Explore an active learning application in the search for dark matter using ATLAS PanDA and iDDS. Investigate Beyond Standard Model physics parameters related to Hidden Abelian Higgs Model and New Scalar with a focus on cross-section limit calculations. Understand the process for generating Monte Car

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Explore the process of modeling in Decision Support Systems for Business Intelligence through images, tables, and examples. Learn about the dimensionality of models, nonlinear relationships, randomness, and Monte Carlo analysis as essential components in business decision-making.

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This course covers the basics of systems modeling, discrete-event simulation, and computer systems performance evaluation. Topics include Monte Carlo simulation, probability models, simulation output analysis, queueing theory, and more. Professor Carey Williamson leads the course with a focus on pra

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Multiple Sequence Alignment (MSA) is essential for various biological analyses like phylogeny estimation and selection quantification. Profile Hidden Markov Models (HMMs) play a crucial role in achieving accurate alignments. This process involves aligning unaligned sequences to create alignments wit

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Discrepancy and Optimization, explored by Nikhil Bansal, delve into irregularities when approximating continuous data with discrete points. This concept addresses the challenge of distributing points uniformly in a grid and optimizing numerical integration or sampling techniques. Additionally, it to

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Polymer degradation involves a reduction in molecular weight due to various factors like heating, mechanical stresses, radiation, oxygen, and moisture. Two main types of degradation include chain end degradation and random degradation, each affecting the polymer structure differently. Chain end degr

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Supply chain attacks pose a significant threat to software developers and suppliers by targeting source codes and build processes to distribute malware. This article discusses the importance of supply chain risk management, the various attack vectors involved, the industries at risk, and the repercu

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Dive into the world of Monte Carlo simulations and probabilistic methods, understanding the basic principles, the Law of Large Numbers, Pseudo-Random Number Generators, and practical Monte Carlo steps. Explore topics like conditional probability, basic geometry, and calculus through engaging exercis

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This lecture covers the kinetics of free radical polymerization, including initiation, propagation, termination, and kinetic chain length concepts. It explains the calculation of kinetic chain length and chain-transfer reactions. Key points include the rate equations for initiation, propagation, and

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Explore Rutgers Business School's innovative Supply Chain Management Curriculum designed for high schools. The curriculum focuses on Project-Based Learning (PBL) and integrates essential elements such as significant content, 21st-century skills, in-depth inquiry, and more. The program is based on th

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Explore the world of randomized algorithms through types like Las Vegas and Monte Carlo, with a focus on classic examples such as Quick Sort. Learn how randomness plays a crucial role in computation and discover the principles behind these algorithms. Dive into the applications of randomized algorit

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Randomized algorithms incorporate randomness into computations, with Las Vegas algorithms always providing the correct answer but varying in time, while Monte Carlo algorithms occasionally give wrong answers. Quick Sort is a classic Las Vegas algorithm that involves pivoting elements for sorting. Ch

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Multiplicity distributions play a crucial role in understanding the cascade of quarks and gluons at the LHC energies, revealing underlying correlations in particle production. Popular models like Monte Carlo and statistical models are used to describe the charged particle multiplicity distributions.

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Andrej Markov and his contributions to the development of Markov chains are explored, highlighting the principles, algorithms, and rules associated with these probabilistic models. The concept of a Markov chain, where transitions between states depend only on the current state, is explained using we

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A Markov model is a chain-structured process where future states depend only on the present state. Hidden Markov Models are Markov chains where the state is only partially observable. Explore state transition and emission probabilities in various scenarios such as weather forecasting and genetic seq

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Explore the key components of supply chains, the importance of supply chain management technology, and strategies to overcome challenges. Learn about supply chain visibility, the structure of supply chains, and the three segments - upstream, internal, and downstream. Discover how organizations acces

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In the realm of Markov Decision Processes (MDPs), tackling infinite horizon problems involves defining value functions, introducing discount factors, and guaranteeing the existence of optimal policies. Computational challenges like policy evaluation and optimization are addressed through algorithms

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Explore the process of obtaining the Cherenkov ring radius using Circular Hough Transform in a modular rich detector simulation. The study, conducted by Cheuk-Ping Wong from Georgia State University, delves into Monte Carlo results, ring finder algorithms, event displays, and radius distributions in

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Monte Carlo Tree Search (MCTS) is an intelligent tree search algorithm that balances exploration and exploitation by using random sampling through simulations. It is widely used in AI applications such as games (e.g., AlphaGo), scheduling, planning, and optimization. This algorithm involves steps li

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Markov Decision Processes (MDPs) involve taking actions that influence the state of the world, leading to optimal policies. Components include states, actions, transition models, reward functions, and policies. Solving MDPs requires knowing transition models and reward functions, while reinforcement

