5 resultados para diffusion process, wavelet estimator, non-parametric rate of convergence, Markov chain, estimation of unknown signal

em Boston University Digital Common


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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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Two new notions of reduction for terms of the λ-calculus are introduced and the question of whether a λ-term is beta-strongly normalizing is reduced to the question of whether a λ-term is merely normalizing under one of the new notions of reduction. This leads to a new way to prove beta-strong normalization for typed λ-calculi. Instead of the usual semantic proof style based on Girard's "candidats de réductibilité'', termination can be proved using a decreasing metric over a well-founded ordering in a style more common in the field of term rewriting. This new proof method is applied to the simply-typed λ-calculus and the system of intersection types.

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This is an addendum to our technical report BUCS TR-94-014 of December 19, 1994. It clarifies some statements, adds information on some related research, includes a comparison with research be de Groote, and fixes two minor mistakes in a proof.

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One-and two-dimensional cellular automata which are known to be fault-tolerant are very complex. On the other hand, only very simple cellular automata have actually been proven to lack fault-tolerance, i.e., to be mixing. The latter either have large noise probability ε or belong to the small family of two-state nearest-neighbor monotonic rules which includes local majority voting. For a certain simple automaton L called the soldiers rule, this problem has intrigued researchers for the last two decades since L is clearly more robust than local voting: in the absence of noise, L eliminates any finite island of perturbation from an initial configuration of all 0's or all 1's. The same holds for a 4-state monotonic variant of L, K, called two-line voting. We will prove that the probabilistic cellular automata Kε and Lε asymptotically lose all information about their initial state when subject to small, strongly biased noise. The mixing property trivially implies that the systems are ergodic. The finite-time information-retaining quality of a mixing system can be represented by its relaxation time Relax(⋅), which measures the time before the onset of significant information loss. This is known to grow as (1/ε)^c for noisy local voting. The impressive error-correction ability of L has prompted some researchers to conjecture that Relax(Lε) = 2^(c/ε). We prove the tight bound 2^(c1log^21/ε) < Relax(Lε) < 2^(c2log^21/ε) for a biased error model. The same holds for Kε. Moreover, the lower bound is independent of the bias assumption. The strong bias assumption makes it possible to apply sparsity/renormalization techniques, the main tools of our investigation, used earlier in the opposite context of proving fault-tolerance.

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The origin of the tri-phasic burst pattern, observed in the EMGs of opponent muscles during rapid self-terminated movements, has been controversial. Here we show by computer simulation that the pattern emerges from interactions between a central neural trajectory controller (VITE circuit) and a peripheral neuromuscularforce controller (FLETE circuit). Both neural models have been derived from simple functional constraints that have led to principled explanations of a wide variety of behavioral and neurobiological data, including, as shown here, the generation of tri-phasic bursts.