Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination

In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the stochastic agreement between multiple classifiers on the unlabeled dataset, the algorithm simultaneously learns multiple high accuracy classifiers. We demonstrate that our proposed method can yield improved performance over previous multi-view learning approaches by comparing performance on three real multi-sensor data sets.Comment: 5 pages, 4 figures, Accepted in 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 15

Robust Learning from Multiple Information Sources

In the big data era, the ability to handle high-volume, high-velocity and high-variety information assets has become a basic requirement for data analysts. Traditional learning models, which focus on medium size, single source data, often fail to achieve reliable performance if data come from multiple heterogeneous sources (views). As a result, robust multi-view data processing methods that are insensitive to corruptions and anomalies in the data set are needed. This thesis develops robust learning methods for three problems that arise from real-world applications: robust training on a noisy training set, multi-view learning in the presence of between-view inconsistency and network topology inference using partially observed data. The central theme behind all these methods is the use of information-theoretic measures, including entropies and information divergences, as parsimonious representations of uncertainties in the data, as robust optimization surrogates that allows for efficient learning, and as flexible and reliable discrepancy measures for data fusion. More specifically, the thesis makes the following contributions: 1. We propose a maximum entropy-based discriminative learning model that incorporates the minimal entropy (ME) set anomaly detection technique. The resulting probabilistic model can perform both nonparametric classification and anomaly detection simultaneously. An efficient algorithm is then introduced to estimate the posterior distribution of the model parameters while selecting anomalies in the training data. 2. We consider a multi-view classification problem on a statistical manifold where class labels are provided by probabilistic density functions (p.d.f.) and may not be consistent among different views due to the existence of noise corruption. A stochastic consensus-based multi-view learning model is proposed to fuse predictive information for multiple views together. By exploring the non-Euclidean structure of the statistical manifold, a joint consensus view is constructed that is robust to single-view noise corruption and between-view inconsistency. 3. We present a method for estimating the parameters (partial correlations) of a Gaussian graphical model that learns a sparse sub-network topology from partially observed relational data. This model is applicable to the situation where the partial correlations between pairs of variables on a measured sub-network (internal data) are to be estimated when only summary information about the partial correlations between variables outside of the sub-network (external data) are available. The proposed model is able to incorporate the dependence structure between latent variables from external sources and perform latent feature selection efficiently. From a multi-view learning perspective, it can be seen as a two-view learning system given asymmetric information flow from both the internal view and the external view.PHDElectrical & Computer Eng PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138599/1/tianpei_1.pd

Learning to classify with possible sensor failures

In this paper, we propose an efficient algorithm to train a robust large-margin classifier, when corrupt measurements caused by sensor failure might be present in the training set. By incorporating a non-parametric prior based on the empiri-cal distribution of the training data, we propose a Geometric-Entropy-Minimization regularized Maximum Entropy Dis-crimination (GEM-MED) method to perform classification and anomaly detection in a joint manner. We demonstrate that our proposed method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate using sim-ulated data and real footstep data. Index Terms — corrupt measurements, robust large-margin training, anomaly detection, maximum entropy dis-crimination 1

Chimonanthus nitens var. salicifolius Aqueous Extract Protects against 5-Fluorouracil Induced Gastrointestinal Mucositis in a Mouse Model

Gastrointestinal mucositis is a major side effect of chemotherapy, leading to life quality reduction in patients and interrupting the therapy of cancer. Chimonanthus nitens var. salicifolius (CS) is a traditional Chinese herb for enteral disease. Considering the protective effect of CS on intestine, we hypothesize that the aqueous extract of CS could be benefcial to gastrointestinal mucositis. To verify this, a mouse mucositis model was induced by 5-Fluorouracil (5-Fu). Male Balb/C mice were treated with CS aqueous extract (5, 10, and 20 g/kg) or loperamide (0.2 mg/kg) intragastrically for 11 days, and the severity of mucositis was evaluated. Furthermore, the chemical compounds of CS aqueous extract were also analysed by high-performance liquid chromatography (HPLC). Our results demonstrated that CS aqueous extract improved mice body weight, diarrhoea, and faecal blood, maintained the liver function and intestinal length, alleviated villus shortening, and suppressed the apoptosis and inflammation in small intestine. We concluded that CS could protect mice against 5-Fu induced mucositis by inhibiting apoptosis and inflammation, and this protective effect might be associated with the 3 flavonoids (rutin, quercetin, and kaempferol) identified in CS aqueous extract

