19 resultados para Technical literature


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This paper presents a systematic review of the literature pertaining to orphans and vulnerable children in sub-Saharan Africa, with a particular focus on research in countries heavily impacted by HIV/AIDS. Despite study and data limitations, the literature provides evidence of growing orphan-based disparities, difficulties within households providing care, and insufficient capacity among social services. Still, additional research is urgently needed, including better OVC surveillance methods, qualitative data than answers persisting questions, the inclusion of more useful indicators in national household surveys, and longitudinal studies to determine the mechanisms by which parental HIV status and death impacts children, caregiving impacts households, and the orphan epidemic impacts communities and social systems.

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The impacts of antiretroviral therapy on quality of life, mental health, labor productivity, and economic wellbeing for people living with HIV/AIDS in developing countries are only beginning to be measured. We conducted a systematic literature review to analyze the effect of antiretroviral therapy (ART) on these non-clinical indicators in developing countries and assess the state of research on these topics. Both qualitative and quantitative studies were included, as were peer-reviewed articles, gray literature, and conference abstracts and presentations. Findings are reported from 12 full-length articles, 7 abstracts, and 1 presentation (representing 16 studies). Compared to HIV-positive patients not yet on treatment, patients on ART reported significant improvements in physical, emotional and mental health and daily function. Work performance improved and absenteeism decreased, with the most dramatic changes occurring in the first three months of treatment and then leveling off. Little research has been done on the impact of ART on household wellbeing, with modest changes in child and family wellbeing within households where adults are receiving ART reported so far. Studies from developing countries have not yet assessed non-clinical outcomes of therapy beyond the first year; therefore, longitudinal outcomes are still unknown. As ART roll out extends throughout high HIV prevalence, low-resource countries and is sustained over years and decades, both positive and adverse non-clinical outcomes need to be empirically measured and qualitatively explored in order to support patient adherence and maximize treatment benefits.

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Coherent shared memory is a convenient, but inefficient, method of inter-process communication for parallel programs. By contrast, message passing can be less convenient, but more efficient. To get the benefits of both models, several non-coherent memory behaviors have recently been proposed in the literature. We present an implementation of Mermera, a shared memory system that supports both coherent and non-coherent behaviors in a manner that enables programmers to mix multiple behaviors in the same program[HS93]. A programmer can debug a Mermera program using coherent memory, and then improve its performance by selectively reducing the level of coherence in the parts that are critical to performance. Mermera permits a trade-off of coherence for performance. We analyze this trade-off through measurements of our implementation, and by an example that illustrates the style of programming needed to exploit non-coherence. We find that, even on a small network of workstations, the performance advantage of non-coherence is compelling. Raw non-coherent memory operations perform 20-40~times better than non-coherent memory operations. An example application program is shown to run 5-11~times faster when permitted to exploit non-coherence. We conclude by commenting on our use of the Isis Toolkit of multicast protocols in implementing Mermera.

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By utilizing structure sharing among its parse trees, a GB parser can increase its efficiency dramatically. Using a GB parser which has as its phrase structure recovery component an implementation of Tomita's algorithm (as described in [Tom86]), we investigate how a GB parser can preserve the structure sharing output by Tomita's algorithm. In this report, we discuss the implications of using Tomita's algorithm in GB parsing, and we give some details of the structuresharing parser currently under construction. We also discuss a method of parallelizing a GB parser, and relate it to the existing literature on parallel GB parsing. Our approach to preserving sharing within a shared-packed forest is applicable not only to GB parsing, but anytime we want to preserve structure sharing in a parse forest in the presence of features.

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Various restrictions on the terms allowed for substitution give rise to different cases of semi-unification. Semi-unification on finite and regular terms has already been considered in the literature. We introduce a general case of semi-unification where substitutions are allowed on non-regular terms, and we prove the equivalence of this general case to a well-known undecidable data base dependency problem, thus establishing the undecidability of general semi-unification. We present a unified way of looking at the various problems of semi-unification. We give some properties that are common to all the cases of semi-unification. We also the principality property and the solution set for those problems. We prove that semi-unification on general terms has the principality property. Finally, we present a recursive inseparability result between semi-unification on regular terms and semi-unification on general terms.

