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A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data

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Tsinghua Science and Technology 2019年第4期24卷 446-454页

作者： Xiang Chen Min Li Ruiqing Zheng Siyu Zhao Jianxin Wang Fang-Xiang Wu Yaohang Li the School of Computer Science and Engineering Central South University the Department of Mechanical Engineering and Division of Biomedical Engineering University of Saskatchewan the Department of Computer Science Old Dominion University

Inferring Gene Regulatory Networks(GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network Inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure Inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information(PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theorybased network Inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

关键词： gene regulatory networks network Inference path consistency algorithm

Adaptive network fuzzy Inference system based navigation controller for mobile robotAdaptive network fuzzy Inference system based navigation controller for mobile robot

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Frontiers of Information Technology & Electronic Engineering 2019年第2期20卷 141-151页

作者： Panati SUBBASH Kil To CHONG Department of Electronics and Information Engineering Chonbuk National University Advanced Electronics and Information Research Center Chonbuk National University

Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy Inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.

关键词： Adaptive network fuzzy Inference system Additive white Gaussian noise Autonomous navigation Mobile robot

Bayesian Inference for dynamical systems

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Infectious Disease Modelling 2020年第1期5卷 221-232页

作者： Weston C.Roda Department of Mathematical and Statistical Sciences University of AlbertaEdmontonAB T6G 2G1Canada

Bayesian Inference is a common method for conducting parameter estimation for dynamical systems.Despite the prevalent use of Bayesian Inference for performing parameter estimation for dynamical systems,there is a need for a formalized and detailed methodology.This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian Inference and it covers utilizing different distributions,Markov Chain Monte Carlo(MCMC)sampling,obtaining credible intervals for parameters,and prediction intervals for solutions.A logistic growth example is given to illustrate the methodology.

关键词： Bayesian Inference Model fitting Data Dynamical system Mathematical model

Nonlinear joint PP-PS AVO inversion based on improved Bayesian Inference and LSSVM

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Applied Geophysics 2019年第1期16卷 64-76页

作者： Xie Wei Wang Yan-Chun Liu Xue-Qing Bi Chen-Chen Zhang Feng-Qi Fang Yuan Tahir Azeem School of Geophysics and Information Technology China University of Geosciences (Beijing)Beijing 100083China Beijing Energy Oil & Gas Resources Development Co. Ltd. Beijing 100022China Petroleum Exploration and Production Research Institute Beijing 100083China Development and Research Center China Geological SurveyBeijing 100037China Department of Earth Sciences Quaid-I-Azam UniversityIslamabad 45320Pakistan

Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian Inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian Inference are optimized via particle swarm optimization (PSO). In improved Bayesian Inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.

关键词： Nonlinear problem joint PP-PS AVO inversion particle swarm optimization Bayesian Inference least squares support vector machine

The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations

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Computer Modeling in Engineering & Sciences 2019年第10期121卷 49-82页

作者： Yang Cheng Dehua Li Wenbin Jiang School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China.

Beta Process is a typical nonparametric Bayesian model.and the Beta Bernoulli Process provides a Bayesian nonparametric prior for models involving collections of binary valued features.Some previous studies considered the Beta Process Inference problem by giving the Stick-Breaking sampling method.This paper focuses on analyzing the form of precise probability distribution based on a Stick-Breaking approach,that is,the joint probability distribution is derived from any finite number of observable samples:It not only determines the probability distribution function of the Beta Process with finite observation(represented as a group of number between[0,1]),but also gives the distribution function of the Beta Bernoulli Process with the same finite dimension(represented as a matrix with element value of 0 or,1)by using this distribution as a prior.

关键词： Beta process joint distribution beta Bernoulli process exact Inference

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第1期34卷 72-80页

作者： Zhu Haihua Li Jing Wang Yingcong College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

The process Inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent Inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative Inference engine are introduced.Then,the Inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of Inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

关键词： ontology semantic web rule language (SWRL) process plan knowledge semantic Inference

Optimizing deep learning Inference on mobile devices with neural network accelerators

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High Technology Letters 2019年第4期25卷 417-425页

作者：曾惜 Xu Yunlong Zhi Tian Intelligent Processor Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China Cambricon Technologies Corporation Limited Beijing 100191P.R.China

Deep learning has now been widely used in intelligent apps of mobile devices.In pursuit of ultra-low power and latency,integrating neural network accelerators(NNA)to mobile phones has become a trend.However,conventional deep learning programming frameworks are not well-developed to support such devices,leading to low computing efficiency and high memory-occupation.To address this problem,a 2-stage pipeline is proposed for optimizing deep learning model Inference on mobile devices with NNAs in terms of both speed and memory-footprint.The 1 st stage reduces computation workload via graph optimization,including splitting and merging nodes.The 2 nd stage goes further by optimizing at compilation level,including kernel fusion and in-advance compilation.The proposed optimizations on a commercial mobile phone with an NNA is evaluated.The experimental results show that the proposed approaches achieve 2.8×to 26×speed up,and reduce the memory-footprint by up to 75%.

关键词： machine learning Inference neural network accelerator(NNA) low latency kernel fusion in-advance compilation

On the Foundations of Statistical Mechanics: Ergodicity, Many Degrees of Freedom and Inference

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Communications in Theoretical Physics 2014年第10期61卷 469-475页

作者： Sergio Chibbaro Lamberto Rondoni Angelo Vulpiani Sorbonne Universités UPMC Univ Paris 06CNRSUMR7190Institut Jean Le Rond d’AlembertF-75005 ParisFrance Kavli Institute for Theoretical Physics China CAS Dip.Scienze Matematiche Politecnico di TorinoC.Duca degli Abruzzi 24TorinoItaly INFN Sezione di TorinoVia P.Giuria 1TorinoItaly Dipartimento di Fisica Università "La Sapienza" RomaItaly ISC-CNR Piazzale A.Moro 2 – 00185 RomaItaly

We review the main aspects of the foundations of statistical mechanics. In particular we explain why many degrees of freedom are necessary, while chaos(in the sense of positive Lyapunov exponents) is only marginally r... 详细信息

关键词： ergodic hypothesis chaos emergent properties statical Inference

Scalar Alternatives and Scalar Inference Involving Adjectives:A Comment on van Tiel,et al.(2016)

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外国语 2019年第3期42卷 2-12页

作者： Louise McNally Universitat Pompeu Fabra

Doran, et al.(2009, 2012) and van Tiel, et al.(2016) have presented experimental evidence that gradable adjectives (e.***.cool) yield scalar Inferences (e.***.to not cold) significantly less often than do numerals, quantifiers and modals;their data also reveal considerable variability within the class of adjectives in the frequency with which Inferences are likely to be generated.Van Tiel, et al.consider two main explanations for this variation:differences among pairs of adjectives in what they call scale distinctness (how easy or difficult it is to differentiate the adjectives), and differences in scale availability (how salient the stronger scalar alternative is).They argue that only scale distinctness plays a role, and a rather small one at that;they leave most of the variation unexplained.In this comment, I argue that their measures of scale availability were too crude to detect a role for contextual variation in the scalar alternatives that subjects consider, but that this latter variation is, in fact, a very plausible part of the explanation for the results.I discuss some specific ways adjectives might yield different scalar alternatives in different contexts, under sufficiently sophisticated assumptions about their lexical semantics and about the rhetorical structure of discourse.

关键词： scalar implicature adjectives Inference alternatives