Study on Resource Configuration on Cloud Manufacturing

The purpose of manufacturing is to realize the requirement of customer. In manufacturing process of cloud system, there exist a lot of resource services which have similar functional characteristics to realize the requirement. It makes the manufacturing process more diverse. To develop the quality and reduce cost, a resource configuration model on cloud-manufacturing platform is put forward in this paper. According to the generalized six-point location principle, a growth design from the requirement of customers to entities with geometric constraints is proposed. By the requirement growing up to product, a configuration process is used to match the entities with the instances which the resources in the database could supply. Different from most existing studies, this paper studies the tolerance design with multiple candidate resource suppliers on cloud manufacturing to make the market play a two-level game considering the benefit of customers and the profit of resources to give an optimal result. A numerical case study is used to illustrate the proposed model and configuration process. The performance and advantage of the proposed method are discussed at the end

Connection and Control Strategy of PV Converter Integrated into Railway Traction Power Supply System

In order to supply the single-phase locomotive load and mitigate the negative sequence current, this paper develops a V/V transformer-based connection and control strategy of three-phase photovoltaic (PV) converters integrated into railway traction power supply systems. In this V/V transformer-based connection, the two-phase traction voltage is converted into the three-phase voltage. This approach can offer a common low voltage AC bus, which is more convenient for more access to three-phase PV converters. Based on this V/V transformer-based connection, an individual phase current control strategy with the hybrid current reference is fully designed. In this control strategy, the current reference, containing two parts, is generated. One is the asymmetrical part for powering the single-phase locomotive load and mitigating the negative sequence current. The other is the symmetrical part for feeding the surplus power back to the utility grid. Then, each phase current replaces the dual-sequence current to be controlled to track the corresponding phase current reference. Consequently, PV converters can flexibly inject the symmetrical and asymmetrical currents without the dual-sequence extraction for a simpler implementation. Finally, the effectiveness of the developed connection and control strategy is validated by the simulation studies

AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

VQ3D: Learning a 3D-Aware Generative Model on ImageNet

Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human faces, animal faces, or cars. However, these models struggle on larger, more complex datasets. To model diverse and unconstrained image collections such as ImageNet, we present VQ3D, which introduces a NeRF-based decoder into a two-stage vector-quantized autoencoder. Our Stage 1 allows for the reconstruction of an input image and the ability to change the camera position around the image, and our Stage 2 allows for the generation of new 3D scenes. VQ3D is capable of generating and reconstructing 3D-aware images from the 1000-class ImageNet dataset of 1.2 million training images. We achieve an ImageNet generation FID score of 16.8, compared to 69.8 for the next best baseline method.Comment: 15 pages. For visual results, please visit the project webpage at http://kylesargent.github.io/vq3

Observation of a Nematic Quantum Hall Liquid on the Surface of Bismuth

Nematic quantum fluids with wavefunctions that break the underlying crystalline symmetry can form in interacting electronic systems. We examine the quantum Hall states that arise in high magnetic fields from anisotropic hole pockets on the Bi(111) surface. Spectroscopy performed with a scanning tunneling microscope shows that a combination of local strain and many-body Coulomb interactions lift the six-fold Landau level (LL) degeneracy to form three valley-polarized quantum Hall states. We image the resulting anisotropic LL wavefunctions and show that they have a different orientation for each broken-symmetry state. The wavefunctions correspond precisely to those expected from pairs of hole valleys and provide a direct spatial signature of a nematic electronic phase

Association between the lean nonalcoholic fatty liver disease and risk of incident type 2 diabetes in a healthy population of Northwest China: a retrospective cohort study with a 2-year follow-up period

AimsWe aimed to explore the metabolic features of lean nonalcoholic fatty liver disease (Lean-NAFLD) and its association with the risk of incident type 2 diabetes in young and middle-aged people.MethodsWe conducted a retrospective cohort study of 3001 participants who were enrolled in a health check-up program from January 2018 to December 2020 in the Health Management Center of Karamay People’s Hospital. The age, sex, height, weight, body mass index (BMI), blood pressure, waist circumference (WC), fasting plasma glucose (FPG), lipid profiles, serum uric acid and alanine aminotransferase (ALT) of the subjects were collected. The cutoff point of BMI for lean nonalcoholic fatty liver disease is <25 kg/m2. A COX proportional hazard regression model was used to analyze the risk ratio of lean nonalcoholic fatty liver disease to type 2 diabetes mellitus.ResultsLean NAFLD participants had many metabolic abnormalities, such as overweight and obesity with nonalcoholic fatty liver disease. Compared with lean participants without nonalcoholic fatty liver disease, the fully adjusted hazard ratio (HR) for lean participants with nonalcoholic fatty liver disease was 3.83 (95% CI 2.02-7.24, p<0.01). In the normal waist circumference group (man<90cm, woman<80 cm), compared with lean participants without NAFLD, the adjusted hazard ratios (HRs) of incident type 2 diabetes for lean participants with NAFLD and overweight or obese participants with NAFLD were 1.93 (95% CI 0.70-5.35, p>0.05) and 4.20 (95% CI 1.44-12.22, p<0.05), respectively. For excess waist circumference (man≥90 cm, woman ≥80 cm) compared with lean participants without NAFLD, the adjusted hazard ratios (HRs) of incident type 2 diabetes for lean participants with NAFLD and overweight or obese participants with NAFLD were 3.88 (95% CI 1.56-9.66, p<0.05) and 3.30 (95% CI 1.52-7.14, p<0.05), respectively.ConclusionAbdominal obesity is the strongest risk factor for type 2 diabetes in lean nonalcoholic fatty liver disease