Captures of Hot and Warm Sterile Antineutrino Dark Matter on EC-decaying Ho-163 Nuclei

Capturing low-energy electron antineutrinos on radioactive Ho-163 nuclei, which decay into Dy-163 via electron capture (EC), is a noteworthy opportunity to detect relic sterile antineutrinos. Such hypothetical particles are more or less implied by current experimental and cosmological data, and they might be a part of hot dark matter or a candidate for warm dark matter in the Universe. Using the isotope Ho-163 as a target and assuming reasonable active-sterile antineutrino mixing angles, we calculate the capture rate of relic electron antineutrinos against the corresponding EC-decay background in the presence of sterile antineutrinos at the sub-eV or keV mass scale. We show that the signature of hot or warm sterile antineutrino dark matter should in principle be observable, provided the target is big enough and the energy resolution is good enough.Comment: 16 pages, 6 figures, more discussions and references added. To appear in JCA

Contrastive self-supervised learning for automated multi-modal dance performance assessment

A fundamental challenge of analyzing human motion is to effectively represent human movements both spatially and temporally. We propose a contrastive self-supervised strategy to tackle this challenge. Particularly, we focus on dancing, which involves a high level of physical and intellectual abilities. Firstly, we deploy Graph and Residual Neural Networks with Siamese architecture to represent the dance motion and music features respectively. Secondly, we apply the InfoNCE loss to contrastively embed the high-dimensional multimedia signals onto the latent space without label supervision. Finally, our proposed framework is evaluated on a multi-modal Dance- Music-Level dataset composed of various dance motions, music, genres and choreographies with dancers of different expertise levels. Experimental results demonstrate the robustness and improvements of our proposed method over 3 baselines and 6 ablation studies across tasks of dance genres, choreographies classification and dancer expertise level assessment