Research on Typical Cases of Integration of Jiangsu Logistics Industry and Manufacturing Industry

This project aims to comprehensively investigate the current status of the modern logistics industry and advanced manufacturing development in Jiangsu, China. Its primary goal is to gain a precise understanding of the challenges and shortcomings in the integration of the logistics and manufacturing sectors in Jiangsu, as well as to delve deeply into the factors constraining this integration. Additionally, through empirical research on representative cases and study areas, it intends to propose policy recommendations for the development of the integration between the logistics and manufacturing industries. These recommendations aim to promote high-quality development of manufacturing in the context of the new development pattern, strengthen the foundational capabilities for logistics-manufacturing integration, and foster innovative models and formats for the integration of logistics and manufacturing

Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods.Comment: Accepted by IEEE TPAM

Non-simple systoles on random hyperbolic surfaces for large genus

In this paper, we investigate the asymptotic behavior of the non-simple systole, which is the length of a shortest non-simple closed geodesic, on a random closed hyperbolic surface on the moduli space $\mathcal{M}_g$ of Riemann surfaces of genus $g$ endowed with the Weil-Petersson measure. We show that as the genus $g$ goes to infinity, the non-simple systole of a generic hyperbolic surface in $\mathcal{M}_g$ behaves exactly like $\log g$.Comment: 47 pages, 11 figures. Comments welcom

An Adaptive Resilience Testing Framework for Microservice Systems

Resilience testing, which measures the ability to minimize service degradation caused by unexpected failures, is crucial for microservice systems. The current practice for resilience testing relies on manually defining rules for different microservice systems. Due to the diverse business logic of microservices, there are no one-size-fits-all microservice resilience testing rules. As the quantity and dynamic of microservices and failures largely increase, manual configuration exhibits its scalability and adaptivity issues. To overcome the two issues, we empirically compare the impacts of common failures in the resilient and unresilient deployments of a benchmark microservice system. Our study demonstrates that the resilient deployment can block the propagation of degradation from system performance metrics (e.g., memory usage) to business metrics (e.g., response latency). In this paper, we propose AVERT, the first AdaptiVE Resilience Testing framework for microservice systems. AVERT first injects failures into microservices and collects available monitoring metrics. Then AVERT ranks all the monitoring metrics according to their contributions to the overall service degradation caused by the injected failures. Lastly, AVERT produces a resilience index by how much the degradation in system performance metrics propagates to the degradation in business metrics. The higher the degradation propagation, the lower the resilience of the microservice system. We evaluate AVERT on two open-source benchmark microservice systems. The experimental results show that AVERT can accurately and efficiently test the resilience of microservice systems

De novo assembly and transcriptome characterization: novel insights into the natural resistance mechanisms of Microtus fortis against Schistosoma japonicum

BACKGROUND: Microtus fortis is a non-permissive host of Schistosoma japonicum. It has natural resistance against schistosomes, although the precise resistance mechanisms remain unclear. The paucity of genetic information for M. fortis limits the use of available immunological methods. Thus, studies based on high-throughput sequencing technologies are required to obtain information about resistance mechanisms against S. japonicum. RESULTS: Using Illumina single-end technology, a de novo assembly of the M. fortis transcriptome produced 67,751 unigenes with an average length of 868 nucleotides. Comparisons were made between M. fortis before and after infection with S. japonicum using RNA-seq quantification analysis. The highest number of differentially expressed genes (DEGs) occurred two weeks after infection, and the highest number of down-regulated DEGs occurred three weeks after infection. Simultaneously, the strongest pathological changes in the liver were observed at week two. Gene ontology terms and pathways related to the DEGs revealed that up-regulated transcripts were involved in metabolism, immunity and inflammatory responses. Quantitative real-time PCR analysis showed that patterns of gene expression were consistent with RNA-seq results. CONCLUSIONS: After infection with S. japonicum, a defensive reaction in M. fortis commenced rapidly, increasing dramatically in the second week, and gradually decreasing three weeks after infection. The obtained M. fortis transcriptome and DEGs profile data demonstrated that natural and adaptive immune responses, play an important role in M. fortis immunity to S. japonicum. These findings provide a better understanding of the natural resistance mechanisms of M. fortis against schistosomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/1471-2164-15-417) contains supplementary material, which is available to authorized users

DiffSeer: Difference-based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs