Analysis of the strong vertices of ΣcND∗\Sigma_cND^{*} and ΣbNB∗\Sigma_bNB^{*} in QCD sum rules

The strong coupling constant is an important parameter which can help us to understand the strong decay behaviors of baryons. In our previous work, we have analyzed strong vertices $\Sigma_{c}^{*}ND$, $\Sigma_{b}^{*}NB$, $\Sigma_{c}ND$, $\Sigma_{b}NB$ in QCD sum rules. Following these work, we further analyze the strong vertices $\Sigma_{c}ND^{*}$ and $\Sigma_{b}NB^{*}$ using the three-point QCD sum rules under Dirac structures $q\!\!\!/p\!\!\!/\gamma_{\alpha}$ and $q\!\!\!/p\!\!\!/p_{\alpha}$. In this work, we first calculate strong form factors considering contributions of the perturbative part and the condensate terms $\langle\overline{q}q\rangle$, $\langle\frac{\alpha_{s}}{\pi}GG\rangle$ and $\langle\overline{q}g_{s}\sigma Gq\rangle$. Then, these form factors are used to fit into analytical functions. According to these functions, we finally determine the values of the strong coupling constants for these two vertices $\Sigma_{c}ND^{*}$ and $\Sigma_{b}NB^{*}$.Comment: arXiv admin note: text overlap with arXiv:1705.0322

Stability of internet-based control systems with uncertainties and multiple time-varying delays

In this paper, based on remote control and local control strategy, a class of hybrid multi-rate control models with uncertainties and multiple time-varying delays is formulated and their robust stability properties are investigated. By Lyapunov-Krasovskii functions and apply it to a descriptor model transformation, some new criteria of robust stability for such Internet-based control systems are established. Numerical example and simulation are given to illustrate the effectiveness of the theoretical results

Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding

Open-vocabulary querying in 3D space is challenging but essential for scene understanding tasks such as object localization and segmentation. Language-embedded scene representations have made progress by incorporating language features into 3D spaces. However, their efficacy heavily depends on neural networks that are resource-intensive in training and rendering. Although recent 3D Gaussians offer efficient and high-quality novel view synthesis, directly embedding language features in them leads to prohibitive memory usage and decreased performance. In this work, we introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks. Instead of embedding high-dimensional raw semantic features on 3D Gaussians, we propose a dedicated quantization scheme that drastically alleviates the memory requirement, and a novel embedding procedure that achieves smoother yet high accuracy query, countering the multi-view feature inconsistencies and the high-frequency inductive bias in point-based representations. Our comprehensive experiments show that our representation achieves the best visual quality and language querying accuracy across current language-embedded representations, while maintaining real-time rendering frame rates on a single desktop GPU

The Detection and Phylogenetic Analysis of Bovine Hepacivirus in China

Hepacivirus has been identified in cattle in Africa, Europe, and South America. In this survey of bovine hepacivirus (BovHepV) in 131 serum samples from Chinese cattle herds using RT-PCR, five of 131 sera were BovHepV positive, with the infection rate of 3.82%. Phylogenetic analysis based on the partial NS3 coding sequence showed that the BovHepV of the five positive samples clustered with other BovHepV but formed a separate branch. The results indicated that these new BovHepV represent emerging and novel strains. Further investigations are necessary to determine the epidemiology and viral pathogenesis of these BovHepV strains, as well as the potential threat to ruminant and livestock workers in China

A Factory-based Approach to Support E-commerce Agent Fabrication

With the development of Internet computing and software agent technologies, agent-based e-commerce is emerging. How to create agents for e-commerce applications has become an important issue along the way to success. We propose a factory-based approach to support agent fabrication in e-commerce and elaborate a design based on the SAFER (Secure Agent Fabrication, Evolution & Roaming) framework. The details of agent fabrication, modular agent structure, agent life cycle, as well as advantages of agent fabrication are presented. Product-brokering agent is employed as a practical agent type to demonstrate our design and Java-based implementation

Chrion: Optimizing Recurrent Neural Network Inference by Collaboratively Utilizing CPUs and GPUs

Deploying deep learning models in cloud clusters provides efficient and prompt inference services to accommodate the widespread application of deep learning. These clusters are usually equipped with host CPUs and accelerators with distinct responsibilities to handle serving requests, i.e. generalpurpose CPUs for input preprocessing and domain-specific GPUs for forward computation. Recurrent neural networks play an essential role in handling temporal inputs and display distinctive computation characteristics because of their high inter-operator parallelism. Hence, we propose Chrion to optimize recurrent neural network inference by collaboratively utilizing CPUs and GPUs. We formulate the model deployment in the CPU-GPU cluster as an NP-hard scheduling problem of directed acyclic graphs on heterogeneous devices. Given an input model in the ONNX format and user-defined SLO requirement, Chrion firstly preprocesses the model by model parsing and profiling, and then partitions the graph to select execution devices for each operator. When an online request arrives, Chrion performs forward computation according to the graph partition by executing the operators on the CPU and GPU in parallel. Our experimental results show that the execution time can be reduced by 19.4% at most in the latency-optimal pattern and GPU memory footprint by 67.5% in the memory-optimal pattern compared with the execution on the GPU

MiR-1254 inhibits proliferation, migration and invasion of human brain tumour cell lines

Purpose: To investigate the expression of miR-1254 in 5 astrocytoma cell lines, and the mechanism involved.Methods: Total RNA was isolated by RNeasy RNA isolation kit while cDNA was prepared by RevertAid cDNA synthesis kit. The transcripts were analysed by real-time polymerase chain reaction (RT-PCR). Transfection of miR-1254 was carried out using FuGENE HD (Promega). Apoptosis was determined by DAPI, acridine orange (AO)/ethidium bromide (EB) and annexin V/PI double staining. Cell migration and invasion were investigated by wound healing and Martigel invasion assays, respectively. Protein expression was measured by western blotting.Results: Expression of miR-1254 was significantly down-regulated in the astrocytoma cell lines when compared to normal astrocyte cells (p < 0.05). Ectopic expression of miR-1254 in astrocytoma SW 1088 cells inhibited cell proliferation via initiation of apoptosis and cell cycle arrest. Over-expression of miR- 1254 also led to significant decrease in cell migration and invasion of SW 1088 astrocytoma cells (p < 0.05).Conclusion: The results show that the expression of miR-1254 is down-regulated in astrocytoma cell lines, but over-expression of miR-1254 inhibits proliferation of the cell lines via cell cycle arrest and apoptosis. Thus, miR-1254 has promising potential for use in the treatment of brain tumour.Keywords: Brain tumour, Astrocytoma, miR-1254, Apoptosis, Cell migratio