Emotional representations of space vary as a function of peoples' affect and interoceptive sensibility

Most research on people’s representation of space has focused on spatial appraisal and navigation. But there is more to space besides navigation and assessment: people have different emotional experiences at different places, which create emotionally tinged representations of space. Little is known about the emotional representation of space and the factors that shape it. The purpose of this study was to develop a graphic methodology to study the emotional representation of space and some of the environmental features (non-natural vs. natural) and personal features (affective state and interoceptive sensibility) that modulate it. We gave participants blank maps of the region where they lived and asked them to apply shade where they had happy/sad memories, and where they wanted to go after Covid-19 lockdown. Participants also completed self-reports on affective state and interoceptive sensibility. By adapting methods for analyzing neuroimaging data, we examined shaded pixels to quantify where and how strong emotions are represented in space. The results revealed that happy memories were consistently associated with similar spatial locations. Yet, this mapping response varied as a function of participants’ affective state and interoceptive sensibility. Certain regions were associated with happier memories in participants whose affective state was more positive and interoceptive sensibility was higher. The maps of happy memories, desired locations to visit after lockdown, and regions where participants recalled happier memories as a function of positive affect and interoceptive sensibility overlayed significantly with natural environments. These results suggest that people’s emotional representations of their environment are shaped by the naturalness of places, and by their affective state and interoceptive sensibility

Symbolic Reachability Analysis of B through ProB and LTSmin

We present a symbolic reachability analysis approach for B that can provide a significant speedup over traditional explicit state model checking. The symbolic analysis is implemented by linking ProB to LTSmin, a high-performance language independent model checker. The link is achieved via LTSmin's PINS interface, allowing ProB to benefit from LTSmin's analysis algorithms, while only writing a few hundred lines of glue-code, along with a bridge between ProB and C using ZeroMQ. ProB supports model checking of several formal specification languages such as B, Event-B, Z and TLA. Our experiments are based on a wide variety of B-Method and Event-B models to demonstrate the efficiency of the new link. Among the tested categories are state space generation and deadlock detection; but action detection and invariant checking are also feasible in principle. In many cases we observe speedups of several orders of magnitude. We also compare the results with other approaches for improving model checking, such as partial order reduction or symmetry reduction. We thus provide a new scalable, symbolic analysis algorithm for the B-Method and Event-B, along with a platform to integrate other model checking improvements via LTSmin in the future

Лекарственный анафилактический и псевдоаллергический шок: патогенез, клиника, дифференциальная диагностика, подходы к терапии

Представлены особенности патогенеза анафилактического и псевдоаллергического шока, обусловленного лекарственными средствами, сходства и различия в клиническом их течении, субъективные и объективные признаки дифференциальной диагностики, подходы к терапии.The peculiarities of the pathogenesis of anaphylactic and pseudoallergic shock caused by medications, similarity and differences of the clinical course, subjective and objective signs of differential diagnosis, approaches to therapy are presented

The exact evaluation of the corner-to-corner resistance of an M x N resistor network: Asymptotic expansion

We study the corner-to-corner resistance of an M x N resistor network with resistors r and s in the two spatial directions, and obtain an asymptotic expansion of its exact expression for large M and N. For M = N, r = s =1, our result is R_{NxN} = (4/pi) log N + 0.077318 + 0.266070/N^2 - 0.534779/N^4 + O(1/N^6).Comment: 12 pages, re-arranged section

The Effect of Hints and Model Answers in a Student-Controlled Problem-Solving Program for Secondary Physics Education

Many students experience difficulties in solving applied physics problems. Most programs that want students to improve problem-solving skills are concerned with the development of content knowledge. Physhint is an example of a student-controlled computer program that supports students in developing their strategic knowledge in combination with support at the level of content knowledge. The program allows students to ask for hints related to the episodes involved in solving a problem. The main question to be answered in this article is whether the program succeeds in improving strategic knowledge by allowing for more effective practice time for the student (practice effect) and/or by focusing on the systematic use of the available help (systematic hint-use effect). Analysis of qualitative data from an experimental study conducted previously show that both the expected effectiveness of practice and the systematic use of episode-related hints account for the enhanced problem-solving skills of students

Deep learning for inferring cause of data anomalies

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.Comment: Presented at ACAT 2017 conference, Seattle, US

Uniform tiling with electrical resistors

The electric resistance between two arbitrary nodes on any infinite lattice structure of resistors that is a periodic tiling of space is obtained. Our general approach is based on the lattice Green's function of the Laplacian matrix associated with the network. We present several non-trivial examples to show how efficient our method is. Deriving explicit resistance formulas it is shown that the Kagom\'e, the diced and the decorated lattice can be mapped to the triangular and square lattice of resistors. Our work can be extended to the random walk problem or to electron dynamics in condensed matter physics.Comment: 22 pages, 14 figure