1000 resultados para Adaptive LBP


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It was the end of the academic year in 2020. On the third floor of the Southern Health University's administrative building, the five Vice-Chancellors of the Health Universities were gathering. Outside it was raining, but the patter of drops on the window only served to enhance the coziness of the meeting room. This annual event would take three days. The Vice-Chancellors enjoyed this meeting. They seldom saw each other during the busy academic year and under Professor Hope Brightview's 6 years of chairmanship they had been able to work well together to formulate a rational and profitable distribution of teaching, research and consultation services.

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Perceptions of wellness are often used by athletes and coaches to assess adaptive responses to training. The purpose of this research was to describe how players were coping with the demands of elite level Australian football over a competitive season using subjective ratings of physical and psychological wellness and to assess the ecological validity of such a monitoring approach. Twenty-seven players completed ratings for 9 items (fatigue, general muscle, hamstring, quadriceps, pain/stiffness, power, sleep quality, stress, well-being). Players subjectively rated each item as they arrived at the training or competition venue on a 1–5 visual analog scale, with 1 representing the positive end of the continuum. A total of 2,583 questionnaires were analyzed from completions on 183 days throughout the season (92 ± 24 per player, 103 ± 20 per week; mean ± SD). Descriptive statistics and multilevel modelling were used to understand how player ratings of wellness varied over the season and during the week leading into game day and whether selected player characteristics moderated these relationships. Results indicated that subjective ratings of physical and psychological wellness were sensitive to weekly training manipulations (i.e., improve steadily throughout the week to a game day low, p < 0.001), to periods of unloading during the season (i.e., a week of no competition, p < 0.05) and to individual player characteristics (e.g., muscle strain after a game was poorer in players with high maximum speed, p < 0.01). It is concluded that self-reported player ratings of wellness provide a useful tool for coaches and practitioners to monitor player responses to the rigorous demands of training, competition, and life as a professional athlete.

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Dynamic variations in channel behavior is considered in transmission power control design for cellular radio systems. It is well known that power control increases system capacity, improves Quality of Service (QoS), and reduces multiuser interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss dynamics of the fading channel is presented. Formulating power control decisions based on the measured received power levels allows modeling the fading channel pathloss dynamics in terms of a Hidden Markov Model (HMM). Applying the online HMM identification algorithm enables accurate estimation of the real pathloss ensuring efficient performance of the suggested power control scheme.

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In the context of collaborative filtering, the well known data sparsity issue makes two like-minded users have little similarity, and consequently renders the k nearest neighbour rule inapplicable. In this paper, we address the data sparsity problem in the neighbourhood-based CF methods by proposing an Adaptive-Maximum imputation method (AdaM). The basic idea is to identify an imputation area that can maximize the imputation benefit for recommendation purposes, while minimizing the imputation error brought in. To achieve the maximum imputation benefit, the imputation area is determined from both the user and the item perspectives; to minimize the imputation error, there is at least one real rating preserved for each item in the identified imputation area. A theoretical analysis is provided to prove that the proposed imputation method outperforms the conventional neighbourhood-based CF methods through more accurate neighbour identification. Experiment results on benchmark datasets show that the proposed method significantly outperforms the other related state-of-the-art imputation-based methods in terms of accuracy.

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Diel vertical migration (DVM) by zooplankton is a universal feature in all the World's oceans, as well as being common in freshwater environments. The normal pattern involves movement from shallow depths at night to greater depths during the day. For many herbivorous and omnivorous mesozooplankton that feed predominantly near the surface on phytoplankton and microzooplankton, minimising the risk of predation from fish seems to be the ultimate factor behind DVM. These migrants appear to use deep water as a dark daytime refuge where their probability of being detected and eaten is lower than if they remained near the surface. Associated with these vertical movements of mesozooplankton, predators at higher trophic levels, including invertebrates, fish, marine mammals, birds and reptiles, may modify their behaviour to optimise the exploitation of their vertically migrating prey. Recent advances in biotelemetry promise to allow the interaction between migrating zooplankton and diving air-breathing vertebrates to be explored in far more detail than hitherto.

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How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.

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1. Sex allocation theory has received considerable attention, yet the mechanism(s) by which mothers skew offspring sex ratios remain unknown. In birds, females are the heterogametic sex, which potentially gives them control of whether gametes will be male or female. How females might control the sex of the gamete is unclear, but one possibility is that variation in steroid hormones may mediate this process. 2. We experimentally altered circulating levels of corticosterone in female Gouldian finches (Erythrura gouldiae), a species that demonstrates both extreme stress responses and extreme offspring sex ratio biases when breeding with a low-quality (genetically incompatible) partner. 3. During egg production, individual females received both corticosterone and metyrapone (a corticosterone-synthesis inhibitor) implants, in random order, to induce both high and low levels of circulating stress hormones (within physiological limits). 4. We found that females with elevated corticosterone levels produced male-biased sex ratios, but when the same females were treated with metyrapone they produced female-biased offspring sex ratios. 5. These stress responses are adaptive because females constrained to breeding with low-quality males can substantially increase their fitness by overproducing sons. Changes in maternal corticosterone levels during stressful situations, such as the quality of a breeding partner, may provide an endocrine mechanism that can be exploited for strategic sex allocation.

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Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multidimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). In this paper, we investigate efficient strategies for information discovery in large-scale multidimensional WSNs and propose the Adaptive MultiDimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery and adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results showing the optimal routing structure depends on the frequency of events and query occurrence in the network. It also balances push and pull operations in large scale networks enabling significant QoS improvements and energy savings.

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Without genetic variation, species cannot cope with changing environments, and evolution does not proceed. In endangered species, adaptive potential may be eroded by decreased population sizes and processes that further reduce gene flow such as philopatry and local adaptations. Here, we focused on the philopatric and endangered loggerhead sea turtle (Caretta caretta) nesting in Cape Verde as a model system to investigate the link between adaptive potential and philopatry. We produced a dataset of three complementary genomic regions to investigate female philopatric behaviour (mitochondrial DNA), male-mediated gene flow (microsatellites) and adaptive potential (major histocompatibility complex, MHC). Results revealed genetically distinct nesting colonies, indicating remarkably small-scale philopatric behaviour of females. Furthermore, these colonies also harboured local pools of MHC alleles, especially at the margins of the population's distribution, which are therefore important reserves of additional diversity for the population. Meanwhile, directional male-mediated gene flow from the margins of distribution sustains the adaptive potential for the entire rookery. We therefore present the first evidence for a positive association between philopatry and locally adapted genomic regions. Contrary to expectation, we propose that philopatry conserves a high adaptive potential at the margins of a distribution, while asymmetric gene flow maintains genetic connectivity with the rest of the population.