967 resultados para Asymptotically optimal policy


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Evidence-based policy is a means of ensuring that policy is informed by more than ideology or expedience. However, what constitutes robust evidence is highly contested. In this paper, we argue policy must draw on quantitative and qualitative data. We do this in relation to a long entrenched problem in Australian early childhood education and care (ECEC) workforce policy. A critical shortage of qualified staff threatens the attainment of broader child and family policy objectives linked to the provision of ECEC and has not been successfully addressed by initiatives to date. We establish some of the limitations of existing quantitative data sets and consider the potential of qualitative studies to inform ECEC workforce policy. The adoption of both quantitative and qualitative methods is needed to illuminate the complex nature of the work undertaken by early childhood educators, as well as the environmental factors that sustain job satisfaction in a demanding and poorly understood working environment.

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EcoP15I is a type III restriction enzyme that requires two recognition sites in a defined orientation separated by up to 3.5 kbp to efficiently cleave DNA. The mechanism through which site- bound EcoP15I enzymes communicate between the two sites is unclear. Here, we use atomic force microscopy to study EcoP15I-DNA pre-cleavage complexes. From the number and size distribution of loops formed, we conclude that the loops observed do not result from translocation, but are instead formed by a contact between site- bound EcoP15I and a nonspecific region of DNA. This conclusion is confirmed by a theoretical polymer model. It is further shown that translocation must play some role, because when translocation is blocked by a Lac repressor protein, DNA cleavage is similarly blocked. On the basis of these results, we present a model for restriction by type III restriction enzymes and highlight the similarities between this and other classes of restriction enzymes.

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We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Theta(opt) bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form d(opt)((P) over bar (t)) x Theta(opt) with d(opt) scaling as (P) over bar (1/eta)(t), where (P) over bar (t) is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then provide a simple characterisation of the optimal operating point.

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The aim of the study was to examine the influence of school smoking policy and school smoking prevention programs on the smoking behaviour of students in high schools in Prince Edward Island using the School Health Action Planning Evaluation System (SHAPES). A total sample included 13,131 observations of students in grades 10-12 in ten high schools in Prince Edward Island over three waves of data collection (1999, 2000, and 2001). Changes in prevalence of smoking and factors influencing smoking behaviour were analyzed using descriptive statistics and Chi-Square tests. Multi-level logistic regression analyses were used to examine how both school and student characteristics were associated with smoking behaviour (I, II, III, IV). Since students were located within schools, a basic 2-level nested structure was used in which individual students (level 1) were nested within schools (level 2). For grade 12 students, the combination of both school policies and programs was not associated with the risk of smoking and the presence of the new policy was not associated with decreased risk of smoking, unless there were clear rules in place (I). For the grade 10 study, (II) schools with both policies and programs were not associated with decreased risk of smoking. However, the smoking behaviour of older students (grade 12) at a school was associated with younger students’ (grade 10) smoking behaviour. Students first enrolled in a high school in grade 9, rather than grade 10, were at increased risk of occasional smoking. For students who transitioned from grade 10 to 12 (III), close friends smoking had a substantial influence on smoking behaviour for both males and females (III). Having one or more close friends who smoke (Odds Ratio (OR) = 37.46; 95% CI = 19.39 to 72.36), one or more smokers in the home (OR = 2.35; 95% CI = 1.67 to 3.30) and seeing teachers and staff smoking on or near school property (OR=1.78; 95% CI = 1.13 to 2.80), were strongly associated with increased risk of smoking for grade 12 students. Smoking behaviour increased for both junior (Group 1) and senior (Group 2) students (IV). Group 1 students indicated a greater decrease in smoking behaviour and factors influencing smoking behaviour compared to those of Group 2. Students overestimating the percentage of youth their age who smoke was strongly associated with increased likelihood of smoking. Smoking rates showed a decreasing trend (1999, 2000, and 2001). However, policies and programs alone were not successful in influencing smoking behaviour of youth. Rather, factors within the students and schools contextual environment influenced smoking behaviour. Comprehensive approaches are required for school-based tobacco prevention interventions. Keywords: schools, policy, programs, smoking prevention, adolescents Subject Terms: school-based programming, public health, health promotion

