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#	Item
981	MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Particle Filtering for Online Motion Planning with Task Specifications Berntorp, K.; Di Cairano, S. Add to Reading List Source URL: www.merl.com Language: English - Date: 2016-07-07 10:22:15 Statistical mechanics Robot control Sampling techniques Stochastic simulation Mechanical engineering Rapidly-exploring random tree Motion planning Particle filter Kalman filter Work Kinematics Acceleration
982	Concise Integer Linear Programming Formulations for Dependency Parsing ∗ Andr´e F. T. Martins∗† Noah A. Smith∗ Eric P. Xing∗ School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA Add to Reading List Source URL: homes.cs.washington.edu Language: English - Date: 2015-07-30 17:51:06 Operations research Compiler construction Formal languages Machine learning Dependency grammar Natural language parsing LL parser Integer programming Parsing Linear programming Context-free grammar Conditional random field
983	The development of a new docking score, taking account of the fourth dimension Rob Smith, Ben Tehan, Andrea Bortolato, Simon Cross Heptares Therapeutics Ltd., BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire, Add to Reading List Source URL: cisrg.shef.ac.uk Language: English - Date: 2016-07-22 08:25:51 Computational chemistry Medicinal chemistry Computational neuroscience Drug discovery Deep learning Artificial neural network Random forest Docking TensorFlow Scoring rule
984	Chapter 3 Measurements uncertainties In this chapter we describe how to treat and combine the statistical and systematic uncertainties associated to measurements. 3.1 Add to Reading List Source URL: ihp-lx.ethz.ch Language: English - Date: 2015-03-31 05:26:54 Covariance and correlation Algebra of random variables Summary statistics Estimation theory Regression analysis Covariance Propagation of uncertainty Variance Central limit theorem Normal distribution Correlation and dependence Expected value
985	PHYSICAL REVIEW E 93, Efficient community detection of network flows for varying Markov times and bipartite networks Masoumeh Kheirkhahzadeh,1,2 Andrea Lancichinetti,2 and Martin Rosvall2,* 1 Add to Reading List Source URL: www.mapequation.org Language: English - Date: 2016-03-19 03:43:10 Stochastic processes Markov processes Markov models Probability theory Network theory Markov chain Modularity Ergodicity Random walk
986	Digital Volunteerism During Disaster: Crowdsourcing Information Processing Kate Starbird University of Colorado – ATLAS Institute 430 UCB, University of Colorado, Boulder Add to Reading List Source URL: crowdresearch.org Language: English - Date: 2011-03-24 21:28:49 Disaster preparedness Crowdsourcing Internet-based activism Media in Kenya Ushahidi Disaster informatics Random Hacks of Kindness Disaster response Twitter Emergency management Tweet
987 $Fractals / Functional analysis / Signal processing / Dimension theory / Multifractal system / Continuous function / Wavelet / Distribution / Singularity spectrum$	MULTIFRACTAL ANALYSIS OF COMPLEX RANDOM CASCADES JULIEN BARRAL AND XIONG JIN Abstract. We achieve the multifractal analysis of a class of complex valued statistically self-similar continuous functions. For we use multifr Add to Reading List Source URL: personalpages.manchester.ac.uk Language: English - Date: 2013-09-19 07:58:47 Fractals Functional analysis Signal processing Dimension theory Multifractal system Continuous function Wavelet Distribution Singularity spectrum
988	Microsoft PowerPoint - class-5-out.ppt [Compatibility Mode] Add to Reading List Source URL: people.cs.pitt.edu Language: English - Date: 2014-09-09 17:58:53 Graphical models Probability theory Belief propagation Coding theory Markov random field Factor graph Variable elimination Tree decomposition Logarithm Centrality
989	Calibrated Structured Prediction Percy Liang Department of Computer Science Stanford University Stanford, CA 94305 Add to Reading List Source URL: cs.stanford.edu Language: English - Date: 2015-10-31 00:27:55 Statistical classification Machine learning Bayesian statistics Marginal distribution Probability theory Expected value Joint probability distribution Platt scaling Support vector machine Conditional random field Mutual information Structured support vector machine
990	The freezing threshold for k-colourings of a random graph.∗ Michael Molloy† April 28, 2016 Abstract We determine the exact value of the freezing threshold, rk , for k-colourings of a random graph when Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2016-04-28 21:24:02 Graph coloring Kempe chain Neighbourhood Tree Regular graph Degree

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