University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

The future of crowd work

Abstract: Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

A reconfigurable fabric for accelerating large-scale datacenter services

Abstract: Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost It is challenging to improve all of these factors simultaneously To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA) Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth networkWe describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput In other words, the reconfigurable fabric enables the same throughput using only half the number of servers

Structural equation modeling and its application to network analysis in functional brain imaging

Abstract: The analysis of brain imaging data has recently focused on the examination of the covariances of activity among neural regions during different behaviors. We present some of the theoretical and technical issues surrounding one of these covariance-based methods: structural equation modeling. In structural equation modeling, connections between brain areas are based on known neuroanatomy, and the interregional covariances of activity are used to calculate path coefficients representing the magnitude of the influence of each directional path. The logic behind the use of structural equation modeling stems from the suggestion that brain function is the result of changes in the covariances of activity among neural elements. The technical foundations for neural structural equation models are presented, emphasizing the ability to make inferential comparisons to evaluate the experimental changes in path coefficients. Simulated data sets were used to test the effects of omitted regions and omitted connections. The results suggested that structural modeling algorithms can give hints as to possible external influences and missing paths, but that the final decision as to model modifications requires the guidance of the researcher. The utility of anatomically based models to distinguish between the direct effect of one region on another, and indirect effects of darkness or patterned light on the metabolic activity in the rat visual system. The anatomical framework for the structural equation models revealed that the total impact of ascending thalamocortical influences was modified by corticocortical interactions. Extensions of structural equation modeling to human brain imaging experiments are presented. We conclude by suggesting that neural covariances may be a more accurate way to examine the dynamic functional organization of the central nervous system. ©1994 Wiley-Liss, Inc.