Rutgers University

About: Rutgers University is a education organization based out in New Brunswick, New Jersey, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 68736 authors who have published 159418 publications receiving 6713860 citations. The organization is also known as: Rutgers, The State University of New Jersey & Rutgers.

Improved Dark Energy Constraints From ~ 100 New CfA Supernova Type Ia Light Curves

Abstract: We combine the CfA3 supernovae Type Ia (SN Ia) sample with samples from the literature to calculate improved constraints on the dark energy equation of state parameter, w. The CfA3 sample is added to the Union set of Kowalski et al. to form the Constitution set and, combined with a BAO prior, produces 1 + w = 0.013+0.066 –0.068 (0.11 syst), consistent with the cosmological constant. The CfA3 addition makes the cosmologically useful sample of nearby SN Ia between 2.6 and 2.9 times larger than before, reducing the statistical uncertainty to the point where systematics play the largest role. We use four light-curve fitters to test for systematic differences: SALT, SALT2, MLCS2k2 (RV = 3.1), and MLCS2k2 (RV = 1.7). SALT produces high-redshift Hubble residuals with systematic trends versus color and larger scatter than MLCS2k2. MLCS2k2 overestimates the intrinsic luminosity of SN Ia with 0.7 < Δ < 1.2. MLCS2k2 with RV = 3.1 overestimates host-galaxy extinction while RV 1.7 does not. Our investigation is consistent with no Hubble bubble. We also find that, after light-curve correction, SN Ia in Scd/Sd/Irr hosts are intrinsically fainter than those in E/S0 hosts by 2σ, suggesting that they may come from different populations. We also find that SN Ia in Scd/Sd/Irr hosts have low scatter (0.1 mag) and reddening. Current systematic errors can be reduced by improving SN Ia photometric accuracy, by including the CfA3 sample to retrain light-curve fitters, by combining optical SN Ia photometry with near-infrared photometry to understand host-galaxy extinction, and by determining if different environments give rise to different intrinsic SN Ia luminosity after correction for light-curve shape and color.

Machining induced surface integrity in titanium and nickel alloys: A review

Abstract: Titanium and nickel alloys represent a significant metal portion of the aircraft structural and engine components. When these critical structural components in aerospace industry are manufactured with the objective to reach high reliability levels, surface integrity is one of the most relevant parameters used for evaluating the quality of finish machined surfaces. The residual stresses and surface alteration (white etch layer and depth of work hardening) induced by machining of titanium alloys and nickel-based alloys are very critical due to safety and sustainability concerns. This review paper provides an overview of machining induced surface integrity in titanium and nickel alloys. There are many different types of surface integrity problems reported in literature, and among these, residual stresses, white layer and work hardening layers, as well as microstructural alterations can be studied in order to improve surface qualities of end products. Many parameters affect the surface quality of workpieces, and cutting speed, feed rate, depth of cut, tool geometry and preparation, tool wear, and workpiece properties are among the most important ones worth to investigate. Experimental and empirical studies as well as analytical and Finite Element modeling based approaches are offered in order to better understand machining induced surface integrity. In the current state-of-the-art however, a comprehensive and systematic modeling approach based on the process physics and applicable to the industrial processes is still missing. It is concluded that further modeling studies are needed to create predictive physics-based models that is in good agreement with reliable experiments, while explaining the effects of many parameters, for machining of titanium alloys and nickel-based alloys.

Stochastic dual coordinate ascent methods for regularized loss

Abstract: Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.