Notes

Good quality enhancement and reconciliation method for automatic dishing out cupboard treatment changes.
Continual learning is the ability of a learning system to solve new tasks by utilizing previously acquired knowledge from learning and performing prior tasks without having significant adverse effects on the acquired prior knowledge. Continual learning is key to advancing machine learning and artificial intelligence. Progressive learning is a deep learning framework for continual learning that comprises three procedures curriculum, progression, and pruning. The curriculum procedure is used to actively select a task to learn from a set of candidate tasks. The progression procedure is used to grow the capacity of the model by adding new parameters that leverage parameters learned in prior tasks, while learning from data available for the new task at hand, without being susceptible to catastrophic forgetting. The pruning procedure is used to counteract the growth in the number of parameters as further tasks are learned, as well as to mitigate negative forward transfer, in which prior knowledge unrelated to the task at hand may interfere and worsen performance. Progressive learning is evaluated on a number of supervised classification tasks in the image recognition and speech recognition domains to demonstrate its advantages compared with baseline methods. It is shown that, when tasks are related, progressive learning leads to faster learning that converges to better generalization performance using a smaller number of dedicated parameters.Detecting the locations of multiple actions in videos and classifying them in real-time are challenging problems termed "action localization and prediction" problem. Convolutional neural networks (ConvNets) have achieved great success for action localization and prediction in still images. https://www.selleckchem.com/products/Decitabine.html A major advance occurred when the AlexNet architecture was introduced in the ImageNet competition. ConvNets have since achieved state-of-the-art performances across a wide variety of machine vision tasks, including object detection, image segmentation, image classification, facial recognition, human pose estimation, and tracking. However, few works exist that address action localization and prediction in videos. The current action localization research primarily focuses on the classification of temporally trimmed videos in which only one action occurs per frame. Moreover, nearly all the current approaches work only offline and are too slow to be useful in real-world environments. In this work, we propose a fast and accurate deep-learning approach to perform real-time action localization and prediction. The proposed approach uses convolutional neural networks to localize multiple actions and predict their classes in real time. This approach starts by using appearance and motion detection networks (known as "you only look once" (YOLO) networks) to localize and classify actions from RGB frames and optical flow frames using a two-stream model. We then propose a fusion step that increases the localization accuracy of the proposed approach. Moreover, we generate an action tube based on frame level detection. The frame by frame processing introduces an early action detection and prediction with top performance in terms of detection speed and precision. The experimental results demonstrate this superiority of our proposed approach in terms of both processing time and accuracy compared to recent offline and online action localization and prediction approaches on the challenging UCF-101-24 and J-HMDB-21 benchmarks.Locality preserving projection (LPP), as a well-known technique for dimensionality reduction, is designed to preserve the local structure of the original samples which usually lie on a low-dimensional manifold in the real world. However, it suffers from the undersampled or small-sample-size problem, when the dimension of the features is larger than the number of samples which causes the corresponding generalized eigenvalue problem to be ill-posed. To address this problem, we show that LPP is equivalent to a multivariate linear regression under a mild condition, and establish the connection between LPP and a least squares problem with multiple columns on the right-hand side. Based on the developed connection, we propose two regularized least squares methods for solving LPP. Experimental results on real-world databases illustrate the performance of our methods.We prove some new results concerning the approximation rate of neural networks with general activation functions. link2 Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation function. We extend the dimension independent approximation rates previously obtained to this new class of activation functions. Our second result gives a weaker, but still dimension independent, approximation rate for a larger class of activation functions, removing the polynomial decay assumption. This result applies to any bounded, integrable activation function. Finally, we show that a stratified sampling approach can be used to improve the approximation rate for polynomially decaying activation functions under mild additional assumptions.RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. Considering no correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing marginal distribution divergence between the entire RGB and IR sets. However, this set-level alignment strategy may lead to misalignment of some instances, which limit the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged features. