Deep Learning for Semantic Part Segmentation with High-Level Guidance

In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an discriminative fashion, as a hidden CRF, demonstrating that prior information can yield additional improvements. We also investigate the performance of our approach ``in the wild'', without information concerning the objects' bounding boxes, using an object detector to guide a multi-scale segmentation scheme. We evaluate the performance of our approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing and face labelling respectively. We show superior performance with respect to competitive methods that have been extensively engineered on these benchmarks, as well as realistic qualitative results on part segmentation, even for occluded or deformable objects. We also provide quantitative and extensive qualitative results on three classes from the PASCAL Parts dataset. Finally, we show that our multi-scale segmentation scheme can boost accuracy, recovering segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table

Learning morphological phenomena of Modern Greek an exploratory approach

This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive open‐ended multimedia environment. The proposed model introduces a new language metaphor, the ‘puzzle‐metaphor’ (similar to the existing ‘turtle‐metaphor’ for concepts from mathematics and physics), based on a visualized unification‐like mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching

Deep Filter Banks for Texture Recognition, Description, and Segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture represenations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another

Development of an optimized method for the detection of airborne viruses with real-time PCR analysis

<p>Abstract</p> <p>Background</p> <p>Airborne viruses remain one of the major public health issues worldwide. Detection and quantification of airborne viruses is essential in order to provide information regarding public health risk assessment.</p> <p>Findings</p> <p>In this study, an optimized new, simple, low cost method for sampling of airborne viruses using Low Melting Agarose (LMA) plates and a conventional microbial air sampling device has been developed. The use of LMA plates permits the direct nucleic acids extraction of the captured viruses without the need of any preliminary elution step. Molecular detection and quantification of airborne viruses is performed using real-time quantitative (RT-)PCR (Q(RT-)PCR) technique. The method has been tested using Adenoviruses (AdVs) and Noroviruses (NoVs) GII, as representative DNA and RNA viruses, respectively. Moreover, the method has been tested successfully in outdoor experiments, by detecting and quantifying human adenoviruses (HAdVs) in the airborne environment of a wastewater treatment plant.</p> <p>Conclusions</p> <p>The great advantage of LMA is that nucleic acids extraction is performed directly on the LMA plates, while the eluted nucleic acids are totally free of inhibitory substances. Coupled with QPCR the whole procedure can be completed in less than three (3) hours.</p

Properties and characterization of biodiesel from selected microalgea stains

The demand for alternative fuels has increased in the past several years[1]. Biofuels are gaining importance as significant substitutes for the depleting fossil fuels. The fact that biofuels are renewable fuels with very low emissions of CO2 in the lifecycle offers them a competitive advantage[2]. However, the first produced biodiesel derived from edible oil seed crops (first generation feedstocks), lurking a serious risk of disturbing the overall worldwide balance of food reserves and safety. The second generation feedstocks for biodiesel production obtained from non-edible oil seed crops, waste cooking oil, animal fats, etc., but these feedstocks are not sufficient to cover the present energy needs. Recent focus is on microalgae as the third generation feedstock[3]. Mi l d t t f l d b t th i lt ( ) b kih(l ) df h Microalgae do not compete for land, but they can grow in salty sea), brackish (lagoons) and fresh (lakes) water. Moreover, microalgae have high photosynthetic efficiency using solar energy, water and carbon dioxide to produce higher quantities of biomass than other feedstocks. In the present research work, two indigenous fresh water (ChlorF1, ChlorF2) and two marine (ChlorM1, ChlorM2) Chlorophyte strains have been cultivated successfully under laboratory conditions using commercial fertilizer (Nutrileaf 30-10-10, initial concentration=70 g/m3) as nutrient source. The produced biodiesel from the microalgae biomass achieved a range of 2.2 - 10.6% total lipid content and an unsaturated FAME content between 48 mol% and 59 mol%. The iodine value, the cetane number, the cold filter plugging point (CFPP) and the oxidative stability of the ultimate biodiesels were determined, based on the compositions of the four (4) microalgae strains and compared with the specifications in the EU and US standards, EN 14214 and ASTM D6751 respectively

Molecular detection of multiple viral targets in untreated urban sewage from Greece

