Review of Cross-border Electronic Retail Logistics Research in the Last Five Years

Nearly five years, retail in cross-border e-commerce (CBER) has gradually attracted more attention with the expansion of domestic e-commerce in several retail major economies. Scholars both here and abroad have become increasingly interested in CBER. As the important supporting services, logistics services of CBER have also be deeply concerned. However, compared with the rich practice, the theoretical outputs of logistics services in CBER are still less. Previous studies have focused on the relationship between cross-border e-commerce and logistics services , and international distribution network. So far, there is little research on content-based logistics services concerning CBER. Based on the literature review, this paper has determined a series of possible research directions, including strategic significance of cooperation in forming CBER distribution structure and how to implement customer-driven logistics service improvement. Finally, some future research directions are proposed

Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance

Relevance module plays a fundamental role in e-commerce search as they are responsible for selecting relevant products from thousands of items based on user queries, thereby enhancing users experience and efficiency. The traditional approach models the relevance based product titles and queries, but the information in titles alone maybe insufficient to describe the products completely. A more general optimization approach is to further leverage product image information. In recent years, vision-language pre-training models have achieved impressive results in many scenarios, which leverage contrastive learning to map both textual and visual features into a joint embedding space. In e-commerce, a common practice is to fine-tune on the pre-trained model based on e-commerce data. However, the performance is sub-optimal because the vision-language pre-training models lack of alignment specifically designed for queries. In this paper, we propose a method called Query-LIFE (Query-aware Language Image Fusion Embedding) to address these challenges. Query-LIFE utilizes a query-based multimodal fusion to effectively incorporate the image and title based on the product types. Additionally, it employs query-aware modal alignment to enhance the accuracy of the comprehensive representation of products. Furthermore, we design GenFilt, which utilizes the generation capability of large models to filter out false negative samples and further improve the overall performance of the contrastive learning task in the model. Experiments have demonstrated that Query-LIFE outperforms existing baselines. We have conducted ablation studies and human evaluations to validate the effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been deployed on Miravia Search, resulting in improved both relevance and conversion efficiency

Is Individualism-Collectivism Associated with Self-Control? Evidence from Chinese and U.S. Samples

Self-control plays an important role in human’s daily life. In the recent two decades, scholars have exerted tremendous effort to examine the etiologies of the individual differences in self-control. Among numerous predictors of self-control, the role of culture has been relatively overlooked. In this study, the influences of cultural orientation on self-control were examined based on the collectivism-individualism framework using both self-report and behavioral task to assess self-control. A convenience sample of 542 Chinese and 446 U.S. undergraduates participated in the research. They were invited to fill out self-report questionnaires reporting their levels of attitudinal self-control and individualistic-collectivistic orientation after completing a computer-based Stroop task. Results of hierarchical regression models showed that Chinese participants reported less attitudinal self-control but had higher behavioral self-control than their U.S. counterparts. Moreover, individual-level individualism and collectivism was negatively and positively related to attitudinal self-control in both countries, respectively. Individual-level collectivism was significantly related to better behavioral self-control, but no significant results were found for the relationship between individual-level individualism and behavioral self-control. In sum, individualism and collectivism have some influences on individual differences in self-control. Implications for future research were discussed

Energy trading and pricing in microgrids with uncertain energy supply:A three-stage hierarchical game approach

This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme

Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries

Accurate segmentation of surgical instrument tip is an important task for enabling downstream applications in robotic surgery, such as surgical skill assessment, tool-tissue interaction and deformation modeling, as well as surgical autonomy. However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures. Although much effort has been made on visual-based methods, existing segmentation models still suffer from low robustness thus not usable in practice. Fortunately, kinematics data from the robotic system can provide reliable prior for instrument location, which is consistent regardless of different surgery types. To make use of such multi-modal information, we propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. Next, a cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation. We have conducted experiments on a private paired visual-kinematics dataset including multiple procedures, i.e., prostatectomy, total mesorectal excision, fundoplication and distal gastrectomy on cadaver, and distal gastrectomy on porcine. The leave-one-procedure-out cross validation demonstrated that our proposed multi-modal segmentation method significantly outperformed current image-based state-of-the-art approaches, exceeding averagely 11.2% on Dice.Comment: Accepted to IROS 202

StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS

Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture meaningful data, resulting in point clouds with severe artifacts. Without a high-quality point cloud, existing methods are not applicable to challenging transparent objects. To tackle this problem, we present StereoPose, a novel stereo image framework for category-level object pose estimation, ideally suited for transparent objects. For a robust estimation from pure stereo images, we develop a pipeline that decouples category-level pose estimation into object size estimation, initial pose estimation, and pose refinement. StereoPose then estimates object pose based on representation in the normalized object coordinate space~(NOCS). To address the issue of image content aliasing, we further define a back-view NOCS map for the transparent object. The back-view NOCS aims to reduce the network learning ambiguity caused by content aliasing, and leverage informative cues on the back of the transparent object for more accurate pose estimation. To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions. Extensive experiments on the public TOD dataset demonstrate the superiority of the proposed StereoPose framework for category-level 6D transparent object pose estimation.Comment: 7 pages, 6 figures, Project homepage: https://appsrv.cse.cuhk.edu.hk/~kaichen/stereopose.htm

MicroRNA-200c overexpression inhibits tumorigenicity and metastasis of CD117+CD44+ ovarian cancer stem cells by regulating epithelial-mesenchymal transition

BACKGROUND: Cancer stem cells (CSCs) are believed to be ‘seed cell’ in cancer recurrence and metastasis. MicroRNAs (miRNAs) can play an important role in the progression of primary tumor towards metastasis by regulating the epithelial-mesenchymal transition (EMT). The goal of this study was to investigate the effect of miRNA-200c overexpression on the EMT, tumorigenicity and metastasis of epithelial ovarian cancer (EOC) CSCs. METHODS: The EOC CD117(+)CD44(+)CSCs were isolated from the human ovarian cancer cell line SKOV3 by using a magnetic-activated cell sorting system, and the lentivirus miR-200c transduced CSCs were then selected for the study. The assays of colony forming, wound healing, cellular migration in vitro and tumor progression in vivo were performed. RESULTS: The miR-200c expression was reduced in the CD117(+)CD44(+)CSCs compared with the non-CD117(+)CD44(+)CSCs. However, the stable overexpression of the miR-200c in the CD117(+)CD44(+)CSCs resulted in a significant down-regulation of ZEB-1 and the Vimentin expression, an upregulation of the E-cadherin expression as well as a decrease of colony forming, migratory and invasion in vitro. Importantly, the miR-200c overexpression significantly inhibited the CD117(+)CD44(+)CSCs xenograft growth and lung metastasis in vivo in nude mice by inhibition of the EMT. In addition, the down-regulation of ZEB-1 showed the same efficacy as the miR-200c overexpression in the CD117(+)CD44(+)CSCs. CONCLUSION: These findings from this study suggest that the miR-200c overexpression may be considered a critical approach for the EOC CD117(+)CD44(+)CSCs in clinical trials

MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC

Spinal toll like receptor 3 is involved in chronic pancreatitis-induced mechanical allodynia of rat

<p>Abstract</p> <p>Background</p> <p>Mechanisms underlying pain in chronic pancreatitis (CP) are incompletely understood. Our previous data showed that astrocytes were actively involved. However, it was unclear how astrocytic activation was induced in CP conditions. In the present study, we hypothesized that toll-like receptors (TLRs) were involved in astrocytic activation and pain behavior in CP-induced pain.</p> <p>Results</p> <p>To test our hypothesis, we first investigated the changes of TLR2-4 in the rat CP model induced by intrapancreatic infusion of trinitrobenzene sulfonic acid (TNBS). Western blot showed that after TNBS infusion, TLR3, but not TLR2 or TLR4, was increased gradually and maintained at a very high level for up to 5 w, which correlated with the changing course of mechanical allodynia. Double immunostaining suggested that TLR3 was highly expressed on astrocytes. Infusion with TLR3 antisense oligodeoxynucleotide (ASO) dose-dependently attenuated CP-induced allodynia. CP-induced astrocytic activation in the spinal cord was also significantly suppressed by TLR3 ASO. Furthermore, real-time PCR showed that IL-1β, TNF-α, IL-6 and monocyte chemotactic protein-1 (MCP-1) were significantly increased in spinal cord of pancreatic rats. In addition, TLR3 ASO significantly attenuated CP-induced up-regulation of IL-1β and MCP-1.</p> <p>Conclusions</p> <p>These results suggest a probable "TLR3-astrocytes-IL-1β/MCP-1" pathway as a positive feedback loop in the spinal dorsal horn in CP conditions. TLR3-mediated neuroimmune interactions could be new targets for treating persistent pain in CP patients.</p