prevention algorithms in real-time scenarios has been analyzed in MEC systems. The metrics used for a comparative analysis in this research include Round-trip time, Queue wait-time and CPU utilization. The proposed algorithm shows promising results when compared with prevalent techniques.

关键词： System recovery real-time systems Reliability Energy consumption 6G mobile communication Quality of service Servers Intelligent autonomous transport systems collaborative edge MEC deadlock real-time algorithms Banker's algorithm

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Decomposing Two-Stage Stochastic Scheduling Problems on a continuous-time basis via Slot Similarity 22

Decomposing Two-Stage Stochastic Scheduling Problems on a co...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Montes, Daniel Pitarch, Jose Luis de Prada, Cesar Univ Valladolid Dept Syst Engn & Automat Control Valladolid Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Ai2 Valencia Spain Univ Valladolid Inst Sustainable Proc Valladolid Spain

ISBN: (纸本)9781713872344

Incorporating uncertainty into process scheduling by explicitly considering future forecasts is crucial for achieving optimal and feasible solutions in practice. However, the complexity of two-stage stochastic scheduling formulations increases exponentially with the number of scenarios, making it difficult to solve large-scale problems in real-time using mathematical programming, which is often necessary for production-maintenance scheduling. This is where decomposition strategies on a scenario basis can be advantageous. Recently, the authors developed a method for decomposing two-stage scheduling problems (TSSPs) formulated on a discrete-time basis using the Similarity Index. This paper extends this concept to TSSPs formulated on a continuous-time basis by fuzzifying discrete decisions among slots instead of time periods, and incorporating the progressive-hedging algorithm to manage non-anticipativity in continuous decisions. The proposed algorithm is tested in a case study of a multiproduct plant consisting of a single processing unit from the literature. Results show that the proposed decomposition algorithm solves the problem faster than the monolithic formulation. Copyright (c) 2023 The Authors.

关键词： Efficient strategies for large-scale complex systems Modelling and decision-making in complex systems Two-Stage Stochastic Scheduling Decomposition Algorithm real-time algorithms scheduling and programming

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From Closing Triangles to Higher-Order Motif Closures for Better Unsupervised Online Link Prediction 21

From Closing Triangles to Higher-Order Motif Closures for Be...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Rossi, Ryan A. Rao, Anup Kim, Sungchul Koh, Eunyee Ahmed, Nesreen K. Wu, Gang Adobe Res San Francisco CA 94107 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9781450384469

This paper introduces higher-order link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as online visitor stitching, web search, and online recommendation. In such applications, real-time performance is critical. The proposed methods do not require any explicit training data, nor do they derive an embedding from the graph data, or perform any explicit learning. Most existing unsupervised methods with the above desired properties are all based on closing triangles (common neighbors, Jaccard similarity, and the ilk). In this work, we develop unsupervised techniques based on the notion of closing higher-order motifs that generalize beyond closing simple triangles. Through extensive experiments, we find that these higher-order motif closures often outperform triangle-based methods, which are commonly used in practice. This result implies that one should consider other motif closures beyond simple triangles. We also find that the "best" motif closure depends highly on the underlying network and its structural properties. Furthermore, all methods described in this work are fast for link prediction-based applications requiring real-time performance. The experimental results indicate the importance of closing higher-order motifs for unsupervised link prediction. Finally, these new higher-order motif closures can serve as a basis for studying and developing better unsupervised real-time link prediction and ranking methods.

关键词： Motif closure higher-order motif closure unsupervised link prediction network motifs graphlets real-time algorithms higher-order link prediction online algorithms

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Fast Allan Variance (FAVAR) and Dynamic Fast Allan Variance (D-FAVAR) algorithms for both Regularly and Irregularly Sampled Data

Fast Allan Variance (FAVAR) and Dynamic Fast Allan Variance ...

