319 research outputs found
Jackknife empirical likelihood tests for error distributions in regression models
AbstractRegression models are commonly used to model the relationship between responses and covariates. For testing the error distribution, some classical test statistics such as Kolmogorov–Smirnov test and Cramér–von-Mises test suffer from the complicated limiting distribution due to the plug-in estimate for the unknown parameters. Hence some ad hoc procedure such as bootstrap method is needed to obtain critical points. Recently, Khmaladze and Koul (2004) [7] have proposed an asymptotically distribution free test via some Martingale transforms. However, the calculation of such a test becomes quite involved, which usually requires numeric integration when the Cramér–von-Mises type of test is employed. In this paper we propose a novel jackknife empirical likelihood method which is easy to compute and has a chi-square limit so that critical values are ready at hand. A simulation study confirms that the new test has an accurate size and is powerful too
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling
Stochastic sampling based trackers have shown good performance for abrupt
motion tracking so that they have gained popularity in recent years. However,
conventional methods tend to use a two-stage sampling paradigm, in which the
search space needs to be uniformly explored with an inefficient preliminary
sampling phase. In this paper, we propose a novel sampling-based method in the
Bayesian filtering framework to address the problem. Within the framework,
nearest neighbor field estimation is utilized to compute the importance
proposal probabilities, which guide the Markov chain search towards promising
regions and thus enhance the sampling efficiency; given the motion priors, a
smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate
the posterior distribution through a smoothing weight-updating scheme.
Moreover, to track the abrupt and the smooth motions simultaneously, we develop
an abrupt-motion detection scheme which can discover the presence of abrupt
motions during online tracking. Extensive experiments on challenging image
sequences demonstrate the effectiveness and the robustness of our algorithm in
handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin
Deep Domain Adaptation for Pavement Crack Detection
Deep learning-based pavement cracks detection methods often require
large-scale labels with detailed crack location information to learn accurate
predictions. In practice, however, crack locations are very difficult to be
manually annotated due to various visual patterns of pavement crack. In this
paper, we propose a Deep Domain Adaptation-based Crack Detection Network
(DDACDN), which learns to take advantage of the source domain knowledge to
predict the multi-category crack location information in the target domain,
where only image-level labels are available. Specifically, DDACDN first
extracts crack features from both the source and target domain by a two-branch
weights-shared backbone network. And in an effort to achieve the cross-domain
adaptation, an intermediate domain is constructed by aggregating the
three-scale features from the feature space of each domain to adapt the crack
features from the source domain to the target domain. Finally, the network
involves the knowledge of both domains and is trained to recognize and localize
pavement cracks. To facilitate accurate training and validation for domain
adaptation, we use two challenging pavement crack datasets CQU-BPDD and
RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement
Multi-label Disease Dataset named CQU-BPMDD, which contains 38994
high-resolution pavement disease images to further evaluate the robustness of
our model. Extensive experiments demonstrate that DDACDN outperforms
state-of-the-art pavement crack detection methods in predicting the crack
location on the target domain.Comment: 12 pages, 10 figure
Recurrent exercise-induced acute kidney injury by idiopathic renal hypouricemia with a novel mutation in the SLC2A9 gene and literature review
OBJETIVO: Comparar a sensibilidade do método de difusão em ágar e do método de extração utilizando as linhagens celulares RC-IAL (células fibroblásticas de rim de coelho) e HeLa (células epiteliais de carcinoma do colo do útero humano), na avaliação da citotoxicidade "in vitro" de materiais de uso médico-hospitalar. MATERIAL E MÉTODO: Foram testadas 50 amostras escolhidas por sorteio, entre as já conhecidamente positivas e negativas e identificadas como: algodão, espuma, borracha, látex, celulose e acrílico. Além, das amostras citadas foram testadas experimentalmente várias concentrações de SDS (duodecil sulfato de sódio) nas culturas celulares RC-IAL e HeLa. RESULTADOS: Das 50 amostras testadas , 44 (88%) foram positivas para os dois métodos. Mas quando comparado o SDS nos dois métodos foram observados resultados positivos nas concentrações de 0,5 a 0,05 µg/ml no método de difusão em ágar e no método de extração somente foi observado efeito citotóxico até a concentração de 0,25 µg/ml. CONCLUSÃO: Os resultados encontrados são similares aos observados por outros autores que testaram materiais como, por exemplo, ligas metálicas. Quando foi usado o SDS observou-se, nas duas linhagens celulares, diferenças favoráveis ao método de difusão em ágar em duas concentrações, isto é, a sensibilidade deste método foi significantemente maior, por inspecção, em relação ao método de extração, além de se constituir em método mais simples de ser realizado
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