7,998 research outputs found
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulationâoptimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
4. generĂĄciĂłs mobil rendszerek kutatĂĄsa = Research on 4-th Generation Mobile Systems
A 3G mobil rendszerek szabvĂĄnyosĂtĂĄsa a vĂ©gĂ©hez közeledik, legalĂĄbbis a meghatĂĄrozĂł kĂ©pessĂ©gek tekintetĂ©ben. EzĂ©rt lĂ©tfontossĂĄgĂș azon technikĂĄk, eljĂĄrĂĄsok vizsgĂĄlata, melyek a következĆ, 4G rendszerekben meghatĂĄrozĂł szerepet töltenek majd be. Több ilyen kutatĂĄsi irĂĄnyvonal is lĂ©tezik, ezek közĂŒl projektĂŒnkben a fontosabbakra koncentrĂĄltunk. A következĆben felsoroljuk a kutatott terĂŒleteket, Ă©s röviden összegezzĂŒk az elĂ©rt eredmĂ©nyeket. SzĂłrt spektrumĂș rendszerek KifejlesztettĂŒnk egy Ășj, rĂĄdiĂłs interfĂ©szen alkalmazhatĂł hĂvĂĄsengedĂ©lyezĂ©si eljĂĄrĂĄst. SzimulĂĄciĂłs vizsgĂĄlatokkal tĂĄmasztottuk alĂĄ a megoldĂĄs hatĂ©konysĂĄgĂĄt. A projektben kutatĂłkĂ©nt rĂ©sztvevĆ Jeney GĂĄbor sikeresen megvĂ©dte Ph.D. disszertĂĄciĂłjĂĄt neurĂĄlis hĂĄlĂłzatokra Ă©pĂŒlĆ többfelhasznĂĄlĂłs detekciĂłs technikĂĄk tĂ©mĂĄban. Az elĂ©rt eredmĂ©nyek Imre SĂĄndor MTA doktori disszertĂĄciĂłjĂĄba is beĂ©pĂŒltek. IP alkalmazĂĄsa mobil rendszerekben TovĂĄbbfejlesztettĂŒk, teszteltĂŒk Ă©s ĂĄltalĂĄnosĂtottuk a projekt keretĂ©ben megalkotott Ășj, gyƱrƱ alapĂș topolĂłgiĂĄra Ă©pĂŒlĆ, a jelenleginĂ©l nagyobb megbĂzhatĂłsĂĄgĂș IP alapĂș hozzĂĄfĂ©rĂ©si koncepciĂłt. A tĂ©makörben Szalay MĂĄtĂ© Ph.D. disszertĂĄciĂłja mĂĄr a nyilvĂĄnos vĂ©dĂ©sig jutott. Kvantum-informatikai mĂłdszerek alkalmazĂĄsa 3G/4G detekciĂłra Ăj, kvantum-informatikai elvekre Ă©pĂŒlĆ többfelhasznĂĄlĂłs detekciĂłs eljĂĄrĂĄst dolgoztunk ki. Ehhez Ășj kvantum alapĂș algoritmusokat is kifejlesztettĂŒnk. Az eredmĂ©nyeket nemzetközi folyĂłiratok mellett egy sajĂĄt könyvben is publikĂĄltuk. | The project consists of three main research directions. Spread spectrum systems: we developed a new call admission control method for 3G air interfaces. Project member Gabor Jeney obtained the Ph.D. degree and project leader Sandor Imre submitted his DSc theses from this area. Application of IP in mobile systems: A ring-based reliable IP mobility mobile access concept and corresponding protocols have been developed. Project member MĂĄtĂ© Szalay submitted his Ph.D. theses from this field. Quantum computing based solutions in 3G/4G detection: Quantum computing based multiuser detection algorithm was developed. Based on the results on this field a book was published at Wiley entitled: 'Quantum Computing and Communications - an engineering approach'
Comparing discrete choice models: some housing market examples
Introduction: Since the mid nineteen seventies there has been strong interest within variolls branches of social science in the adaptation of the discrete choice modeling methodology towards a wide range of research problems. This has required recognition of a wide variety of alternative decision-contexts (Landau et a1. 1982) and behaviour-patterns (Lerman, 1979), and has also raised general issues concerning the variable extent to which individual or subgroup choices may be restricted by spatial and temporal constraints. Further interest has been expressed about the spatial and temporal transferability of alternative discrete choice models (Atherton and Ben-Akiva, 1976: Galbraith and Hensher, 1982). This substantive diversification has been accompanied by a variety of technical and methodological refinements of the multinomiallogit (MNL) and multinomial probit (MNP) models, ranging from new estimation procedures (Hausman and Wise, 1978) to the development of less-restrictive, computationally tractable discrete choice model forms (for example, Williams, 1977: Daly and Zachary, 1978). Faced with both a wider selection of methodological tools and a broader
spectrum of substantive enquiry, there exists a clear need for formal comparison procedures which the analyst can call upon to evaluate a given model specification or framework.
