TransNFCM: Translation-Based Neural Fashion Compatibility Modeling

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items in a unified embedding space where a category-specific relation (category-comp-category) is modeled as a vector translation operating on the embeddings of compatible items from the corresponding categories. By this way, we not only capture the specific notion of compatibility conditioned on a specific pair of complementary categories, but also preserve the global notion of compatibility. We also design a deep fashion item encoder which exploits the complementary characteristic of visual and textual features to represent the fashion products. To the best of our knowledge, this is the first work that uses category-specific complementary relations to model the category-aware compatibility between items in a translation-based embedding space. Extensive experiments demonstrate the effectiveness of TransNFCM over the state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

A Framework for BIM-Enabled Life-Cycle Information Management of Construction Project

BIM has been widely used in project management, but on the whole the applications have been scattered and the BIM models have not been deployed throughout the whole project life-cycle. Each participant builds their own BIM, so there is a major problem in how to integrate these dynamic and fragmented data together. In order to solve this problem, this paper focuses on BIM-based life-cycle information management and builds a framework for BIM-enabled life-cycle information management. To organize the life-cycle information well, the information components and information flow during the project life-cycle are defined. Then, the application of BIM in life-cycle information management is analysed. This framework will provide a unified platform for information management and ensure data integrit

NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation. However, one major obstacle of NAS is the extremely demanding computation resource requirements and time-consuming iterations particularly when the dataset scales. In this paper, targeting at the emerging vision transformer (ViT), we present NasHD, a hyperdimensional computing based supervised learning model to rank the performance given the architectures and configurations. Different from other learning based methods, NasHD is faster thanks to the high parallel processing of HDC architecture. We also evaluated two HDC encoding schemes: Gram-based and Record-based of NasHD on their performance and efficiency. On the VIMER-UFO benchmark dataset of 8 applications from a diverse range of domains, NasHD Record can rank the performance of nearly 100K vision transformer models with about 1 minute while still achieving comparable results with sophisticated models

Simulation of Plasma Electrolytic Oxidation (PEO) of AM50 Mg Alloys and its Experimental Validation

The PEO process is a useful surface technology to improve wear performance and corrosion resistance on light metals such as Mg, Al and Ti and their alloys. It has been studied for several decades, but up to now, its mechanism is still not fully understood. In order to improve the understanding of PEO process and get better process design for industrial application, it is worth studying numerical approaches of the PEO process. This thesis provides a modeling and simulation approach to study the PEO process on Mg alloy AM50 under constant voltage mode. A numerical model has been built to describe the PEO process using Finite Element Method in COMSOL. Experiments are performed to provide data input for the modeling and simulation, and also to validate the correctness and usefulness of the modeling. From comparison, the simulation result of coating thickness is in good agreement with the experimental result. The application of the model was verified by studies investigating the influences of different voltage, a complex substrate geometry and the electrode distance on the formation of PEO coatings. The approach of the model can predict the coating thickness close to the experimental results and explain the effects of the main process parameters on the coating growth reasonably. Therefore, the methodology provided by the numerical model has been demonstrated to be a useful tool for predicting the coating thickness and explaining the effects of different parameters on Mg alloy under constant voltage mode

From Building Information Modeling to City Information Modeling

With the development of Geographic Information System (GIS), the concept of digital city is implemented widely. However, in practice, most of the GIS models are relatively poorly attributed, semantically. Building Information Modeling (BIM) is a process involving the generation and management of digital representations of physical and functional characteristics of building, which is most used in small scale projects. In order to address the target problem of completing the semantic attribution of 3D digital city model, a framework of integrating BIM technology into GIS is demonstrated. A new concept of city information modeling (CIM) is proposed with the goal of bringing great benefits to the urban construction and city management. The composition of city information model is discussed. The data schema behind BIM and GIS (i.e. IFC and CityGML) are compared and mapped with each other. A case study of land planning of campus is demonstrated to present the potential benefits of CIM