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Regular version of the site

Why High-performing Peer Groups Can Undermine Student Success

Being part of a very capable class at school can undermine some students' self-esteem and academic achievement.

How Corporate Values ​​Affect Bank Profits

Russian companies' corporate values are not associated with higher profits.

July 07

Decision-making Rules Least Susceptible to Manipulation, According to Science

HSE researchers have used computer modelling to demonstrate the varying manipulability of decision-making procedures and to identify those least susceptible to manipulation. Their findings are published in the paper 'Manipulability of Majority Relation-based Collective Decision Rules'.


Russian legal realism and transdisciplinarity in Social sciences at the turn of the XXth Century

his paper examines the academic context in which the Russian-Polish legal scholar Leon Petrazycki formed a transdisciplinary approach in legal philosophy, which served as a basis for development of legal sociology by his followers. The author contends that Petrazycki’s legal conception included “social engineering”, “living law”, and other aspects that allow characterizing his conception as one of the branches of legal realism. These realist stances were afterwards reconsidered by Gurvitch, Timasheff and other his followers who placed Petrazycki’s legal ideas into a framework of sociological jurisprudence. Going back to the beginnings of this approach, the author studies the common places in legal and economic sciences at the turn of the XX century, and foremost the predominating orientation to empirical data with discrimination of metaphysical speculation. The author asserts that the prevailing orientations at that epoque formed similar attitudes to understanding of legal and economic behaviors of social actors in countries seemingly belonging to different intellectual cultures. In this context, the author draws certain parallels between the methodological programs of Petrazycki and von Schmoller.

Revista europea de historia de las ideas políticas y de las instituciones públicas. 2017. No. 10.
Jun 23, 2017

More than the verbal stimulus matters: Visual attention in Language assessment for people with aphasia using multiple-choice image displays.

Purpose: Language comprehension in people with aphasia (PWA) is frequently evaluated using multiple-choice displays: PWA are asked to choose the image that best corresponds to the verbal stimulus in a display. When a nontarget image is selected, comprehension failure is assumed. However stimulus-driven factors unrelated to linguistic comprehension may influence performance. In this study we explore the influence of physical image characteristics of multiple-choice image displays on visual attention allocation of PWA.

Methods: Eye fixations of 41 PWA were recorded while they viewed 40 multiple-choice image sets presented with and without verbal stimuli. Within each display, three images (majority images) were the same and one image (singleton image) differed in terms of one image characteristic. The mean proportion of fixation duration (PFD) allocated across majority images was compared against the PFD allocated to singleton images.

Results: PWA allocated significantly greater PFD to the singleton than to the majority images in both nonverbal and verbal conditions.  Those with greater severity of comprehension deficits allocated greater PFD to nontarget singleton images in the verbal condition.

Conclusions: When using tasks that rely on multiple-choice displays and verbal stimuli, one cannot assume that verbal stimuli will override the effect of visual stimulus characteristics.

Heuer S., Ivanova M., Hallowell B.
Journal of Speech, Language, and Hearing Research. 2017. Vol. 60. P. 1348-1361.
Jun 16, 2017

Deep Convolutional Neural Networks and Maximum-Likelihood Principle in Approximate Nearest Neighbor Search

Deep convolutional neural networks are widely used to extract high-dimensional features in various image recognition tasks. If the count of classes is relatively large, performance of the classifier for such features can be insufficient to be implemented in real-time applications, e.g., in video-based recognition. In this paper we propose the novel approximate nearest neighbor algorithm, which sequentially chooses the next instance from the database, which corresponds to the maximal likelihood (joint density) of distances to previously checked instances. The Gaussian approximation of the distribution of dissimilarity measure is used to estimate this likelihood. Experimental study results in face identification with LFW and YTF datasets are presented. It is shown that the proposed algorithm is much faster than an exhaustive search and several known approximate nearest neighbor methods.

Lecture Notes in Computer Science. 2017. Vol. 10255. P. 42-49.
Jun 14, 2017

Navigation satellite systems as the audit foundation for mining companies

Integrated quality management system in the modern world are an essential element of functioning and development of any production, to a great extent determines the competitiveness of the enterprise and prospects of its activities. Currently the mining industry is actively developing a field of knowledge, called "satellite technology", which has diverse practical applications in geodesy, mine surveying, control systems, mining transport and safety and control of risks, the development of tools and methods for implementing energy efficiency strategies. The examples of current uses of satellite technology over the last decade at the leading enterprises of the mining sector.
The creation of new methods of technical and economic audit determines the need for and justification of the most common and objective criteria and evaluation indicators in the design phase and launch of the satellite equipment. To extend the lifetime of spacecraft is proposed in the design stage to calculate possible effects of electrostatic discharges and to give recommendations for reducing their negative influence. Developed a new method that allows for 2-3 orders of magnitude to reduce the complexity of calculations and to reduce costs for introduction and development of satellite technologies in mining.
Eurasian Mining*. 2017. No. 1.
Jun 12, 2017

Detecting interethnic relations with the data from social media

The ability of social media to rapidly disseminate judgements on ethnicity and to influence offline ethnic relations creates demand for the methods of automatic monitoring of ethnicity-related online content. In this study we seek to measure the overall volume of ethnicity-related discussion in the Russian-language social media and to develop an approach that would automatically detect various aspects of attitudes to those ethnic groups. We develop a comprehensive list of ethnonyms and related bigrams that embrace 97 Post-Soviet ethnic groups and obtain all messages containing one of those words from a two-year period from all Russian-language social media (N=2,660,222 texts). We hand-code 7,181 messages where rare ethnicities are over-represented and train a number of classifiers to recognize different aspects of authors’ attitudes and other text features. After calculating a number of standard quality metrics, we find that we reach good quality in detecting intergroup conflict, positive intergroup contact, and overall negative and positive sentiment. Relevance to the topic of ethnicity and general attitude to an ethnic group are least well predicted, while some aspects such as calls for violence against an ethnic group are not sufficiently present in the data to be predicted.

Koltsova O., Nikolenko S. I., Alexeeva S. V. et al.
Communications in Computer and Information Science. 2017.
Jun 11, 2017

Multidimensional mutual ordering of patterns using a set of pre-trained artificial neural networks

The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns "Strangers" that are input to a neural network trained to recognize only examples of the pattern "familiar". The neural network after training reduces the entropy of the examples of the pattern "Familiar" and increases the entropy of examples of pattern "Stranger." It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern "Familiar", the second to the inversion of the pattern "Familiar". It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.

Kulagin V., Ivanov A. I., Kuznetsov Y. M. et al.
Journal of Physics: Conference Series. 2017. Vol. 803. No. 1.
Jun 9, 2017


Marie Arsalidou "How the Brain Processes Numbers and Calculations"