Glassy Random Matrix Models

This paper discusses Random Matrix Models which exhibit the unusual phenomena of having multiple solutions at the same point in phase space. These matrix models have gaps in their spectrum or density of eigenvalues. The free energy and certain correlation functions of these models show differences for the different solutions. Here I present evidence for the presence of multiple solutions both analytically and numerically. As an example I discuss the double well matrix model with potential $V(M)= -{\mu \over 2}M^2+{g \over 4}M^4$ where $M$ is a random $N\times N$ matrix (the $M^4$ matrix model) as well as the Gaussian Penner model with $V(M)={\mu\over 2}M^2-t \ln M$. First I study what these multiple solutions are in the large $N$ limit using the recurrence coefficient of the orthogonal polynomials. Second I discuss these solutions at the non-perturbative level to bring out some differences between the multiple solutions. I also present the two-point density-density correlation functions which further characterizes these models in a new university class. A motivation for this work is that variants of these models have been conjectured to be models of certain structural glasses in the high temperature phase.Comment: 25 pages, Latex, 7 Figures, to appear in PR

Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Long-term windspeed prediction is crucial for establishing the viability of wind as a clean energy option, including the selection of wind farm locations, feasibility studies on energy potential and the operation of wind energy conversion systems with minimal investment risk. To deliver this vital societal need, data-inexpensive artificial intelligence models relying on historical inputs can be a useful scientific contrivance by energy analysts, engineers and climate-policy advocates. In this paper, a novel approach is adopted to construct a multilayer perceptron (MLP) hybrid model integrated with the Firefly Optimizer algorithm (MLP-FFA) trained with a limited set of historical (monthly) data (2004–2014) for a group of neighboring stations to predict windspeed at target sites in north-west Iran. Subsequently, the MLP-FFA model is developed to minimize the error rate of the resulting hybrid model and applied at each of the eight target sites one-by-one (namely: Tabriz, Jolfa, Sarab, Marand, Sahand, Kaleybar, Maraghe and Mianeh) such that the seven neighboring (reference) sites are used for training and the remainder eighth site for testing purposes. To ascertain conclusive results, the hybrid model's ability to predict windspeed at each target site is cross-validated with the MLP model without the FFA optimizer and the statistical performance is benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (E NS ), Willmott's Index (d) and the Legates and McCabes Index (E 1 ), including relative errors. For all eight target sites, the testing performance of the MLP-FFA model is found to be significantly superior than the classical MLP, resulting in lower values of the RMSE (0.202–0.50 ms -1 relative to 0.236–0.664 ms -1 ) and larger values of E NS , d and E 1 (0.686–0.953 vs. 0.529–0.936, 0.874–0.976 vs. 0.783–0.966, 0.417–0.800 vs. 0.303–0.748). Despite a more accurate performance of hybrid models tested at each target site, the preciseness registered a distinct geographic signature with the least accurate result (for Kaleybar) and the most accurate result (for Jolfa). To accord with this result, we conclude that the utilization of the FFA as an add-in optimizer in a hybrid data-intelligent model leads to a significant improvement in the predictive accuracy, presumably due to the optimal weights attained in the hidden layer that allows a more robust feature extraction process. Accordingly, we establish that the hybrid MLP-FFA model can be explored further in a problem of long-term windspeed prediction with reference station input data, and feasibility studies on wind energy investments in data-scarce regions where a limited set of neighboring reference site data can be employed to forecast the target site windspeed. © 2017 Elsevier LtdChinese Academy of Agricultural Sciences: ADOSP 2016The data were acquired from Iranian Meteorological Organization which is greatly acknowledged. Dr R C Deo was supported by a grant through Chinese Academy of Science Presidential Fellowship and Academic Development and Outside Studies Program (ADOSP 2016) in writing phase, and short-term ADOSP funding (s-ADOSP 2017) in the revision phase. We acknowledge all three reviewers and Editor-in-Chief Prof. Soteris Kalogirou for their critical comments that have improved the clarity of our final paper

Ensemble neural network approach detecting pain intensity from facial expressions

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, threestream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients’ pain level accurately.Ghazal Bargshady, Xujuan Zhou, Ravinesh C. Deo, Jeffrey Soar, Frank Whittaker, Hua Wan

A hybrid heuristic for the diameter constrained minimum spanning tree problem

Spanning trees, Diameter constrained spanning trees, Heuristics, GRASP, Iterated local search,

Human PABP binds AU-rich RNA via RNA-binding domains 3 and 4

Poly(A) binding protein (PABP) binds mRNA poly(A) tails and affects mRNA stability and translation. We show here that there is little free PABP in NIH3T3 cells, with the vast majority complexed with RNA. We found that PABP in NIH3T3 cytoplasmic lysates and recombinant human PABP can bind to AU-rich RNA with high affinity. Human PABP bound an AU-rich RNA with Kd in the nm range, which was only sixfold weaker than the affinity for oligo(A) RNA. Truncated PABP containing RNA recognition motif domains 3 and 4 retained binding to both AU-rich and oligo(A) RNA, whereas a truncated PABP containing RNA recognition motif domains 1 and 2 was highly selective for oligo(A) RNA. The inducible PABP, iPABP, was found to be even less discriminating than PABP in RNA binding, with affinities for AU-rich and oligo(A) RNAs differing by only twofold. These data suggest that iPABP and PABP may in some situations interact with other RNA regions in addition to the poly(A) tail