How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms

This paper reviews and compares twenty-one different model selection algorithms (MSAs) representing a diversity of approaches, including (i) information criteria such as AIC and SIC; (ii) selection of a “portfolio” or best subset of models; (iii) general-to-specific algorithms, (iv) forward-stepwise regression approaches; (v) Bayesian Model Averaging; and (vi) inclusion of all variables. We use coefficient unconditional mean-squared error (UMSE) as the basis for our measure of MSA performance. Our main goal is to identify the factors that determine MSA performance. Towards this end, we conduct Monte Carlo experiments across a variety of data environments. Our experiments show that MSAs differ substantially with respect to their performance on relevant and irrelevant variables. We relate this to their associated penalty functions, and a bias-variance tradeoff in coefficient estimates. It follows that no MSA will dominate under all conditions. However, when we restrict our analysis to conditions where automatic variable selection is likely to be of greatest value, we find that two general-to-specific MSAs, Autometrics, do as well or better than all others in over 90% of the experiments.Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics

Single Molecule Michaelis-Menten Equation beyond Quasi-Static Disorder

The classic Michaelis-Menten equation describes the catalytic activities for ensembles of enzyme molecules very well. But recent single-molecule experiment showed that the waiting time distribution and other properties of single enzyme molecule are not consistent with the prediction based on the viewpoint of ensemble. It has been contributed to the slow inner conformational changes of single enzyme in the catalytic processes. In this work we study the general dynamics of single enzyme in the presence of dynamic disorder. We find that at two limiting cases, the slow reaction and nondiffusion limits, Michaelis-Menten equation exactly holds although the waiting time distribution has a multiexponential decay behaviors in the nondiffusion limit.Particularly, the classic Michaelis-Menten equation still is an excellent approximation other than the two limits.Comment: 10 pages, 1 figur

Combining Canonical Variate Analysis, Probability Approach and Support Vector Regression for Failure Time Prediction

Reciprocating compressors are widely used in oil and gas industry for as transport, lift and injection. Critical reciprocating compressors that operate under high-speed conditions and compress hazardous gases are target equipment on maintenance improvement lists due to downtime risks and safety hazards. Estimating performance deterioration and failure time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. This study presents an application of Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models to estimate failure degradation and remaining useful life based on sensory data acquired from an operational industrial reciprocating compressor. CVA was used to extract a one-dimensional health indicator from the multivariate data sets, thereby reducing the dimensionality of the original data matrix. The failure rate was obtained by using the CPHM based on historical failure times. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and the one-dimensional performance measures obtained from the CVA model. The trained SVR model was then utilized to estimate the failure degradation rate and remaining useful life. The results indicate that the proposed method can be effectively used in real industrial processes to predict performance degradation and failure time

Canonical Variable Analysis for Fault Detection, System Identification and Performance Estimation

Condition monitoring of industrial processes can minimize downtime and maintenance costs while enhancing the safety of operation of plants and increasing the quality of products. Multivariate statistical methods are widely used for condition monitoring in industrial plants due to the rapid growth and advancement in data acquisition technology. However, the effectiveness of these methodologies in real industrial processes has not been fully investigated. This paper proposes a CVA-based approach for process fault identification, system modelling and performance estimation. The effectiveness of the proposed method was tested using data acquired from an operational industrial centrifugal compressor. The results indicate that CVA can be effectively used to identify abnormal operating conditions and predict performance degradation after the appearance of faults

Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.Comment: 19 pages, 2 tables, and 5 figure

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented.Comment: Resubmitted to Physics in Medicine and Biology. Text has been modified according to referee comments, and typos in the equations have been correcte

Essential Role of SIRT1 Signaling in the Nucleus Accumbens in Cocain and Morphine Action

Sirtuins (SIRTs), class III histone deacetylases, are well characterized for their control of cellular physiology in peripheral tissues, but their influence in brain under normal and pathological conditions remains poorly understood. Here, we establish an essential role for brain reward region. We show that chronic cocain administration increases SIRT1 and SIRT2 expression in the mouse NAc, while chronic morphine administration induces SIRT1 expression alone, with no regulation of all other sirtuin family members observed. Drug induction of SIRT1 and SIRT2 is mediated in part at the transcriptional level via the drug-induced transcription factor ΔFosB and is associated with robust histone modifications at the Sirt1 and Sirt2 genes. Viral-mediated overexpression of SIRT1 or SIRT2 in the NAc enhances the rewarding effects of both cocain and morphine. In contrast, the local knockdown of SIRT1 from the NAc of floxed Sirt1 mice decreases drug reward. Such behavioral effects of SIRT1 occur in concert with its regulation of numerous synaptic proteins in NAc as well as with SIRT1-mediated induction of dendritic spines on NAc medium spiny neurons. These studies establish sirtuins as key mediators of the molecular and cellular plasticity induced by drugs of abuse in NAc, and of the associated behavioral adaptations, and point towards novel signaling pathways involved in drug action

ATLAS Z Excess in Minimal Supersymmetric Standard Model

Recently the ATLAS collaboration reported a 3 sigma excess in the search for the events containing a dilepton pair from a Z boson and large missing transverse energy. Although the excess is not sufficiently significant yet, it is quite tempting to explain this excess by a well-motivated model beyond the standard model. In this paper we study a possibility of the minimal supersymmetric standard model (MSSM) for this excess. Especially, we focus on the MSSM spectrum where the sfermions are heavier than the gauginos and Higgsinos. We show that the excess can be explained by the reasonable MSSM mass spectrum.Comment: 13 pages, 7 figures; published versio

Machine learning for cardiac ultrasound time series data

We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.UCLA through the Physical Sciences Division; Entrepreneurship and Innovation Fund; Department of Mathematics; NSF [DMS-1045536, DMS-1417674]; ONR [N00014-16-1-2119]; Cross-disciplinary Scholars in Science and Technology (CSST) program at UCLACPCI-S(ISTP)1013