Results 1 
4 of
4
Thermodynamics and Garbage Collection
 In ACM Sigplan Notices
, 1994
"... INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over time (i.e., constants of motion). Second, statistical thermodynamics successfully reduces macroscopic properties of a system to the statistical behavior of large numbers of microscopic processes. As computer systems become large assemblages of small components, an explanation of their macroscopic behavior may also be obtained as the aggregate statistical behavior of its component parts. If not, the elegance of the statistical thermodynamical approach can at least provide inspiration for new classes of models. 1 Third, the components of computer systems are approaching the same size as the microscopic pr
Analysis and Simulation of a Stochastic, DiscreteIndividual Model of STD Transmission with Partnership Concurrency
 Department of Probability and Statistics, University of Sheffield
, 2002
"... Deterministic differential equation models indicate that partnership concurrency and nonhomogeneous mixing patterns play an important role in the spread of sexuallytransmitted infections. Stochastic discreteindividual simulation studies arrive at similar conclusions, but from a very different mod ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Deterministic differential equation models indicate that partnership concurrency and nonhomogeneous mixing patterns play an important role in the spread of sexuallytransmitted infections. Stochastic discreteindividual simulation studies arrive at similar conclusions, but from a very different modeling perspective. This paper presents a stochastic discreteindividual infection model that helps to unify these two approaches to infection modeling. The model allows for both partnership concurrency, as well as the infection, recovery, and reinfection of an individual from repeated contact with a partner, as occurs with many mucosal infections. The simplest form of the model is a networkvalued Markov Chain, whose nodes are individuals and arcs represent partnerships. Connections between the differential equation and discreteindividual approaches are constructed with largepopulation limits that approximate endemic levels and equilibrium probability distributions that describe partnersh...
ABSTRACT GERMS: AN EPIDEMIOLOGIC SIMULATION TOOL FOR STUDYING GEOGRAPHIC AND SOCIAL EFFECTS ON INFECTION TRANSMISSION
"... The analysis, surveillance, and control of infectious diseases are important functions of public health organizations around the world. This article describes the design and implementation of simulation tools that include several innovations for modeling infectious disease transmission. These tools ..."
Abstract
 Add to MetaCart
(Show Context)
The analysis, surveillance, and control of infectious diseases are important functions of public health organizations around the world. This article describes the design and implementation of simulation tools that include several innovations for modeling infectious disease transmission. These tools address several important issues for understanding the epidemiology of sexually transmitted infections. The model accounts for realistic infection transmission systems by explicitly modeling (i) heterogeneous populations of individuals with varying social and geographic characteristics, (ii) complex interaction between individuals to characterize opportunities for transmission, (iii) infection characteristics such as transmission probabilities and infection duration, and (iv) contact and infection histories. Since public health organizations collect and use information regarding infected individuals, including geographic location and partnership data, the tool is well equipped to help evaluate the effectiveness of interventions based on that data. We outline design decisions and present results of initial simulation analysis. We also discuss shortterm goals for extending the simulation toolkit to address specific needs of the Centers for Disease Control. 1
Quotes: Are all Computer Scientists computer literate?
"... at his inaugural lecture, after the computer running his PowerPoint presentation crashed. 2. I HATE COMPUTERS!: Prominent Computer Scientist, in the tea room. 3. Computers zijn fijne dinge, zolank hulle werk: (Computers are nice, but only when they work): MD of an International IT organization, afte ..."
Abstract
 Add to MetaCart
(Show Context)
at his inaugural lecture, after the computer running his PowerPoint presentation crashed. 2. I HATE COMPUTERS!: Prominent Computer Scientist, in the tea room. 3. Computers zijn fijne dinge, zolank hulle werk: (Computers are nice, but only when they work): MD of an International IT organization, after attempting for many hours to get his home network up. 4. I don't have an email address. Donald E Knuth. 5. Switch the power off at the plug! Computer Scientist, after struggling with the remote control of the aircon. 6. My son/daughter is very good with computers: On many occasions, parent of failed first year student. 7. I am now at first year university level: Secretary, after completing a week long course in MSWord.