Simultaneous Quantitative Determination of Polyphenolic Compounds in Blumea balsamifera (Ai-Na-Xiang, Sembung) by High-Performance Liquid Chromatography with Photodiode Array Detector

A high-performance liquid chromatography method was developed for simultaneous quantification of 18 polyphenolic compounds from the leaves of Blumea balsamifera, including 17 flavonoids and 1 phenylethanone. The B. balsamifera extraction was separated by a Kromasil C18 column (250 × 4.6 mm, 5 μm) with a binary gradient mobile phase consisting of acetonitrile and 0.2% aqueous acetic acid. A photodiode array detector (PDA) was used to record the signals of investigated constituents. The linearity, sensitivity, stability, precision, and accuracy of the established assay methods were assessed to meet the requirements of quantitative determination. Samples extracted by reflux in 25 mL of 80% methanol for 30 minutes were selected for the extraction method. The 18 compounds were accurately identified by comparing with the reference compounds. The purity of each peak was confirmed by the base peak in the mass spectrum. The contents of 18 compounds in Blumea samples from four different regions were successfully determined. The results also showed that 3,3′,5,7-tetrahydroxy-4′-methoxyflavanone was the most abundant constituent, which could be used as a potential chemical marker for quality control of B. balsamifera and Chinese patent medications containing B. balsamifera herb

Salvianolic acid B inhibits growth of head and neck squamous cell carcinoma in vitro and in vivo via cyclooxygenase-2 and apoptotic pathways

Overexpression of cyclooxygenase-2 (COX-2) in oral mucosa has been associated with increased risk of head and neck squamous cell carcinoma (HNSCC). Celecoxib is a nonsteroidal anti-inflammatory drug, which inhibits COX-2 but not COX-1. This selective COX-2 inhibitor holds promise as a cancer preventive agent. Concerns about cardiotoxicity of celecoxib, limits its use in long-term chemoprevention and therapy. Salvianolic acid B (Sal-B) is a leading bioactive component of Salvia miltiorrhiza Bge, which is used for treating neoplastic and chronic inflammatory diseases in China. The purpose of this study was to investigate the mechanisms by which Sal-B inhibits HNSCC growth. Sal-B was isolated from S. miltiorrhiza Bge by solvent extraction followed by 2 chromatographic steps. Pharmacological activity of Sal-B was assessed in HNSCC and other cell lines by estimating COX-2 expression, cell viability and caspase-dependent apoptosis. Sal-B inhibited growth of HNSCC JHU-022 and JHU-013 cells with IC50 of 18 and 50 lM, respectively. Nude mice with HNSCC solid tumor xenografts were treated with Sal-B (80 mg/kg/day) or celecoxib (5 mg/ kg/day) for 25 days to investigate in vivo effects of the COX-2 inhibitors. Tumor volumes in Sal-B treated group were signifi-cantly lower than those in celecoxib treated or untreated control groups (p \u3c 0.05). Sal-B inhibited COX-2 expression in cultured HNSCC cells and in HNSCC cells isolated from tumor xenografts. Sal-B also caused dose-dependent inhibition of prostaglandin E 2 synthesis, either with or without lipopolysaccharide stimulation. Taken together, Sal-B shows promise as a COX-2 targeted anticancer agent for HNSCC prevention and treatment. © 2008 Wiley-Liss, Inc

Oxygen Vacancies Confined in Nickel Molybdenum Oxide Porous Nanosheets for Promoted Electrocatalytic Urea Oxidation

The direct urea fuel cell (DUFC), as an efficient technology for generating power from urea, shows great potential for energy-sustainable development but is greatly hindered by the slow kinetics of the urea oxidation reaction (UOR). Herein, we highlighted a defect engineering strategy to design oxygen vacancy-rich NiMoO₄ nanosheets as a promising platform to study the relationship between O vacancies and UOR activity. Experimental/theoretical results confirm that the rich O vacancies confined in NiMoO₄ nanosheets successfully bring synergetic effects of higher exposed active sites, faster electron transport, and lower adsorption energy of urea molecules, giving rise to largely improved UOR activity. As expected, the r-NiMoO₄/NF 3D electrode exhibits a higher current density of 249.5 mA cm^–2, which is about 1.9 and 5.0 times larger than those of p-NiMoO₄/NF and Ni-Mo precursor/NF at a potential of 0.6 V. Our finding will be a promising pathway to develop non-noble materials as highly efficient UOR catalysts