Manitest: Are classifiers really invariant?

Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-of-the-art classifiers are invariant to basic transformations such as rotations and translations. This is mainly due to the lack of general methods that properly measure such an invariance. In this paper, we propose a rigorous and systematic approach for quantifying the invariance to geometric transformations of any classifier. Our key idea is to cast the problem of assessing a classifier's invariance as the computation of geodesics along the manifold of transformed images. We propose the Manitest method, built on the efficient Fast Marching algorithm to compute the invariance of classifiers. Our new method quantifies in particular the importance of data augmentation for learning invariance from data, and the increased invariance of convolutional neural networks with depth. We foresee that the proposed generic tool for measuring invariance to a large class of geometric transformations and arbitrary classifiers will have many applications for evaluating and comparing classifiers based on their invariance, and help improving the invariance of existing classifiers.Comment: BMVC 201

A reduced Hsieh–Clough–Tocher element with splitting based on an arbitrary interior point

AbstractWe present formulas for a reduced Hsieh–Clough–Tocher (rHCT) element with splitting based on an arbitrary interior point. These formulas use local barycentric coordinates in each of the subtriangles and are not significantly more complicated than formulas for an rHCT element with splitting based on the centroid

Students of Dana Zhou

Shinichi SuzukiAlbert von TilzertraditionalJohn WilliamsCarl Maria von WeberJean-Baptiste LullyNiccolo PaganiniFranz WohlfahrtAbel Korzeniowski, arr. L. Hsieh & D. ZhouJoseph HaydnJohann Sebastian Bac

Book Review: Hsieh Liang-tso and the Analects of Confucius: Humane Learning as a Religious Quest

Hsieh Liang-tso is the first volume to explore Chinese traditions in the Academy Series sponsored by Oxford and the American Academy of Religion. Most previous titles in the series focus on Christianity, which perhaps explains Selover’s attention to the perspectives of comparative religions and comparative theology in his introduction. There he briefly traces the history of the issues concerning the religious dimensions of the Chinese literati tradition and outlines a comparative framework for approaching eleventh-century Chinese thought. Inspired by Robert Neville’s Beyond the Masks of God, Selover focuses in the introduction on four themes—scripture, tradition, reason, and experience. This framework, however, does not figure prominently until the conclusion. [excerpt

Further Evidence on Hedge Funds Performance.

In this analysis we identify dynamic hedge fund strategies quantitatively pursuing a Principal Component Analysis following Fung and Hsieh (1997). We extract five dominant hedge fund strategies each representing similar investment styles and analyse the performance of each strategy by employing a multi-factor model comprising both market indices and passive option strategies along the lines of Agerwal and Naik (2000). We find that the majority of the five homogenous strategies show superior performance. However, correcting for survivorship bias this superior performance disappears.Hedge funds; Investment in securities; Performance; Dynamic strategies; Hedge funds performance;

Self phase modulation in Highly nonlinear hydrogenated amorphous silicon

We study self phase modulation in submicron amorphous silicon-on-insulator waveguides. We extract both the real and imaginary part of the nonlinear parameter gamma from a 1 cm long waveguide with a cross-section of 500x220nm(2). The real and imaginary part of the nonlinear parameter are found to be 767W(-1)m(-1) and -28W(-1)m(-1) respectively. The figure of merit (FOM) is found to be 3.6 times larger than the FOM in crystalline silicon (c-Si)

«Performance analysis of niche alternatives and hedge fund strategies»

The interest of institutional investors in hedge funds as alternative investments has grown substantially over the last decade. The key reason for adding alternative investments to a well-diversified institutional portfolio is the risk-return profile, which is achieved by reducing the risk through diversification and enhancing the returns through alpha. In addition to the well-known hedge fund investment strategies, the Swiss investment company Progressive Capital Partners Ltd. offers its own specialized niche alternative assets consisting of music royalties, appraisal and litigation rights. Due to their performance characteristics, the alternative investments are intended to provide an opportunity for pension fund portfolios. The purpose of this master thesis is to analyze the monthly returns of twelve hedge fund strategies, and niche alternatives of Progressive Capital. In addition, the performance of a self-created representative Swiss pension fund portfolio is examined quantitatively with niche alternatives as an alternative asset class. The methodology for the analysis is based on a combination of principal component analysis with three different multi-factor models to explain the returns of hedge fund strategies. An extensive aggregated hedge fund database and a universe of 25 risk factors are employed for the full sample period from August 2007 to December 2018. Furthermore, a portfolio optimization analysis is used on the Swiss pension fund portfolio to evaluate the niche alternatives and other traditional alternative assets based on pension fund investment restrictions. The results showed small differences in the alphas resulting from the three different multi-factor models. The average monthly alpha is highest 0.22 % for the Fung and Hsieh eight-factor model, 0.19 % for the stepwise regression model and lowest with 0.16 % for Fung and Hsieh seven-factor model over all thirteen hedge fund strategies including the niche alternatives. According to these results, Progressive Capital performs better in all three models than the average alphas do. The highest alpha of 0.47 % was gained by the stepwise regression, followed by 0.44 % in the Fung and Hsieh eight-factor model, and 0.37 % in the Fung and Hsieh seven-factor model