Pushing the Limits: Cognitive, Affective, and Neural Plasticity Revealed by an Intensive Multifaceted Intervention.

Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex (PCC) and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus (MTG) that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed

Automated Circuit Approximation Method Driven by Data Distribution

We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best trade-offs between the classification accuracy and power consumption of two image classifiers based on neural networks.Comment: Accepted for publication at Design, Automation and Test in Europe (DATE 2019). Florence, Ital

Determinants of Managerial Pay in the Czech Republic

The purpose of this paper is to examine the determinants of the variation in Czech managers' pay levels. Among the questions we attempt to answer are: Are the managers in state-owned firms compensated differently than those in private owned firms? How much of the difference in pay is explained by differences in individual characteristics and job levels? What is the importance of the regional location or the industry affiliation of the firms for managerial pay differentials? We use data from a cross-section of Czech managers in 1998 and estimate earnings equations augmented with a host of explanatory variables related to firm and job characteristics.http://deepblue.lib.umich.edu/bitstream/2027.42/39694/3/wp310.pd

Murine fecal microbiota transplantation lowers gastrointestinal pathogen loads and dampens pro-inflammatory immune responses in Campylobacter jejuni infected secondary abiotic mice

Conventional mice are protected from Campylobacter jejuni infection by the murine host-specific gut microbiota composition. We here addressed whether peroral fecal microbiota transplantation (FMT) might be an antibiotics-independent option to lower even high gastrointestinal C. jejuni loads in the infected vertebrate host. To address this, secondary abiotic mice were generated by broad-spectrum antibiotic treatment and perorally infected with C. jejuni by gavage. One week later, mice were stably colonized with more than 109 C. jejuni and subjected to peroral FMT from murine donors on three consecutive days. Two weeks post-intervention, gastrointestinal C. jejuni loads were up to 7.5 orders of magnitude lower following murine FMT versus mock challenge. Remarkably, FMT reversed C. jejuni induced colonic epithelial apoptosis, but enhanced proliferative and regenerative responses in the colon thereby counteracting pathogenic cell damage. Furthermore, FMT dampened both, innate and adaptive immune cell responses in the large intestines upon C. jejuni infection that were accompanied by less C. jejuni-induced colonic nitric oxide secretion. Our study provides strong evidence that novel probiotic formulations developed as alternative option to FMT in severe intestinal inflammatory morbidities including Clostridoides difficile infection might be effective to treat campylobacteriosis and lower pathogen loads in colonized vertebrates including farm animals

Creating brands online: third party opinions and their effect on consumers\u27 trust in brands and purchase intentions

Consumer lack of trust in online vendors and brands is identified as one of the biggest obstacles in the growth of e-commerce. This study examined how third-party product reviews help in building consumers’ trust, in consumers’ perception of product quality, their brand attitudes and consumers’ purchase intention. The six cell experimental design tested the effect of consumer and expert online product reviews on fictitious web sites for high-involvement and low-involvement products. The findings indicate that online consumer product reviews perform better than online expert product reviews and no product reviews. Online product reviews affected visitors to a web site with a high-involvement product the most. The study implies that online consumer product reviews significantly affect consumers in a high-involvement condition and are more effective than online expert product reviews

autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain from tens to thousands of approximate implementations for a single arithmetic operation it is intractable to find an optimal combination of approximate circuits in the library even for an application consisting of a few operations. An open problem is "how to effectively combine circuits from these libraries to construct complex approximate accelerators". This paper proposes a novel methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application. To enable fast design space generation and exploration, the methodology utilizes machine learning techniques to create computational models estimating the overall quality of processing and hardware cost without performing full synthesis at the accelerator level. Using the methodology, we construct hundreds of approximate accelerators (for a Sobel edge detector) showing different but relevant tradeoffs between the quality of processing and hardware cost and identify a corresponding Pareto-frontier. Furthermore, when searching for approximate implementations of a generic Gaussian filter consisting of 17 arithmetic operations, the proposed approach allows us to identify approximately $10^3$ highly important implementations from $10^{23}$ possible solutions in a few hours, while the exhaustive search would take four months on a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US