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Algorithms by Sedgewick%2c Robert%2c 1946

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1The Relational And Psychological Dynamics Of Social Media Algorithms

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An investigation into the antecedents and outcomes of the sorting on Twitter's algorithm.

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2Microsoft Research Audio 103751: Numerical Algorithms In Material Science

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The talk will consist of two parts. In the first part, I will present prior work aimed at developing new algorithms for materials science applications. I developed a novel numerical method to optimize the shape of mechanical parts and maximize their strength. This optimization problem was extremely challenging as it involved changing the topology and therefore classical optimization methods do not apply. A similar idea can be applied to model shape memory alloys and plasticity in metals. In this project, the optimization led to the construction of a tree of laminated microstructures. Constraints from mechanical equilibrium were used to construct an adaptive tree and to determine the optimal number of levels. The second part involves more recent work. I will present the implementation of a new heat flux algorithm to understand failures in micro-devices in LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator); LAMMPS is a high performance code which was benchmarked on Blue Gene L with 90% parallel efficiency. Also I am currently extending the capabilities of ParaDiS (Parallel Dislocation Simulator), another high performance code which has run on 135k processors of Blue Gene L. ©2008 Microsoft Corporation. All rights reserved.

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38WLV-5LAB: Provocation Piece | Governing Algorithms

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4Enhancing Clinical Prediction Using Brain Connectivity Metrics As Inputs To Machine Learning Algorithms: Application To Depression And Obsessive-compulsive Disorder

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In this dissertation, a series of studies are designed to investigate the brain connectivity patterns (both functional connectivity and effective connectivity), comparing subjects with depression history vs. control and subjects with OCD vs. control. Depression and OCD are examples of trans-diagnostic disorders, which may be more likely due to alteration of connectivity patterns (ex., hyper-connectivity in default mode network or other networks) rather than localized brain abnormality.

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5Wiki - List Of Open Problems In Sublinear Algorithms

Wiki - List of Open Problems in Sublinear Algorithms

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6The Influence Of Unrealistic Recommendations By Algorithms An Law Experts On Unexperienced Judges

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Algorithms are increasingly implemented in the criminal justice system to assist and support judges especially in decisions requiring a prognosis (e.g., parole decisions). The aim of these algorithmic support systems is to assist but also to avoid biases and heuristics in the decision process. The aim of the present study is to explore the impact of algorithmic as well as legal human experts’ recommendations on judicial decisions in the context of parole decisions because these judgments require a prognosis of recidivism. Trainee lawyers are presented ten different (pretested) fictional cases. The cases have a high complexity because they contain mixed information (in favor, in disfavor, and irrelevant information) about the defendant. The trainee lawyers’ task is to review all ten cases and to make a parole decision, to assess the risk of recidivism, and to self-asses their confidence in their decision. In three recommendation conditions manipulated between the trainee lawyer, they receive a recommendation on how to decide on the question of parole in form of a risk score. This risk score is allegedly either provided by a supervision group of experienced judges (legal human experts’ recommendation) or by an algorithmic system that presumably bases its recommendation on data of comparable past cases (algorithmic recommendation). For the purpose of the study, the risk score is chosen randomly so that it is perceived as more realistic. We investigate the impact of these recommendations on judicial decisions by comparing the recommendation conditions (legal human experts vs. algorithm) to a control condition in which the trainee lawyers do not receive a risk score as a recommendation (i.e., baseline condition).

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7When Can Humans-in-the-loop Create Rather Than Mitigate Bias In Algorithms

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Experiment in which humans are used to provide feedback to an algorithm used to give recommendations about which social support applications, submitted by immigrant and locals, are approved. The algorithm is varied to be neutral, biased towards immigrants, or biased towards locals. The aim is to test whether these humans in the loop create rather than mitigate bias in algorithms based on their political orientation (democrat voters favoring benefits to immigrants more than republican voters; republican voters favoring benefits to locals more than republican voters).

