Holistic context models for visual recognition

A novel framework to context modeling based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availability of robust appearance classifiers. Images are represented by their posterior probabilities with respect to a set of contextual models, built upon the bag-of-features image representation, through two layers of probabilistic modeling. The first layer represents the image in a semantic space, where each dimension encodes an appearance-based posterior probability with respect to a concept. Due to the inherent ambiguity of classifying image patches, this representation suffers from a certain amount of contextual noise. The second layer enables robust inference in the presence of this noise by modeling the distribution of each concept in the semantic space. A thorough and systematic experimental evaluation of the proposed context modeling is presented. It is shown that it captures the contextual “gist” of natural images. Scene classification experiments show that contextual classifiers outperform their appearance-based counterparts, irrespective of the precise choice and accuracy of the latter. The effectiveness of the proposed approach to context modeling is further demonstrated through a comparison to existing approaches on scene classification and image retrieval, on benchmark data sets. In all cases, the proposed approach achieves superior results.     

Evaluation of pneumatic inclined deck separator for high-ash Indian coals

International Journal of Coal Science & Technology - Application of pneumatic separators in coal beneficiation is increasing rapidly over the last decade primarily due to their low capital and...     

Acute Generalized Exanthematous Pustulosis: Pathogenesis,Genetic Background,Clinical Variants and Therapy

Acute generalized exanthematous pustulosis (AGEP) is a severe, usually drug-related reaction, characterized by an acute onset of mainly small non-follicular pustules on an erythematous base and spontaneous resolution usually within two weeks. Systemic involvement occurs in about 20% of cases. The course is mostly benign, and only in rare cases complications lead to life-threatening situations. Recent studies highlight the importance of genetic variations in interleukin-36 receptor antagonist gene (IL-36RN) in the pathogenesis of this disease. The physiopathology of AGEP remains unclear, but an involvement of innate and acquired immune cells together with resident cells (keratinocytes), which recruit and activate neutrophils via production of cytokines/chemokines such as IL-17, IL-36, granulocyte-macrophage colony-stimulating factor (GM-CSF), tumor necrosis factor alpha (TNFα) and chemokine (C-X-C motif) ligand 8 (CXCL8)/IL-8, has been postulated. Treatment is based on the removal of the causative drug, supportive care, infection prevention and use of potent topical or systemic steroids.     

Design and test of a Pareto optimal flat pad aerostatic bearing

This paper discusses the design and assessment of a Pareto optimal aerostatic flat pad bearing. The design of such bearings is a multi-variable, multi-criteria problem. The criteria used were load capacity, flow rate and stiffness. The design technique does not rely on weighting the criteria at the outset but allows a final design to be selected from the Pareto set. The final design was assessed using an automated experimental rig to obtain load/deflection data, mass flow rates, and film pressure profiles for a range of gap heights. The experimental performance of the Pareto optimal bearing is shown to compare well with the theoretical prediction at lower gap heights.     

Variability of waste copper slag concrete and its effect on the seismic safety of reinforced concrete building: A case study

Proven research output on the behavior of structures made of waste copper slag concrete can improve its utilization in the construction industry and thereby help to develop a sustainable built environment. Although numerous studies on waste copper slag concrete can be found in the published literature, no research has focused on the structural application of this type of concrete. In particular, the variability in the strength properties of waste copper slag concrete, which is required for various structural applications, such as limit state design formulation, reliability-based structural analysis, etc., has so far not attracted the attention of researchers. This paper quantifies the uncertainty associated with the compressive-, flexural- and split tensile strength of hardened concrete with different dosages of waste copper slag as fine aggregate. Best-fit probability distribution models are proposed based on statistical analyses of strength data generated from laboratory experiments. In addition, the paper presents a reliability-based seismic risk assessment of a typical waste copper slag incorporated reinforced concrete framed building, considering the proposed distribution model. The results show that waste copper slag can be safely used for seismic resistant structures as it results in an identical probability of failure and dispersion in the drift demand when compared with a conventional concrete building made of natural sand.     

Applications of Polymer Matrix Syntactic Foams

A collection of applications of polymer matrix syntactic foams is presented in this article. Syntactic foams are lightweight porous composites that found their early applications in marine structures due to their naturally buoyant behavior and low moisture absorption. Their light weight has been beneficial in weight sensitive aerospace structures. Syntactic foams have pushed the performance boundaries for composites and have enabled the development of vehicles for traveling to the deepest parts of the ocean and to other planets. The high volume fraction of porosity in syntactic foams also enabled their applications in thermal insulation of pipelines in oil and gas industry. The possibility of tailoring the mechanical and thermal properties of syntactic foams through a combination of material selection, hollow particle volume fraction, and hollow particle wall thickness has helped in rapidly growing these applications. The low coefficient of thermal expansion and dimensional stability at high temperatures are now leading their use in electronic packaging, composite tooling, and thermoforming plug assists. Methods have been developed to tailor the mechanical and thermal properties of syntactic foams independent of each other over a wide range, which is a significant advantage over other traditional particulate and fibrous composites.     

Genetic programming for simultaneous feature selection and classifier design.

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.