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Modeling, Estimation and Control Conference (MECC)

作者： Maddipatla, Satya Prasad Haeri, Hossein Jerath, Kshitij Brennan, Sean Penn State Univ University Pk PA 16802 USA Univ Massachusetts Lowell Lowell MA 01854 USA

This work develops algorithms demonstrating fast implementations of Allan variance (AVAR) for regularly and irregularly sampled signals. AVAR is a technique first developed to study the frequency stability of atomic clocks. Typical AVAR algorithms calculate changes in means between differently-sized groupings of data and thus are useful in many data aggregation processes: to select the appropriate window length or timescales for estimating a signal's moving average, to find the minimum variance of a signal, or to estimate the change in variance of a signal with complex noise contributions as a function of the number of collected data points. Unfortunately, AVAR typically involves very large signal lengths, yet the typical time required to compute AVAR increases quickly with the length of the time-series data. This paper presents a recursive algorithm inspired by the Fast Fourier Transform (FFT), specifically data organization into power-of-two groupings. This enables a fast AVAR implementation, called FAVAR, shown first for regularly sampled data. The results show a computational speed increase of three orders of magnitude versus typical AVAR calculations for data lengths often used with AVAR. Next, the FAVAR algorithm is extended to compute AVAR of irregularly sampled data by modeling these data as weighted but regularly sampled data clusters. Finally, this work analyzes Dynamic Allan variance implementations of FAVAR, called D-FAVAR, wherein AVAR is calculated at every timestep to capture window-varying statistical properties of the data stream. The recursion methods used in FAVAR, when extended to compute D-FAVAR, further increase computational speed by an additional factor of ten compared to computing the FAVAR at every timestep. They result in approximately four orders of magnitude speed improvements versus repeated calculation of AVAR with typical methods. These fast algorithms are demonstrated on signals that illustrate classical Allan variance curves, and th

关键词： Allan variance Dynamic Allan variance real-time algorithms

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Detection of Glucose Sensor Faults in an Artificial Pancreas via Whiteness Test on Kalman Filter Residuals 19

Detection of Glucose Sensor Faults in an Artificial Pancreas...

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19th IFAC Symposium on System Identification (SYSID)

作者： Manzoni, Eleonora Rampazzo, Mirco Del Favero, Simone Univ Padua Dept Informat Engn Via G Gradenigo 6-B I-35131 Padua Italy

Continuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans. Copyright (C) 2021 The Authors.

关键词： Fault detection and diagnosis Biomedical system modeling simulation and visualization Artificial pancreas or organs Statistical methods/signal analysis for FDI real-time algorithms scheduling programming

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Technical Devices Degradation Self-Analysis for Self-Maintenance Strategy: Crane Case Study 17th

Technical Devices Degradation Self-Analysis for Self-Mainten...

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17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Szpytko, Janusz Duarte, Yorlandys Salgado AGH Univ Sci & Technol Ave A Mickiewicza 30 PL-30059 Krakow Poland

Self-learning from historical data and using the resulting information to improve the future self-decision-making focused on technical objects, results machine learning approach, is a major goal in today's devices exploitation process, including also self-maintenance. Simulation-based risk-oriented approach into the broad machine learning machinery usually fits independent theorical distributions to historical degradation data of the device in order to forecast risk indicators and evaluate in the final stage the maintenance process standards The study case of the paper are critical overhead cranes operating with hazardous and continuous operation conditions. The subject of the paper is a technical devices degradation self-analysis for self-maintenance strategy discussed on crane case study, also including device representative data sensitivity analysis. The proposed subject directly impacts the device risk control management and maintenance scheduling processes, and the analysis proves the robustness of the intelligent self-analysis system by introducing additional information for future decision making The devices degradation representative data analysis impact on the preferred exploitation focused decisions have been discussed. The device features have been presented via stochastic non-linear optimization model with bounded constraint that assess a risk global -system indicator based on Monte Carlo simulations. Copyright (C) 2021 The Authors.

