Five times sit-to-stand test completion times among older women: Influence of seat height and arm position

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SAFE: Scale Aware Feature Encoder for Scene Text Recognition

In this paper, we address the problem of having characters with different scales in scene text recognition. We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales. SAFE is composed of a multi-scale convolutional encoder and a scale attention network. The multi-scale convolutional encoder targets at extracting character features under multiple scales, and the scale attention network is responsible for selecting features from the most relevant scale(s). SAFE has two main advantages over the traditional single-CNN encoder used in current state-of-the-art text recognizers. First, it explicitly tackles the scale problem by extracting scale-invariant features from the characters. This allows the recognizer to put more effort in handling other challenges in scene text recognition, like those caused by view distortion and poor image quality. Second, it can transfer the learning of feature encoding across different character scales. This is particularly important when the training set has a very unbalanced distribution of character scales, as training with such a dataset will make the encoder biased towards extracting features from the predominant scale. To evaluate the effectiveness of SAFE, we design a simple text recognizer named scale-spatial attention network (S-SAN) that employs SAFE as its feature encoder, and carry out experiments on six public benchmarks. Experimental results demonstrate that S-SAN can achieve state-of-the-art (or, in some cases, extremely competitive) performance without any post-processing.Comment: ACCV201

The expression and regulation of enzymes mediating the biosynthesis of triglycerides and phospholipids in keratinocytes/epidermis

Triglycerides and phospholipids play an important role in epidermal permability barrier formation and function. They are synthesized de novo in the epidermis via the glycerol-3-phosphate pathway, catalyzed sequentially by a group of enzymes that have multiple isoforms including glycerol-3-phosphate acyltransferase (GPAT), 1-acylglycerol-3-phosphate acyltransferase (AGPAT), Lipin and diacylglycerol acyltransferase (DGAT). Here we review the current knowledge of GPAT, AGPAT, Lipin and DGAT enzymes in keratinocytes/epidermis focusing on the expression levels of the various isoforms and their localization in mouse epidermis. Additionally, the factors regulating their gene expression, including calcium induced differentiation, PPAR and LXR activators, and the effect of acute permeability barrier disruption will be discussed

Human-Centered Design with Autistic University Students: Interface, Interaction and Information Preferences

This paper reports on a study aimed at creating an online support toolkit for young autistic people to navigate the transition from school to university, thereby empowering this group in developing their full potential. It is part of the Autism&Uni project, a European-funded initiative to widen access to Higher Education for students on the autism spectrum. Our particular focus is on the Human-Computer Interaction elements of the toolkit, namely the visual design of the interface, the nature of interactions and navigation, and the information architecture. Past research in this area tended to focus on autistic children, often with learning difficulties, and their preferences in terms of interface and interaction design. Our research revealed that the preferences of young autistic adults who are academically competent and articulate, differ considerably from those of autistic children. Key findings are that text is preferred over visual material; visual design should be minimal; content ought to be organized in a logical and hierarchical manner; the tone of language ought to be genuine yet not too negative or patronizing; and images or video are only useful if they illustrate places or people, in other words information that cannot easily be conveyed in other ways

Finite Element Modelling and Damage Detection of Seam Weld

© Springer Nature Singapore Pte Ltd 2020. Seam welds are widely used in assembled structures for connecting components. However, the dynamic effects of a seam weld are often difficult to characterise in numerical models for several reasons: (1) it is often not wise to build a fine mesh on the seam line which will add considerable computational cost for a structure with many welds, (2) the mechanical properties of weld materials are not well known; (3) sometimes some geometric information about welds is not known beforehand. In this work, the finite element model of a welding connection part is developed by employing CSEAM element in NASTRAN and its feasibility for representing a seam weld is investigated. Based on this result, a damage detection method by updating the properties of the built CSEAM elements is also proposed for welding quality assurance. The damage takes the form of a gap in the weld which causes a sharp change of model strain energy at the edges of the gap for certain vibration modes. Specifically, the model strain energy shape is used as the objective function. A Kriging model is introduced for efficiency and simulation of a T-shaped welded plate structure to demonstrate the effectiveness of this method

Synthetically Supervised Feature Learning for Scene Text Recognition

We address the problem of image feature learning for scene text recognition. The image features in the state-of-the-art methods are learned from large-scale synthetic image datasets. However, most meth- ods only rely on outputs of the synthetic data generation process, namely realistically looking images, and completely ignore the rest of the process. We propose to leverage the parameters that lead to the output images to improve image feature learning. Specifically, for every image out of the data generation process, we obtain the associated parameters and render another “clean” image that is free of select distortion factors that are ap- plied to the output image. Because of the absence of distortion factors, the clean image tends to be easier to recognize than the original image which can serve as supervision. We design a multi-task network with an encoder-discriminator-generator architecture to guide the feature of the original image toward that of the clean image. The experiments show that our method significantly outperforms the state-of-the-art methods on standard scene text recognition benchmarks in the lexicon-free cate- gory. Furthermore, we show that without explicit handling, our method works on challenging cases where input images contain severe geometric distortion, such as text on a curved path

Insensitivity of Human iPS Cells-derived Mesenchymal Stem Cells to Interferon-γ -induced HLA Expression Potentiates Repair Efficiency of Hind Limb Ischemia in Immune Humanized NOD Scid Gamma Mice

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Deceptive body movements reverse spatial cueing in soccer

This article has been made available through the Brunel Open Access Publishing Fund.The purpose of the experiments was to analyse the spatial cueing effects of the movements of soccer players executing normal and deceptive (step-over) turns with the ball. Stimuli comprised normal resolution or point-light video clips of soccer players dribbling a football towards the observer then turning right or left with the ball. Clips were curtailed before or on the turn (-160, -80, 0 or +80 ms) to examine the time course of direction prediction and spatial cueing effects. Participants were divided into higher-skilled (HS) and lower-skilled (LS) groups according to soccer experience. In experiment 1, accuracy on full video clips was higher than on point-light but results followed the same overall pattern. Both HS and LS groups correctly identified direction on normal moves at all occlusion levels. For deceptive moves, LS participants were significantly worse than chance and HS participants were somewhat more accurate but nevertheless substantially impaired. In experiment 2, point-light clips were used to cue a lateral target. HS and LS groups showed faster reaction times to targets that were congruent with the direction of normal turns, and to targets incongruent with the direction of deceptive turns. The reversed cueing by deceptive moves coincided with earlier kinematic events than cueing by normal moves. It is concluded that the body kinematics of soccer players generate spatial cueing effects when viewed from an opponent's perspective. This could create a reaction time advantage when anticipating the direction of a normal move. A deceptive move is designed to turn this cueing advantage into a disadvantage. Acting on the basis of advance information, the presence of deceptive moves primes responses in the wrong direction, which may be only partly mitigated by delaying a response until veridical cues emerge