GARNETT, A. Campbell: The perceptual process.

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MARTÍNEZ FREIRE, P.: Filosofía de la Ciencia Empírica .

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Effectiveness in Executive Decision-Making Bodies: The Role of Conflict

The theory and practice related to team processes such as communication, coordination, conflict, and their impact on team performance and effectiveness has been focused on principles applied to teams in general and to specific subsets of teams/situations. Executive decision-making bodies are a specialized subset of teams charged with managing strategic and enterprise-wide change typically consisting of talented team members who make decisions having a broad impact on the organizations they lead. Executive decision-making bodies have been largely unexplored in the team research literature. This study presents an in-depth review of prior research exploring the role of input factors and conflict on executive teams and identifies the most critical variables that have been found to predict outcomes in executive teams. This paper concludes by presenting the framework to be used on a mixed methods study on a field setting focusing on critical input variables that contribute to executive team effectiveness and the role of conflict as a moderator of that relationship. This mixed-methods study seeks to improve our understanding of the dynamic relationships between team input variables, conflict, and performance in executive teams. Individual interviews, surveys, and observational sessions will be conducted to gather data on over thirty executive teams in a 15,000+ employee civilian-driven, military-led engineering government enterprise varying by geography and functional responsibility. Paper includes motivation for the study, research framework, findings to date, and some early conclusions

Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems

Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries.In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search

Improved outer boundary conditions for Einstein's field equations

In a recent article, we constructed a hierarchy B_L of outer boundary conditions for Einstein's field equations with the property that, for a spherical outer boundary, it is perfectly absorbing for linearized gravitational radiation up to a given angular momentum number L. In this article, we generalize B_2 so that it can be applied to fairly general foliations of spacetime by space-like hypersurfaces and general outer boundary shapes and further, we improve B_2 in two steps: (i) we give a local boundary condition C_2 which is perfectly absorbing including first order contributions in 2M/R of curvature corrections for quadrupolar waves (where M is the mass of the spacetime and R is a typical radius of the outer boundary) and which significantly reduces spurious reflections due to backscatter, and (ii) we give a non-local boundary condition D_2 which is exact when first order corrections in 2M/R for both curvature and backscatter are considered, for quadrupolar radiation.Comment: accepted Class. Quant. Grav. numerical relativity special issue; 17 pages and 1 figur

Development of permeable reactive biobarrier for the removal of PAHs by Trichoderma longibrachiatum

In this work, the formation of permeable reactive biobarriers (PRBBs) using Trichoderma longibrachiatum over nylon sponge as bioreactive medium for removal of polycyclic aromatic hydrocarbons (PAHs) was studied. Colony formation was pretested without PAH presence by inoculation of fungus into nylon sponge. The fungus formed a large quantity of strongly adhesive biofilm among nylon sponge. Afterwards, the ability of the developed bioreactive medium was tested to remediate phenanthrene in aqueous medium and in soil. In aqueous medium, a 90% of phenanthrene concentration reduction was observed after 14 d. However, the pollutant removal in soil requires previous fungus colonization and the attained level was around 70% after 28 d. Subsequently, the formed bioreactive material was used in a glass column reactor to evaluate its application as PRBBs. Mixtures of phenanthrene, benzo[a]anthracene and pyrene at several concentrations, from 100 to 400 μM, were treated. In all cases, the performance of the PRBB was satisfactory and total PAH removals were achieved. These results suggest that PRBBs of T. longibrachiatum supported on nylon sponge can be an effective method for the treatment of PAHs.This research was funded by Spanish Ministry of Science and Innovation and FEDER Funds (Project CTM 2011-25389) and for financial support of Marta Pazos under the Ramon y Cajal programme and Marta Cobas under the final project master grant "Campus do Mar Knowledge in depth"

PEDESTRIAN PATHFINDING in URBAN ENVIRONMENTS: PRELIMINARY RESULTS

With the rise of urban population, many initiatives are focused upon the smart city concept, in which mobility of citizens arises as one of the main components. Updated and detailed spatial information of outdoor environments is needed to accurate path planning for pedestrians, especially for people with reduced mobility, in which physical barriers should be considered. This work presents a methodology to use point clouds to direct path planning. The starting point is a classified point cloud in which ground elements have been previously classified as roads, sidewalks, crosswalks, curbs and stairs. The remaining points compose the obstacle class. The methodology starts by individualizing ground elements and simplifying them into representative points, which are used as nodes in the graph creation. The region of influence of obstacles is used to refine the graph. Edges of the graph are weighted according to distance between nodes and according to their accessibility for wheelchairs. As a result, we obtain a very accurate graph representing the as-built environment. The methodology has been tested in a couple of real case studies and Dijkstra algorithm was used to pathfinding. The resulting paths represent the optimal according to motor skills and safety

Solving Large Scale Instances of the Distribution Design Problem Using Data Mining

In this paper we approach the solution of large instances of the distribution design problem. The traditional approaches do not consider that the instance size can significantly reduce the efficiency of the solution process. We propose a new approach that includes compression methods to transform the original instance into a new one using data mining techniques. The goal of the transformation is to condense the operation access pattern of the original instance to reduce the amount of resources needed to solve the original instance, without significantly reducing the quality of its solution. In order to validate the approach, we tested it proposing two instance compression methods on a new model of the replicated version of the distribution design problem that incorporates generalized database objects. The experimental results show that our approach permits to reduce the computational resources needed for solving large instances by at least 65%, without significantly reducing the quality of its solution. Given the encouraging results, at the moment we are working on the design and implementation of efficient instance compression methods using other data mining techniques

Protein co-evolution, co-adaptation and interactions

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution

Breakdown transients in high-k multilayered MOS stacks: Role of the oxide-oxide thermal boundary resistance

In this work, breakdown transients of multilayered gate oxide stacks were analyzed to study the impact of the interfaces between oxides on the heat dissipation considering an electromigration-based progressive breakdown model. Using two distinct measurement setups on four different sets of samples, featuring two layers and three layers of Al 2 O 3 and HfO 2 interspersed, the breakdown transients were captured and characterized in terms of the degradation rate. Experimental results show that the number of oxide-oxide interfaces present in the multilayered stack has no visible impact on the breakdown growth rate among our samples. This strongly supports the interpretation of the bulk materials dominating the heat transfer to the surroundings of a fully formed conductive filament that shows no electrical differences between our various multilayered stack configurations.Fil: Boyeras Baldomá, Santiago. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pazos, Sebastián Matías. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; ArgentinaFil: Aguirre, F. L.. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; ArgentinaFil: Palumbo, Felix Roberto Mario. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin