A comprehensive analysis of the social cost of greenhouse gases, including recent RFF research and EPA estimates, using a stochastic discounting approach. Explore a modular framework for calculating the SCC and RFF Socioeconomic Projections on global CO2 emissions and economic growth. Learn about th

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Delve into the intricate world of travel demand modeling systems, where complexity arises from dynamic feedback, stochastic effects, uncertainty, and system structure. Discover the balance needed to minimize complicatedness while maximizing behavioral complexity in regional travel modeling. Uncover

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Explore the innovative methods and practical applications of Stochastic Storm Transposition (SST) in the context of HEC-HMS. Delve into the history, fundamentals, simulation procedures, and benefits of using SST for watershed-averaged precipitation frequency analysis. Learn about the non-parametric

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Understanding the dynamics of opinion formation and control in social networks is a critical area of research. This study, supervised by Susana Gomes and Marie-Therese Wolfram, explores the manipulation of collective behavior through various models including ODE, agent-based, and stochastic analysis

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The LNG forecast presentation provides insights into the expected LNG deliveries, LDC LNG demand, and future assumptions in NEPOOL Markets and Reliability Committees. It discusses winter de-rated capacity for gas-fired resources, stochastic forecasts for gas availability, LNG terminal infrastructure

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Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

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Queuing Theory is the study of waiting lines and service levels in businesses. It involves analyzing customer arrival patterns, service configurations, and queuing processes such as FIFO vs. LIFO disciplines. Characteristics include the generation of customers, homogeneity of populations, and determ

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Explore the concept of interaction effects in composite-based structural equation modeling (SEM) through topics like the logic of interaction, estimating effects, multigroup analysis, and visualizing effects. Learn about moderators, their role in relationships between variables, and techniques for a

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Explore the similarities between Computable General Equilibrium (CGE) models and Dynamic Stochastic General Equilibrium (DSGE) models, their equilibrium concepts, and the use of descriptive equilibria in empirical modeling. Learn how CGE and DSGE models simulate the operation of commodity and factor

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Dive into Session 7 of the ARIDE program to explore the prevalence of drug and alcohol use, the concept of polydrug impairment, and the potential effects of combining different substances. Learn about null effects, overlapping effects, and how various drug combinations can impact impairment indicato

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Factorial designs in experiments allow researchers to study the effects of multiple independent variables simultaneously. This type of design enables the examination of main effects and interactions between factors, providing a comprehensive understanding of the research variables. Main effects refe

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In this lecture by Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky, an overview of mini-batch gradient descent is provided. The discussion includes the error surfaces for linear neurons, convergence speed in quadratic bowls, challenges with learning rates, comparison with stochastic gradient d

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Introducing xtsfkk, a new Stata command for fitting panel stochastic frontier models with endogeneity, offering better control for endogenous variables in the frontier and/or the inefficiency term in longitudinal settings compared to standard estimators. Learn about the significance of stochastic fr

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Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and

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This study delves into the generalization of Empirical Risk Minimization (ERM) in stochastic convex optimization, focusing on minimizing true objective functions while considering generalization errors. It explores the application of ERM in machine learning and statistics, particularly in supervised

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Exploring high probability generalization bounds for uniformly stable algorithms, the relationship between dataset, loss function, and estimation error, and the implications of low sensitivity on generalization. Known bounds and new theoretical perspectives are discussed, along with approaches like

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Optimization is essential in neural networks to find the minimum value of a function. Techniques like local search, gradient descent, and stochastic gradient descent are used to minimize non-linear objectives with multiple local minima. Challenges such as overfitting and getting stuck in local minim

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This lecture by Dr. Zaid Shaker Naji delves into the biological effects of radiation, including deterministic and stochastic effects. It covers mechanisms of damage at the cellular level, such as direct and indirect damage, and discusses somatic and genetic damages that can arise following exposure.