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85Y4P-43BE: Injustice Ex Machina: Predictive Algorithms In Cr…

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9The Problems Of Regulating Algorithms Are Solvable

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Nathan Matias is a professor at Cornell University and leads the Citizens and Technology Lab. He believes that the strong tradition of scrappy, responsive, citizen science (which has led to positive changes in food safety and quality assurance regulations) can also bring positive changes to how algorithms impact our lives. The Citizens and Technology Lab, Cornell UniversityMozilla Foundation- volunteer opportunities  Algorithmic Justice League Joy Buolamwini, TED Talk, Fighting Bias in AlgorithmsThe MarkupConsumer ReportsThe History of the Good Housekeeping InstituteElinor Ostrom's Work on Governing The Commons: An Appreciation, LSEKurt Lewin, American Social Psychologist, Encyclopedia BritanicaDemocratic, Authoritarian, Laissez-Faire: What Type Of Leader Are You?, Forbes 2019 Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.

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10Tue. 04/16 - YouTube's Algorithms Continue To Suck Out Loud

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YouTube's algorithms are abhorrent in a new way, details on the next Playstation console, Google makes nice with developers, and three interesting sides to the TikTok story.Sponsors:EveryKey.com 20% off with promocode RIDE20 Mealime AppLinks: YouTube shows 9/11 link on live videos of unrelated Notre Dame fire (The Verge)EXCLUSIVE: WHAT TO EXPECT FROM SONY'S NEXT-GEN PLAYSTATION (Wired) 15 MONTHS OF FRESH HELL INSIDE FACEBOOK (Wired) Next major macOS version will include standalone Music, Podcasts, and TV apps, Books app gets major redesign (9to5Mac) Improving the update process with your feedback (Android Developer's Blog) AT&T sells Hulu stake, valuing the video streaming company at $15 billion (CNBC) Short video app TikTok extends reach in global markets, sees in-app purchases surge more than 200 per cent (South China Morning Post)TikTok downloads ordered to be blocked on iOS and Android in India over porn and other illegal content (TechCrunch) TikTok sensation Lil Nas X rewrites the rules of country music (Axios)

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11E86M-3D3R: Algorithms + Data Structures = Programs

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12Using Machine Learning Algorithms On Longitudinal Electronic Health Records For The Early Detection And Prevention Of Diseases: A Scoping Review

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Longitudinal EHRs contain a wealth of clinical information and can potentially support diagnostic tasks that target disease prevention. Although the exploding amount of data increases information, clinicians cannot assess this during consultation and, therefore, do not exploit the full potential. There is accumulating evidence that machine learning can assist in analyzing large-scale EHRs, but studies mainly focus on designing techniques and lack healthcare focus. Because machine learning is a black box for clinicians, it is significant to map the generated knowledge and benefits for preventive healthcare.

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13Melissa Williams: We OWN Climate Change Science™ & Ensure Google Algorithms ONLY Show UN Information

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Because Diversity™ means everyone MUST think alike about the agenda of 'diversity™' United Nations, Melissa Fleming says “They *own* the science on climate change”, and they have worked with Google to make sure the algorithms shows only UN information. "We own the science, and we think that the world should know it". 'Incredibly distorted information right at the top...'  means its information that doesn't fit OUR agenda. Globohomo plutocrats take for GRANTED the right to coerce you, manipulate what you see, control what you can research and find, and censor you systemically to pursue their agenda against the public.  They need to be punished, arrested, and fined. Not lauded and interviewed about it, ================== Only a fool thinks climate change politics has anything to do with climate change. The climate is changing, the climate is changing!!!' 'So?? ' 'So Hands UP!!! Gimme all your money. ' Tax. tax. tax, You work. Government takes with a gun at your back. NO MORE --------------------------- Posted for educative purposes under THE FAIR USE NOTICE: These Videos may contain copyrighted (©) material the use of which has not always been specifically authorized by the copyright owner. Such material is made available to advance understanding of ecological, political, human rights, economic, democracy, scientific, moral, ethical, and social justice issues, etc. It is believed that this constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, this material is distributed without profit to those who have expressed a prior general interest in receiving similar information for research and educational purposes. For more information go to: http://www.law.cornell.edu/uscode​. "Copyright Disclaimer Under Section 107 of the Copyright Act 1976. (https://creativecommons.org/licenses/by-sa/3.0/legalcode)

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14MISQ WHEN JUSTICE IS BLIND TO ALGORITHMS: MULTILAYERED BLACKBOXING OF ALGORITHMIC DECISION MAKING IN THE PUBLIC SECTOR

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Transparency material applicable to the MISQ article (WHEN JUSTICE IS BLIND TO ALGORITHMS: MULTILAYERED BLACKBOXING OF ALGORITHMIC DECISION MAKING IN THE PUBLIC SECTOR) containing: coding examples, media headlines and reports, and legal court case argumentation.

