Remarks on CLEO New Measurements for Upsilon(1S) Decays to Charmonium Final States and Investigations on Associate Strange Particle Enhancement in Upsilon to J/Psi +X

The recent measurements by CLEO Collaboration for the inclusive J/Psi and Psi(2S) production in Upsilon(1S) decay and our previous calculation are analyzed. The J/Psi momentum spectrum and the production ratio of Psi(2S) versus J/Psi favour Upsilon to J/Psi(Psi(2S)) + ccbar g as the dominant contribution. We point out that the differences between the experimental data and our previous results are mainly originated from the setting of the parameter charm quark mass. We further suggest the associate strange particle enhancement as a probe for the open charm particles in Upsilon to J/Psi (Psi(2S)) + ccbar g.Comment: minor corrections during proof-readin

Characteristics of papillae in wild, cultivated and hybrid sea cucumbers (Apostichopus japonicus)

We investigated the characteristics of papillae of living, boiled and dried specimens of three wild populations, cultivated and hybrid Apostichopus japonicus in the laboratory. Visual observation showed that the appearance of the papillae was obviously different among the populations. For the first time, we established a standard criterion for estimating papilla number (PL/L ≥ 0.05, PL: the length of the papilla, L: body length) in living, boiled and dried A. japonicus. According to the criterion, we estimated the papilla number in living, boiled and dried specimens of wild, cultured and hybrid A. japonicus. Results show that the number of both dorsal and ventral papillae was significantly different among the groups. The largest total number of papillae was found in the Russian population (66.25 ± 8.11), which was significantly larger than those in the other groups (P<0.01). It was followed by those in the Japanese population and two hybrid groups (53.64 ± 7.88 for JJ, 54.65 ± 4.74 for CR and 46.29 ± 6.91 for CK). The differences between the number of dorsal and ventral papilla in Russian, Japanese and Chinese♂×Russian♀ hybrid populations were significantly larger than those in the other groups. This study increases our understanding of papilla traits in different populations of A. japonicus and has a direct application potential for selective breeding and its aquaculture.Key words: Apostichopus japonicus, papilla, criterion, hybrid

A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. The generative adversarial network (GAN) has proven to be an effective deep learning framework for image super-resolution. However, the optimisation process of existing GAN-based models frequently suffers from the problem of mode collapse, leading to the limited capacity of spectral-spatial invariant reconstruction. This may cause the spectral-spatial distortion on the generated HSI, especially with a large upscaling factor. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples. Essentially, we treat an HSI as a high-dimensional manifold embedded in a latent space. Thus, the optimisation of GAN models is converted to the problem of learning the distributions of high-resolution HSI samples in the latent space, making the distributions of the generated super-resolution HSIs closer to those of their original high-resolution counterparts. We have conducted experimental evaluations on the model performance of super-resolution and its capability in alleviating mode collapse. The proposed approach has been tested and validated based on two real HSI datasets with different sensors (i.e. AVIRIS and UHD-185) for various upscaling factors and added noise levels, and compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR, BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure

Dirac-Surface-State Modulated Spin Dynamics in a Ferrimagnetic Insulator at Room Temperature

This work demonstrates dramatically modified spin dynamics of magnetic insulator (MI) by the spin-momentum locked Dirac surface states of the adjacent topological insulator (TI) which can be harnessed for spintronic applications. As the Bi-concentration x is systematically tuned in 5 nm thick (BixSb1-x)2Te3 TI film, the weight of the surface relative to bulk states peaks at x = 0.32 when the chemical potential approaches the Dirac point. At this concentration, the Gilbert damping constant of the precessing magnetization in 10 nm thick Y3Fe5O12 MI film in the MI/TI heterostructures is enhanced by an order of magnitude, the largest among all concentrations. In addition, the MI acquires additional strong magnetic anisotropy that favors the in-plane orientation with similar Bi-concentration dependence. These extraordinary effects of the Dirac surface states distinguish TI from other materials such as heavy metals in modulating spin dynamics of the neighboring magnetic layer

Vertically-aligned graphene nanowalls grown via plasma-enhanced chemical vapor deposition as a binder-free cathode in Li-O_2 batteries

In the present report, vertically-aligned graphene nanowalls are grown on Ni foam (VA-G/NF) using plasma-enhanced chemical vapor deposition method at room temperature. Optimization of the growth conditions provides graphene sheets with controlled defect sites. The unique architecture of the vertically-aligned graphene sheets allows sufficient space for the ionic movement within the sheets and hence enhancing the catalytic activity. Further modification with ruthenium nanoparticles (Ru NPs) drop-casted on VA-G/NF improves the charge overpotential for lithium–oxygen (Li–O_2) battery cycles. Such reduction we believe is due to the easier passage of ions between the perpendicularly standing graphene sheets thereby providing ionic channels

CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System

Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Transformer and its variants have become the de facto backbone for LTSF, offering exceptional capabilities in processing long sequence data. However, existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs, indicating potential underutilization of the Transformer's capacity. One of the reasons that contribute to this overfitting is data redundancy arising from the rolling forecasting settings in the data augmentation process, particularly evident in longer sequences with highly similar adjacent data. In this paper, we propose a novel approach to address this issue by employing curriculum learning and introducing a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time-series data and leverages temporal relationships to facilitate the forecasting process. The experimental results on six real-life LTSF benchmarks demonstrate that our approach can be seamlessly plugged into varying Transformer-based models, with our approach enhancing the LTSF performances of various Transformer-based models by maximally 30%.Comment: Tech repor