In this paper, I attempt to review briefly some trends amongst recent housing choice studies which employ discrete choice modeling methods. A new procedure is then presented (Hubert and Golledge, 1981; Halperin et al. 1984) which may be used to compare discrete choice models specified and/or structured in accordance with different a priori hypotheses. It is argued that this method fills a gap between existing discrete choice model comparison-procedures which are inapplicable to 'nonnested' model specifications, that is, to competing discrete choice models which comprise totally different variable specifications and that such procedures can usefully aid selection of the discrete choice model most appropriate to any given decision context
Data-driven satisficing measure and ranking
We propose an computational framework for real-time risk assessment and
prioritizing for random outcomes without prior information on probability
distributions. The basic model is built based on satisficing measure (SM) which
yields a single index for risk comparison. Since SM is a dual representation
for a family of risk measures, we consider problems constrained by general
convex risk measures and specifically by Conditional value-at-risk. Starting
from offline optimization, we apply sample average approximation technique and
argue the convergence rate and validation of optimal solutions. In online
stochastic optimization case, we develop primal-dual stochastic approximation
algorithms respectively for general risk constrained problems, and derive their
regret bounds. For both offline and online cases, we illustrate the
relationship between risk ranking accuracy with sample size (or iterations).Comment: 26 Pages, 6 Figure
Chemotherapy planning and multi-appointment scheduling: formulations, heuristics and bounds
The number of new cancer cases is expected to increase by about 50% in the
next 20 years, and the need for chemotherapy treatments will increase
accordingly. Chemotherapy treatments are usually performed in outpatient cancer
centers where patients affected by different types of tumors are treated. The
treatment delivery must be carefully planned to optimize the use of limited
resources, such as drugs, medical and nursing staff, consultation and exam
rooms, and chairs and beds for the drug infusion. Planning and scheduling
chemotherapy treatments involve different problems at different decision
levels. In this work, we focus on the patient chemotherapy multi-appointment
planning and scheduling problem at an operational level, namely the problem of
determining the day and starting time of the oncologist visit and drug infusion
for a set of patients to be scheduled along a short-term planning horizon. We
use a per-pathology paradigm, where the days of the week in which patients can
be treated, depending on their pathology, are known. We consider different
metrics and formulate the problem as a multi-objective optimization problem
tackled by sequentially solving three problems in a lexicographic
multi-objective fashion. The ultimate aim is to minimize the patient's
discomfort. The problems turn out to be computationally challenging, thus we
propose bounds and ad-hoc approaches, exploiting alternative problem
formulations, decomposition, and -opt search. The approaches are tested on
real data from an Italian outpatient cancer center and outperform
state-of-the-art solvers.Comment: 28 pages, 3 figure
Chance Constrained Mixed Integer Program: Bilinear and Linear Formulations, and Benders Decomposition
In this paper, we study chance constrained mixed integer program with
consideration of recourse decisions and their incurred cost, developed on a
finite discrete scenario set. Through studying a non-traditional bilinear mixed
integer formulation, we derive its linear counterparts and show that they could
be stronger than existing linear formulations. We also develop a variant of
Jensen's inequality that extends the one for stochastic program. To solve this
challenging problem, we present a variant of Benders decomposition method in
bilinear form, which actually provides an easy-to-use algorithm framework for
further improvements, along with a few enhancement strategies based on
structural properties or Jensen's inequality. Computational study shows that
the presented Benders decomposition method, jointly with appropriate
enhancement techniques, outperforms a commercial solver by an order of
magnitude on solving chance constrained program or detecting its infeasibility
On the evolutionary optimisation of many conflicting objectives
This inquiry explores the effectiveness of a class of modern evolutionary algorithms, represented by Non-dominated Sorting Genetic Algorithm (NSGA) components, for solving optimisation tasks with many conflicting objectives. Optimiser behaviour is assessed for a grid of mutation and recombination operator configurations. Performance maps are obtained for the dual aims of
proximity to, and distribution across, the optimal trade-off surface. Performance sweet-spots for both variation operators are observed to contract as the number of objectives is increased. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance for higher numbers of objectives, even when large population
sizes are used. Explanations for this behaviour are offered via the concepts of dominance resistance and active diversity promotion
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
© 2007 EUCA.A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means of a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
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