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Predictive prefetching on the web and its potential impact in the wide area

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2004年第2期7卷 143-179页

作者： Bouras, C Konidaris, A Kostoulas, D Univ Patras Comp Engn & Informat Dept GR-26500 Patras Greece CTI GR-26221 Patras Greece

The rapid increase of World Wide Web users and the development of services with high bandwidth requirements have caused the substantial increase of response times for users on the Internet. Web latency would be significantly reduced, if browser, proxy or Web server software could make predictions about the pages that a user is most likely to request next, while the user is viewing the current page, and prefetch their content. In this paper we study Predictive Prefetching on a totally new Web system architecture. This is a system that provides two levels of caching before information reaches the clients. This work analyses prefetching on a Wide Area Network with the above mentioned characteristics. We first provide a structured overview of predictive prefetching and show its wide applicability to various computer systems. The WAN that we refer to is the GRNET academic network in Greece. We rely on log files collected at the network's Transparent cache (primary caching point), located at GRNET's edge connection to the Internet. We present the parameters that are most important for prefetching on GRNET's architecture and provide preliminary results of an experimental study, quantifying the benefits of prefetching on the WAN. Our experimental study includes the evaluation of two prediction algorithms: an "n most popular document" algorithm and a variation of the PPM (prediction by Partial Matching) prediction algorithm. Our analysis clearly shows that Predictive prefetching can improve Web response times inside the GRNET WAN without substantial increase in network traffic due to prefetching.

关键词： web prefetching prediction algorithms caching trace based simulation

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PANTR: A Proximal Algorithm With Trust-Region Updates for Nonconvex Constrained Optimization

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 2389-2394页

作者： Bodard, Alexander Pas, Pieter Patrinos, Panagiotis Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

This letter presents PANTR, an efficient solver for nonconvex constrained optimization problems, that is well-suited as an inner solver for an augmented Lagrangian method. The proposed scheme combines forward-backward iterations with solutions to trust-region subproblems: the former ensures global convergence, whereas the latter enables fast update directions. We discuss how the algorithm is able to exploit exact Hessian information of the smooth objective term through a linear Newton approximation, while benefiting from the structure of box-constraints or $\ell _{1}$ -regularization. An open-source C++ implementation of PANTR is made available as part of the NLP solver library ALPAQA. Finally, the effectiveness of the proposed method is demonstrated in nonlinear model predictive control applications.

关键词： Convergence Optimization Minimization Approximation algorithms Reliability Predictive control prediction algorithms Optimization algorithms predictive control for nonlinear systems numerical algorithms

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Decision-making and Planning Framework with prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections 25

Decision-making and Planning Framework with Prediction-Guide...

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25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China Nanjing University of Science and Technology Nanjing 210014 China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468817

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations. © 2022 IEEE.

关键词： Uncertainty Decision making prediction algorithms Planning Trajectory Complexity theory Behavioral sciences

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MCLFIQ: Mobile Contactless Fingerprint Image Quality

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

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IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE 2024年第2期6卷 272-287页

作者： Priesnitz, Jannis Weissenfeld, Axel Ruzicka, Laurenz Rathgeb, Christian Strobl, Bernhard Lessmann, Ralph Busch, Christoph Hsch Darmstadt Da Sec Biometr & Secur Res Grp D-64295 Darmstadt Germany AIT Austrian Inst Technol Ctr Digital Safety & Secur A-1210 Vienna Austria HID Global D-07743 Jena Germany

We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images and the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurately and is more robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a starting point for the development of a new standard algorithm for contactless fingerprint quality assessment.