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Third, our method learns a latent manifold space. In the space, we can random sample and generate lots of images of unseen classes. Training with those images, the learned identity feature space is more smooth can generalize better when test. Finally, extensive experimental results on two standard benchmarks demonstrate that the proposed model favorably against state-of-the-art methods.Generalized anxiety disorder (GAD) is one of the most prevalent anxiety disorders among children and adolescents. Objectives The purpose of this study is to determine the prevalence, sociodemographic variables, and comorbidity of GAD among children and adolescents to suggest the main predictors, using an analytical cross-sectional study. Method Data were collected via a multistage random-cluster sampling method from 29,709 children and adolescents aged 6-18 years old in Iran. We used the Persian present and lifetime version of the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-PL). Then, we analyzed the data via descriptive analysis and multivariate logistic regression analysis methods. Results The lifetime prevalence rate for GAD was 2.6 % (95 % Cl, 2.4%-2.8%). Overall, logistic regression analyses revealed five variables with significant unique contributions to the prediction of GAD. Significant predictors were age, sex, mother history of psychiatric hospitalization, mother education, and residence. Participants with these risk factors were between 0.23-2.91 times more likely to present with GAD. Besides, the highest and lowest comorbidity rates of psychiatric disorder with GAD was 57.6 % and 0.3 % related to anxiety and eating disorders, respectively. Age or sex also affects the comorbidity of GAD and some mental disorders including behavioral, neurodevelopmental, elimination, and mood disorders. Conclusion This study, which was conducted in Iran, is located at the low end of the range of international estimates for GAD. Awareness of the predictors and comorbidity of GAD could be used in the prevention of GAD in children and adolescents.Dynein axonemal heavy chain 5 (DNAH5) is part of a microtubule-associated protein complex found within the cilia of the lung. Mutations in the DNAH5 gene lead to impaired ciliary function and are linked to primary ciliary dyskinesia (PCD), a rare autosomal recessive disorder. link3 We established two human induced pluripotent stem cell (hiPSC) lines generated from a patient with PCD and homozygous mutation in the corresponding DNAH5 gene. These cell lines represent an excellent tool for modeling the ciliary dysfunction in PCD.Unlike other modalities in breast imaging, breast ultrasound is very operator dependent. Proper characterization and management of breast lesions depends on the knowledge and skill of the radiologist performing the exam. Breast ultrasound optimization continues to evolve as the technology of ultrasound machines improves. The American College of Graduate Medical Education (ACGME) program requirements for graduate medical education in diagnostic radiology recently updated requirements to include competency in ultrasound physics, knobology, and image generation. As trainees are held to high ultrasound optimization standards, practicing radiologists who perform breast ultrasound exams must keep up to date on current breast ultrasound optimization techniques to avoid mischaracterizing and mismanaging breast ultrasound findings. In this paper, ultrasound optimization techniques, including patient positioning, transducer frequency, transducer contact and pressure, depth, gain, focus, tissue harmonic imaging, spatial compounding, dynamic range, speed of sound imaging, Doppler evaluation, and elastography will be described. Multiple ultrasound case examples will be used to illustrate application of these skills. After reading this paper, the reader will be able to apply these techniques to their own breast ultrasound practice, improving characterization and management of breast lesions in their patients.Background Aluminium is an environmental neurotoxin to which human beings are extensively exposed. However, the molecular mechanism of aluminium toxicity remains unclear. Methods The changes in cognitive function of aluminum exposed workers under long-term occupational exposure were evaluated, and the relationship between cognitive changes, plasma memory related BDNF and EGR1 protein expression, and variations of epigenetic markers H3K4me3, H3K9me2, H3K27me3 expression levels in blood was explored. Results MMSE, DSFT, DST scores in cognitive function and the levels of plasma BDNF and EGR1 protein expression decreased with the increase of blood aluminum level. H3K4me3, H3K9me2, H3K27me3 expression levels in peripheral blood lymphocytes of aluminum exposed workers were statistically different (all P less then 0.05). H3K4me3, H3K9me2 and H3K27me3 expression levels in lymphocytes were correlated with blood aluminum level. BDNF, EGR1 protein level and H3K4me3, H3K9me2, H3K27me3 expression levels have different degrees of correlation.
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