<p>Abstract</p> <p>Background</p> <p>Urban sewage virological analysis may produce important information about the strains that cause clinical and subclinical infections in the population, thus supporting epidemiological studies.</p> <p>Methods</p> <p>In the present study, a twenty one-month survey (November 2007 to July 2009) was conducted in order to evaluate the presence of human adenoviruses (hAdV), hepatitis A viruses (HAV), hepatitis E viruses (HEV), Noroviruses (NoV), and human Polyomaviruses (hPyV) in untreated sewage samples collected from the inlet of Patras' municipal biological wastewater treatment plant, located in southwestern Greece. Nucleic acid amplification techniques were applied for viral nucleic acid detection. Positive samples were confirmed by sequencing and comparative phylogenetic analysis was performed on the isolated viral strains.</p> <p>Results</p> <p>In total, viruses were detected in 87.5% (42/48) of sewage samples. AdVs, PyVs, HAV, and NoVs were detected in 45.8% (22/48), 68.8% (33/48), 8.3% (4/48), and 6.3% (3/48) of the samples collected from the plant's inlet, while HEV was not detected at all. Adenovirus types 8 (Ad8), 40 (Ad40) and 41 (Ad41) were recognized, while JC and BK polyomaviruses were recorded. Noroviruses were identified as GII.4. HAV was typed as genotype IA.</p> <p>Conclusions</p> <p>Our study demonstrates the advantages of environmental surveillance as a tool to elucidate the molecular epidemiology of community circulating viruses. We underline the need of environmental surveillance programs in countries such as Greece with inadequate and problematic epidemiological surveillance system and no environmental surveillance system currently in action.</p

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

Long-term survival after CABG in diabetics with aggressive risk factor management

Objectives: Diabetes is a well-established risk factor for cardiovascular disease, and diabetics have a threefold increase in risk of death from cardiovascular disease compared to non-diabetics. Following coronary artery bypass grafting, tight glycemic control improves short-term outcomes, however limited data exist for long-term outcomes. Here we examine these outcomes in diabetics using aggressive risk factor management. Methods: A retrospective review of all patients under-going coronary artery bypass between 1991 and 2000 at a single Veterans Affairs Medical Center was undertaken. 973 patients were included, 313 with diabetes and 660 without. Strict glucose control was maintained for all patients. Additional risk factor modification, including anti-platelets medications, statins, and beta blockers were also used. Survival analysis was performed. Results: The diabetic group was at higher risk, with age, BSA, and NYHA class all being greater (p \u3c 0.05). The mean follow-up time was 6.7 ± 3 years. There were 28 deaths/1000 person-years for non-diabetics, and 48 deaths/1000 person-years for diabetics. Survival rates were significantly higher for non-diabetics (72% versus 58% in the diabetic group, p \u3c 0.001). Cox proportional hazard analysis demonstrated mortality risk was 57% higher for diabetic patients (hazard ratio = 1.57; CI: 1.19 - 2.09; p = 0.002). The mortality risk in diabetics with and without prior MI was similar (HR = 0.83; CI: 0.54 - 1.28; p = 0.40). Conclusions: Diabetics undergoing coronary bypass have poorer long-term survival than non-diabetics despite perioperative glycemic control and risk factor modification. The long-term survival decrease in diabetics with history of MI is attenuated with surgical revascularization

Impact of unhealthy lifestyle on cardiorespiratory fitness and heart rate recovery of medical science students

Background: Medical science students represent valuable labour resources for better future medicine and medical technology. However, little attention was given to the health and well-being of these early career medical science professionals. The aim of this study is to investigate the impact of lifestyle components on cardiorespiratory fitness and heart rate recovery measured after moderate exercise in this population. Methods: Volunteers without documented medical condition were recruited randomly and continuously from the first-year medical science students during 2011-2014 at the University of Surrey, UK. Demographics and lifestyle components (the levels of smoking, alcohol intake, exercise, weekend outdoor activity and screen-time, daily sleep period, and self-assessment of fitness) were gathered through pre-exercise questionnaire. Cardiorespiratory fitness (VO2max) and heart rate recovery were determined using Åstrand–Rhyming submaximal cycle ergometry test. Data were analysed using SPSS version 25. Results: Among 614 volunteers, 124 had completed both lifestyle questionnaire and the fitness test and were included for this study. Within 124 participants (20.6±4 years), 46.8% were male and 53.2% were female, 11.3% were overweight and 8.9% were underweight, 8.9% were current smokers and 33.1% consumed alcohol beyond the UK recommendation. There were 34.7% of participants admitted to have <3 h/week of moderate physical activity assessed according to UK Government National Physical Activity Guidelines and physically not fit (feeling tiredness). Fitness test showed that VO2max distribution was inversely associated with heart rate recovery at 3 min and both values were significantly correlated with the levels of exercise, self-assessed fitness and BMI. Participants who had <3h/week exercise, or felt not fit or were overweight had significantly lower VO2max and heart rate recovery than their peers. Conclusion: One in three new medical science students were physically inactive along with compromised cardiorespiratory fitness and heart rate recovery, which put them at risk of cardiometabolic diseases. Promoting healthy lifestyle at the beginning of career is crucial in keeping medical science professionals healthy