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In this paper we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we design a novel auction which we call the OPT (optimal) auction. The OPT mechanism maximizes the search engine's expected revenue while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We show that the OPT mechanism is superior to two of the most commonly used mechanisms for sponsored search namely (1) GSP (Generalized Second Price) and (2) VCG (Vickrey-Clarke-Groves). We then show an important revenue equivalence result that the expected revenue earned by the search engine is the same for all the three mechanisms provided the advertisers are symmetric and the number of sponsored slots is strictly less than the number of advertisers.

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In this paper a nonlinear optimal controller has been designed for aerodynamic control during the reentry phase of the Reusable Launch Vehicle (RLV). The controller has been designed based on a recently developed technique Optimal Dynamic Inversion (ODI). For full state feedback the controller has required full information about the system states. In this work an Extended Kalman filter (EKF) is developed to estimate the states. The vehicle (RLV) has been has been consider as a nonlinear Six-Degree-Of-Freedom (6-DOF) model. The simulation results shows that EKF gives a very good estimation of the states and it is working well with ODI. The resultant trajectories are very similar to those obtained by perfect state feedback using ODI only.

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This paper presents a detailed analysis of a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts in an area fire situation. Lanchester linear law attrition model is used to develop the dynamical equations governing the variation in force strength. Here we address a static resource allocation problem namely, Time-Zero-Allocation (TZA) where the resource allocation is done only at the initial time. Numerical examples are given to support the analytical results.

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We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

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We consider the problem of centralized routing and scheduling for IEEE 802.16 mesh networks so as to provide Quality of Service (QoS) to individual real and interactive data applications. We first obtain an optimal and fair routing and scheduling policy for aggregate demands for different source- destination pairs. We then present scheduling algorithms which provide per flow QoS guarantees while utilizing the network resources efficiently. Our algorithms are also scalable: they do not require per flow processing and queueing and the computational requirements are modest. We have verified our algorithms via extensive simulations.

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To mitigate the effects of climate change, countries worldwide are advancing technologies to reduce greenhouse gas emissions. This paper proposes and measures optimal production resource reallocation using data envelopment analysis. This research attempts to clarify the effect of optimal production resource reallocation on CO2 emissions reduction, focusing on regional and industrial characteristics. We use finance, energy, and CO2 emissions data from 13 industrial sectors in 39 countries from 1995 to 2009. The resulting emissions reduction potential is 2.54 Gt-CO2 in the year 2009, with former communist countries having the largest potential to reduce CO2 emissions in the manufacturing sectors. In particular, basic material industry including chemical and steel sectors has a lot of potential to reduce CO2 emissions.

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Due to their non-stationarity, finite-horizon Markov decision processes (FH-MDPs) have one probability transition matrix per stage. Thus the curse of dimensionality affects FH-MDPs more severely than infinite-horizon MDPs. We propose two parametrized 'actor-critic' algorithms to compute optimal policies for FH-MDPs. Both algorithms use the two-timescale stochastic approximation technique, thus simultaneously performing gradient search in the parametrized policy space (the 'actor') on a slower timescale and learning the policy gradient (the 'critic') via a faster recursion. This is in contrast to methods where critic recursions learn the cost-to-go proper. We show w.p 1 convergence to a set with the necessary condition for constrained optima. The proposed parameterization is for FHMDPs with compact action sets, although certain exceptions can be handled. Further, a third algorithm for stochastic control of stopping time processes is presented. We explain why current policy evaluation methods do not work as critic to the proposed actor recursion. Simulation results from flow-control in communication networks attest to the performance advantages of all three algorithms.

