StarSpace: Embed All The Things!

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task. Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not

Hyperbolic three-manifolds that embed geodesically

We prove that every complete finite-volume hyperbolic 3-manifold $M$ that is tessellated into right-angled regular polyhedra (dodecahedra or ideal octahedra) embeds geodesically in a complete finite-volume connected orientable hyperbolic 4-manifold $W$, which is also tessellated into right-angled regular polytopes (120-cells and ideal 24-cells). If $M$ is connected, then Vol($W$) < $2^{49}$Vol($M$). This applies for instance to the Whitehead and the Borromean links complements. As a consequence, the Borromean link complement bounds geometrically a hyperbolic 4-manifold.Comment: 11 pages, 6 figures. Minor corrections from the previous version

Embed Zee Neutrino Mass Model into SUSY

In this talk, I summarize a work done in collaboration with Otto Kong on the Zee neutrino mass model. We show that the MSSM with explicit R-parity violation actually contains the Zee model with the right-handed sleptons as the Zee singlet. We determine the conditions on the parameter space such that the neutrino mass matrix provides a viable texture that explains the atmospheric and solar data.Comment: 11 pages, invited talk given at 5th International Workshop on Particle Phyiscs Phenomenology, Chi-Pen, Taitung, Taiwan, 8-11 Nov 200

KROME - a package to embed chemistry in astrophysical simulations

Chemistry plays a key role in many astrophysical situations regulating the cooling and the thermal properties of the gas, which are relevant during gravitational collapse, the evolution of disks and the fragmentation process. In order to simplify the usage of chemical networks in large numerical simulations, we present the chemistry package KROME, consisting of a Python pre-processor which generates a subroutine for the solution of chemical networks which can be embedded in any numerical code. For the solution of the rate equations, we make use of the high-order solver DLSODES, which was shown to be both accurate and efficient for sparse networks, which are typical in astrophysical applications. KROME also provides a large set of physical processes connected to chemistry, including photochemistry, cooling, heating, dust treatment, and reverse kinetics. The package presented here already contains a network for primordial chemistry, a small metal network appropriate for the modelling of low metallicities environments, a detailed network for the modelling of molecular clouds, a network for planetary atmospheres, as well as a framework for the modelling of the dust grain population. In this paper, we present an extended test suite ranging from one-zone and 1D-models to first applications including cosmological simulations with ENZO and RAMSES and 3D collapse simulations with the FLASH code. The package presented here is publicly available at http://kromepackage.org/ and https://bitbucket.org/krome/krome_stableComment: accepted for publication in MNRA

Perceptual Copyright Protection Using Multiresolution Wavelet-Based Watermarking And Fuzzy Logic

In this paper, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This paper deals with a fuzzy inference filter to choose the larger entropy of coefficients to embed watermarks. Unlike most previous watermarking frameworks which embedded watermarks in the larger coefficients of inner coarser subbands, the proposed technique is based on utilizing a context model and fuzzy inference filter by embedding watermarks in the larger-entropy coefficients of coarser DWT subbands. The proposed approaches allow us to embed adaptive casting degree of watermarks for transparency and robustness to the general image-processing attacks such as smoothing, sharpening, and JPEG compression. The approach has no need the original host image to extract watermarks. Our schemes have been shown to provide very good results in both image transparency and robustness.Comment: 13 pages, 7 figure

Some remarks on universality properties of ℓ∞/c0\ell_\infty / c_0

We prove that if continuum is not a Kunen cardinal, then there is a uniform Eberlein compact space $K$ such that the Banach space $C(K)$ does not embed isometrically into $\ell_\infty/c_0$. We prove a similar result for isomorphic embeddings. We also construct a consistent example of a uniform Eberlein compactum whose space of continuous functions embeds isomorphically into $\ell_\infty/c_0$, but fails to embed isometrically. As far as we know it is the first example of this kind