关键词： real-time algorithms scheduling and programming Intelligent maintenance systems Cyber-Physical Systems Robustness analysis Simulation of stochastic systems Modeling for control optimization

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A local reactive steering law for 2D collision avoidance with curvature constraints and constant speed

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ROBOTICS AND AUTONOMOUS SYSTEMS 2022年 155卷

作者： Marchidan, Andrei Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech 210 E 24th St Stop C0600 Austin TX 78712 USA

Motion planning algorithms for dynamic environments that explicitly take into account actuator constraints require a lot of computational effort due to replanning or optimizing trajectories. This makes them limited in use, especially for autonomous reactive behaviors that need to be computed on-board. Motivated by this need, we present a new real-time method for reactive collision avoidance for systems with bounded curvature in static and dynamic environments. Our approach relies on the implementation of a local steering law that satisfies a predefined bound on path curvature. The steering law depends on a user-defined parametric function that determines the transition between obstaclefree motion and collision avoidance by enforcing an obstacle impenetrability constraint. As such, we propose a systematic procedure which modulates the velocity vector to enforce curvature constraints in complex 2D environments characterized by static and moving obstacles. We provide theoretical guarantees for collision avoidance and we demonstrate this methodology through simulations. (c) 2022 Elsevier B.V. All rights reserved.

关键词： real-time algorithms Collision avoidance Steering law Curvature constraints Path planning Moving obstacles

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Model Predictive Control Prototyping and Validation for a Large Central Cooling System

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IFAC-PapersOnLine 2022年第16期55卷 338-343页

作者： Rahmat Heidari Julio H. Braslavsky Commonwealth Scientific and Industrial Research Organisation CSIRO Energy Centre Newcastle Australia The University of Newcastle Australia

In this paper we present an application of Model Predictive Control (MPC) to a commercial building cooling system. The MPC models are a combination of white, grey and black box models derived from the experimental dataset and ensure energy and mass conservation is the system. Individual equipment models are validated against the actual measurements to ensure they accurately represent the physical system. A comparison of MPC performance to that of a business-as-usual operation scenario indicates up to 25% savings in operating costs with MPC.

关键词： Modelling for control optimisation Industrial applications of optimal control Building automation real-time algorithms scheduling programming Grey box modelling

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REMaDD: Resource-Efficient Malicious Domains Detector in Large-Scale Networks

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IEEE ACCESS 2020年 8卷 66327-66337页

作者： Kdosha, Ofir Erets Rosenthal, Gilad Cohen, Kobi Freund, Alon Bartik, Avishay Ron, Aviv Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel IBM Cyber Secur Ctr Excellence IL-8489325 Beer Sheva Israel

Detecting malicious activities in cyber systems is a major challenge of cybersecurity service providers. Due to the large amount of network traffic, it is often likened to finding a needle in a haystack. Domain name system (DNS) is one of the fundamental protocols of the internet, and therefore it can give a broad view of those malicious activities, which abuse it and leave fingerprints as part of their attack vector. In this collaborative research between Ben-Gurion University, and IBM, a significant performance improvement was achieved in detecting malicious domains as compared to the state-of-the-art software solutions. Specifically, we establish a novel algorithm to detect malicious domains in large-scale DNS traffic, named Resource-Efficient Malicious Domain Detector (REMaDD), with the following desired properties. First, the algorithm does not require prior knowledge on historical malicious activities in its real-time operations. Second, the development used real live streaming data from The Inter-University Computation Center (IUCC), and operated on real-time IBM system. The algorithm is highly computational efficient and satisfies real-time requirements in terms of running time and computational complexity. REMaDD demonstrated strong performance in terms of both detection accuracy and computational efficiency as compared to existing algorithms. Specifically, experimental results on IBM production environment demonstrated that REMaDD achieved 89.4 & x0025;Precision score, and 82.9 & x0025;Recall score. By contrast, the DomainObserver, and *** algorithms achieved only 76.7 & x0025;, 67.2 & x0025;Precision score, and 81.7 & x0025;, 75.3 & x0025;Recall score, respectively.

关键词： Cyber security domain name system (DNS) detection algorithms real-time algorithms

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