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The etiology of aging in both biological and inanimate systems explores the concept that aging may not be driven by genetic programming but rather result from stochastic processes. The loss of molecular structure is a key feature of age-related changes. The Second Law of Thermodynamics is suggested

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This contribution presents preliminary THz channel modeling results for future wireless data center scenarios. Ray tracing simulations are conducted for various channel types, utilizing RMS delay spread and RMS angular spread to measure multipath richness. A stochastic channel model is developed and

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SCRUM2 project aims to enhance CMEMS through regional/coastal ocean-biogeochemical uncertainty modelling, ensemble consistency verification, probabilistic forecasting, and data assimilation. The research team plans to contribute significant advancements in ensemble techniques and reliability assessm

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Regression models help analyze effects of independent variables (IVs) on dependent variables (DVs, like weight loss from exercise time). Interactions explore how one IV's effect can be modified by another IV (moderating variable, MV). In this seminar's purpose, techniques to estimate, test, and grap

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Exploring efficiency methodologies in analyzing prisons service quality, this study focuses on parametric and non-parametric approaches such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). It delves into benchmarking techniques, productivity analysis, and the implications

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Explore the simplest model of population growth and the assumptions it relies on. Delve into the challenges of real-world scenarios, such as stochastic effects caused by demographic and environmental variations in birth and death rates. Learn how these factors impact predictions and models.

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Understanding causal effects through methods like propensity score weighting is crucial in institutional research. This approach helps in estimating the impact of various interventions, such as a writing program, by distinguishing causation from correlation. The use of propensity score matching aids

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This presentation delves into the analysis and optimality of multiserver stochastic scheduling, focusing on the theory of large-scale computing systems, queueing theory, and prior work on single-server and multiserver scheduling. It explores optimizing response time and resource efficiency in modern

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This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta

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American psychologists have traditionally believed in strong media effects, attributing direct influence on audiences. However, the limited effects theory emerged in the 1940s, challenging this notion by suggesting media's negligible impact on behaviors such as voting. On the other hand, proponents

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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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Exploring the use of a stochastic weather generator combined with downscaled General Circulation Models for climate change analysis in the California Department of Water Resources. The presentation outlines the motivation, weather-regime based generator description, scenario generation, and a case s

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Explore the concepts of Brownian motion, integration of stochastic differential equations, and derivations by Einstein and Langevin. Learn about the assumptions, forces, and numerical integration methods in the context of stochastic processes. Discover the key results and equations that characterize

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This course dives into animation and visual effects techniques through Narrative Storytelling and Motion Graphics with Adobe After Effects. Learn to create visually rich and impactful animated films synced to audio, expressing complex ideas through various modes of storytelling. Practice key motion

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Explore Generalized Stochastic Petri Nets (GSPN) to model manufacturing systems and evaluate steady-state performances. Learn about stochastic Petri nets, inhibitors, priorities, and their applications through examples. Delve into models of unreliable machines, productions systems with priorities, a

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Narcotic analgesics, such as opiates and opioids derived from opium, interact with specific opioid receptors in the body to produce analgesic effects. Different opioid receptors have varying effects, with mu (MOP) being a good analgesic but with adverse effects, delta (DOP) and kappa (KOP) have nuan

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Collective effects play a crucial role in Higgs factories and high-energy physics facilities. Impedance effects are proportional to beam-induced voltage, with peak bunch current impacting SB effects and average current affecting MB effects. Factors like beam loading compensation and detuning of the

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Stochastic algorithms, including Monte Carlo and Las Vegas variations, leverage randomness to tackle complex tasks efficiently. While Monte Carlo algorithms prioritize speed with some margin of error, Las Vegas algorithms guarantee accuracy but with variable runtime. They play a vital role in primal

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Explore an optimal stopping approach for early drought detection, focusing on setting trigger levels based on precipitation measures. The goal is to determine the best time to send humanitarian aid by maximizing expected rewards and minimizing expected costs through suitable gain/risk functions. Tas

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Explore the world of viral marketing and user behavior optimization through stochastic optimal control in the realm of human-centered machine learning. Discover strategies to maximize user activity in social networks by steering behaviors and understanding endogenous and exogenous events. Dive into

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Explore the relationship between sample and space complexity in stochastic streams to estimate distribution properties and solve various problems. The research delves into the tradeoff between the number of samples required to solve a problem and the space needed for the algorithm, covering topics s

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Training dense linear models on FPGA with low-precision data offers increased hardware efficiency while maintaining statistical efficiency. This approach leverages stochastic rounding and multivariate trade-offs to optimize performance in machine learning tasks, particularly using Stochastic Gradien

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