Smartphones Сome in Handy for the Rare Cosmic Particles Search
Researchers from the Laboratory of Methods for Big Data Analysis (LAMBDA) at the Higher School of Economics have improved their way of analyzing ultra-high energy cosmic rays (UHECR) with the use of mobile phones.
Survival Strategies for Women in a Youth-centric World
The idea of ageing well assumes that a mature individual remains active, healthy, and attractive. Society places this demand on women in particular. HSE researchers have published an article in Ageing & Society that looks at the strategies women over 50 choose.
How Neurotechnologies Impact Risk Appetite
Researchers from the Higher School of Economics have shown that by stimulating the frontal cortex, a person’s financial risk appetite can be increased temporarily. Their article on the cognitive mechanisms of risky decision-making was published in eNeuro, an international peer-reviewed scientific journal published by the Society for Neuroscience.
Scientists Learned to Predict Public Corruption with Neural Networks
Scientists from Higher School of Economics (HSE) and University of Valladolid have developed a neural network prediction model of corruption based on economic and political factors. The results of the research were published in Social Indicators Research.
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.…
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.…
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.…
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.…