A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Global prevalence and genotype distribution of hepatitis C virus infection in 2015 : A modelling study

Publisher Copyright: © 2017 Elsevier LtdBackground The 69th World Health Assembly approved the Global Health Sector Strategy to eliminate hepatitis C virus (HCV) infection by 2030, which can become a reality with the recent launch of direct acting antiviral therapies. Reliable disease burden estimates are required for national strategies. This analysis estimates the global prevalence of viraemic HCV at the end of 2015, an update of—and expansion on—the 2014 analysis, which reported 80 million (95% CI 64–103) viraemic infections in 2013. Methods We developed country-level disease burden models following a systematic review of HCV prevalence (number of studies, n=6754) and genotype (n=11 342) studies published after 2013. A Delphi process was used to gain country expert consensus and validate inputs. Published estimates alone were used for countries where expert panel meetings could not be scheduled. Global prevalence was estimated using regional averages for countries without data. Findings Models were built for 100 countries, 59 of which were approved by country experts, with the remaining 41 estimated using published data alone. The remaining countries had insufficient data to create a model. The global prevalence of viraemic HCV is estimated to be 1·0% (95% uncertainty interval 0·8–1·1) in 2015, corresponding to 71·1 million (62·5–79·4) viraemic infections. Genotypes 1 and 3 were the most common cause of infections (44% and 25%, respectively). Interpretation The global estimate of viraemic infections is lower than previous estimates, largely due to more recent (lower) prevalence estimates in Africa. Additionally, increased mortality due to liver-related causes and an ageing population may have contributed to a reduction in infections. Funding John C Martin Foundation.publishersversionPeer reviewe

Mesenchymal stromal cell turnover in the normal adult lung revisited

Fulltext embargoed for: 12 months post date of publicationWe have employed a simple and robust noninvasive method of continuous in vivo long-term bromodeoxyuridine (BrdU) labeling to analyze lung mesenchymal stromal cell turnover in adult mice in the steady state. Mathematical modeling of BrdU uptake in flow cytometrically sorted CD45(neg)CD31(neg)Sca-1(pos) lung cells following long-term feeding of BrdU to mice in their drinking water reveals that lung mesenchymal stromal cells cycle continuously throughout life. Analysis of BrdU incorporation during long-term feeding and during chasing (delabeling) following replacement of BrdU-water with normal water shows that the CD45(neg)CD31(neg)Sca-1(pos) lung mesenchymal stromal cell compartment turns over at a rate of ∼2.26% per day with a time to half-cycled of 44 days, an estimated cell proliferation rate of 0.004/day, and a cell death rate of 0.018/day

Optimisation of Mechanical Properties in Saw-Dust/Woven-Jute Fibre/Polyester Structural Composites under Liquid Nitrogen Environment Using Response Surface Methodology

Natural fibre-based composites are replacing traditional materials in a wide range of structural applications that are used in different environments. Natural fibres suffer from thermal shocks, which affects the use of these composites in cold environment. Considering these, a goal was set in the present research to investigate the impact of cryogenic conditions on natural fibre composites. Composites were developed using polyester as matrix and jute-fibre and waste Teak saw-dust as reinforcement and filler, respectively. The effects of six parameters, viz., density of saw-dust, weight ratio of saw-dust, grade of woven-jute, number of jute layers, duration of cryogenic treatment of composite and duration of alkaline treatment of fibres on the mechanical properties of the composite was evaluated with an objective to maximise hardness, tensile, impact and flexural strengths. Taguchi method was used to design the experiments and response-surface methodology was used to model, predict and plot interactive surface plots. Results indicated that the duration of cryogenic treatment had a significant effect on mechanical properties, which was better only up to 60 min. The models were found to be statistically significant. The study concluded that saw-dust of density 300 kg/m3 used as a filler with a weight ratio of 13 wt.% and a reinforcement of a single layer of woven-jute-fibre mat of grade 250 gsm subjected to alkaline treatment for 4 h in a composite that has undergone 45 min of cryogenic treatment presented an improvement of 64% in impact strength, ca. 21% in flexural strength, ca. 158% in tensile strength and ca. 28% in hardness