The performance of socially responsible mutual funds: the role of fees and management companies

In this paper, we shed light on the debate about the financial performance of socially responsible investment (SRI) mutual funds by separately analyzing the contributions of before-fee performance and fees to SRI funds' performance and by investigating the role played by fund management companies in the determination of those variables. We apply the matching estimator methodology to obtain our results and find that in the period 1997-2005, US SRI funds had significantly higher fees and better before- and after-fee performance than conventional funds with similar characteristics. Differences, however, were driven exclusively by SRI funds run by management companies specialized in socially responsible investment.Socially responsible investment, Mutual fund fees, Mutual fund performance, Matching estimators

Triclinic modification of N-[(1,1-di­methyl­ethoxy)carbon­yl]-3-[(R)-prop-2-en-1-ylsulfin­yl]-(R)-alanine ethyl ester at 120 (1) K

There are two independent mol­ecules in the asymmetric unit of the title compound, C13H23NO5S. In the crystal structure, inter­molecular N—H⋯O hydrogen bonds link mol­ecules into two independent one-dimensional chains along [100]. The crystal studied was found to be a non-merohedral twin with a ratio of 0.615 (6):0.385 (1) for the refined components. At 200 (1) K [Singh et al. (2009 ▶). Acta Cryst. E65, o1385–o1386] the crystal structure of the title compound contains one disordered mol­ecule in the asymmetric unit of a monoclinic unit cell

Dynamical replica theoretic analysis of CDMA detection dynamics

We investigate the detection dynamics of the Gibbs sampler for code-division multiple access (CDMA) multiuser detection. Our approach is based upon dynamical replica theory which allows an analytic approximation to the dynamics. We use this tool to investigate the basins of attraction when phase coexistence occurs and examine its efficacy via comparison with Monte Carlo simulations.Comment: 18 pages, 2 figure

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Radio-frequency dressed state potentials for neutral atoms

Potentials for atoms can be created by external fields acting on properties like magnetic moment, charge, polarizability, or by oscillating fields which couple internal states. The most prominent realization of the latter is the optical dipole potential formed by coupling ground and electronically excited states of an atom with light. Here we present an experimental investigation of the remarkable properties of potentials derived from radio-frequency (RF) coupling between electronic ground states. The coupling is magnetic and the vector character allows to design state dependent potential landscapes. On atom chips this enables robust coherent atom manipulation on much smaller spatial scales than possible with static fields alone. We find no additional heating or collisional loss up to densities approaching $10^{15}$ atoms / cm$^3$ compared to static magnetic traps. We demonstrate the creation of Bose-Einstein condensates in RF potentials and investigate the difference in the interference between two independently created and two coherently split condensates in identical traps. All together this makes RF dressing a powerful new tool for micro manipulation of atomic and molecular systems

Monoclinic modification of N-[(1,1-dimethyl­ethoxy)carbon­yl]-3-[(R)-prop-2-en-1-ylsulfin­yl]-(R)-alanine ethyl ester at 200 (1) K

In the monoclinic polymorph of the title compound, C13H23NO5S, inter­molecular N—H⋯O hydrogen bonds link mol­ecules into one-dimensional chains along [100]. The atoms of the terminal propenyl group are disordered over two sets of sites with refined occupancies of 0.69 (2) and 0.31 (2)

Resolving the ancestry of Austronesian-speaking populations

There are two very different interpretations of the prehistory of Island Southeast Asia (ISEA), with genetic evidence invoked in support of both. The “out-of-Taiwan” model proposes a major Late Holocene expansion of Neolithic Austronesian speakers from Taiwan. An alternative, proposing that Late Glacial/postglacial sea-level rises triggered largely autochthonous dispersals, accounts for some otherwise enigmatic genetic patterns, but fails to explain the Austronesian language dispersal. Combining mitochondrial DNA (mtDNA), Y-chromosome and genome-wide data, we performed the most comprehensive analysis of the region to date, obtaining highly consistent results across all three systems and allowing us to reconcile the models. We infer a primarily common ancestry for Taiwan/ISEA populations established before the Neolithic, but also detected clear signals of two minor Late Holocene migrations, probably representing Neolithic input from both Mainland Southeast Asia and South China, via Taiwan. This latter may therefore have mediated the Austronesian language dispersal, implying small-scale migration and language shift rather than large-scale expansion

Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices

Compressed sensing is a signal processing method that acquires data directly in a compressed form. This allows one to make less measurements than what was considered necessary to record a signal, enabling faster or more precise measurement protocols in a wide range of applications. Using an interdisciplinary approach, we have recently proposed in [arXiv:1109.4424] a strategy that allows compressed sensing to be performed at acquisition rates approaching to the theoretical optimal limits. In this paper, we give a more thorough presentation of our approach, and introduce many new results. We present the probabilistic approach to reconstruction and discuss its optimality and robustness. We detail the derivation of the message passing algorithm for reconstruction and expectation max- imization learning of signal-model parameters. We further develop the asymptotic analysis of the corresponding phase diagrams with and without measurement noise, for different distribution of signals, and discuss the best possible reconstruction performances regardless of the algorithm. We also present new efficient seeding matrices, test them on synthetic data and analyze their performance asymptotically.Comment: 42 pages, 37 figures, 3 appendixe