Co-interest Person Detection from Multiple Wearable Camera Videos

Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views. In this paper, we tackle a new problem of locating the co-interest person (CIP), i.e., the one who draws attention from most camera wearers, from temporally synchronized videos taken by multiple wearable cameras. Our basic idea is to exploit the motion patterns of people and use them to correlate the persons across different videos, instead of performing appearance-based matching as in traditional video co-segmentation/localization. This way, we can identify CIP even if a group of people with similar appearance are present in the view. More specifically, we detect a set of persons on each frame as the candidates of the CIP and then build a Conditional Random Field (CRF) model to select the one with consistent motion patterns in different videos and high spacial-temporal consistency in each video. We collect three sets of wearable-camera videos for testing the proposed algorithm. All the involved people have similar appearances in the collected videos and the experiments demonstrate the effectiveness of the proposed algorithm.Comment: ICCV 201

Effects of Neuropeptide y on Stem Cells and Their Potential Applications in Disease Therapy

Neuropeptide Y (NPY), a 36-amino acid peptide, is widely distributed in the central and peripheral nervous systems and other peripheral tissues. It takes part in regulating various biological processes including food intake, circadian rhythm, energy metabolism, and neuroendocrine secretion. Increasing evidence indicates that NPY exerts multiple regulatory effects on stem cells. As a kind of primitive and undifferentiated cells, stem cells have the therapeutic potential to replace damaged cells, secret paracrine molecules, promote angiogenesis, and modulate immunity. Stem cell-based therapy has been demonstrated effective and considered as one of the most promising treatments for specific diseases. However, several limitations still hamper its application, such as poor survival and low differentiation and integration rates of transplanted stem cells. The regulatory effects of NPY on stem cell survival, proliferation, and differentiation may be helpful to overcome these limitations and facilitate the application of stem cell-based therapy. In this review, we summarized the regulatory effects of NPY on stem cells and discussed their potential applications in disease therapy

Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models

We study mean-field variational inference in a Bayesian linear model when the sample size n is comparable to the dimension p. In high dimensions, the common approach of minimizing a Kullback-Leibler divergence from the posterior distribution, or maximizing an evidence lower bound, may deviate from the true posterior mean and underestimate posterior uncertainty. We study instead minimization of the TAP free energy, showing in a high-dimensional asymptotic framework that it has a local minimizer which provides a consistent estimate of the posterior marginals and may be used for correctly calibrated posterior inference. Geometrically, we show that the landscape of the TAP free energy is strongly convex in an extensive neighborhood of this local minimizer, which under certain general conditions can be found by an Approximate Message Passing (AMP) algorithm. We then exhibit an efficient algorithm that linearly converges to the minimizer within this local neighborhood. In settings where it is conjectured that no efficient algorithm can find this local neighborhood, we prove analogous geometric properties for a local minimizer of the TAP free energy reachable by AMP, and show that posterior inference based on this minimizer remains correctly calibrated.Comment: 79 pages, 5 figure

Selection of Latent Variables for Multiple Mixed-Outcome Models

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. As the formulation of a latent variable model is often unknown a priori, misspecification could distort the dependence structure and lead to unreliable model inference. More- over, the multiple outcomes are often of varying types (e.g., continuous and ordinal), which presents analytical challenges. In this article, we present a class of general latent variable models that can accommodate mixed types of outcomes, and further propose a novel selection approach that simultaneously selects latent variables and estimates model parameters. We show that the proposed estimators are consistent, asymptotically normal, and have the Oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of a dataset collected in the World Values Survey (WVS), a global research project that explores peoples\u27 values and beliefs and what social and personal characteristics might influence them

Combining hydrogen peroxide addition with sunlight regulation to control algal blooms

The concentration, light conditions during treatment, and the number of hydrogen peroxide (H2O2) additions as well as the H2O2 treatment combined with subsequent shading to control algal blooms were studied in the field (Lake Dianchi, China). The cyanobacterial stress and injury due to H2O2 were dose dependent, and the control effectiveness and degradation of H2O2 were better and faster under full light than under shading. However, H2O2 was only able to control a bloom for a short time, so it may have promoted the recovery of algae and allowed the biomass to rebound due to the growth of eukaryotic algae. A second addition of H2O2 at the same dose had no obvious effect on algal control in the short term, suggesting that a higher concentration or a delayed addition should be considered, but these alternative strategies are not recommended so that the integrity of the aquatic ecosystem is maintained and algal growth is not promoted. Moreover, shading (85%) after H2O2 addition significantly reduced the algal biomass during the enclosure test, no restoration was observed for nearly a month, and the proportion of eukaryotic algae declined. It can be inferred that algal blooms can be controlled by applying a high degree of shading after treatment with H2O2.</p