Dark viscous fluid described by a unified equation of state in cosmology

We generalize the $\Lambda$CDM model by introducing a unified EOS to describe the Universe contents modeled as dark viscous fluid, motivated by the fact that a single constant equation of state (EOS) $p=-p_0$ ($p_0>0$) reproduces the $\Lambda$CDM model exactly. This EOS describes the perfect fluid term, the dissipative effect, and the cosmological constant in a unique framework and the Friedmann equations can be analytically solved. Especially, we find a relation between the EOS parameter and the renormalizable condition of a scalar field. We develop a completely numerical method to perform a $\chi^2$ minimization to constrain the parameters in a cosmological model directly from the Friedmann equations, and employ the SNe data with the parameter $\mathcal{A}$ measured from the SDSS data to constrain our model. The result indicates that the dissipative effect is rather small in the late-time Universe.Comment: 4 pages, 2 figures. v2: new materials added. v3: matches the version to appear in IJMP

Market Orientation and Export Performance: The Moderation of Channel and Institutional Distance

Purpose: Market orientation (MO) has been shown to provide a valuable resource-based advantage in domestic markets. How internationalizing firms from emerging markets can benefit from this capability is more complex while facing institutional distance. This research develops and tests theory to suggest that although MO capabilities can enhance export performance, the structure where they are deployed, namely the export channel a firm uses and the market in terms of institutional distance from home, can affect the benefits derived from MO. Design/methodology/approach: With a sample of Chinese exporters and data collected via questionnaire survey, this research uses a multiple regression model to test the hypotheses. Findings: It finds that firms with stronger MO capabilities can improve export performance by using hierarchical channels and by exporting to more institutionally distant markets where MO provide greater value. Originality/value: This research claims to make several important contributions to the literature by providing a better understanding of how firms can successfully deploy MO capabilities when exporting

Approximation of conformal mappings by circle patterns

A circle pattern is a configuration of circles in the plane whose combinatorics is given by a planar graph G such that to each vertex of G corresponds a circle. If two vertices are connected by an edge in G, the corresponding circles intersect with an intersection angle in $(0,\pi)$. Two sequences of circle patterns are employed to approximate a given conformal map $g$ and its first derivative. For the domain of $g$ we use embedded circle patterns where all circles have the same radius decreasing to 0 and which have uniformly bounded intersection angles. The image circle patterns have the same combinatorics and intersection angles and are determined from boundary conditions (radii or angles) according to the values of $g'$ ($|g'|$ or $\arg g'$). For quasicrystallic circle patterns the convergence result is strengthened to $C^\infty$-convergence on compact subsets.Comment: 36 pages, 7 figure

Exactly solvable models and ultracold Fermi gases

Exactly solvable models of ultracold Fermi gases are reviewed via their thermodynamic Bethe Ansatz solution. Analytical and numerical results are obtained for the thermodynamics and ground state properties of two- and three-component one-dimensional attractive fermions with population imbalance. New results for the universal finite temperature corrections are given for the two-component model. For the three-component model, numerical solution of the dressed energy equations confirm that the analytical expressions for the critical fields and the resulting phase diagrams at zero temperature are highly accurate in the strong coupling regime. The results provide a precise description of the quantum phases and universal thermodynamics which are applicable to experiments with cold fermionic atoms confined to one-dimensional tubes.Comment: based on an invited talk at Statphys24, Cairns (Australia) 2010. 16 pages, 6 figure

Microphotonic Forces From Superfluid Flow

In cavity optomechanics, radiation pressure and photothermal forces are widely utilized to cool and control micromechanical motion, with applications ranging from precision sensing and quantum information to fundamental science. Here, we realize an alternative approach to optical forcing based on superfluid flow and evaporation in response to optical heating. We demonstrate optical forcing of the motion of a cryogenic microtoroidal resonator at a level of 1.46 nN, roughly one order of magnitude larger than the radiation pressure force. We use this force to feedback cool the motion of a microtoroid mechanical mode to 137 mK. The photoconvective forces demonstrated here provide a new tool for high bandwidth control of mechanical motion in cryogenic conditions, and have the potential to allow efficient transfer of electromagnetic energy to motional kinetic energy.Comment: 5 pages, 6 figure

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods

Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

ARPES studies of cuprate Fermiology: superconductivity, pseudogap, and quasiparticle dynamics

We present angle-resolved photoemission spectroscopy (ARPES) studies of the cuprate high-temperature superconductors which elucidate the relation between superconductivity and the pseudogap and highlight low-energy quasiparticle dynamics in the superconducting state. Our experiments suggest that the pseudogap and superconducting gap represent distinct states, which coexist below T$_c$. Studies on Bi-2212 demonstrate that the near-nodal and near-antinodal regions behave differently as a function of temperature and doping, implying that different orders dominate in different momentum-space regions. However, the ubiquity of sharp quasiparticles all around the Fermi surface in Bi-2212 indicates that superconductivity extends into the momentum-space region dominated by the pseudogap, revealing subtlety in this dichotomy. In Bi-2201, the temperature dependence of antinodal spectra reveals particle-hole asymmetry and anomalous spectral broadening, which may constrain the explanation for the pseudogap. Recognizing that electron-boson coupling is an important aspect of cuprate physics, we close with a discussion of the multiple 'kinks' in the nodal dispersion. Understanding these may be important to establishing which excitations are important to superconductivity.Comment: To appear in a focus issue on 'Fermiology of Cuprates' in New Journal of Physic