Sustainable investment in Turkey 2010

The main objectives of this report are as follows: 1 To understand and provide a review of the current state of the Sustainable Investment (SI) market in Turkey, 2 To identify the drivers and obstacles for sustainable investments, and assess the commercial feasibility of different approaches and initiatives that may stimulate the SI market in Turkey, 3 To analyze the institutional prerequisites and interventions that will fuel the development of investments, which would, in turn, encourage a betterallocation of local and international capital to sustainable enterprises and hence support sustainable development of the Turkish economy. This study forms part of a series of assessments of Sustainable Investment (SI) in Brazil (2009), India (2009) and China (2009), and draws upon earlier reports published by IFC jointly with the Economist Intelligence Unit: Sustainable Invest ing in Emerging Markets: Unscathed by the Financial Crises (2010) and with Mercer; Gaining Ground, Integrating Environmental, Social and Governance (ESG) Factors into Investment Processes in Emerging Markets (2009)

Nonlocality in many-body quantum systems detected with two-body correlators

Contemporary understanding of correlations in quantum many-body systems and in quantum phase transitions is based to a large extent on the recent intensive studies of entanglement in many-body systems. In contrast, much less is known about the role of quantum nonlocality in these systems, mostly because the available multipartite Bell inequalities involve high-order correlations among many particles, which are hard to access theoretically, and even harder experimentally. Standard, "theorist- and experimentalist-friendly" many-body observables involve correlations among only few (one, two, rarely three...) particles. Typically, there is no multipartite Bell inequality for this scenario based on such low-order correlations. Recently, however, we have succeeded in constructing multipartite Bell inequalities that involve two- and one-body correlations only, and showed how they revealed the nonlocality in many-body systems relevant for nuclear and atomic physics [Science 344, 1256 (2014)]. With the present contribution we continue our work on this problem. On the one hand, we present a detailed derivation of the above Bell inequalities, pertaining to permutation symmetry among the involved parties. On the other hand, we present a couple of new results concerning such Bell inequalities. First, we characterize their tightness. We then discuss maximal quantum violations of these inequalities in the general case, and their scaling with the number of parties. Moreover, we provide new classes of two-body Bell inequalities which reveal nonlocality of the Dicke states---ground states of physically relevant and experimentally realizable Hamiltonians. Finally, we shortly discuss various scenarios for nonlocality detection in mesoscopic systems of trapped ions or atoms, and by atoms trapped in the vicinity of designed nanostructures.Comment: 46 pages (25.2 + appendices), 7 figure

Detecting non-locality in multipartite quantum systems with two-body correlation functions

Bell inequalities define experimentally observable quantities to detect non-locality. In general, they involve correlation functions of all the parties. Unfortunately, these measurements are hard to implement for systems consisting of many constituents, where only few-body correlation functions are accessible. Here we demonstrate that higher-order correlation functions are not necessary to certify nonlocality in multipartite quantum states by constructing Bell inequalities from one- and two-body correlation functions for an arbitrary number of parties. The obtained inequalities are violated by some of the Dicke states, which arise naturally in many-body physics as the ground states of the two-body Lipkin-Meshkov-Glick Hamiltonian.Comment: 10 pages, 2 figures, 1 tabl

Leveraging triplet loss for unsupervised action segmentation

In this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, without requiring any training data. Our method is a deep metric learning approach rooted in a shallow network with a triplet loss operating on similarity distributions and a novel triplet selection strategy that effectively models temporal and semantic priors to discover actions in the new representational space. Under these circumstances, we successfully recover temporal boundaries in the learned action representations with higher quality compared with existing unsupervised approaches. The proposed method is evaluated on two widely used benchmark datasets for the action segmentation task and it achieves competitive performance by applying a generic clustering algorithm on the learned representations.Comment: Accepted to the Workshop on Learning with Limited Labelled Data in conjunction with CVPR 202

Validation of p53 Immunohistochemistry (PAb240 Clone) in Canine Tumors with Next-Generation Sequencing (NGS) Analysis

In human medicine, p53 immunohistochemistry (IHC) is a common method that is used for the identification of tumors with TP53 mutations. In veterinary medicine, several studies have performed IHC for p53 in canine tumors, but it is not known how well it actually predicts the mutation. The aim of this study was to estimate the accuracy of the IHC method for p53 (clone PAb240) using a lab-developed NGS panel to analyze TP53 mutations in a subset of malignant tumors in dogs. A total of 176 tumors were analyzed with IHC and then 41 were subjected to NGS analysis; among them, 15 were IHC positive and 26 were negative, and 16 out of 41 (39%) were found to be inadequate for NGS analysis. Excluding the non-evaluable cases at NGS, of the remaining eight IHC-positive cases, six were mutants and two were wild-type. Among the 17 IHC-negative cases, 13 were wild type, and 4 were mutants. The sensitivity was 60%, specificity was 86.7%, and the accuracy was 76%. These results suggest that when using IHC for p53 with this specific antibody to predict mutation, up to 25% wrong predictions can be expected