Analyzing structural characteristics of object category representations from their semantic-part distributions

Studies from neuroscience show that part-mapping computations are employed by human visual system in the process of object recognition. In this work, we present an approach for analyzing semantic-part characteristics of object category representations. For our experiments, we use category-epitome, a recently proposed sketch-based spatial representation for objects. To enable part-importance analysis, we first obtain semantic-part annotations of hand-drawn sketches originally used to construct the corresponding epitomes. We then examine the extent to which the semantic-parts are present in the epitomes of a category and visualize the relative importance of parts as a word cloud. Finally, we show how such word cloud visualizations provide an intuitive understanding of category-level structural trends that exist in the category-epitome object representations

Efficient Handwritten Digit Classification using User-defined Classification Algorithm

In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

Common fixed point theorems through generalized altering distance functions

In this paper we obtain a unique common fixed point theorem, for four self maps using a generalized altering distance function in four variables , which generalizes and improves the main theorem of Choudhury [2]. We also obtain a Gregus type common fixed point theorem for four maps as a corollary

Bioanalytical Method Development and Validation of Memantine in Human Plasma by High Performance Liquid Chromatography with Tandem Mass Spectrometry: Application to Bioequivalence Study

A simple, sensitive, and rapid HPLC-MS/MS method was developed and validated for quantitative estimation of memantine in human plasma. Chromatography was performed on Zorbax SB-C18 (4.6 × 75 mm, 3.5 μm) column. Memantine (ME) and internal standard Memantine-d6(MED6) were extracted by using liquid-liquid extraction and analyzed by LC-ESI-MS/MS using multiple-reaction monitoring (MRM) mode. The assay exhibited a linear dynamic range of 50.00–50000.00 pg/ml for ME in human plasma. This method demonstrated an intra- and interday precision within the range of 2.1–3.7 and 1.4–7.8%, respectively. Further intra- and interday accuracy was within the range of 95.6–99.8 and 95.7–99.1% correspondingly. The mean recovery of ME and MED6 was 86.07 ± 6.87 and 80.31 ± 5.70%, respectively. The described method was successfully employed in bioequivalence study of ME in Indian male healthy human volunteers under fasting conditions

Clinical manifestation and prevalence of peripheral neuropathy and nerve dysfunction in patients with chronic kidney disease

Background: Chronic Kidney Disease (CKD) is recognized as a major health problem. Prevalence of CKD is rising continuously; mostly CKD is affecting the elderly aged population and/or patients with diabetes and hypertension. Present study was aimed to explore clinical manifestation and evaluate the prevalence of peripheral neuropathy and peripheral nerve dysfunction in CKD patients attending our hospital with reference to the severity and duration of the CKD.Methods: The present cross sectional study was conducted in 74 patients affected with chronic kidney disease, of different age groups at the medical wards of King George Hospital, Visakhapatnam. The presence of peripheral nerve dysfunction was assessed by nerve dysfunction clinically (motor or sensory symptoms and signs) and electrophysiological nerve conduction studies.Results: Out of 74 patients, 65% of study population was suffering from chronic kidney disease with peripheral nerve dysfunction. The peripheral nerves dysfunction was more prevalent in elder age (>65 years) subjects when compared to subjects with age <65 years. Moreover, the results shown that the rate of prevalence of peripheral nerves dysfunction was observed higher in subjects with longer duration of CKD. Male subjects were affected more when creatinine clearance is <15 ml/minute. Both sexes were affected equally when creatinine clearance is between 30-59 ml/minute.Conclusion: This study enlightens the prevalence and clinical presentation of peripheral nerve dysfunction in patients with CKD. The CKD was found to cause peripheral neuropathy including overt and subclinical neuropathy, of which distal symmetrical sensory motor neuropathy was common in CKD. The prevalence of peripheral neuropathy was directly proportional to duration and severity of CKD.

A new validated stability-indicating gradient RP-HPLC method for the determination of pemetrexed disodium and its process related substances

Pemetrexed disodium is used for the treatment of malignant pleural mesothelioma and lung cancer. In the present study a simple stability indicating RP-HPLC method was developed and validated for the determination of Pemetrexed disodium. The process related substances such as Dimer-1 impurity, Dimer-2 impurity, N-Methyl Pemetrexed, Pemetrexed diethyl ester, Alanine derivative of Pemetrexed, DMF derivative of Pemetrexed, Acid intermediate, Oxidation impurity and D-isomer were separated on gradient mode and quantified. Forced degradation studies were performed to prove the specificity. Hypersil BDS C18 100 x 4.6mm, 3µm was used for the separation (at 27°C) with mobile phase mixture consisting of (0.02M sodium dihydrogen phosphate with 0.1% HCOOH and pH 3.8 with dilute sodium hydroxide): Acetonitrile (40:60 v/v) (pH 3.8) with a flow rate of 1.2 mL/min. Methanol: water (1:1) was used as diluent and the eluted compounds were monitored at 240 nm. 0.5-1500 µg/mL with linear regression equation y = 20588x - 9294.1 (R2=0.9999). The degradation products observed during the forced degradation studies were well resolved from the drug peak and proving that the method is a stability-indicating method. The method was validated as per ICH guidelines. Keywords: Pemetrexed disodium, RP-HPLC, gradient mode, Related substances, Stability indicating, Validation