The Experts below are selected from a list of 31080 Experts worldwide ranked by ideXlab platform
Vicky Choi - One of the best experts on this subject based on the ideXlab platform.
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minor Embedding in adiabatic quantum computation ii minor universal graph design
Quantum Information Processing, 2011Co-Authors: Vicky ChoiAbstract:In Choi (Quantum Inf Process, 7:193---209, 2008), we introduced the notion of minor-Embedding in adiabatic quantum optimization. A minor-Embedding of a graph G in a quantum hardware graph U is a subgraph of U such that G can be obtained from it by contracting edges. In this paper, we describe the intertwined adiabatic quantum architecture design problem, which is to construct a hardware graph U that satisfies all known physical constraints and, at the same time, permits an efficient minor-Embedding Algorithm. We illustrate an optimal complete-graph-minor hardware graph. Given a family $${\mathcal{F}}$$ of graphs, a (host) graph U is called $${\mathcal{F}}$$ -minor-universal if for each graph G in $${\mathcal{F}, U}$$ contains a minor-Embedding of G. The problem for designing a $${{\mathcal{F}}}$$ -minor-universal hardware graph U sparse in which $${{\mathcal{F}}}$$ consists of a family of sparse graphs (e.g., bounded degree graphs) is open.
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minor Embedding in adiabatic quantum computation ii minor universal graph design
arXiv: Quantum Physics, 2010Co-Authors: Vicky ChoiAbstract:In [Choi08], we introduced the notion of minor-Embedding in adiabatic quantum optimization. A minor-Embedding of a graph G in a quantum hardware graph U is a subgraph of U such that G can be obtained from it by contracting edges. In this paper, we describe the intertwined adiabatic quantum architecture design problem, which is to construct a hardware graph U that satisfies all known physical constraints and, at the same time, permits an efficient minor-Embedding Algorithm. We illustrate an optimal complete-graph-minor hardware graph. Given a family F of graphs, a (host) graph U is called F-minor-universal if for each graph G in F, U contains a minor-Embedding of G. The problem for designing a F-minor-universal hardware graph U_{sparse} in which F consists of a family of sparse graphs (e.g., bounded degree graphs) is open.
Kui Wang - One of the best experts on this subject based on the ideXlab platform.
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deep learning enables accurate clustering with batch effect removal in single cell rna seq analysis
Nature Communications, 2020Co-Authors: Yafei Lyu, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Kui Wang, Huize Pan, Muredach P ReillyAbstract:Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep Embedding Algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering Algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.
Wenzhi Zhao - One of the best experts on this subject based on the ideXlab platform.
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spectral spatial feature extraction for hyperspectral image classification a dimension reduction and deep learning approach
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Wenzhi Zhao, Shihong DuAbstract:In this paper, we propose a spectral–spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant Embedding Algorithm is proposed for spectral feature extraction from high-dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial-related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple-feature-based classifier is trained for image classification. Experimental results on well-known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
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spectral spatial feature extraction for hyperspectral image classification a dimension reduction and deep learning approach
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Wenzhi ZhaoAbstract:In this paper, we propose a spectral–spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant Embedding Algorithm is proposed for spectral feature extraction from high-dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial-related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple-feature-based classifier is trained for image classification. Experimental results on well-known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
Muredach P Reilly - One of the best experts on this subject based on the ideXlab platform.
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deep learning enables accurate clustering with batch effect removal in single cell rna seq analysis
Nature Communications, 2020Co-Authors: Yafei Lyu, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Kui Wang, Huize Pan, Muredach P ReillyAbstract:Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep Embedding Algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering Algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.
Nasir M Rajpoot - One of the best experts on this subject based on the ideXlab platform.
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cell phenotyping in multi tag fluorescent bioimages
Neurocomputing, 2014Co-Authors: Adnan Mujahid Khan, Shaneahmed Raza, Michael Khan, Nasir M RajpootAbstract:Multi-tag bioimaging systems have recently emerged as powerful tools which provide spatiotemporal localization of several different proteins in the same tissue specimen. The analysis of such multivariate bioimages requires sophisticated analytical methods that extract a molecular signature of various types of cells and assist in analyzing interaction behaviors of functional protein complexes. Previous studies were mainly focused on pixel-level analysis which essentially ignore cellular structures as units which can be crucial when analyzing cancerous cells. In this paper, we present a framework in order to overcome these limitations by incorporating cell-level analysis. We use this framework to identify cell phenotypes based on their high-dimensional co-expression profiles contained within the images generated by the robotically controlled TIS microscope installed at Warwick. The proposed paradigm employs a refined cell segmentation Algorithm followed by a locality preserving nonlinear Embedding Algorithm which is shown to produce significantly better cell classification and phenotype distribution results as compared to its linear counterpart.