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Supported housing for individuals with severe mental illness strives to provide the services necessary to place and keep individuals in independent housing that is integrated into the community and in which the consumer has choice and control over his or her services and supports. Supported housing can be contrasted to an earlier model called the “linear residential approach” in which individuals are moved from the most restrictive settings (e.g., inpatient settings) through a series of more independent settings (e.g., group homes, supervised apartments) and then finally to independent housing. This approach has been criticized as punishing the client due to frequent moves, and as being less likely to result in independent housing. In the supported housing model (Anthony & Blanch, 1988) consumers have choice and control over their living environment, their treatment, and supports (e.g., case management, mental health and substance abuse services). Supports are flexible and faded in and out depending on needs. Results of this systematic review of supported housing suggest that there are several well-controlled studies of supported housing and several studies conducted with less rigorous designs. Overall, our synthesis suggests that supported housing can improve the living situation of individuals who are psychiatrically disabled, homeless and with substance abuse problems. Results show that supported housing can help people stay in apartments or homes up to about 80% of the time over an extended period. These results are contrary to concerns expressed by proponents of the linear residential model and housing models that espoused more restrictive environments. Results also show that housing subsidies or vouchers are helpful in getting and keeping individuals housed. Housing services appear to be cost effective and to reduce the costs of other social and clinical services. In order to be most effective, intensive case management services (rather than traditional case management) are needed and will generally lead to better housing outcomes. Having access to affordable housing and having a service system that is well-integrated is also important. Providing a person with supported housing reduces the likelihood that they will be re-hospitalized, although supported housing does not always lead to reduced psychiatric symptoms. Supported housing can improve clients’ quality of life and satisfaction with their living situation. Providing supported housing options that are of decent quality is important in order to keep people housed and satisfied with their housing. In addition, rapid entry into housing, with the provision of choices is critical. Program and clinical supports may be able to mitigate the social isolation that has sometimes been associated with supported housing.

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Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.

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We study the problem of preprocessing a large graph so that point-to-point shortest-path queries can be answered very fast. Computing shortest paths is a well studied problem, but exact algorithms do not scale to huge graphs encountered on the web, social networks, and other applications. In this paper we focus on approximate methods for distance estimation, in particular using landmark-based distance indexing. This approach involves selecting a subset of nodes as landmarks and computing (offline) the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, we can estimate it quickly by combining the precomputed distances of the two nodes to the landmarks. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. Given a budget of memory for the index, which translates directly into a budget of landmarks, different landmark selection strategies can yield dramatically different results in terms of accuracy. A number of simple methods that scale well to large graphs are therefore developed and experimentally compared. The simplest methods choose central nodes of the graph, while the more elaborate ones select central nodes that are also far away from one another. The efficiency of the suggested techniques is tested experimentally using five different real world graphs with millions of edges; for a given accuracy, they require as much as 250 times less space than the current approach in the literature which considers selecting landmarks at random. Finally, we study applications of our method in two problems arising naturally in large-scale networks, namely, social search and community detection.

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In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods.

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Understanding the nature of the workloads and system demands created by users of the World Wide Web is crucial to properly designing and provisioning Web services. Previous measurements of Web client workloads have been shown to exhibit a number of characteristic features; however, it is not clear how those features may be changing with time. In this study we compare two measurements of Web client workloads separated in time by three years, both captured from the same computing facility at Boston University. The older dataset, obtained in 1995, is well-known in the research literature and has been the basis for a wide variety of studies. The newer dataset was captured in 1998 and is comparable in size to the older dataset. The new dataset has the drawback that the collection of users measured may no longer be representative of general Web users; however using it has the advantage that many comparisons can be drawn more clearly than would be possible using a new, different source of measurement. Our results fall into two categories. First we compare the statistical and distributional properties of Web requests across the two datasets. This serves to reinforce and deepen our understanding of the characteristic statistical properties of Web client requests. We find that the kinds of distributions that best describe document sizes have not changed between 1995 and 1998, although specific values of the distributional parameters are different. Second, we explore the question of how the observed differences in the properties of Web client requests, particularly the popularity and temporal locality properties, affect the potential for Web file caching in the network. We find that for the computing facility represented by our traces between 1995 and 1998, (1) the benefits of using size-based caching policies have diminished; and (2) the potential for caching requested files in the network has declined.

