The Method of Using the Maxima System for Operations Research Learning

У статті досліджено проблеми використання систем комп’ютерної математики (СКМ) як інструменту підтримки навчальної та дослідницької діяльності у галузі навчання інформатики та математики. Визначено роль СКМ у процесі підготовки бакалаврів інформатики та особливості педагогічних застосувань цих систем у навчанні «Дослідження операцій». Метою статті є обґрунтування доцільності використання системи Maxima у процесі навчання «Дослідження операцій» у педагогічному університеті як засобу забезпечення дослідницького підходу до навчання та визначення перспектив його впровадження. Розглянуто основні характеристики СКМ Maxima та способи організації доступу до цього засобу як в локальній, так і в хмарній реалізації. Висвітлено результати педагогічного експерименту щодо використання системи Maxima як засобу підтримування навчання "Дослідження операцій" та здійснено аналіз його висновків.In the article, the problems of using the systems of computer mathematics (SCM) as a tool to support the teaching and research activities in the field of informatics and mathematics disciplines training are investigated. The role of SCM in the process of bachelors of informatics training and special aspects of pedagogical applications of these systems in the “Operations research” study is defined. The aim of the article is the justification of the Maxima system use of in the process of “Operations research” teaching in pedagogical university as enchasing the investigative approach to learning and determination of the perspective ways of its introduction. The main characteristics of SCM Maxima and the ways of access organizing to it both in local and the cloud-oriented implementation are considered. The results of the pedagogical experiment on the Maxima application to support the investigative approach to operation research study and the analysis of its conclusions are reported

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Generalization of form in visual pattern classification.

Human observers were trained to criterion in classifying compound Gabor signals with sym- metry relationships, and were then tested with each of 18 blob-only versions of the learning set. General- ization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propo- sitional scheme of representation operated upon by a rule-based classification process remains an open question