University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Top Management Team Characteristics and Corporate Illegal Activity

Abstract: From a review of previous theoretical and empirical research in corporate illegal activity and top management team (TMT) characteristics, we develop several propositions that suggest a relationship between the two TMT characteristics such as length of service, functional background, formal business education, age, and military service, as well as homogeneity in each of these characteristics, are hypothesized to neutralize or enhance relationships between context and corporate illegal activity Implications of the proposed relationships and their limitations are also discussed

Discrimination-aware channel pruning for deep neural networks

Abstract: Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we investigate a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose those channels that really contribute to discriminative power. To this end, we introduce additional discrimination-aware losses into the network to increase the discriminative power of intermediate layers and then select the most discriminative channels for each layer by considering the additional loss and the reconstruction error. Last, we propose a greedy algorithm to conduct channel selection and parameter optimization in an iterative way. Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels outperforms the baseline model by 0.39% in top-1 accuracy.

Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks

Abstract: Recent work has shown that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in jointly improving the amount of data collected and reducing the energy consumption. To address this issue, we propose a novel data collection scheme, called the Maximum Amount Shortest Path (MASP), that increases network throughput as well as conserves energy by optimizing the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol based on zone partition is designed to implement the MASP scheme. We also develop a practical distributed approximate algorithm to solve the MASP problem. In addition, the impact of different overlapping time partition methods is studied. The proposed algorithms and protocols are validated through simulation experiments using OMNET++.

The DØ detector

Abstract: The DO detector is a large general purpose detector for the study of short-distance phenomena in high energy antiproton-proton collisions, now in operation at the Fermilab Tevatron collider. The detector focusses upon the detection of electrons, muons, jets and missing transverse momentum. We describe the design and performance of the major elements of the detector, including the tracking chambers, transition radiation detector, liquid argon calorimetry and muon detection. The associated electronics, triggering systems and data acquisition systems are presented. The global mechanical, high voltage, and experiment monitoring and control systems which support the detector are described. We also discuss the design and implementation of software and software support systems that are specific to DO.