关键词： Fingerprint recognition Quality assessment Databases Biometrics (access control) prediction algorithms Image matching Fingers Biometric sample quality fingerprint contactless fingerprint

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Identification of two catalytic residues in RAG1 that define a single active site within the RAG1/RAG2 protein complex

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MOLECULAR CELL 2000年第1期5卷 97-107页

作者： Fugmann, SD Villey, IJ Ptaszek, LM Schatz, DG Yale Univ Sch Med Howard Hughes Med Inst Immunobiol Sect New Haven CT 06520 USA CUNY Mt Sinai Sch Med Ruttenberg Canc Ctr New York NY 10029 USA

During V(D)J recombination, the RAG1 and RAG2 proteins cooperate to catalyze a series of DNA bond breakage and strand transfer reactions. The structure, location, and number of active sites involved in RAG-mediated catalysis have as yet not been determined. Using protein secondary structure prediction algorithms, we have identified a region of RAG1 with possible structural similarities to the active site regions of transposases and retroviral integrases. Based on this information, we have identified two aspartic acid residues in RAG1 (D600 and D708) that function specifically in catalysis. The results support a model in which RAG1 contains a single, divalent metal ion binding active site structurally related to the active sites of transposases/integrases and responsible for all catalytic functions of the RAG protein complex.

关键词： Aspartic Acid DNA-Binding Proteins Homeodomain Proteins DNA Nucleotidyltransferases 天冬氨酸 DNA结合蛋白质类同源域蛋白质类 RAG-1 Genes DNA-Binding Proteins Catalysis transposase Aspartic Acid Active Site V(D)J Recombination prediction algorithms DNA Nucleotidyltransferases Homeodomain Proteins

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IMPROVED PEL-RECURSIVE MOTION COMPENSATION

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IEEE TRANSACTIONS ON COMMUNICATIONS 1984年第10期32卷 1128-1134页

作者： WALKER, DR RAO, KR Department of Electrical Engineering University of Technology Arlington TX USA

A pel-recursive motion estimation technique for television coding was introduced by Netravali and Robbins [1]. This method basically involves computing a motion displacement and then separating the pels into predictable and unpredictable segments. The addresses required to identify these segments and thus enable the motion estimation algorithm are transmitted along with pel error data. The original algorithms are slow to converge, i.e., many iterations are required to obtain an accurate displacement estimate. This research introduces extensions to the basic algorithm of [1] and offers two significant improvements. First, convergence speed is improved substantially, which means that the algorithms can better tolerate motion changes and diverse motion within the picture. Secondly, explicit address information is not required, as it is contained implicitly in the motion compensation algorithm. For the sequences examined, this method proved superior to that of explicitly transmitting the address data. This algorithm has much better convergence rates and has prediction rates better than the original.

关键词： Motion compensation Motion estimation Signal processing algorithms Convergence prediction algorithms Bandwidth Digital TV Chromium Redundancy Cameras

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SPEECH DATA RATE REDUCTION .2. APPLICABILITY OF SENSITIVITY AND ERROR ANALYSIS

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1973年第2期AES9卷 139-150页

作者： GUNN, JE SAGE, AP SO METHODIST UNIV INST TECHNOL DALLAS TX 75222 USA

This paper deals with the application of modern estimation techniques to the problem of speech data rate reduction. It is desirable to adaptively identify and quantitize the parameters of the speech model. These paramaters cannot be identified and quantized exactly; the performance of the predictor is thereby degraded and this could prevent data reduction. In many cases it is desirable to emply a suboptimal predictor in order to simplify the algorithms, and predictor performance is again degraded. This paper develops sensitivity and error analysis as a potential method for determining quantitatively how speech data reduction system performance is degraded by imprecise parameter knowledge or suboptimal filtering. An intended use of the sensitivity and error analysis algorithms is to determine parameter identification and model structure requirements of configuration concepts for adaptive speech digitizers. First, sensitivity and error analysis algorithms are presented that form the basis for the remainder of the work. The algorithms are then used to determine how imprecise knowledge of vocal tract parameters degrades predictor performance in speech. Transversal filters have previously been proposed for this application. The sensitivity analysis algorithms are then used to determine when and by how much the transverse filter is suboptimal to the Kalman filter. In particular, the question of how effectively a higher order of all-pole model approximates a system with zeros is answered, as this question is of considerable importance in speech. Finally, the physical significance of the innovations process in speech data rate reduction is studied.

