Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems

Abstract: This paper presents a similarity-based approach for estimating the Remaining Useful Life (RUL) in prognostics. The approach is especially suitable for situations in which abundant run-to-failure data for an engineered system are available. Data from multiple units of the same system are used to create a library of degradation patterns. When estimating the RUL of a test unit, the data from it will be matched to those patterns in the library and the actual life of those matched units will be used as the basis of estimation. This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated. Results show that the similarity-based approach is very effective in performing RUL estimation.

Localized excitations in (2+1)-dimensional systems.

Abstract: By means of a special variable separation approach, a common formula with some arbitrary functions has been obtained for some suitable physical quantities of various (2+1)-dimensional models such as the Davey-Stewartson (DS) model, the Nizhnik-Novikov-Veselov (NNV) system, asymmetric NNV equation, asymmetric DS equation, dispersive long wave equation, Broer-Kaup-Kupershmidt system, long wave-short wave interaction model, Maccari system, and a general (N+M)-component Ablowitz-Kaup-Newell-Segur (AKNS) system. Selecting the arbitrary functions appropriately, one may obtain abundant stable localized interesting excitations such as the multidromions, lumps, ring soliton solutions, breathers, instantons, etc. It is shown that some types of lower dimensional chaotic patterns such as the chaotic-chaotic patterns, periodic-chaotic patterns, chaotic line soliton patterns, chaotic dromion patterns, fractal lump patterns, and fractal dromion patterns may be found in higher dimensional soliton systems. The interactions between the traveling ring type soliton solutions are completely elastic. The traveling ring solitons pass through each other and preserve their shapes, velocities, and phases. Some types of localized weak solutions, peakons, are also discussed. Especially, the interactions between two peakons are not completely elastic. After the interactions, the traveling peakons also pass through each other and preserve their velocities and phases, however, they completely exchange their shapes.

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

Abstract: This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.

3D Bioprinting for Tissue and Organ Fabrication

Abstract: The field of regenerative medicine has progressed tremendously over the past few decades in its ability to fabricate functional tissue substitutes. Conventional approaches based on scaffolding and microengineering are limited in their capacity of producing tissue constructs with precise biomimetic properties. Three-dimensional (3D) bioprinting technology, on the other hand, promises to bridge the divergence between artificially engineered tissue constructs and native tissues. In a sense, 3D bioprinting offers unprecedented versatility to co-deliver cells and biomaterials with precise control over their compositions, spatial distributions, and architectural accuracy, therefore achieving detailed or even personalized recapitulation of the fine shape, structure, and architecture of target tissues and organs. Here we briefly describe recent progresses of 3D bioprinting technology and associated bioinks suitable for the printing process. We then focus on the applications of this technology in fabrication of biomimetic constructs of several representative tissues and organs, including blood vessel, heart, liver, and cartilage. We finally conclude with future challenges in 3D bioprinting as well as potential solutions for further development.