Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract)

This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the binarization constraint but also the balance and decorrelation constraints. Although those additional discrete constraints make the optimization problem of EDMH look a lot more complicated, we are actually able to develop a fast iterative learning algorithm in the alternating optimization framework for it, as after introducing a couple of auxiliary variables each subproblem of optimization turns out to have closed-form solutions. It has been confirmed by extensive experiments that EDMH can consistently deliver better retrieval performances than state-of-the-art MH methods at lower computational costs

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells. © 2012 Shen et al

Characterization of Geographical and Meteorological Parameters

[EN]This chapter is devoted to the introduction of some geographical and meteorological information involved in the numerical modeling of wind fields and solar radiation. First, a brief description of the topographical data given by a Digital Elevation Model and Land Cover databases is provided. In particular, the Information System of Land Cover of Spain (SIOSE) is considered. The study is focused on the roughness length and the displacement height parameters that appear in the logarithmic wind profile, as well as in the albedo related to solar radiation computation. An extended literature review and characterization of both parameters are reported. Next, the concept of atmospheric stability is introduced from the Monin–Obukhov similarity theory to the recent revision of Zilitinkevich of the Neutral and Stable Boundary Layers (SBL). The latter considers the effect of the free-flow static stability and baroclinicity on the turbulent transport of momentum and of the Convective Boundary Layers (CBL), more precisely, the scalars in the boundary layer, as well as the model of turbulent entrainment

Determination and Modeling of Solubility for CaSO4 center dot 2H(2)O-NH4+-Cl--SO42--NO3--H2O System

The solubility of gypsum (CaSO4 center dot 2H(2)O) in ammonium solutions plays a significant role to prevent gypsum scaling on the heater and tower in the treatment of ammonium-N wastewater bearing sulfate ions by the steam stripping process. In this work solubilities of calcium sulfate dihydrate in NH4Cl, NH4NO3, and mixed NH4Cl and (NH4)(2)SO4 solutions up to 343.15 K were measured using the classic isothermal dissolution method. The investigated concentration (at ambient temperature) is up to 1.50 mol.dm(-3) for both NH4Cl and NH4NO3. The solubility of CaSO4 center dot 2H(2)O was found to increase sharply with either NH4Cl or NH4NO3 concentration, whereas the temperature has a limited effect. The XRD analysis of equilibrated solids for these systems shows that CaSO4 center dot 2H(2)O is stable in all cases over the temperature range (298.15 to 343.15) K. The electrolyte nonrandom two-liquid (the electrolyte NRTL) model embedded in AspenPlus was applied to model the solubility of CaSO4 center dot 2H(2)O in the above systems. The newly obtained model parameters were used to well estimate the solubility of CaSO4 center dot 2H(2)O in all cases with a relative deviation of 1.52 %

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization

Ordinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often used to model such ordinal data. But these modeling approaches conditionally depict the mean characteristic of response variable on a cluster of predictive variables, which often results in non-robust estimation results. As a considerable alternative, composite quantile regression (CQR) approach is usually employed to gain more robust and relatively efficient results. In this paper, we propose a Bayesian CQR modeling approach for ordinal latent regression model. In order to overcome the recognizability problem of the considered model and obtain more robust estimation results, we advocate to using the Bayesian relative CQR approach to estimate regression parameters. Additionally, in regression modeling, it is a highly desirable task to obtain a parsimonious model that retains only important covariates. We incorporate the Bayesian L1/2 penalty into the ordinal latent CQR regression model to simultaneously conduct parameter estimation and variable selection. Finally, the proposed Bayesian relative CQR approach is illustrated by Monte Carlo simulations and a real data application. Simulation results and real data examples show that the suggested Bayesian relative CQR approach has good performance for the ordinal regression models

Association between gene expression of three genes [TOP2A (A), DBI (B) and PMVK(C)] and drug response in ER positive and ER negative breast cell lines.

<p>The x-axis represents cell line drug response, represented as AUC value; higher AUC values are correlated with drug resistance, while low AUC values are correlated with drug sensitivity. The y-axis represents the expression of genes in cell lines.</p

Heatmap of gene-drug correlation.

<p>Each block represents a gene-drug correlation in ER positive or ER negative cell lines. Red boxes represent high negative gene-drug correlations, i.e., cell lines with higher gene expression tend to be more resistant, and green boxes represent high positive gene-drug correlations, i.e. cell lines with higher gene expression tend to be more sensitive. The bar across the top indicates the multidrug response genes identified in ER positive and ER negative cell lines. Yellow corresponds to ER negative and blue corresponds to ER positive.</p

2D scatter plot of chemotherapeutic agents with respect to the first and second principal components.

<p>2D scatter plot of chemotherapeutic agents with respect to the first and second principal components.</p