The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform

Chengfei Liu - One of the best experts on this subject based on the ideXlab platform.

  • holistic constraint Preserving Transformation from relational schema into xml schema
    Database Systems for Advanced Applications, 2008
    Co-Authors: Rui Zhou, Chengfei Liu
    Abstract:

    In this paper, we propose a holistic scheme of transforming a relational schema into an XML Schema with integrity constraints preserved. This scheme facilitates constructing a schema for the published XML views of relational data. With this schema, users are able to issue qualified queries against XML views, and discover update anomalies in advance before propagating the view updates into relational database. Compared to the previous work which splits the Transformation process into two steps, we establish a holistic solution to directly transform a relational schema into an XML Schema without building a reference graph. We achieve this by classifying the underlying relations in a more concise and effective way, and applying the converting rules wisely. The rules are also devised to be more compact and less complicated in contrast to those in our previous work. Finally, we manage to crack another hard nut which was seldom touched before, i.e. converting circularly referenced relations into recursive XML Schema.

  • constraint Preserving Transformation from relational schema to xml schema
    World Wide Web, 2006
    Co-Authors: Chengfei Liu, Millist W Vincent, Jixue Liu
    Abstract:

    XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic Transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.

Qionghai Dai - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised content Preserving Transformation for optical microscopy
    Light-Science & Applications, 2021
    Co-Authors: Guoxun Zhang, Hui Qiao, Feng Bao, Yue Deng, Jingping Yun, Xing Lin, Hao Xie, Haoqian Wang, Qionghai Dai
    Abstract:

    The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image Transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image Transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-Preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image Transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image Transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

  • unsupervised content Preserving Transformation for optical microscopy
    bioRxiv, 2019
    Co-Authors: Guoxun Zhang, Hui Qiao, Xing Lin, Hao Xie, Haoqian Wang, Qionghai Dai
    Abstract:

    The advent of deep learning and the open access to a substantial collection of imaging data provide a potential solution to computational image Transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current deep-learning implementations usually operate in a supervised manner, and the reliance on a laborious and error-prone data annotation procedure remains a barrier towards more general applicability. Here, we propose an unsupervised image Transformation enlightened by cycle-consistent generative adversarial networks (cycleGANs) to facilitate the utilization of deep learning in optical microscopy. By incorporating the saliency constraint into cycleGAN, the unsupervised approach, dubbed as content-Preserving cycleGAN (c2GAN), can learn the mapping between two image domains and avoid the misalignment of salient objects without paired training data. We demonstrate several image Transformation tasks such as fluorescence image restoration, whole-slide histological coloration, and virtual fluorescent labeling. Quantitative evaluations prove that c2GAN achieves robust and high-fidelity image Transformation across different imaging modalities and various data configurations. We anticipate that our framework will encourage a paradigm shift in training neural networks and democratize deep learning algorithms for optical society.

Jixue Liu - One of the best experts on this subject based on the ideXlab platform.

  • constraint Preserving Transformation from relational schema to xml schema
    World Wide Web, 2006
    Co-Authors: Chengfei Liu, Millist W Vincent, Jixue Liu
    Abstract:

    XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic Transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.

Mikhail Gordin - One of the best experts on this subject based on the ideXlab platform.

  • Limit theorems for von Mises statistics of a measure Preserving Transformation
    Probability Theory and Related Fields, 2014
    Co-Authors: Manfred Denker, Mikhail Gordin
    Abstract:

    For a measure Preserving Transformation $$T$$ T of a probability space $$(X,\mathcal{F },\mu )$$ ( X , F , μ ) and some $$d \ge 1$$ d ≥ 1 we investigate almost sure and distributional convergence of random variables of the form $$\begin{aligned} x \rightarrow \frac{1}{C_n} \sum _{0\le i_1,\ldots ,\,i_d

  • limit theorems for von mises statistics of a measure Preserving Transformation
    Probability Theory and Related Fields, 2014
    Co-Authors: Manfred Denker, Mikhail Gordin
    Abstract:

    For a measure Preserving Transformation \(T\) of a probability space \((X,\mathcal{F },\mu )\) and some \(d \ge 1\) we investigate almost sure and distributional convergence of random variables of the form $$\begin{aligned} x \rightarrow \frac{1}{C_n} \sum _{0\le i_1,\ldots ,\,i_dsubspace in some \(L_p(X^d\!,\, \mathcal{F }^{\otimes d}\!,\,\mu ^d)\). We establish a form of the individual ergodic theorem for such sequences. Using a filtration compatible with \(T\) and the martingale approximation, we prove a central limit theorem in the non-degenerate case; for a class of canonical (totally degenerate) kernels and \(d=2\), we also show that the convergence holds in distribution towards a quadratic form \(\sum _{m=1}^{\infty } \lambda _m\eta ^2_m\) in independent standard Gaussian variables \(\eta _1, \eta _2, \ldots \).

  • limit theorems for von mises statistics of a measure Preserving Transformation
    arXiv: Dynamical Systems, 2011
    Co-Authors: Manfred Denker, Mikhail Gordin
    Abstract:

    For a measure Preserving Transformation $T$ of a probability space $(X,\mathcal F,\mu)$ we investigate almost sure and distributional convergence of random variables of the form $$x \to \frac{1}{C_n} \sum_{i_1random variables are well defined and belong to $L_r(\mu)$ provided that the kernel is chosen from the projective tensor product $$L_p(X_1,\mathcal F_1, \mu_1) \otimes_{\pi}...\otimes_{\pi} L_p(X_d,\mathcal F_d, \mu_d)\subset L_p(\mu^d)$$ with $p=d\,r,\, r\ \in [1, \infty).$ We establish a form of the individual ergodic theorem for such sequences. Next, we give a martingale approximation argument to derive a central limit theorem in the non-degenerate case (in the sense of the classical Hoeffding's decomposition). Furthermore, for $d=2$ and a wide class of canonical kernels $f$ we also show that the convergence holds in distribution towards a quadratic form $\sum_{m=1}^{\infty} \lambda_m\eta^2_m$ in independent standard Gaussian variables $\eta_1, \eta_2,...$. Our results on the distributional convergence use a $T$--\,invariant filtration as a prerequisite and are derived from uni- and multivariate martingale approximations.

Catherine Vermandele - One of the best experts on this subject based on the ideXlab platform.