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Information enhanced model selection for Gaussian graphical model with application to metabolomic data

BIOSTATISTICS 2021年第3期23卷 926-948页

作者： Zhou, Jie Hoen, Anne G. Mcritchie, Susan Pathmasiri, Wimal Viles, Weston D. Nguyen, Quang P. Madan, Juliette C. Dade, Erika Karagas, Margaret R. Gui, Jiang Dartmouth Coll Geisel Sch Med Dept Biomed Data Sci 3 Rope Ferry Rd Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Dept Epidemiol 3 Rope Ferry Rd Hanover NH 03755 USA Univ North Carolina Chapel Hill Nutr Res Inst Dept Nutr Sch Publ Hlth 500 Laureate Way Kannapolis NC 28081 USA Univ Southern Maine Dept Math & Stat 96 Falmouth St Portland ME 04103 USA

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

关键词： Chow-Liu algorithm Metabolite pathway analysis Model pool Prior structure Structural BIC Structure learning algorithm two-step algorithm Undirected Gaussian graphical model

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Reconstructing Genetic Regulatory Networks Using two-step algorithms with the Differential Equation Models of Neural Networks

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2018年第4期10卷 823-835页

作者： Chen, Chi-Kan Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

BackgroundThe identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time *** present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RERNN/RERMLP), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RERNN-RNN and RERMLP-RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair *** unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RERMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RERNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RERMLP-RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The fram

关键词： GRN Reconstruction Gene expression time series RNN RMLP two-step algorithm

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On the Minimax Misclassification Ratio of Hypergraph Community Detection

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第12期65卷 8095-8118页

作者： Chien, I. (Eli) Lin, Chung-Yi Wang, I-Hsiang Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Univ Illinois Dept Elect & Comp Engn Champaign IL 61820 USA Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan CALTECH Dept Elect Engn Pasadena CA 91125 USA

Community detection in hypergraphs is explored. Under a generative hypergraph model called "d-wise hypergraph stochastic block model" (d-hSBM), which naturally extends the stochastic block model (SBM) from graphs to d-uniform hypergraphs, the fundamental limit of the misclassification ratio (the loss function in the community detection problem) is studied. For the converse part, a lower bound of the minimax risk, that is, the minimax expected misclassification ratio, is derived. Asymptotically, it decays exponentially fast to zero as the number of nodes tends to infinity, and the rate function is a weighted combination of several divergence terms, each of which is the Renyi divergence of order 1/2 between two Bernoulli distributions. The Bernoulli distributions involved in the characterization of the rate function are those governing the random instantiation of hyperedges in d-hSBM. For the achievability part, we propose a two-step polynomial-time algorithm which, with high probability, has a misclassification ratio with a decaying exponent that is asymptotically greater than or equal to that of our proposed lower bound. The first step of the algorithm is a hypergraph spectral clustering method, which achieves partial recovery to a certain precision level. The second step is a local refinement method, which leverages the underlying probabilistic model along with parameter estimation from the outcome of the first step. To characterize the asymptotic performance of the proposed algorithm, we first derive a sufficient condition for attaining weak consistency in the hypergraph spectral clustering step. Then, under the guarantee of weak consistency in the first step, we upper bound the loss (with high probability) attained in the local refinement step by an exponentially decaying function of the size of the hypergraph and characterize the decaying rate. Compared to existing works in SBM, the main technical challenge lies in the complex structure of error events since com

关键词： Community detection stochastic block model (SBM) hypergraph clustering asymptotic theory hypergraph Laplacian two-step algorithm

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Improved two-step algorithm in Geosynchronous SAR

Improved Two-Step Algorithm in Geosynchronous SAR

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IET International Radar Conference

作者： W. Yang Z. Ding Y. Zhu C.Ni C. Hu School of Information and Eletronic Beijing Institute of Technology Chinese Academy of Space Technology

ISBN: (纸本)9781629931340

In Geosynchronous Synthetic Aperture Radar (GEO SAR),the very large swath width makes the classical line trajectory model invalid and the imaging parameters space-variant. In this paper, an accurate slant range model is firstly given for GEO SAR. Next, an improved two-step algorithm which can overcome the difficulties introduced by space-variance of imaging parameters is proposed. Furthermore, simulation results fully verify the correctness and effectiveness of the derived algorithm.

