A new decay mode of higher charmonium

We calculate the $\Lambda_c\bar{\Lambda}_c$ partial decay width of the excited vector charmonium states around 4.6 GeV with the quark pair creation model. We find that the partial decay width of the $\Lambda_c\bar{\Lambda}_c$ mode can reach up to several MeV for $\psi(4S,~5S,~6S)$. In contrast, the partial $\Lambda_c\bar{\Lambda}_c$ decay width of the states $\psi(3D,~4D,~5D)$ is less than one MeV. If the enhancement $Y(4630)$ reported by the Belle Collaboration in $\Lambda_c\bar{\Lambda}_c$ invariant-mass distribution is the same structure as $Y(4660)$, the $Y(4660)$ resonance is most likely to be a $S$-wave charmonium state.Comment: 8 pages, 4 figure

Bis[2-(2-pyridyl­sulfan­yl)eth­yl]ammonium perchlorate

The cation and anion of the title salt, C14H18N3S2 +·ClO4 −, lie on a twofold rotation axis. The cation is a W-shaped entity with the aromatic rings at the ends; the ammonium NH2 + group is a hydrogen-bond donor to the pyridyl N atoms. The perchlorate ion has one O atom disordered over two sites in a 0.50:0.50 ratio

Image Super-Resolution using Efficient Striped Window Transformer

Transformers have achieved remarkable results in single-image super-resolution (SR). However, the challenge of balancing model performance and complexity has hindered their application in lightweight SR (LSR). To tackle this challenge, we propose an efficient striped window transformer (ESWT). We revisit the normalization layer in the transformer and design a concise and efficient transformer structure to build the ESWT. Furthermore, we introduce a striped window mechanism to model long-term dependencies more efficiently. To fully exploit the potential of the ESWT, we propose a novel flexible window training strategy that can improve the performance of the ESWT without additional cost. Extensive experiments show that ESWT outperforms state-of-the-art LSR transformers, and achieves a better trade-off between model performance and complexity. The ESWT requires fewer parameters, incurs faster inference, smaller FLOPs, and less memory consumption, making it a promising solution for LSR.Comment: SOTA lightweight super-resolution transformer. 8 pages, 9 figures and 6 tables. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLa