No abstract

An experiment with a newly developed high-resolution kaon spectrometer (HKS) and a scattered electron spectrometer with a novel configuration was performed in Hall C at Jefferson Lab (JLab). The ground state of a neutron-rich hypernucleus,

A spectroscopy of a 10 Λ Be hypernucleus was carried out at JLab Hall C using the (e, e ′ K + ) reaction. A new magnetic spectrometer system (SPL+HES+HKS), specifically designed for high resolution hypernuclear spectroscopy, was used to obtain an energy spectrum with a resolution of ∼ 0.78 MeV (FWHM). The well-calibrated spectrometer system of the present experiment using p(e, e ′ K + )Λ,Σ 0 reactions allowed us to determine the energy levels, and the binding energy of the ground state peak (mixture of 1 − and 2 − states) was obtained to be B Λ = 8.55 ± 0.07(stat.) ± 0.11(sys.) MeV. The result indicates that the ground state energy is shallower than that of an emulsion study by about 0.5 MeV which provides valuable experimental information on Charge Symmetry Breaking (CSB) effect in the ΛN interaction.

The understanding of body measurements and body shapes in and between populations is important and has many applications in medicine, surveying, the fashion industry, fitness, and entertainment. Body measurement using 3D surface scanning technologies is faster and more convenient than measurement with more traditional methods and at the same time provides much more data, which requires automatic processing. A multitude of 3D scanning methods and processing pipelines have been described in the literature, and the advent of deep learning-based processing methods has generated an increased interest in the topic. Also, over the last decade, larger public 3D human scanning datasets have been released. This paper gives a comprehensive survey of body measurement techniques, with an emphasis on 3D scanning technologies and automatic data processing pipelines. An introduction to the three most common 3D scanning technologies for body measurement, passive stereo, structured light, and time-of-flight, is provided, and their merits w.r.t. body measurement are discussed. Methods described in the literature are discussed within the newly proposed framework of five common processing stages: preparation, scanning, feature extraction, model fitting, and measurement extraction. Synthesizing the analyzed prior works, recommendations on specific 3D body scanning technologies and the accompanying processing pipelines for the most common applications are given. Finally, an overview of about 80 currently available 3D scanners manufactured by about 50 companies, as well as their taxonomy regarding several key characteristics, is provided in the Appendix.

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.