Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines.For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn)
Catalytic membrane reactors are multifunctional reactors, which provide improved performance over conventional reactors. These are used mainly for conducting hydrogenation/ dehydrogenation reactions, and synthesis of oxyorganic compounds by using inorganic membranes. In this paper, comprehensive model has been developed for a tubular membrane reactor, which is applicable to Pd or Pd alloys membrane, porous inorganic membranes. The model accounts for the reaction on either side, tube or shell, isothermal and adiabatic conditions, reactive and non reactive sweep gas, multicomponent diffusion through gas films on both sides of membrane, and pressure variations. Equations governing the diffusion of gaseous components through stagnant gas film, and membranes have been identified and described. The model has been validated with the experimental results available in literature. By using the developed model catalytic dehydrogenation of ethylbenzene to produce styrene in a tubular membrane reactor have been simulated. Four catalysts available for this reaction have been evaluated for their performance. It is our view that the model may be used to develop general purpose software for the analysis and design of tubular catalytic membrane reactors through numerical simulation.
X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system’s spatial resolution. Here we show that dark-field creates a texture which is characteristic of the imaged material, and that its combination with conventional attenuation leads to an improved discrimination of threat materials. We show that remaining ambiguities can be resolved by exploiting the different energy dependence of the dark-field and attenuation signals. Furthermore, we demonstrate that the dark-field texture is well-suited for identification through machine learning approaches through two proof-of-concept studies. In both cases, application of the same approaches to datasets from which the dark-field images were removed led to a clear degradation in performance. While the small scale of these studies means further research is required, results indicate potential for a combined use of dark-field and deep neural networks in security applications and beyond.
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [10,26]. However, a question of paramount importance is somewhat unanswered in deep learning research -is the selected CNN optimal for the dataset in terms of accuracy and model size?In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations.We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [15] and , with architectures of that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method.
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