Unsupervised String Transformation Learning for Entity Consolidation

Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clusters of duplicate records as input and produces a single "golden record" for each cluster, which contains the canonical value for each attribute. Truth discovery and data fusion methods, as well as Master Data Management (MDM) systems, can be used for entity consolidation. However, to achieve better results, the variant values (i.e., values that are logically the same with different formats) in the clusters need to be consolidated before applying these methods. For this purpose, we propose a data-driven method to standardize the variant values based on two observations: (1) the variant values usually can be transformed to the same representation (e.g., "Mary Lee" and "Lee, Mary") and (2) the same transformation often appears repeatedly across different clusters (e.g., transpose the first and last name). Our approach first uses an unsupervised method to generate groups of value pairs that can be transformed in the same way (i.e., they share a transformation). Then the groups are presented to a human for verification and the approved ones are used to standardize the data. In a real-world dataset with 17,497 records, our method achieved 75% recall and 99.5% precision in standardizing variant values by asking a human 100 yes/no questions, which completely outperformed a state of the art data wrangling tool

The Role Of Genetics, Nutrition, And Cigarette Smoking In The Longitudinal Change In Lung Function

Lung function is an important predictor of population morbidity and mortality. Decline in lung function is a natural part of aging, but accelerated loss in lung function over time is a harbinger of chronic obstructive pulmonary disease (COPD), a leading cause of death globally. Smoking is widely recognized as the key risk factor for reduced lung function and COPD, although additional risk factors, such as genetics and nutrition, have been suggested to also play important roles in contributing to changes in lung function. The overall aim of this research was to investigate the role of, and interaction between, genetics, nutrition, and cigarette smoking in relation to the longitudinal change in lung function, as an indicator of COPD susceptibility. First, we explored the association between genetic variation within a network of antioxidant enzyme genes and the rate of change in lung function in a prospective cohort study of African and European American elderly adults; this study also investigated gene-bysmoking interaction. Evidence of association was identified for genetic variants in several candidate genes, among which were two novel genes (mGST3 and IDH3B) that interacted with smoking in both races/ethnicities. Second, to expand the scope of investigation to all common genetic variants iii throughout the entire human genome, we conducted a large-scale meta-analysis of genomewide association studies of longitudinal change in lung function in a consortium of 14 individual cohort studies of adults of European ancestry. We found evidence of association at two novel genetic loci (IL16/STARD5/TMC3 and ME3) in the meta-analysis and performed additional gene expression analyses to demonstrate that both loci harbor candidate genes with biologically plausible functional links to lung function. Finally, we explored the role of nutrition directly by investigating the relation between overall dietary patterns and longitudinal change in lung function in a prospective cohort of male adults, considering diet-by-smoking interaction. We identified two distinct dietary patterns by applying principal component analysis to food frequency questionnaire data, and found that a prudent diet rich in fruits, vegetables, fish, and poultry attenuated the accelerated decline in lung function in cigarette smokers, but had no association in non-smokers. i

The Internationalization of Chinese Firms: Case Analysis of Some Key Dimensions

Under the background of rapid economic globalization and the information revolution, internationalization has become a trend for firms which face intensified international competition and pursue further development. Employing a case-study approach, this dissertation examines some key issues related to the internationalization of Chinese firms. Specifically, the research investigates the key factors that possibly determine the success of Chinese firms internationalization, the routes through which Chinese firms should implement for their aspiration of internationalization, the foreign market entry strategy of Chinese internationalized firms. In examining these issues, the dissertation aims to find how firms from emerging economies such as China can improve their international competitiveness through internationalization. A number of interesting research findings have emerged from this study. First, the results from our case study show that the motivation for internationalization varies from firm to firm, ranging from escape investment, to expansion of market and to strategic assets-seeking. This demonstrates that no single theory can explain internationalization of firms from developing countries such as China. Second, the strategy for internationalization also varies from firm to firm, ranging from establishment of joint ventures to wholly-owned greenfield production facilities. From this one can conclude that firms employ different strategies depending on the type of their competitive advantages and specific market conditions. It seems that the classic Uppsala model can not explain the process of internationalization for some firms. Third, the nature of competitive advantages varies from firm to firm in our case study. This explains why the three companies adopt different strategies for internationalization, either joint venture or wholly-owned operation. The study has also discussed the limitations of the research and avenues for future research

