The role of data & program code archives in the future of economic research

This essay examines the role of data and program-code archives in making economic research "replicable." Replication of published results is recognized as an essential part of the scientific method. Yet, historically, both the "demand for" and "supply of" replicable results in economics has been minimal. "Respect for the scientific method" is not sufficient to motivate either economists or editors of professional journals to ensure the replicability of published results. We enumerate the costs and benefits of mandatory data and code archives, and argue that the benefits far exceed the costs. Progress has been made since the gloomy assessment of Dewald, Thursby and Anderson some twenty years ago in the American Economic Review, but much remains to be done before empirical economics ceases to be a "dismal science" when judged by the replicability of its published results.Econometrics ; Research

Secret-Sharing for NP

A computational secret-sharing scheme is a method that enables a dealer, that has a secret, to distribute this secret among a set of parties such that a "qualified" subset of parties can efficiently reconstruct the secret while any "unqualified" subset of parties cannot efficiently learn anything about the secret. The collection of "qualified" subsets is defined by a Boolean function. It has been a major open problem to understand which (monotone) functions can be realized by a computational secret-sharing schemes. Yao suggested a method for secret-sharing for any function that has a polynomial-size monotone circuit (a class which is strictly smaller than the class of monotone functions in P). Around 1990 Rudich raised the possibility of obtaining secret-sharing for all monotone functions in NP: In order to reconstruct the secret a set of parties must be "qualified" and provide a witness attesting to this fact. Recently, Garg et al. (STOC 2013) put forward the concept of witness encryption, where the goal is to encrypt a message relative to a statement "x in L" for a language L in NP such that anyone holding a witness to the statement can decrypt the message, however, if x is not in L, then it is computationally hard to decrypt. Garg et al. showed how to construct several cryptographic primitives from witness encryption and gave a candidate construction. One can show that computational secret-sharing implies witness encryption for the same language. Our main result is the converse: we give a construction of a computational secret-sharing scheme for any monotone function in NP assuming witness encryption for NP and one-way functions. As a consequence we get a completeness theorem for secret-sharing: computational secret-sharing scheme for any single monotone NP-complete function implies a computational secret-sharing scheme for every monotone function in NP

Parametrization of dark energy equation of state Revisited

A comparative study of various parametrizations of the dark energy equation of state is made. Astrophysical constraints from LSS, CMB and BBN are laid down to test the physical viability and cosmological compatibility of these parametrizations. A critical evaluation of the 4-index parametrizations reveals that Hannestad-M\"{o}rtsell as well as Lee parametrizations are simple and transparent in probing the evolution of the dark energy during the expansion history of the universe and they satisfy the LSS, CMB and BBN constraints on the dark energy density parameter for the best fit values.Comment: 11 page

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

High - Temperature Superconductivity in Iron Based Layered Compounds

We present a review of basic experimental facts on the new class of high - temperature superconductors - iron based layered compounds like REOFeAs (RE=La,Ce,Nd,Pr,Sm...), AFe_2As_2 (A=Ba,Sr...), AFeAs (A=Li,...) and FeSe(Te). We discuss electronic structure, including the role of correlations, spectrum and role of collective excitations (phonons, spin waves), as well as the main models, describing possible types of magnetic ordering and Cooper pairing in these compounds.Comment: 43 pages, 30 figures, review talk on 90th anniversary of Physics Uspekh

Efficiency Ranking of IT Service-Producing Firms: Case of Indian Multinationals

Production functions often study the output of physical products with capital and labor inputs. Instead, we use 2004 to 2016 data for 55 In- dian multinational companies to assess the production of services. Our estimates of flexible production functions yield estimates of scale elasticity (SCE) and elasticity of substitution (EOS) for pooled data. A subset of 31 companies with relatively complete data yields their individual SCE and EOS values, revealing their heterogeneity. Sorting the 31 companies by their SCE help name scale-efficient (high SCE) and scale inefficient (low SCE) multinationals. Similarly, a listing of 31 companies sorted by EOS allows us to name companies that are (and are not) robust to input price shocks. Using stock market data on these publicly traded companies, we report the values of three stock market criteria for top ranking companies by SCE. We also study empirical causal paths from the market criteria to EOS and SCE, suggesting that SCE and EOS do drive stock market indicators implying efficient markets. Our pooled and detailed results are relevant for government policy toward the IT sector and corporate governance issues