University of Illinois at Chicago

About: University of Illinois at Chicago is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 57071 authors who have published 110536 publications receiving 4264936 citations.

Combined Measurement of the Higgs Boson Mass in pp Collisions at √s=7 and 8 TeV with the ATLAS and CMS Experiments

Abstract: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4l decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is mH=125.09±0.21 (stat)±0.11 (syst) GeV.

Examining smart-card security under the threat of power analysis attacks

Abstract: This paper examines how monitoring power consumption signals might breach smart-card security. Both simple power analysis and differential power analysis attacks are investigated. The theory behind these attacks is reviewed. Then, we concentrate on showing how power analysis theory can be applied to attack an actual smart card. We examine the noise characteristics of the power signals and develop an approach to model the signal-to-noise ratio (SNR). We show how this SNR can be significantly improved using a multiple-bit attack. Experimental results against a smart-card implementation of the Data Encryption Standard demonstrate the effectiveness of our multiple-bit attack. Potential countermeasures to these attacks are also discussed.

Transfer Feature Learning with Joint Distribution Adaptation

Abstract: Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.