Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects

Abstract: Online product reviews provided by consumers who previously purchased products have become a major information source for consumers and marketers regarding product quality. This study extends previous research by conducting a more compelling test of the effect of online reviews on sales. In particular, we consider both quantitative and qualitative aspects of online reviews, such as reviewer quality, reviewer exposure, product coverage, and temporal effects. Using transaction cost economics and uncertainty reduction theories, this study adopts a portfolio approach to assess the effectiveness of the online review market. We show that consumers understand the value difference between favorable news and unfavorable news and respond accordingly. Furthermore, when consumers read online reviews, they pay attention not only to review scores but to other contextual information such as a reviewer's reputation and reviewer exposure. The market responds more favorably to reviews written by reviewers with better reputation and higher exposure. Finally, we demonstrate that the impact of online reviews on sales diminishes over time. This suggests that firms need not provide incentives for customers to write reviews beyond a certain time period after products have been released.

Adaptive channel allocation spectrum etiquette for cognitive radio networks

Abstract: In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

Abstract: Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, signicantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.

Role of water in electron-initiated processes and radical chemistry: issues and scientific advances.

Abstract: Chemical Science Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352; Department of Chemistry, ShelbyHall, University of Alabama, Box 870336, Tuscaloosa, Alabama 35487-0336; Notre Dame Radiation Laboratory, University of Notre Dame,Notre Dame, Indiana 46556; Department of Chemistry, Yale University, P.O. Box 208107, New Haven, Connecticut 0520-8107; Argonne NationalLaboratory, 9700 South Cass Avenue, Argonne, Illinois 60439; Department of Computer Science and Department of Physics, 2710 University Drive,Washington State University, Richland, Washington 99352-1671; Lawrence Berkeley National Laboratory, 1 Cyclotron Road Mailstop 1-0472,Berkeley, California 94720; Department of Chemistry and Biochemistry, University of Texas at Austin, 1 University Station A5300,Austin, Texas 78712; Office of Basic Energy Sciences, U.S. Department of Energy, SC-141/Germantown Building, 1000 Independence Avenue,S.W., Washington, D.C. 20585-1290; Department of Physics and Engineering Physics, Stevens Institute of Technology, Castle Point on Hudson,Hoboken, New Jersey 07030; Department of Chemistry, Johns Hopkins University, 34th and Charles Streets, Baltimore, Maryland 21218;Department of Chemistry, University of Southern California, Los Angeles, California 90089-1062; Department of Chemistry, The Ohio StateUniversity, 100 West 18th Avenue, Columbus, Ohio 43210-1185; Department of Chemistry, Columbia University, Box 3107, Havemeyer Hall,New York, New York 10027; Department of Chemistry, University of Pittsburgh, Parkman Avenue and University Drive,Pittsburgh, Pennsylvania 15260; Chemistry Department, Brookhaven National Laboratory, Upton, New York 11973-5000; Department of Physics andAstronomy, Rutgers, The State University of New Jersey, 136 Frelinghuysen Road, Piscataway, New Jersey 08854-8019; Department of Chemistry,516 Rowland Hall, University of California, Irvine, Irvine, California 92697-2025; Stanford Synchrotron Radiation Laboratory, Stanford LinearAccelerator Center, 2575 Sand Hill Road, Mail Stop 69, Menlo Park, California 94025; School of Chemistry and Biochemistry, Georgia Institute ofTechnology, 770 State Street, Atlanta, Georgia 30332-0400; Geology Department, University of California, Davis, One Shields Avenue,Davis, California 95616-8605; Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue,Cambridge, Massachusetts 02139-4307; Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907-2084Received July 23, 2004