University of Nevada, Reno

About: University of Nevada, Reno is a education organization based out in Reno, Nevada, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13561 authors who have published 28217 publications receiving 882002 citations. The organization is also known as: University of Nevada & Nevada State University.

The use of social network analysis in logistics research

Abstract: This paper introduces and provides an overview of social network theory and social network analysis (SNA) and its potential applications to logistics and supply chain management research. It then provides an example of the use of SNA via the introduction of hypotheses related to informal and formal structure and influence within a social network. These hypotheses are tested within the context of the development and implementation of a complex reporting system that evolved as the result of warehousing safety and environmental concerns within an organization.

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Abstract: Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

Double-ended calibration of fiber-optic Raman spectra distributed temperature sensing data

Abstract: Over the past five years, Distributed Temperature Sensing (DTS) along fiber optic cables using Raman backscattering has become an important tool in the environmental sciences. Many environmental applications of DTS demand very accurate temperature measurements, with typical RMSE < 0.1 K. The aim of this paper is to describe and clarify the advantages and disadvantages of double-ended calibration to achieve such accuracy under field conditions. By measuring backscatter from both ends of the fiber optic cable, one can redress the effects of differential attenuation, as caused by bends, splices, and connectors. The methodological principles behind the double-ended calibration are presented, together with a set of practical considerations for field deployment. The results from a field experiment are presented, which show that with double-ended calibration good accuracies can be attained in the field.