A production function study of manufacturing establishments of France, India, Israel Japan and Yugoslavia

This thesis is an empirical inquiry into the nature of production functions of manufacturing establishments of France, India, Israel Japan and Yugoslavia. It uses the difference between the nature of economic and technical variables to review several forms of production functions in the literature. Fifteen production relations are selected for a cross section analysis of the data of each country. Various criteria of grouping the establishment data are examined. It is found that meaningful results can be obtained from mixed establishment data which can represent the manufacturing sector of a country. It is found that in international comparisons based on production function analysis, nations are more relevant than industries or groups of manufacturing establishments. The intrinsic features of the data are best revealed when the production relation contains at least one suitable economic and one suitable technical variable on the explanatory side. By grouping the data according to various criteria and applying statistical tests, it is shown that there is homogeneity between groups of establishments within each country and that this homogeneity is revealed in almost all cases when the grouping of the data is based on a variable which is not a dependent variable in the production relation used in the analysis

DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

Orthogonally Regularized Deep Networks For Image Super-resolution

Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR) version in the spatial domain. Aiming for faster inference and more efficient solutions than solving the SR problem in the spatial domain, we propose a novel network structure for learning the SR mapping function in an image transform domain, specifically the Discrete Cosine Transform (DCT). As a first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer. We further extend the network to allow the CDCT layer to become trainable (i.e. optimizable). Because this layer represents an image transform, we enforce pairwise orthogonality constraints on the individual basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR) simplifies the SR task by taking advantage of image transform domain while adapting the design of transform basis to the training image set

Sorption and Drug Release Studies from Semi-interpenetrating Polymer Networks of Chitosan and Xanthan Gum

Hydrogel films of Chitosan (CS) and Xanthan gum (XA) of compositions 100/0, 90/10, 80/20, 70/30, 60/40 and 50/50 (w/w) % were prepared and swollen in simulated gastric fluid (SGF) of pH 1.2 and simulated intestinal fluid (SIF) of pH 7.4. To impart stability in acidic environment, semi-interpenetrating polymer network (semi-IPNs) films were formed using glutaraldehyde (GA) as the crosslinking agent. With increase in XA concentration, equilibrium degree of swelling reduced in SGF as well as SIF indicating maximum intermolecular interactions for 50/50 CS/XA semi-IPN. The swelling data was observed to follow second order kinetics. Spectroscopic and thermal analyses of these semi-IPN films also suggest maximum intermolecular interactions for 50/50 CS/XA semi-IPN. The potential of using 50/50 semi-IPN in drug delivery was studied using amoxicillin. In-vitro drug release studies indicated higher drug release in SGF than in SIF suggesting dependence of amoxicillin release kinetics and diffusion coefficient on pH of the environment and drug loading. The results suggest that CS-based semi-IPNs with different crosslinker and XA concentration could be promising candidates for formulation in oral gastrointestinal delivery systems

Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art

Security Games for Node Localization through Verifiable Multilateration

Most applications of wireless sensor networks (WSNs) rely on data about the positions of sensor nodes, which are not necessarily known beforehand. Several localization approaches have been proposed but most of them omit to consider that WSNs could be deployed in adversarial settings, where hostile nodes under the control of an attacker coexist with faithful ones. Verifiable multilateration (VM) was proposed to cope with this problem by leveraging on a set of trusted landmark nodes that act as verifiers. Although VM is able to recognize reliable localization measures, it allows for regions of undecided positions that can amount to the 40 percent of the monitored area. We studied the properties of VM as a noncooperative two-player game where the first player employs a number of verifiers to do VM computations and the second player controls a malicious node. The verifiers aim at securely localizing malicious nodes, while malicious nodes strive to masquerade as unknown and to pretend false positions. Thanks to game theory, the potentialities of VM are analyzed with the aim of improving the defender's strategy. We found that the best placement for verifiers is an equilateral triangle with edge equal to the power range R, and maximum deception in the undecided region is approximately 0.27R. Moreover, we characterized-in terms of the probability of choosing an unknown node to examine further-the strategies of the players

National BSUG audit of stress urinary incontinence surgery in England

Introduction and hypothesis The aim of the British Society of Urogynaecology (BSUG) 2013 audit for stress urinary incontinence (SUI) surgery was to conduct a national clinical audit looking at the intra- and postoperative complications and provide outcomes for these procedures. This audit was supported by the Healthcare Quality Improvement Partnership (HQIP) and National Health Service (NHS) England. Methods Data were collected for all continence procedures performed in 2013 through the BSUG database. All clinicians in England performing SUI surgery were invited to submit data to a central database. Outcomes data for the different continence procedures were collected and included intraoperative and postoperative complications and the change in continence scores at postoperative follow-up Changing trends in stress incontinence surgery were also assessed. Results We recorded 4993 urinary incontinence procedures from 177 consultants at 110 centres in England: 94.6% were midurethral slings; 86.7% (4331) were submitted by BSUG members with the remaining 13.3% submitted by non-BSUG members. Postoperative follow-up data were available for 3983 (80%) patients: 92.3% (3676) were very much better/much better postoperatively, and 4806 (96.3%) proceeded with no reported complications. There were 187 cases (3.7%) in which a perioperative complication was recorded. Pain persisting >30 days was reported in 1.9% of all patients. Conclusions Surgery for SUI has good outcomes in the short term. Midurethral synthetic slings have been shown to be safe and effective as a treatment option, with >90% being very much/much better at their postoperative follow-up