Improved Measurement of the Muon Lifetime and Determination of the Fermi Constant

The MuLan collaboration has measured the lifetime of the positve muon to a precision of 1.0 parts per million. The Fermi constant is determined to a precision of 0.6 parts per million.Comment: Proceedings of CKM2010, the 6th International Workshop on the CKM Unitarity Triangle, University of Warwick, UK, 6-10 September 201

Polarization-Based Illumination Detection for Coherent Augmented Reality Scene Rendering in Dynamic Environments

A virtual object that is integrated into the real world in a perceptually coherent manner using the physical illumination information in the current environment is still under development. Several researchers investigated the problem producing a high-quality result; however, pre-computation and offline availability of resources were the essential assumption upon which the system relied. In this paper, we propose a novel and robust approach to identifying the incident light in the scene using the polarization properties of the light wave and using this information to produce a visually coherent augmented reality within a dynamic environment. This approach is part of a complete system which has three simultaneous components that run in real-time: (i) the detection of the incident light angle, (ii) the estimation of the reflected light, and (iii) the creation of the shading properties which are required to provide any virtual object with the detected lighting, reflected shadows, and adequate materials. Finally, the system performance is analyzed where our approach has reduced the overall computational cost

Spin-Flip Strengths in 12-C(p,p')12-C at 122 MeV

This work was supported by National Science Foundation Grant PHY 76-84033 and Indiana Universit

Elastic Scattering of Medium-Energy Protons

This work was supported by National Science Foundation Grant PHY 75-00289 and Indiana Universit

Studies of Pi° Production Near Threshold

Supported by the National Science Foundation and Indiana Universit

Systematics of Inclusive Charged Particles Production with Medium Energy Protons

This work was supported by National Science Foundation Grants PHY 76-84033A01, PHY 78-22774, and Indiana Universit

NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.Comment: Camera-ready version for SIGGRAPH Asia 2021. Project Page: https://people.csail.mit.edu/xiuming/projects/nerfactor

The Optical Potential for Medium-Energy Proton Scattering

This work was supported by National Science Foundation Grant PHY 76-84033 and Indiana Universit

Prooxidant/Antioxidant Balance in Hypoxia: A Cross-Over Study on Normobaric vs. Hypobaric “Live High-Train Low”

“Live High-Train Low” (LHTL) training can alter oxidative status of athletes. This study compared prooxidant/antioxidant balance responses following two LHTL protocols of the same duration and at the same living altitude of 2250 m in either normobaric (NH) or hypobaric (HH) hypoxia. Twenty-four well-trained triathletes underwent the following two 18-day LHTL protocols in a cross-over and randomized manner: Living altitude (PIO2 = 111.9 ± 0.6 vs. 111.6 ± 0.6 mmHg in NH and HH, respectively); training “natural” altitude (~1000–1100 m) and training loads were precisely matched between both LHTL protocols. Plasma levels of oxidative stress [advanced oxidation protein products (AOPP) and nitrotyrosine] and antioxidant markers [ferric-reducing antioxidant power (FRAP), superoxide dismutase (SOD) and catalase], NO metabolism end-products (NOx) and uric acid (UA) were determined before (Pre) and after (Post) the LHTL. Cumulative hypoxic exposure was lower during the NH (229 ± 6 hrs.) compared to the HH (310 ± 4 hrs.; P<0.01) protocol. Following the LHTL, the concentration of AOPP decreased (-27%; P<0.01) and nitrotyrosine increased (+67%; P<0.05) in HH only. FRAP was decreased (-27%; P<0.05) after the NH while was SOD and UA were only increased following the HH (SOD: +54%; P<0.01 and UA: +15%; P<0.01). Catalase activity was increased in the NH only (+20%; P<0.05). These data suggest that 18-days of LHTL performed in either NH or HH differentially affect oxidative status of athletes. Higher oxidative stress levels following the HH LHTL might be explained by the higher overall hypoxic dose and different physiological responses between the NH and HH.The study was funded by grants from the Ministère des Sports, de la Jeunesse, de l’Education Populaire et de la Vie Associative (MSJEPVA; France; to L.S. and G.P.M.), Institut National du Sport, de l’Expertise et de la Performance (INSEP; France; to L.S. and G.P.M.) and Institut Universitaire de France (IUF; France; to V.P.)