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

0 views • 17 slides

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

0 views • 42 slides

Characterizing Distributed Systems: Multiple autonomous computers with CPUs, memory, storage, and I/O paths, interconnected geographically, shared state, global invariants. Classifying Distributed Systems: Based on synchrony, communication medium, fault models like crash and Byzantine failures. Comp

9 views • 126 slides

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

3 views • 41 slides

Management in organizations is divided into three levels: operational, tactical, and strategic. Each level requires different information systems to support various activities. Operational systems focus on routine transactions and control processes, while middle-level systems aid in semi-structured

8 views • 39 slides

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

0 views • 29 slides

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

0 views • 23 slides

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

1 views • 33 slides

Recommender systems play a crucial role in providing personalized recommendations to users. This article delves into various types of recommender systems including Collaborative Filtering, Content-Based, Knowledge-Based, and Group Recommender Systems. Collaborative Filtering involves making predicti

0 views • 7 slides

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

1 views • 25 slides

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

4 views • 15 slides

Embedded Systems Design, Chapter 1 provides an insightful overview of embedded systems, distinguishing them from general-purpose computers. The chapter delves into the characteristics of embedded systems, their design considerations, and the various types of embedded computers such as general-purpos

1 views • 7 slides

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

0 views • 31 slides

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

0 views • 13 slides

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

0 views • 12 slides

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

0 views • 11 slides

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

0 views • 8 slides

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

0 views • 9 slides

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

1 views • 38 slides

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

0 views • 28 slides

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.

0 views • 35 slides

Explore advanced concepts in temporal point processes through the lens of human-centered machine learning. Topics include marked temporal point processes, independent identically distributed marks, dependent marks, and mutually exciting marks. Learn about stochastic dynamical systems such as the Sus

0 views • 8 slides

Information systems play a crucial role in organizations, encompassing transaction processing systems, functional area information systems, and enterprise resource planning systems. This content delves into the purpose of transaction processing systems, the support provided by information systems ac

0 views • 30 slides

This research delves into neuromorphic computing, a cutting-edge field that merges principles from biology and silicon technology to advance cognitive processing. The study explores top-down approaches, drawing inspiration from the auditory cortex for DNS, and bottom-up strategies to enhance CPU arc

0 views • 17 slides

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

0 views • 38 slides

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

0 views • 33 slides

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

0 views • 27 slides

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

0 views • 20 slides

Explore the world of Continuous-Time Markov Chains (CTMC) in manufacturing systems through the lens of stochastic processes and performance analysis. Learn about basic definitions, characteristics, and behaviors of CTMC, including homogeneous CTMC and Poisson arrivals. Gain insights into the memoryl

0 views • 50 slides

The JUNO experiment aims to achieve a 3% energy resolution at 1 MeV by utilizing a stereo-calorimetric system with large and small PMT systems. The motivation for stereo calorimetry in JUNO is to improve energy resolution beyond current LS-based neutrino experiments and control non-stochastic term u

0 views • 31 slides

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

0 views • 6 slides

Ken Stanley, a prominent figure in neuroevolution, has made significant contributions to the field, such as co-inventing NEAT and HyperNEAT. Through neuroevolution, complex artifacts like neural networks evolve, with the most complex known to have 100 trillion connections. The combination of evoluti

0 views • 47 slides

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

0 views • 44 slides

Embedded systems are specialized computer systems embedded within larger systems, such as control systems and car controllers. This lecture covers real-time aspects, applications of Cyber-Physical Systems (CPS), and examples like the Boeing 777/Airbus A380 cockpit. It discusses the design process of

0 views • 22 slides

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

0 views • 13 slides

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

0 views • 4 slides

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

0 views • 15 slides

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

0 views • 23 slides

The research delves into stochastic modeling of Solid-State Storage Systems, emphasizing design tradeoffs and optimization strategies. Key aspects covered include the workings of SSDs, challenges such as wear-out, garbage collection, and tradeoff considerations between cleaning cost and wear-levelin

0 views • 24 slides

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

0 views • 26 slides