Semiblind subgraph reconstruction in Gaussian graphical models

Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its customers' dependency subgraph: it is blinded to any external interactions of its customers and this blindness creates false edges in its subgraph. In this paper we address the semiblind scenario where the company has access to a noisy summary of the complementary subgraph connecting external agents, e.g., provided by a consolidator. The proposed framework applies to other applications as well, including field estimation from a network of awake and sleeping sensors and privacy-constrained information sharing over social subnetworks. We propose a penalized likelihood approach in the context of a graph signal obeying a Gaussian graphical models (GGM). We use a convex-concave iterative optimization algorithm to maximize the penalized likelihood.Comment: 7 pages; 5 figures; 2017 5th IEEE Global Conference on Signal and Information Processin

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

Surface/interface nanoengineering for rechargeable Zn–air batteries

Surface/interface nanoengineering of electrocatalysts and air electrodes will promote the rapid development of high-performance rechargeable Zn–air batteries.</p

Temperature-dependent gel-type ionic liquid compounds based on vanadium-substituted polyoxometalates with Keggin structure

A series of temperature-dependent gel-type ionic liquid compounds have been synthesized from 1-(3-sulfonic group) propyl-3-methyl imidazolium (abbreviated as MIMPS) and three vanadium-substituted heteropoly acids H5SiW11VO40, H5SiMo11VO40 and H7SiW9V3O40.</p

Analysis of Mogrosides in <i>Siraitia grosvenorii</i> Fruits at Different Stages of Maturity

Monk fruit extract has been approved as a natural sweetener by many countries. Its major sweet components, mogrosides, display different sweet intensities and profiles. Therefore, it is important to understand the change of mogroside contents in Siraitia grosvenorii at different maturity stages. In this study, monk fruit cultivars were collected from 4 locations in GuangXi, GuiZhou, and HuNan, at different times. Mogroside IIe, mogroside III, mogroside IIIe, mogroside IV, mogroside V, isomogroside V, and siamenoside I in each sample were quantified using high performance liquid chromatography coupled to triple quadrupole mass spectrometer (LC-MS-MS). As a result, mogroside IIe was the major component at the early maturity stage. It is converted to mogroside III from 15 to 45 days, then continued the glycosylation rapidly to yield mogroside V which kept predominant after 60 days. Highly glycosylated mogrosides, such as mogroside V and siamenoside I, which provide a better taste profile, accumulated and stabilized from 75 to 90 days. It is recommended to harvest the fruit after 75 days of pollination. </jats:p

Effect of Extracorporeal Diaphragmatic Pacing Combined with Four-Point Kneeling Position Training on Pulmonary Function in Patients with Ischemic Stroke

ObjectiveTo observe the clinical effect of extracorporeal diaphragmatic pacing combined with four-point knee-ling position training on pulmonary function in patients with ischemic stroke.MethodsA total of 60 patients with ischemic stroke admitted to the Rehabilitation Department of the Fourth Affiliated Hospital of Soochow University from January 1, 2023 to June 30, 2023 were enrolled and randomly divided into control group and treatment group, with 30 cases in each group. Both groups received regular basic training. The control group was treated with extracorporeal diaphragmatic pacing, and the treatment group was treated with extracorporeal diaphragmatic pacing, while combined with four-point kneeling training. After 8 weeks of continuous treatment, the patients were evaluated for changes of clam endexpiratory diaphragmatic thickness (CEEDT), maximum end-inspiratory diaphragm thickness (MEIDT), quiet breathing diaphragm mobility (QBDM), deep breathing diaphragm mobility (DBDM), forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC, peak expiratory flow (PEF), 6-minute walking distance (6MWD) and Borg score of patients were detected and evaluated in both groups.ResultsThere were no significant differences in the pre-treatment general data and baseline evaluation results at baseline, before treatment between the two groups (P&gt;0.05). Compared with the pre-treatment, CEEDT of the unaffected side increased after 8 weeks of treatment in the control group (P&lt;0.05), and CEEDT on the affected side significantly increased after 4 and 8 weeks of treatment (P&lt;0.05); in the treatment group, CEEDT of both the unaffected and affected sides significantly increased after 4 and 8 weeks of treatment (P&lt;0.05). Compared with the control group, the CEEDT of both the unaffected and affected sides increased in the treatment group after 8 weeks of treatment (P&lt;0.05). After 4 and 8 weeks of treatment, the MEIDT, QBDM, DBDM on both the unaffected and affected sides, as well as the patients' FEV1, FVC, PEF, 6MWD were all higher than those before treatment, and Borg score were lower than those before treatment (P&lt;0.05). After 4 and 8 weeks of treatment, the MEIDT, QBDM, and DBDM of the unaffected and affected sides, and FEV1, FVC and PEF in the treatment group were significantly higher than those in the control group (P&lt;0.05). Compared with the control group, FEV1/FVC and 6MWD increased and Borg score decreased in the treatment group after 8 weeks of treatment (P&lt;0.05).ConclusionExtracorporeal diaphragmatic pacing combined with four-point kneeling position training can improve pulmonary function in patients with ischemic stroke