Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201

Patterns of Elbow Fractures and Complications of Those Seen Late in Children Treated at KCMC in Northern Tanzania

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STANDARDISATION AND HPTLC METHOD DEVELOPMENT OF MARKETED AYURVEDIC FORMULATION – BALARISHTA

Objective: The present study aims to standardize four marketed brands of Balarishta, an Ayurvedic formulation viz. Baidyanath-Balarishta (BB), Dabur-Balarishta (DB), Zandu-Balarishta (ZB) and Nagarjuna-Balarishta (NB) with respect to their physicochemical (organoleptic properties, pH, specific gravity, total solid content, ethanol content, reducing and non-reducing sugar content), phytochemical and microbial parameters (total bacterial count, total fungal count and test for specific pathogens viz. P. aeruginosa, E. coli and S. aureus). It also aims to develop and validate a new highperformance thin layer chromatography (HPTLC) method for simultaneous determination of three major phytoconstituents present in Balarishta viz. withaferin A, gallic acid and ephedrine.Methods: ‘Protocol for testing Ayurveda, Siddha and Unani medicines' was used as a reference for conducting standardization experiments. HPTLC method was developed on Camag Linomat-5 using silica gel 60 GF254 as the stationary phase and Toluene: Chloroform: n-propanol: Ethanol: Formic acid (6: 3: 1: 2: 1, v/v/v/v/v) as the mobile phase. The analytical method validation studies were performed as per International Conference on Harmonization-Quality (ICH-Q2 (R1)) guidelines.Results: The results of standardisation tests obtained were compared with specifications mentioned in ‘Ayurvedic Pharmacopoeia of India 2008 Volume 2, Part 2' and a comparative data of each Balarishta formulation was generated for all the quality control parameters performed. A new, accurate, precise and robust HPTLC method was successfully developed with Retardation factor (Rf) of 0.17±0.02, 0.35±0.01 and 0.54±0.02 for ephedrine, gallic acid and withaferin A respectively.Conclusion: The results of this research work will serve as a valuable quality tool for routine quality control analysis of Balarishta formulations.Keywords: Balarishta, Standardisation, Withaferin A, Gallic acid, Ephedrine and HPTLC

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Therapeutic target-site variability in α1-antitrypsin characterized at high resolution

The intrinsic propensity of [alpha]1-antitrypsin to undergo conformational transitions from its metastable native state to hyperstable forms provides a motive force for its antiprotease function. However, aberrant conformational change can also occur via an intermolecular linkage that results in polymerization. This has both loss-of-function and gain-of-function effects that lead to deficiency of the protein in human circulation, emphysema and hepatic cirrhosis. One of the most promising therapeutic strategies being developed to treat this disease targets small molecules to an allosteric site in the [alpha]1-antitrypsin molecule. Partial filling of this site impedes polymerization without abolishing function. Drug development can be improved by optimizing data on the structure and dynamics of this site. A new 1.8 Å resolution structure of [alpha]1-antitrypsin demonstrates structural variability within this site, with associated fluctuations in its upper and lower entrance grooves and ligand-binding characteristics around the innermost stable enclosed hydrophobic recess. These data will allow a broader selection of chemotypes and derivatives to be tested in silico and in vitro when screening and developing compounds to modulate conformational change to block the pathological mechanism while preserving function