关键词： slant range model GEO SAR two-step algorithm

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A Novel two-step Integer-pixel Motion Estimation algorithm for HEVC Encoding on a GPU

A Novel Two-Step Integer-pixel Motion Estimation Algorithm f...

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23rd International Conference on MultiMedia Modeling (MMM)

作者： Chen, Keji Sun, Jun Guo, Zongming Zhao, Dachuan Peking Univ Inst Comp Sci & Technol Beijing Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China Adv Micro Devices Co Ltd Beijing Peoples R China

ISBN: (纸本)9783319518145;9783319518138

Integer-pixel Motion Estimation (IME) is one of the fundamental and time-consuming modules in encoding. In this paper, a novel two-step IME algorithm is proposed for High Efficiency Video Coding (HEVC) on a Graphic Processing Unit (GPU). First, the whole search region is roughly investigated with a predefined search pattern, which is analyzed in detail to effectively reduce the complexity. Then, the search result is further refined in the zones only around the best candidates of the first step. By dividing IME into two steps, the proposed algorithm combines the advantage of one-step algorithms in synchronization and the advantage of multiple-step algorithms in complexity. According to the experimental results, the proposed algorithm achieves up to 3.64 times speedup compared with previous representative algorithms, and the search accuracy is maintained at the same time. Since IME algorithm is independent from other modules, it is a good choice for different GPU-based encoding applications.

关键词： High Efficiency VideoCoding (HEVC) Graphics Processing Unit(GPU) Integer-pixelMotionEstimation (IME) two-step algorithm

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A new two-step algorithm for ionospheric tomography solution

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GPS SOLUTIONS 2012年第1期16卷 89-94页

作者： Wen, Debao Wang, Yong Norman, Robert Changsha Univ Sci & Technol Sch Traff & Transportat Engn Changsha 410004 Hunan Peoples R China Henan Polytech Univ Sch Surveying & Land Informat Engn Jiaozuo 454000 Peoples R China RMIT Univ Ctr Satellite Positioning & Nav Melbourne Vic 3001 Australia

Ionospheric tomography inverse algorithms are usually an ill-conditioned problem because the geometric distribution of continuously operating reference GPS stations is not ideal for this task. In order to cope with such ill-conditioning, a new tomographic algorithm, termed two-step algorithm (TSA), is presented. The electron density is estimated in two steps: Phillips smoothing method (PSM) is first used to resolve the ill-conditioned problem in the ionospheric tomography system, and then, the PSM solution is input as an initial value to the multiplicative algebraic reconstruction technique (MART) and iteratively improved. Numerical simulations using the International Reference Ionosphere 2007 model demonstrate that the TSA is applicable to GPS-based ionospheric tomography reconstruction and is superior to PSM and MART when these techniques are used alone. The new algorithm is applied to reconstruct the ionospheric electron density distribution over China using GPS observations, and a comparison with ionosonde observations is made.

关键词： Computerized ionospheric tomography Total electron content Ionospheric electron density Ill-conditioned two-step algorithm MART

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Parsimonious HMMs for Offline Handwritten Chinese Text Recognition 16

Parsimonious HMMs for Offline Handwritten Chinese Text Recog...