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Temporal locality of reference in Web request streams emerges from two distinct phenomena: the popularity of Web objects and the {\em temporal correlation} of requests. Capturing these two elements of temporal locality is important because it enables cache replacement policies to adjust how they capitalize on temporal locality based on the relative prevalence of these phenomena. In this paper, we show that temporal locality metrics proposed in the literature are unable to delineate between these two sources of temporal locality. In particular, we show that the commonly-used distribution of reference interarrival times is predominantly determined by the power law governing the popularity of documents in a request stream. To capture (and more importantly quantify) both sources of temporal locality in a request stream, we propose a new and robust metric that enables accurate delineation between locality due to popularity and that due to temporal correlation. Using this metric, we characterize the locality of reference in a number of representative proxy cache traces. Our findings show that there are measurable differences between the degrees (and sources) of temporal locality across these traces, and that these differences are effectively captured using our proposed metric. We illustrate the significance of our findings by summarizing the performance of a novel Web cache replacement policy---called GreedyDual*---which exploits both long-term popularity and short-term temporal correlation in an adaptive fashion. Our trace-driven simulation experiments (which are detailed in an accompanying Technical Report) show the superior performance of GreedyDual* when compared to other Web cache replacement policies.

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SomeCast is a novel paradigm for the reliable multicast of real-time data to a large set of receivers over the Internet. SomeCast is receiver-initiated and thus scalable in the number of receivers, the diverse characteristics of paths between senders and receivers (e.g. maximum bandwidth and round-trip-time), and the dynamic conditions of such paths (e.g. congestion-induced delays and losses). SomeCast enables receivers to dynamically adjust the rate at which they receive multicast information to enable the satisfaction of real-time QoS constraints (e.g. rate, deadlines, or jitter). This is done by enabling a receiver to join SOME number of concurrent multiCAST sessions, whereby each session delivers a portion of an encoding of the real-time data. By adjusting the number of such sessions dynamically, client-specific QoS constraints can be met independently. The SomeCast paradigm can be thought of as a generalization of the AnyCast (e.g. Dynamic Server Selection) and ManyCast (e.g. Digital Fountain) paradigms, which have been proposed in the literature to address issues of scalability of UniCast and MultiCast environments, respectively. In this paper we overview the SomeCast paradigm, describe an instance of a SomeCast protocol, and present simulation results that quantify the significant advantages gained from adopting such a protocol for the reliable multicast of data to a diverse set of receivers subject to real-time QoS constraints.

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The relative importance of long-term popularity and short-term temporal correlation of references for Web cache replacement policies has not been studied thoroughly. This is partially due to the lack of accurate characterization of temporal locality that enables the identification of the relative strengths of these two sources of temporal locality in a reference stream. In [21], we have proposed such a metric and have shown that Web reference streams differ significantly in the prevalence of these two sources of temporal locality. These finding underscore the importance of a Web caching strategy that can adapt in a dynamic fashion to the prevalence of these two sources of temporal locality. In this paper, we propose a novel cache replacement algorithm, GreedyDual*, which is a generalization of GreedyDual-Size. GreedyDual* uses the metrics proposed in [21] to adjust the relative worth of long-term popularity versus short-term temporal correlation of references. Our trace-driven simulation experiments show the superior performance of GreedyDual* when compared to other Web cache replacement policies proposed in the literature.

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Moving cameras are needed for a wide range of applications in robotics, vehicle systems, surveillance, etc. However, many foreground object segmentation methods reported in the literature are unsuitable for such settings; these methods assume that the camera is fixed and the background changes slowly, and are inadequate for segmenting objects in video if there is significant motion of the camera or background. To address this shortcoming, a new method for segmenting foreground objects is proposed that utilizes binocular video. The method is demonstrated in the application of tracking and segmenting people in video who are approximately facing the binocular camera rig. Given a stereo image pair, the system first tries to find faces. Starting at each face, the region containing the person is grown by merging regions from an over-segmented color image. The disparity map is used to guide this merging process. The system has been implemented on a consumer-grade PC, and tested on video sequences of people indoors obtained from a moving camera rig. As can be expected, the proposed method works well in situations where other foreground-background segmentation methods typically fail. We believe that this superior performance is partly due to the use of object detection to guide region merging in disparity/color foreground segmentation, and partly due to the use of disparity information available with a binocular rig, in contrast with most previous methods that assumed monocular sequences.