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15People Reject Algorithms In Uncertain Decision Domains Because They Have Diminishing Sensitivity To Forecasting Error

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This registration contains all files associated with "People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error" immediately preceding its publication in Psychological Science.

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16AJ2V-DFF5: Algorithms And Bias, Explained - Vox

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17Parallel Algorithms On A Multiprocessor

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18Parallel Scientific Computing In C++ And MPI : A Seamless Approach To Parallel Algorithms And Their Implementation

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19| 0/1 Knapsack Problem | Algorithms | Adda 247 |

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0/1 Knapsack Problem , Adda 247 ,Algorithms 

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209MXQ-P7BX: Algorithms In The Criminal Justice System: Assess…

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21Parallel Algorithms For Machine Intelligence And Vision

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22Tools And Algorithms For The Construction And Analysis Of Systems: 24th International Conference, TACAS 2018, Held As Part Of The European Joint Conferences On Theory And Practice Of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceed

authentication; computer science; computer software selection and evaluation; cryptography; data privacy; formal logic; formal methods; formal specification; internet; privacy; program compilers; programming languages; security systems; semantics; separation logic; software engineering; verification; world wide web

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23Developing An Intelligent Trading Model For The Ethiopia Commodity Exchange (ECX) Using Deep Reinforcement Learning Algorithms

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The Ethiopian Commodity Exchange (ECX) faces significant challenges, including manual trading processes, market inefficiencies, and data fragmentation, which hinder its ability to operate effectively in a volatile and dynamic environment. This research develops an intelligent trading model leveraging Deep Reinforcement Learning (DRL) algorithms, specifically Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Advantage Actor-Critic (A2C), to address these issues. The proposed framework utilizes DRL to enable agents to learn optimal trading policies through interactions with simulated ECX market environments. The model employs historical market data, representing state features such as price trends, trading volumes, and external economic indicators. Actions are defined as buy, sell, or hold decisions, while reward structures are designed to incentivize profit and penalize excessive risk. The research integrates techniques such as experience replay and target networks in DQN, action evaluation in DDQN, and advantage functions in A2C to enhance model performance and stability. Experimental results demonstrate that the DRL models significantly improve trading efficiency and decision-making accuracy compared to manual processes. DDQN outperforms DQN in managing noisy and volatile market conditions, while A2C excels in handling continuous decision variables, such as dynamic trade volumes. The results highlight the scalability and adaptability of the proposed system in addressing ECX-specific challenges, including risk management and market transparency. The study concludes that the DRL-based trading model offers transformative potential for the ECX by automating decision-making, optimizing trade execution, and promoting equitable participation among stakeholders. This research provides a foundation for integrating advanced machine learning techniques into emerging commodity markets, ensuring their efficiency and competitiveness in a global context.

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24FPG6-FMEW: CES 2018: YouTube’s Algorithms Drive 70% Of What …

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25Parallel Algorithms For Network Routing Problems And Recurrences

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26J4HF-PTY8: Procedural Justice And Risk-Assessment Algorithms…

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27NHW3-J5MU: We Should Treat Algorithms Like Prescription Drug…

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28Comparing Barrier Algorithms

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A barrier is a method for synchronizing a large number of concurrent computer processes. After considering some basic synchronization mechanisms, a collection of barrier algorithms with either linear or logarithmic depth are presented. A graphical model is described that profiles the execution of the barriers and other parallel programming constructs. This model shows how the interaction between the barrier algorithms and the work that they synchronize can impact their performance. One result is that logarithmic tree structured barriers show good performance when synchronizing fixed length work, while linear self-scheduled barriers show better performance when synchronizing fixed length work with an imbedded critical section. The linear barriers are better able to exploit the process skew associated with critical sections. Timing experiments, performed on an eighteen processor Flex/32 shared memory multiprocessor, that support these conclusions are detailed.