关键词： Speech analysis Error analysis Degradation Transversal filters prediction algorithms System performance Filtering Parameter estimation Sensitivity analysis Technological innovation

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An Anomaly Detection Method for Satellites Using Monte Carlo Dropout

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 2044-2052页

作者： Sadr, Mohammad Amin Maleki Zhu, Yeying Hu, Peng Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Natl Res Council Canada Digital Technol Res Ctr Ottawa ON K1A 0R6 Canada

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this article, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several long short-term memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a postprocessing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.

关键词： Time series analysis Uncertainty Artificial neural networks Satellites Telemetry Bayes methods prediction algorithms Anomaly detection satellite communications telemetry time series data uncertainty estimation

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Germline mutations of regulator of telomere elongation helicase 1, RTEL1, in Dyskeratosis congenita

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HUMAN GENETICS 2013年第4期132卷 473-480页

作者： Ballew, Bari J. Yeager, Meredith Jacobs, Kevin Giri, Neelam Boland, Joseph Burdett, Laurie Alter, Blanche P. Savage, Sharon A. NCI Clin Genet Branch Div Canc Epidemiol & Genet Rockville MD 20892 USA NCI Canc Genom Res Lab SAIC Frederick Inc Frederick MD 20877 USA

Dyskeratosis congenita (DC) is an inherited bone marrow failure and cancer predisposition syndrome caused by aberrant telomere biology. The classic triad of dysplastic nails, abnormal skin pigmentation, and oral leukoplakia is diagnostic of DC, but substantial clinical heterogeneity exists;the clinically severe variant Hoyeraal Hreidarsson syndrome (HH) also includes cerebellar hypoplasia, severe immunodeficiency, enteropathy, and intrauterine growth retardation. Germline mutations in telomere biology genes account for approximately one-half of known DC families. Using exome sequencing, we identified mutations in RTEL1, a helicase with critical telomeric functions, in two families with HH. In the first family, two siblings with HH and very short telomeres inherited a premature stop codon from their mother who has short telomeres. The proband from the second family has HH and inherited a premature stop codon in RTEL1 from his father and a missense mutation from his mother, who also has short telomeres. In addition, inheritance of only the missense mutation led to very short telomeres in the proband's brother. Targeted sequencing identified a different RTEL1 missense mutation in one additional DC proband who has bone marrow failure and short telomeres. Both missense mutations affect the helicase domain of RTEL1, and three in silico prediction algorithms suggest that they are likely deleterious. The nonsense mutations both cause truncation of the RTEL1 protein, resulting in loss of the PIP box;this may abrogate an important protein-protein interaction. These findings implicate a new telomere biology gene, RTEL1, in the etiology of DC.

关键词： RTEL1 gene direct current Germ-Line Mutation Nonsense Codon prediction algorithms proband Oral Leukoplakia Dyskeratosis Congenita GalNAc-T3 Missense Mutation Pancytopenia Telomere Mother

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Design of Anomaly Detection Functions for Controller Area Networks

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2021年 2卷 312-321页

作者： Tanksale, Vinayak Ball State Univ Comp Sci Dept Muncie IN 47306 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Vehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units - ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. There is a need for efficient security countermeasures for protecting the CAN from various attacks. In this paper, we present a novel process to efficiently design functions that can be used for anomaly detection. Our earlier work successfully demonstrated the use of Long Short-Term Memory (LSTM) Networks to perform anomaly detection. This paper focuses on the efficient design and testing of functions that are attack-resistant and can be used in our anomaly detection engine. Once trained, our system is capable of efficiently detecting anomalies in real-time. We report the results of our anomaly detection function design process. We also present the results of our overall anomaly detection engine that are used as inputs to our detection engine. Our function design process and anomaly detection engine have been tested on data from real automobiles. We present the results of our experiments and analyze our findings.

关键词： Engines Anomaly detection Security prediction algorithms Logic gates Intrusion detection Intelligent transportation systems Controller area network long short-term memory intrusion detection

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