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16th International Conference on Frontiers in Handwriting Recognition (ICFHR)

作者： Wang, Wenchao Du, Jun Wang, Zi-Rui Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781538658758

Recently, hidden Markov models (HMMs) have achieved promising results for offline handwritten Chinese text recognition. However, due to the large vocabulary of Chinese characters with each modeled by a uniform and fixed number of hidden states, a high demand of memory and computation is required. In this study, to address this issue, we present parsimonious HMMs via the state tying which can fully utilize the similarities among different Chinese characters. two-step algorithm with the data-driven question-set is adopted to generate the tied-state pool using the likelihood measure. The proposed parsimonious HMMs with both Gaussian mixture models (GMMs) and deep neural networks (DNNs) as the emission distributions not only lead to a compact model but also improve the recognition accuracy via the data sharing for the tied states and the confusion decreasing among state classes. Tested on ICDAR-2013 competition database, in the best configured case, the new parsimonious DNN-HMM can yield a relative character error rate (CER) reduction of 6.2%, 25% reduction of model size and 60% reduction of decoding time over the conventional DNN-HMM. In the compact setting case of average 1-state HMM, our parsimonious DNN-HMM significantly outperforms the conventional DNN-HMM with a relative CER reduction of 35.5%.

关键词： Parsimonious HMM character similarity state tying two-step algorithm handwritten Chinese text recognition

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Power Supply Meshes based Line Routing for Urban MV Distribution Network Planning

Power Supply Meshes based Line Routing for Urban MV Distribu...

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作者： Ning Luo Hua Gao Zhuding Wang Molin He Power grid planning research centre of Guizhou Power Grid Co. Ltd State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University

Considering that it is difficult to determine the load points or the locations of user access facilities in urban areas for the planning years, an optimization method for the routing planning of urban medium-voltage(MV) trunk lines is proposed based on power supply meshes. Firstly, based on the results of load forecasting and high voltage substation planning, power supply meshes are generated in the entire planning area according to the principle of selecting standby substations close to loads. Then, based on the thinking line of "trunk firstly, branch then", a shortest path based two-step algorithm is proposed to find the shortest trunk lines for the load centres of power supply meshes and the shortest trunk tie-line be-tween the load centres of each mesh. A numerical example shows that the method is practical and feasible, which can provide a reference for the reasonable routing scheme of medium voltage trunk lines.

关键词： MV line routing planning power supply mesh shortest path algorithm two-step algorithm trunk line

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A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data

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SENSORS 2018年第4期18卷 943-943页

作者： Shang, Mingyang Han, Bing Ding, Chibiao Sun, Jili Zhang, Tao Huang, Lijia Meng, Dadi Chinese Acad Sci Inst Elect Key Lab Technol Geospatial Informat Proc & Appl S Beijing 100190 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Elect Natl Key Lab Microwave Imaging Technol Beijing 100190 Peoples R China

Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.

关键词： SAR GF-3 staring spotlight two-step algorithm curved orbit stop-and-go antenna pattern high-resolution

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two-step fringe pattern analysis with a Gabor filter bank

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OPTICS AND LASERS IN ENGINEERING 2016年 85卷 29-37页

作者： Rivera, Mariano Dalmau, Oscar Gonzalez, Adonai Hernandez-Lopez, Francisco Ctr Invest Matemat AC Guanajuato 36240 Gto Mexico Univ Iberoamericana Leon Dept Ciencias & Ingn Leon 37238 Gto Mexico

We propose a two-shot fringe analysis method for Fringe Patterns (FPs) with random phase-shift and changes in illumination components. These conditions reduce the acquisition time and simplify the experimental setup. Our method builds upon a Gabor Filter (GF) bank that eliminates noise and estimates the phase from the FPs. The GF bank allows us to obtain two phase maps with a sign ambiguity between them. Due to the fact that the random sign map is common to both computed phases, we can correct the sign ambiguity. We estimate a local phase-shift from the absolute wrapped residual between the estimated phases. Next, we robustly compute the global phase-shift. In order to unwrap the phase, we propose a robust procedure that interpolates unreliable phase regions obtained after applying the GF bank. We present numerical experiments that demonstrate the performance of our method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fringe analysis two-step algorithm Phase measurement Phase unwrapping Gabor filters

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