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Explore the world of Markov Decision Processes (MDPs) and optimal policies in Machine Learning. Uncover the concepts of states, actions, transition functions, rewards, and policies. Learn about the significance of Markov property in MDPs, Andrey Markov's contribution, and how to find optimal policie

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This study aims to quantify global agricultural carbon fluxes by considering above- and belowground crop biomass, crop residues, livestock interactions, and human food intake. The methods involve combining inventory data and spatially resolved estimates of Net Carbon Exchange (NCE). Satellite-based

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Synthesizing economic data for comprehensive supply chain analysis, this talk by Krista Chan, Kevin Li, and Christian Moscardi from the U.S. Census Bureau discusses the goals, challenges, supply chain interests, data sources, and desired functionalities to present a holistic view of product supply c

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Explore the significance of randomness in various computational aspects including random sampling, cooking techniques, polling methods, investing strategies, and its role in computer science. It delves into randomized algorithms, Monte Carlo simulations, cryptography, and more.

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The USAID Global Health Supply Chain Program has supported Pakistan in leveraging its logistics management information system (LMIS) to efficiently plan and deliver critical COVID-19 supplies. Through coordination with government entities and use of various LMIS interfaces, Pakistan has enhanced dat

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The value chain involves adding value through a series of activities from producer to consumer, focusing on meeting consumer demands and gaining a competitive advantage. On the other hand, the supply chain focuses on efficient and cost-effective product distribution to meet consumer needs. The prima

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This presentation delves into the critical aspects of supply chain security and IT governance, highlighting the synchronization of IT decisions across supply chains, global supply chain concerns, the cost implications of supply chain security lapses, and the need for more research and strategic alig

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Markov Chain Monte Carlo (MCMC) algorithms play a crucial role in generating sequences of states for various applications. One popular MCMC method, Gibbs Sampling, is particularly useful for Bayesian networks, allowing the random sampling of variables based on probability distributions. This process

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Approximate inference methods in Bayes nets, such as random and rejection sampling, utilize Monte Carlo algorithms for stochastic sampling to estimate complex probabilities. Random sampling involves sampling in topological order, while rejection sampling generates samples from hard-to-sample distrib

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Exploring the concept of Monte Carlo analysis as a method of uncertainty assessment through sampling inputs, running models, and analyzing outputs. Learn how to simulate dice rolls, evaluate probabilities, and assess accuracy with sample size. Monte Carlo approaches are versatile and applicable to v

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This research discusses faster and space-efficient algorithms for Subset Sum and related problems, introducing techniques like Meet-in-the-Middle (MitM) approach and Monte Carlo algorithm. It also covers topics such as Floyds Cycle Finding, Element Distinctness (ED) by BCM, List Disjointness, and Su

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Understanding the importance of plasma etching in semiconductor fabrication, this discourse delves into the challenges faced in modeling modern etch processes. Topics covered include stochastic defect detection, reactor-level plasma physics, and an integrated model hierarchy approach. Techniques suc

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Understanding variance reduction techniques in Monte Carlo simulations is essential for improving program efficiency. Techniques like biasing, absorption weighting, splitting, and forced collision help reduce variance and enhance simulation accuracy. By adjusting particle weights and distributions,

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Simulation study on beam polarization for the Circular Electron Positron Collider (CEPC) using the PTC Poly- morphic Tracking Code. The study includes orbital and spin tracking, equilibrium polarization calculation, and Monte-Carlo simulation of depolarization rate. Comparison with other Monte-Carlo

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Rolling Horizon Evolutionary Algorithms (RHEA) in General Video Game Playing (GVGP) show promise for faster evolution, but there is a lack of clear analysis in the existing literature. This study explores population initialization techniques for RHEA in GVGP, assessing methods like One Step Look Ahe

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Stochastic dynamic programming with Markov chains is used for optimal control of the forest sector, focusing on continuous cover forestry. This approach optimizes forest industry production, harvest levels, and logistic solutions based on market conditions. The method involves solving quadratic prog

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This study focuses on the calibration and reduction of uncertainties in criticality benchmarks by analyzing integral data and correlations not present in differential data. A simplified toy model is used to examine correlations in fission, capture, inelastic, and leakage components across different

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China-Africa Supply Chain Cooperation presents both challenges and opportunities for development. The growth of China-Africa supply chain is crucial, considering Africa's participation in the global supply chain mainly focused on providing primary products. The strategic importance of this relations

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Discussion on the concept of complex probability in solving real-world problems, particularly focusing on the transition probability matrix of discrete Markov chains. The paper introduces a measure more general than conventional probability, leading to the idea of complex probability. Various exampl

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Bayesian statistical modeling often relies on Markov chain Monte Carlo (MCMC) methods for estimating parameters. This involves sampling from full conditional distributions, which can be complex when software limitations arise. In such cases, the need to implement custom MCMC samplers may arise, requ

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