Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning

A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks. The proposed Wyner VAE model is based on two information theoretic problems---distributed simulation and channel synthesis---in which Wyner's common information arises as the fundamental limit of the succinctness of the common representation. The Wyner VAE decomposes a pair of correlated data variables into their common representation (e.g., a shared concept) and local representations that capture the remaining randomness (e.g., texture and style) in respective data variables by imposing the mutual information between the data variables and the common representation as a regularization term. The utility of the proposed approach is demonstrated through experiments for joint and conditional generation with and without style control using synthetic data and real images. Experimental results show that learning a succinct common representation achieves better generative performance and that the proposed model outperforms existing VAE variants and the variational information bottleneck method.Comment: 24 pages, 18 figure

Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices

Daily futures returns on six important commodities are found to be well described as FIGARCH fractionally integrated volatility processes, with small departures from the martingale in mean property. The paper also analyzes several years of high frequency intra day commodity futures returns and finds very similar long memory in volatility features at this higher frequency level. Semi parametric Local Whittle estimation of the long memory parameter supports the conclusions. Estimating the long memory parameter across many different data sampling frequencies provides consistent estimates of the long memory parameter, suggesting that the series are self-similar. The results have important implications for future empirical work using commodity price and returns data.Commodity returns, Futures markets, Long memory, FIGARCH

Dual and opposing roles of primary cilia in medulloblastoma development.

Recent work has shown that primary cilia are essential for Hedgehog (Hh) signaling during mammalian development. It is also known that aberrant Hh signaling can lead to cancer, but the role of primary cilia in oncogenesis is not known. Cerebellar granule neuron precursors (GNPs) can give rise to medulloblastomas, the most common malignant brain tumor in children. The primary cilium and Hh signaling are required for GNP proliferation. We asked whether primary cilia in GNPs have a role in medulloblastoma growth in mice. Genetic ablation of primary cilia blocked medulloblastoma formation when this tumor was driven by a constitutively active Smoothened protein (Smo), an upstream activator of Hh signaling. In contrast, removal of cilia was required for medulloblastoma growth by a constitutively active glioma-associated oncogene family zinc finger-2 (GLI2), a downstream transcription factor. Thus, primary cilia are either required for or inhibit medulloblastoma formation, depending on the initiating oncogenic event. Remarkably, the presence or absence of cilia was associated with specific variants of human medulloblastomas; primary cilia were found in medulloblastomas with activation in HH or WNT signaling but not in most medulloblastomas in other distinct molecular subgroups. Primary cilia could serve as a diagnostic tool and provide new insights into the mechanism of tumorigenesis

Magnetic domain-wall motion by propagating spin waves

We found by micromagnetic simulations that the motion of a transverse wall (TW) type domain wall in magnetic thin-film nanostripes can be manipulated via interaction with spin waves (SWs) propagating through the TW. The velocity of the TW motion can be controlled by changes of the frequency and amplitude of the propagating SWs. Moreover, the TW motion is efficiently driven by specific SW frequencies that coincide with the resonant frequencies of the local modes existing inside the TW structure. The use of propagating SWs, whose frequencies are tuned to those of the intrinsic TW modes, is an alternative approach for controlling TW motion in nanostripes