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29Classification Algorithms

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A barrier is a method for synchronizing a large number of concurrent computer processes. After considering some basic synchronization mechanisms, a collection of barrier algorithms with either linear or logarithmic depth are presented. A graphical model is described that profiles the execution of the barriers and other parallel programming constructs. This model shows how the interaction between the barrier algorithms and the work that they synchronize can impact their performance. One result is that logarithmic tree structured barriers show good performance when synchronizing fixed length work, while linear self-scheduled barriers show better performance when synchronizing fixed length work with an imbedded critical section. The linear barriers are better able to exploit the process skew associated with critical sections. Timing experiments, performed on an eighteen processor Flex/32 shared memory multiprocessor, that support these conclusions are detailed.

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30|Sum Of Subset Problem | Algorithms | Adda 247

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|Subset problem | Algorithms | Adda 247

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315XFT-AQWH: How NIST Tested Facial-Recognition Algorithms For…

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32The Surprising Ways Algorithms Steer Your Life & How To Make Your Ideas Stick

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Do you ever long for the good old days? Nostalgia can be such a wonderful thing. We begin this episode with a look at why the past can seem so idyllic and wonderful and what the positive effects of looking back are. http://science.howstuffworks.com/life/inside-the-mind/human-brain/nostalgia4.htmDo you really understand what algorithms are and how they work? You probably should because algorithms are used so often to influence us in making decisions on what to buy, what movies to watch or who to date. Kartik Hosanagar is a professor of technology, digital business and marketing at the Wharton School at the University of Pennsylvania and author of the book A Human's Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in control (https://amzn.to/2JuStcy). Listen and hear the good and the bad about how algorithms have become part of everyday life."Sit up straight and don't slouch!" How many times have you heard that or said that to someone else? It turns out that posture is really important and I'll explain why. http://www.dailymail.co.uk/health/article-2295420/Stand-straight-stay-fightingfit-From-raised-blood-pressure-bloated-stomach-surprising-effectsbad-posture.html#ixzz2OBJ72FZQSome ideas really stick and others quickly fade away. Why? What makes an idea sticky? Chip Heath, author of the book Made to Stick : Why Some Ideas Thrive and Others Die (https://amzn.to/2Y9YaQu) joins me to talk about his research into what makes a really good idea resonate with people. The advice he gives is very practical and will help you create ideas that people will fall in love with. This Week's Sponsors-Skillshare. For 2 months free access to over 25,000 classes go to www.Skillshare.com/something-ADT. For more information on smart home security go to www.ADT.com -Capital One. Visit www.CapitalOne.com What's in your wallet? Learn more about your ad choices. Visit podcastchoices.com/adchoices

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33Introduction To Mathematical Programming : Applications And Algorithms

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Do you ever long for the good old days? Nostalgia can be such a wonderful thing. We begin this episode with a look at why the past can seem so idyllic and wonderful and what the positive effects of looking back are. http://science.howstuffworks.com/life/inside-the-mind/human-brain/nostalgia4.htmDo you really understand what algorithms are and how they work? You probably should because algorithms are used so often to influence us in making decisions on what to buy, what movies to watch or who to date. Kartik Hosanagar is a professor of technology, digital business and marketing at the Wharton School at the University of Pennsylvania and author of the book A Human's Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in control (https://amzn.to/2JuStcy). Listen and hear the good and the bad about how algorithms have become part of everyday life."Sit up straight and don't slouch!" How many times have you heard that or said that to someone else? It turns out that posture is really important and I'll explain why. http://www.dailymail.co.uk/health/article-2295420/Stand-straight-stay-fightingfit-From-raised-blood-pressure-bloated-stomach-surprising-effectsbad-posture.html#ixzz2OBJ72FZQSome ideas really stick and others quickly fade away. Why? What makes an idea sticky? Chip Heath, author of the book Made to Stick : Why Some Ideas Thrive and Others Die (https://amzn.to/2Y9YaQu) joins me to talk about his research into what makes a really good idea resonate with people. The advice he gives is very practical and will help you create ideas that people will fall in love with. This Week's Sponsors-Skillshare. For 2 months free access to over 25,000 classes go to www.Skillshare.com/something-ADT. For more information on smart home security go to www.ADT.com -Capital One. Visit www.CapitalOne.com What's in your wallet? Learn more about your ad choices. Visit podcastchoices.com/adchoices

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34Social Media Algorithms Control The News -- Debate Clip

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MEP Marietje Schaake argues that social media algorithms are extremely influential. An excerpt from the debate "Constitutional Free Speech Principles Can Save Social Media Principles From Themselves." For the Motion: David French − Senior Writer, National Review Corynne McSherry − Legal Director, Electronic Frontier Foundation Against the Motion: Nathaniel Persily − Professor, Stanford Law Marietje Schaake − Dutch Politician & Member, European Parliament =================================== Cast Your Vote: http://smarturl.it/FakeNewsFreeSpeech Subscribe: http://bit.ly/IQ2onYouTube Official site: iq2us.org IQ2US Twitter: http://bit.ly/IQ2Twitter IQ2US Facebook: http://bit.ly/IQ2onFacebook ===================================

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35Introduction To Algorithms ( 4th Edition) Thomas H. Cormen, Charles E. Leiserson Etc

Introduction to Algorithms 4th edition

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36Introduction To Algorithms, Third Edition 1

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This book is dedicated to the students of computer science and information technology. 

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37A Novel Pseudoderivative-based Mutation Operator For Real-coded Adaptive Genetic Algorithms.

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This article is from F1000Research , volume 2 . Abstract Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

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38Efficient Sequential And Parallel Algorithms For Record Linkage.

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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39Proximal Minimization Algorithms With Cutting Planes

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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40Algorithms, Fractals, And Dynamics

This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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41BAD CHOICES : How Algorithms Can Help You Think Smarter And Live Happier

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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42Computer Animation : Algorithms And Techniques

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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43A Guide To Structured COBOL With Efficiency Techniques And Special Algorithms

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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44Proceedings Of The Ninth Annual ACM-SIAM Symposium On Discrete Algorithms

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This article is from Journal of the American Medical Informatics Association : JAMIA , volume 21 . Abstract Background and objective: Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods: Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results: Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions: We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

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45A Comparative Study Between Different Machine Learning Algorithms For Estimating The Vehicular Delay At Signalized Intersections

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The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.

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46Algorithms - An Invisible Danger : DW : November 12, 2023 8:15pm-9:00pm CET

The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.

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47Just-in-time Scheduling : Models And Algorithms For Computer And Manufacturing Systems

The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.

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489NMG-WYZT: The Next Normal: Algorithms Will Take Over Colleg…

Perma.cc archive of https://www.washingtonpost.com/outlook/next-normal-algorithms-college/2021/11/12/366fe8dc-4264-11ec-a3aa-0255edc02eb7_story.html created on 2022-05-10 17:48:59.344736+00:00.

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49TCP Slow Start, Congestion Avoidance, Fast Retransmit, And Fast Recovery Algorithms

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Modern implementations of TCP contain four intertwined algorithms that have never been fully documented as Internet standards: slow start, congestion avoidance, fast retransmit, and fast recovery. [STANDARDS-TRACK]

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50Network Time Protocol Version 4: Protocol And Algorithms Specification

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The Network Time Protocol (NTP) is widely used to synchronize computer clocks in the Internet. This document describes NTP version 4 (NTPv4), which is backwards compatible with NTP version 3 (NTPv3), described in RFC 1305, as well as previous versions of the protocol. NTPv4 includes a modified protocol header to accommodate the Internet Protocol version 6 address family. NTPv4 includes fundamental improvements in the mitigation and discipline algorithms that extend the potential accuracy to the tens of microseconds with modern workstations and fast LANs. It includes a dynamic server discovery scheme, so that in many cases, specific server configuration is not required. It corrects certain errors in the NTPv3 design and implementation and includes an optional extension mechanism. [STANDARDS-TRACK]

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