The phylogenetically-related pattern recognition receptors EFR and XA21 recruit similar immune signaling components in monocots and dicots

During plant immunity, surface-localized pattern recognition receptors (PRRs) recognize pathogen-associated molecular patterns (PAMPs). The transfer of PRRs between plant species is a promising strategy for engineering broad-spectrum disease resistance. Thus, there is a great interest in understanding the mechanisms of PRR-mediated resistance across different plant species. Two well-characterized plant PRRs are the leucine-rich repeat receptor kinases (LRR-RKs) EFR and XA21 from Arabidopsis thaliana (Arabidopsis) and rice, respectively. Interestingly, despite being evolutionary distant, EFR and XA21 are phylogenetically closely related and are both members of the sub-family XII of LRR-RKs that contains numerous potential PRRs. Here, we compared the ability of these related PRRs to engage immune signaling across the monocots-dicots taxonomic divide. Using chimera between Arabidopsis EFR and rice XA21, we show that the kinase domain of the rice XA21 is functional in triggering elf18-induced signaling and quantitative immunity to the bacteria Pseudomonas syringae pv. tomato (Pto) DC3000 and Agrobacterium tumefaciens in Arabidopsis. Furthermore, the EFR:XA21 chimera associates dynamically in a ligand-dependent manner with known components of the EFR complex. Conversely, EFR associates with Arabidopsis orthologues of rice XA21-interacting proteins, which appear to be involved in EFR-mediated signaling and immunity in Arabidopsis. Our work indicates the overall functional conservation of immune components acting downstream of distinct LRR-RK-type PRRs between monocots and dicots

Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks

The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of the proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN

Preparation of a Peptide-Modified Electrode for Capture and Voltammetric Determination of Endotoxin

Endotoxin is the major structural constituent of the outer membrane of Gram-negative bacteria, which is a great threat to human health. Herein, a sensitive electrochemical biosensor for the detection of endotoxin is established by recording the voltammetric responses of the peptide-modified electrode. The utilized peptide has a high affinity for the target endotoxin, which ensures the high selectivity of this method. After the capture of endotoxin on the electrode surface, a negatively charged layer is formed, and the electron-transfer process is significantly hindered because of the increased steric hindrance and the electrostatic repulsion. The declined electrochemical signal could be used to indicate the concentration of endotoxin. This method is simple but effective, which requires limited reagents. Another highlight of this method is its user-friendly operation. Moreover, its applicability in human blood plasma promises its great potential utility in the near future

DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

Traditional neural objection detection methods use multi-scale features that allow multiple detectors to perform detecting tasks independently and in parallel. At the same time, with the handling of the prior box, the algorithm's ability to deal with scale invariance is enhanced. However, too many prior boxes and independent detectors will increase the computational redundancy of the detection algorithm. In this study, we introduce Dubox, a new one-stage approach that detects the objects without prior box. Working with multi-scale features, the designed dual scale residual unit makes dual scale detectors no longer run independently. The second scale detector learns the residual of the first. Dubox has enhanced the capacity of heuristic-guided that can further enable the first scale detector to maximize the detection of small targets and the second to detect objects that cannot be identified by the first one. Besides, for each scale detector, with the new classification-regression progressive strapped loss makes our process not based on prior boxes. Integrating these strategies, our detection algorithm has achieved excellent performance in terms of speed and accuracy. Extensive experiments on the VOC, COCO object detection benchmark have confirmed the effectiveness of this algorithm