中国における小売企業の物流発展対策に関する一考察

According to the promise of China when entering into the WTO, in December, 2004 Chinese government repealed the restriction on capital contributed by foreign company, the amount of foreign companies and the area they can choose in logistic industry. Besides that in foreign trade and chain deal area some kind of restriction has also been repealed and mitigated. With this chance Wal-Mart and Carrefour and other foreign retailers began their business activities in China formally. The domestic retailer represented by Lianhua Supermarket and Wumei Supermarket Appliance Chain also began their fast spread. In this fierce competitive environment the retail industry\u27s profit rate decreased in a deep degree. How to make use of modern logistic technology to reduce logistic cost become the key of assuring retail industry\u27s profit. This paper firstly points out that the modern logistics develops in our country is not mature and has many problems through the analysis of Chinese retail logistics statuses. Then it analyzes the problems which exists in the domestic retail has carried on the analysis, and based on this proposes the countermeasures to develops the domestic retail

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapaComment: Accepted to NeurIPS 2022. Code is available at https://github.com/poppinace/sap

Turning a CLIP Model into a Scene Text Spotter

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.7% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.56% in text detection and spotting tasks, along with a 48.5% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 5.5% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection. The code is available at https://github.com/wenwenyu/TCM.Comment: arXiv admin note: text overlap with arXiv:2302.1433

Transcriptomic Analysis of Potential “lncRNA–mRNA” Interactions in Liver of the Marine Teleost Cynoglossus semilaevis Fed Diets With Different DHA/EPA Ratios

Long non-coding RNAs (lncRNA) have emerged as important regulators of lipid metabolism and have been shown to play multifaceted roles in controlling transcriptional gene regulation, but very little relevant information has been available in fish, especially in non-model fish species. With a feeding trial on a typical marine teleost tongue sole C. semilaevis followed by transcriptomic analysis, the present study investigated the possible involvement of lncRNA in hepatic mRNA expression in response to different levels of dietary DHA and EPA, which are two most important fatty acids for marine fish. An 80-day feeding trial was conducted in a flow-through seawater system, and in this trial three experimental diets differing basically in DHA/EPA ratio, i.e., 0.61 (D/E-0.61), 1.46 (D/E-1.46), and 2.75 (D/E-2.75), were randomly assigned to 9 tanks of experimental fish. A total of 124.04 G high quality genome-wide clean data about coding and non-coding transcripts was obtained in the analysis of hepatic transcriptome. Compared to diet D/E-0.61, D/E-1.46 up-regulated expression of 178 lncRNAs and 2629 mRNAs, and down-regulated that of 47 lncRNAs and 3059 mRNAs, while D/E-2.75 resulted in much less change in gene expression. The co-expression and co-localization analysis of differentially expressed (DE) lncRNA and mRNA among dietary groups were then conducted. The co-expressed DE lncRNA and mRNA were primarily enriched in GO terms such as Metabolic process, Intracellular organelle, Catalytic activity, and Oxidoreductase activity, as well as in KEGG pathways such as Ribosome and Oxidative phosphorylation. Overlap of co-expression and co-localization analysis, i.e., lncRNA–mRNA matches “XR_523541.1–solute carrier family 16, member 5 (slc16a5)” and “LNC_000285–bromodomain adjacent to zinc finger domain 2A (baz2a),” were observed in all inter-group comparisons, indicating that they might crucially mediate the effects of dietary DHA and EPA on hepatic gene expression in tongue sole. In conclusion, this was the first time in marine teleost to investigate the possible lncRNA–mRNA interactions in response to dietary fatty acids. The results provided novel knowledge of lncRNAs in non-model marine teleost, and will serve as important resources for future studies that further investigate the roles of lncRNAs in lipid metabolism of marine teleost

Effect of Aspect Ratio on Field Emission Properties of ZnO Nanorod Arrays

ZnO nanorod arrays are prepared on a silicon wafer through a multi-step hydrothermal process. The aspect ratios and densities of the ZnO nanorod arrays are controlled by adjusting the reaction times and concentrations of solution. The investigation of field emission properties of ZnO nanorod arrays revealed a strong dependency on the aspect ratio and their density. The aspect ratio and spacing of ZnO nanorod arrays are 39 and 167 nm (sample C), respectively, to exhibit the best field emission properties. The turn-on field and threshold field of the nanorod arrays are 3.83 V/μm and 5.65 V/μm, respectively. Importantly, the sample C shows a highest enhancement of factorβ, which is 2612. The result shows that an optimum density and aspect ratio of ZnO nanorod arrays have high efficiency of field emission