Автоматическая оптимизация технологического комплекса обогащения железных руд по сигналам магнитной индукции сепаратора

Recombinant antibodies can be used to diagnose, treat and prevent disease by exploiting their specific antigen-binding activities. A large number of drugs currently in development are recombinant antibodies and most of these are produced in cultured rodent cells. Although such cells produce authentic functional products, they are expensive, difficult to scale-up and may contain human pathogens. Plants represent a cost-effective, convenient and safe alternative production system and are slowly gaining acceptance. Five plant-derived therapeutic recombinant antibodies (plantibodies) are undergoing clinical evaluation, three of which can be used as prophylactics

Deconvolution Processing for Flaw Signatures

The ultimate resolution of all ultrasonic flaw detection systems is limited by transducer response. Although the system output contains detailed information about the target structure, these details are masked by the system characteristics. Since the output can be described as the convolution of the target response and the impulse response of the system, it should- in principle - be possible to reverse this operation and extract the target response. In practice, it is found that the presence of even relatively small amounts of noise make the deconvolution process impossible. If, however, the flaw detection system has an extremely high output signal-to-noise ratio it is possible to use estimation techniques in the deconvolution process to achieve a good approximation to the actual target response. Results are presented that demonstrate these techniques applied to both simulated and experimental data. Coupling deconvolution processing with feature extraction is shown to yield an order of magnitude increase in range resolution

Stochastic EM methods with variance reduction for penalised PET reconstructions

Expectation-maximisation (EM) is a popular and well-established method for image reconstruction in positron emission tomography (PET) but it often suffers from slow convergence. Ordered subset EM (OSEM) is an effective reconstruction algorithm that provides significant acceleration during initial iterations, but it has been observed to enter a limit cycle. In this work, we investigate two classes of algorithms for accelerating OSEM based on variance reduction for penalised PET reconstructions. The first is a stochastic variance reduced EM algorithm, termed as SVREM, an extension of the classical EM to the stochastic context that combines classical OSEM with variance reduction techniques for gradient descent. The second views OSEM as a preconditioned stochastic gradient ascent, and applies variance reduction techniques, i.e., SAGA and SVRG, to estimate the update direction. We present several numerical experiments to illustrate the efficiency and accuracy of the approaches. The numerical results show that these approaches significantly outperform existing OSEM type methods for penalised PET reconstructions, and hold great potential

A Diagram Is Worth A Dozen Images

Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs

Flexible code safety for Win32

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 90-93).by Andrew R. Twyman.S.B.and M.Eng

Iterative PET Image Reconstruction using Adaptive Adjustment of Subset Size and Random Subset Sampling

Statistical PET image reconstruction methods are often accelerated by the use of a subset of available projections at each iteration. It is known that many subset algorithms, such as ordered subset expectation maximisation, will not converge to a single solution but to a limit cycle. Reconstruction methods exist to relax the update step sizes of subset algorithms to obtain convergence, however, this introduces additional parameters that may result in extended reconstruction times. Another approach is to gradually decrease the number of subsets to reduce the effect of the limit cycle at later iterations, but the optimal iteration numbers for these reductions may be data dependent. We propose an automatic method to increase subset sizes so a reconstruction can take advantage of the acceleration provided by small subset sizes during early iterations, while at later iterations reducing the effects of the limit cycle behaviour providing estimates closer to the maximum a posteriori solution. At each iteration, two image updates are computed from a common estimate using two disjoint subsets. The divergence of the two update vectors is measured and, if too great, subset sizes are increased in future iterations. We show results for both sinogram and list mode data using various subset selection methodologies

Создание полимерных композиционных материалов с непрерывным армированием арамидным волокном для применения в аддитивных технологиях

В данной работе проведено исследование создания композиционного материала на полимерной основе с непрерывным армированием арамидного волокна для применения в аддитивных технологиях. Описаны технологические особенности процесса модификации поверхности арамидного волокна. Исследовано влияние материала модификации поверхности на адгезионные свойства получаемого композиционного материала

Інформаційне законодавство. Основні нормативні акти

Наведено основні нормативні акти з регулювання інформаційних відносин, зокрема, у сфері інформації, інформаційних агентств, телекомунікації, радіочастотного ресурсу України, інформатизації, телебачення тощо. Розраховано на студентів, які здобувають вищу освіту в галузях знань "Право", "Інформаційна безпека", "Комп'ютерні науки", "Телекомунікації"

Proposing the Interactivity-Stimulus-Attention Model (ISAM) to Explain and Predict the Enjoyment, Immersion, and Adoption of Purely Hedonic Systems

Traditional TAM research primarily focuses on utilitarian systems where extrinsic motivations chiefly explain and predict acceptance. We propose a theoretical model, ISAM, which explains the role of intrinsic motivations in building the user attention that leads to hedonic system acceptance. ISAM combines several theories with TAM to explain how interactivity acts as a stimulus in hedonic contexts—fostering curiosity, enjoyment, and the full immersion of cognitive resources. Two experiments involving over 700 participants validated ISAM as a useful model for explaining and predicting hedonic system acceptance. Immersion and PE are shown to be the primary predictors of behavioral intention to use hedonic systems. Unlike traditional utilitarian adoption research, PEOU does not directly impact BIU, and extrinsic motivations are virtually non-existent. The implications of this study extend beyond hedonic contexts, as users of utilitarian systems continue to demand more hedonic features and enjoyment is often more important than PEOU