Who gets privatised? An empirical analysis of Polish manufacturing

This paper employs a multinomial logit model to examine what determines the choice of a particular firm for a given privatisation method. A variety of hypotheses about possible determinants of ownership change are tested using an extensive data set for Polish manufacturing at the beginning of transition. The results at a firm as well as at a sector level give strong support to the hypothesis of the importance of resource constraints on the choice of ownership. Large firms with high financing requirement are more likely to be owned by outsiders. High sectoral capital intensity discourages small insider owned firms while high degree of product differentiation is a constraint for different investors, with the exception of outsiders. We also find that firm quality, measured by profitability and exporting outside the Soviet block, appeals to all types of investors but, additionally, privatisation offers outsiders ways of entering sectors with substantial entry barriers.privatisation methods; ownership change; multinomial logit

Affine Subspace Representation for Feature Description

This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio

Rectification from Radially-Distorted Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Grobner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements when used in a RANSAC-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state-of-the-art. The solvers work on scenes without straight lines and, in general, relax the strong assumptions on scene content made by the state-of-the-art. Accurate rectifications on imagery that was taken with narrow focal length to near fish-eye lenses demonstrate the wide applicability of the proposed method. The method is fully automated, and the code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin

AirNet: Neural Network Transmission over the Air

State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, the employed DNNs are location- and time-dependent, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. This can be considered as a joint source-channel coding (JSCC) problem, in which the goal is not to recover the DNN coefficients with the minimal distortion, but in a manner that provides the highest accuracy in the downstream task. For this purpose we introduce AirNet, a novel training and analog transmission method to deliver DNNs over the air. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to identify the most significant DNN parameters that can be delivered within the available channel bandwidth, knowledge distillation, and nonlinear bandwidth expansion to provide better error protection for the most important network parameters. We show that AirNet achieves significantly higher test accuracy compared to the separation-based alternative, and exhibits graceful degradation with channel quality

Synthesis of Aryl-sulphinyl Acetic Acids and Sulphoximides, and CD of their in situ Complexes with [MO2(OAc)4]

The syntheses of several optically active aryl-sulphinyl acetic acids and sulphoximides are described. They all give two or more Cotton effects in the presence of the metal cluster [MO2(OAc)4]in DMSO solution above 300 nm, from which their absolute configuration can be determined unequivocally

Health-related locus of control and health behaviour among university students in North Rhine Westphalia, Germany

Helmer SM, Krämer A, Mikolajczyk RT. Health-related locus of control and health behaviour among university students in North Rhine Westphalia, Germany. BMC Research Notes. 2012;5(1): 703

Anti‐atherosclerotic effect of the angiotensin 1–7 mimetic AVE0991 is mediated by inhibition of perivascular and plaque inflammation in early atherosclerosis

Background and Purpose: Inflammation plays a key role in atherosclerosis. A protective role of angiotensin-(1-7) in vascular pathologies opened a possibility for therapeutic use of small molecule non-peptide Ang-(1-7) mimetics, such as AVE0991. The mechanisms of these vaso-protective effects of a Mas receptor agonist, AVE0991, remain unclear. Experimental approach: We investigated the effects of AVE0991 on the spontaneous atherosclerosis in ApoE-/- mice, in the context of vascular inflammation and plaque stability. Key Results: AVE0991 has significant anti-atherosclerotic properties in ApoE-/- mice and increases plaque stability, by reducing plaque macrophage content, without effects on collagen. Using descending aorta of chow fed ApoE-/- mice, before significant atherosclerotic plaque develops, we gained insight to early events in atherosclerosis. Interestingly, perivascular adipose tissue (pVAT) and adventitial infiltration with macrophages and T cells precedes atherosclerotic plaque or the impairment of endothelium-dependent NO bioavailability as a measure of endothelial function. AVE0991 inhibited perivascular inflammation, through the reduction of chemokine expression in pVAT, as well as through direct actions on monocytes/macrophages inhibiting their activation, characterized by IL-1β, TNF-α, MCP-1 and CXCL10 and differentiation to M1 phenotype. Pre-treatment with AVE0991 inhibited migration of THP-1 monocytes towards supernatants of activated adipocytes (SW872). Mas receptors were expressed in pVAT and in THP-1 cells in vitro and anti-inflammatory effects of AVE0991 were partially Mas dependent. Conclusions & implications: Selective Mas receptor agonist AVE0991 possesses anti-atherosclerotic and anti-inflammatory properties, affecting monocyte/macrophage differentiation and recruitment to perivascular space at early stages of atherosclerosis in ApoE-/- mice

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration

The brightness clustering transform and locally contrasting keypoints

In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob- detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to- fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors

The chiral 1:2 adduct (S)S(S)C(-)589-ethyl 2-phenylbutyl sulphide-mercury (II) chloride:(-)589[(S)S(S)C-Et(2-PhBu)S.(HgCl2)2]. Stereoselective synthesis, asymmetric oxidation, crystal and molecular structure and circular dichroism spectra

Optically active (-)589ethyl (S)-2-phenylbutyl thioether, (-)(S)C-Et(PhBu)S (I), and its new diastereoisomeric mercury (II) chloride adduct, 1:2, (-)[(S)S(S)C-Et(PhBu)S.(HgCl2)2]2, (II) were stereoselectively synthesized; the absorbance (UV) and circular dichroism (CD) spectra were measured and the crystal and molecular structure of complex (II) was determined by single-crystal X-ray diffraction. Two different Hg centres are present whose coordination environments are built by two short bonds to chloride ligands in one case, and to one chloride and one sulphur in the other one. These originate digonal units. Electroneutrality is achieved by a further chlorine, which can be considered prevalently ionic and bonded to the two Hg centres, forming square bridging systems nearly perpendicular to the digonal molecules. The coordination polyhedra can be interpreted as 2 + 4 tetragonally-compressed octahedra with the four longer contacts lying in the equatorial plane. IR spectroscopic data are consistent with the presence of one bent and one linear Cl–Hg–Cl moiety. The absolute configurations at both stereogenic centres of the formed diastereoisomeric complex (II) are (S). The (S)S absolute configuration at the stereogenic sulphur atom bonded to the mercury(II) atom in complex (II) has been related with the negative Cotton effect assigned in its circular dichroism (CD) spectrum to a charge-transfer transition at ca. 230 nm. The stereoselective oxidation of (I) and (II) with hydrogen peroxide, induced by the stereogenic carbon atom (S)C of the enantiopure sulphide, gave (-)598ethyl (S)C-2-phenylbutyl(S)S-sulphoxide, (-)598[(S)S(S)C-Et(PhBu)SO], (III), having 18.1% de. Oxidations carried out in the presence of a 200 molar excess of mercury(II) chloride gave (-)598ethyl (S)C-2-phenylbutyl(R)S-sulphoxide, (-) 598[(R)S(S)C-Et(PhBu)SO], (IV) with 31% de, showing the cooperative influence of mercury(II) chloride on the selectivity of the oxidation reaction