Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)

We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these "dynamic dependence network" models shows how the individual series can be "decoupled" for sequential analysis, and then "recoupled" for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked-- for later recoupling-- through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models, and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currency, stock indices and commodity prices, the case study includes evaluations of multi-day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table

Puffing of okara/rice blends using a rice cake machine

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (May 18, 2007)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Food science.Okara is the by-product of soymilk and tofu manufactures. It is cheap and nutritious having great potential to be applied in healthy snack foods. In this study, a puffed soy/rice cake product was developed and consumer tests were conducted. Soy/rice cakes were puffed from the mixture of extruded okara/rice pellets and parboiled rice using a rice cake machine. The experiment factorial design was 4 x 2 x 3 x 3 with two replications. This was a Split Plot Design. Main plot was okara pellets and parboiled rice. Subplots were moisture contents, heating temperatures and heating time. The cakes were evaluated for specific volume (SPV), texture, color and integrity. All the processing factors and most interactions had significant effects on the product attributes. The consumer tests indicated that the soy/rice cake containing 70% okara pellets was liked most

Increased Levels of Hydrogen Peroxide Induce a HIF-1-dependent Modification of Lipid Metabolism in AMPK Compromised C. elegans Dauer Larvae

SummaryCells have evolved numerous mechanisms to circumvent stresses caused by the environment, and many of them are regulated by the AMP-activated kinase (AMPK). Unlike most organisms, C. elegans AMPK-null mutants are viable, but they die prematurely in the “long-lived” dauer stage due to exhaustion of triglyceride stores. Using a genome-wide RNAi approach, we demonstrate that the disruption of genes that increase hydrogen peroxide levels enhance the survival of AMPK mutant dauers by altering both the abundance and the nature of the fatty-acid content in the animal by increasing the HIF-1-dependent expression of several key enzymes involved in fatty-acid biosynthesis. Our data provide a mechanistic foundation to explain how an optimal level of an often vilified ROS-generating compound such as hydrogen peroxide can provide cellular benefit, a phenomenon described as hormesis, by instructing cells to readjust their lipid biosynthetic capacity through downstream HIF-1 activation to correct cellular energy deficiencies

Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net

Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS) images can generally be captured at video rate in practice. In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image. In specific, we construct a novel MS/HS fusion model which takes the observation models of low-resolution images and the low-rankness knowledge along the spectral mode of HrHS image into consideration. Then we design an iterative algorithm to solve the model by exploiting the proximal gradient method. And then, by unfolding the designed algorithm, we construct a deep network, called MS/HS Fusion Net, with learning the proximal operators and model parameters by convolutional neural networks. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure

Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach

This paper presents an online data-driven algorithm to detect false data injection attacks towards synchronphasor measurements. The proposed algorithm applies density-based local outlier factor (LOF) analysis to detect the anomalies among the data, which can be described as spatio-temporal outliers among all the synchrophasor measurements from the grid. By leveraging the spatio-temporal correlations among multiple time instants of synchrophasor measurements, this approach could detect false data injection attacks which are otherwise not detectable using measurements obtained from single snapshot. This algorithm requires no prior knowledge on system parameters or topology. The computational speed shows satisfactory potential for online monitoring applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed algorithm