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We consider a scenario in which a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing a function of the measurements and communicating it to an operator station. We restrict ourselves to the class of type-threshold functions (as defined in the work of Giridhar and Kumar, 2005), of which max, min, and indicator functions are important examples: our discussions are couched in terms of the max function. We view the problem as one of message-passing distributed computation over a geometric random graph. The network is assumed to be synchronous, and the sensors synchronously measure values and then collaborate to compute and deliver the function computed with these values to the operator station. Computation algorithms differ in (1) the communication topology assumed and (2) the messages that the nodes need to exchange in order to carry out the computation. The focus of our paper is to establish (in probability) scaling laws for the time and energy complexity of the distributed function computation over random wireless networks, under the assumption of centralized contention-free scheduling of packet transmissions. First, without any constraint on the computation algorithm, we establish scaling laws for the computation time and energy expenditure for one-time maximum computation. We show that for an optimal algorithm, the computation time and energy expenditure scale, respectively, as Theta(radicn/log n) and Theta(n) asymptotically as the number of sensors n rarr infin. Second, we analyze the performance of three specific computation algorithms that may be used in specific practical situations, namely, the tree algorithm, multihop transmission, and the Ripple algorithm (a type of gossip algorithm), and obtain scaling laws for the computation time and energy expenditure as n rarr infin. In particular, we show that the computation time for these algorithms scales as Theta(radicn/lo- g n), Theta(n), and Theta(radicn log n), respectively, whereas the energy expended scales as , Theta(n), Theta(radicn/log n), and Theta(radicn log n), respectively. Finally, simulation results are provided to show that our analysis indeed captures the correct scaling. The simulations also yield estimates of the constant multipliers in the scaling laws. Our analyses throughout assume a centralized optimal scheduler, and hence, our results can be viewed as providing bounds for the performance with practical distributed schedulers.

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Active Fiber Composites (AFC) possess desirable characteristics over a wide range of smart structure applications, such as vibration, shape and flow control as well as structural health monitoring. This type of material, capable of collocated actuation and sensing, call be used in smart structures with self-sensing circuits. This paper proposes four novel applications of AFC structures undergoing torsion: sensors and actuators shaped as strips and tubes; and concludes with a preliminary failure analysis. To enable this, a powerful mathematical technique, the Variational Asymptotic Method (VAM) was used to perform cross-sectional analyses of thin generally anisotropic AFC beams. The resulting closed form expressions have been utilized in the applications presented herein.

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Our attention, is focused on designing an optimal procurement mechanism which a buyer can use for procuring multiple units of a homogeneous item based on bids submitted by autonomous, rational, and intelligent suppliers. We design elegant optimal procurement mechanisms for two different situations. In the first situation, each supplier specifies the maximum quantity that can be supplied together with a per unit price. For this situation, we design an optimal mechanism S-OPT (Optimal with Simple bids). In the more generalized case, each supplier specifies discounts based on the volume of supply. In this case, we design an optimal mechanism VD-OPT (Optimal with Volume Discount, bids). The VD-OPT mechanism uses the S-OPT mechanism as a building block. The proposed mechanisms minimize the cost to the buyer, satisfying at the same time, (a) Bayesian, incentive compatibility and (b) interim individual rationality.

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This workshop is jointly organized by EFMI Working Groups Security, Safety and Ethics and Personal Portable Devices in cooperation with IMIA Working Group "Security in Health Information Systems". In contemporary healthcare and personal health management the collection and use of personal health information takes place in different contexts and jurisdictions. Global use of health data is also expanding. The approach taken by different experts, health service providers, data subjects and secondary users in understanding privacy and the privacy expectations others may have is strongly context dependent. To make eHealth, global healthcare, mHealth and personal health management successful and to enable fair secondary use of personal health data, it is necessary to find a practical and functional balance between privacy expectations of stakeholder groups. The workshop will highlight these privacy concerns by presenting different cases and approaches. Workshop participants will analyse stakeholder privacy expectations that take place in different real-life contexts such as portable health devices and personal health records, and develop a mechanism to balance them in such a way that global protection of health data and its meaningful use is realized simultaneously. Based on the results of the workshop, initial requirements for a global healthcare information certification framework will be developed.