The Cryogenic Target for the G0^0 Experiment at Jefferson Lab

A cryogenic horizontal single loop target has been designed, built, tested and operated for the G$^0$ experiment in Hall C at Jefferson Lab. The target cell is 20 cm long, the loop volume is 6.5 l and the target operates with the cryogenic pump fully immersed in the fluid. The target has been designed to operate at 30 Hz rotational pump speed with either liquid hydrogen or liquid deuterium. The high power heat exchanger is able to remove 1000 W of heat from the liquid hydrogen, while the nominal electron beam with current of 40 $\mu$A and energy of 3 GeV deposits about 320 W of heat into the liquid. The increase in the systematic uncertainty due to the liquid hydrogen target is negligible on the scale of a parity violation experiment. The global normalized yield reduction for 40 $\mu$A beam is about 1.5 % and the target density fluctuations contribute less than 238 ppm (parts per million) to the total asymmetry width, typically about 1200 ppm, in a Q$^2$ bin.Comment: 27 pages, 14 figure

Learning 3D Human Pose from Structure and Motion

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose

VALIDATION OF A 3-DIMENSIONAL VIDEO MOTION CAPTURE SYSTEM FOR DETERMINING BARBELL POWER AND VELOCITY DURING THE BENCH PRESS

Andrew C. Fry, Luke Bradford, Trent Herda, Joseph Weir FACSM, Michael Lane, Matthew Andre, Andrea Hudy, J. Deckert and J. Siedlik.Neuromechanics Laboratory and Kansas Athletics Inc., University of Kansas, Lawrence, KS Analyses of barbell kinetics and kinematics have typically required the use of force plates, tether-based position transducers, or digitized video analysis. PURPOSE: To determine the validity of a 3-dimensional video markerless motion capture system for determining barbell kinetics and kinematics. METHODS: Two 3-D video cameras sampling at 30 Hz and mounted on the top of a power rack were interfaced with a self-contained computer and software system, and operated with a touch screen (EliteForm, Lincoln, NE). For laboratory comparison purposes, a ceiling–mounted linear position transducer (Unimeasure, Corvallis, OR) was attached via a tether to the barbell. Data from the position transducer was sampled at 1000 Hz using a BioPac data acquisition system (Goleta, CA). Velocity (m.s-1) and power (W) were derived using LabView software (National Instruments, Austin, TX). One weight-trained male subject (age = 25 yrs, hgt = 1.75 m, BW = 82.6 kg, 1 RM = 161.0 kg) performed the barbell bench press exercise for 10 sets x 1 repetition at 30, 40, 50, 60, 70 and 80% 1 RM loads using maximal acceleration during the concentric phase. Dependent variables included peak (PV) and X̅ velocity (MV) and peak (PP) and X̅ power (MP). Linear regressions between lab-derived and 3-D video-derived data provided correlation coefficients, and regression slopes (b). Bland-Altman plots were used to determine X̅ differences, from which effect sizes (Cohen’s D) and % error for the 3-D camera system was determined. RESULTS: Lab-derived mean values for all loads ranged as follows; MV = 0.36 – 1.00 m.s-1, PV = 0.47 – 1.60 m.s-1, MP = 460.9 – 621.6 W, and PP = 619.9 – 1055.6 W. CONCLUSION: The 3-D video markerless motion capture system provided accurate and valid barbell velocity and power data for the bench press exercise. Supported in part by Nebraska Global LL

Using skeleton-based tracking to increase the reliability of optical motion capture

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the user's part before the complete animation is ready for use. Here, the authors present an approach to increasing the robustness of a motion capture system by using an anatomical human model. It includes a reasonably precise description of the skeleton's mobility and an approximated envelope. It allows the authors to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of the marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3-D reconstruction proces

Skeleton-based motion capture for robust reconstruction of human motion

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the part of the animator before the virtual characters are ready for their screen debuts. In this paper, we present an approach to increasing the robustness of a motion capture system by using a sophisticated anatomic human model. It includes a precise description of the skeleton's mobility and an approximated envelope. It allows us to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3D reconstruction proces

Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body is a time consuming and labour intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework

Effects of β-alanine supplementation and high-intensity interval training on endurance performance and body composition in men; a double-blind trial

Background: Intermittent bouts of high-intensity exercise result in diminished stores of energy substrates, followed by an accumulation of metabolites, promoting chronic physiological adaptations. In addition, β-alanine has been accepted has an effective physiological hydrogen ion (H+) buffer. Concurrent high-intensity interval training (HIIT) and β-alanine supplementation may result in greater adaptations than HIIT alone. The purpose of the current study was to evaluate the effects of combining β-alanine supplementation with high-intensity interval training (HIIT) on endurance performance and aerobic metabolism in recreationally active college-aged men. Methods: Forty-six men (Age: 22.2 ± 2.7 yrs; Ht: 178.1 ± 7.4 cm; Wt: 78.7 ± 11.9; VO2peak: 3.3 ± 0.59 l·min-1) were assessed for peak O2 utilization (VO2peak), time to fatigue (VO2TTE), ventilatory threshold (VT), and total work done at 110% of pre-training VO2peak (TWD). In a double-blind fashion, all subjects were randomly assigned into one either a placebo (PL – 16.5 g dextrose powder per packet; n = 18) or β-alanine (BA – 1.5 g β-alanine plus 15 g dextrose powder per packet; n = 18) group. All subjects supplemented four times per day (total of 6 g/day) for the first 21-days, followed by two times per day (3 g/day) for the subsequent 21 days, and engaged in a total of six weeks of HIIT training consisting of 5–6 bouts of a 2:1 minute cycling work to rest ratio. Results: Significant improvements in VO2peak, VO2TTE, and TWD after three weeks of training were displayed (p < 0.05). Increases in VO2peak, VO2TTE, TWD and lean body mass were only significant for the BA group after the second three weeks of training. Conclusion: The use of HIIT to induce significant aerobic improvements is effective and efficient. Chronic BA supplementation may further enhance HIIT, improving endurance performance and lean body mass