Crosslingual Document Embedding as Reduced-Rank Ridge Regression

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. Moreover, since our algorithm uses the singular value decomposition as its core operation, it is highly scalable. Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task. Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus

Quasi-Monte Carlo, Discrepancies and Error Estimates

We discuss the problem of defining an estimate for the error in quasi-Monte Carlo integration. The key issue is the definition of an ensemble of quasi-random point sets that, on the one hand, includes a sufficiency of equivalent point sets, and on the other hand uses information on the degree of uniformity of the point set actually used, in the form of a discrepancy or diaphony. A few examples of such discrepancies are given. We derive the distribution of our error estimate in the limit of large number of points. In many cases, Gaussian central limits are obtained. We also present numerical results for the quadratic star-discrepancy for a number of quasi-random sequences

Pricing and Hedging Asian Basket Options with Quasi-Monte Carlo Simulations

In this article we consider the problem of pricing and hedging high-dimensional Asian basket options by Quasi-Monte Carlo simulation. We assume a Black-Scholes market with time-dependent volatilities and show how to compute the deltas by the aid of the Malliavin Calculus, extending the procedure employed by Montero and Kohatsu-Higa (2003). Efficient path-generation algorithms, such as Linear Transformation and Principal Component Analysis, exhibit a high computational cost in a market with time-dependent volatilities. We present a new and fast Cholesky algorithm for block matrices that makes the Linear Transformation even more convenient. Moreover, we propose a new-path generation technique based on a Kronecker Product Approximation. This construction returns the same accuracy of the Linear Transformation used for the computation of the deltas and the prices in the case of correlated asset returns while requiring a lower computational time. All these techniques can be easily employed for stochastic volatility models based on the mixture of multi-dimensional dynamics introduced by Brigo et al. (2004).Comment: 16 page

Effect of high-temperature annealing on the residual strain and bending of freestanding GaN films grown by hydride vapor phase epitaxy

The effect of high-temperature high-pressure annealing on the residual strain, bending, and point defect redistribution of freestanding hydride vapor phase epitaxial GaN films was studied. The bending was found to be determined by the difference in the in-plane lattice parameters in the two faces of the films. The results showed a tendency of equalizing the lattice parameters in the two faces with increasing annealing temperature, leading to uniform strain distribution across the film thickness. A nonmonotonic behavior of structural parameters with increasing annealing temperature was revealed and related to the change in the point defect content under the high-temperature treatment.Peer reviewe

Anisotropic strain and phonon deformation potentials in GaN

We report optical phonon frequency studies in anisotropically strained c-plane- and a-plane-oriented GaN films by generalized infrared spectroscopic ellipsometry and Raman scattering spectroscopy. The anisotropic strain in the films is obtained from high-resolution x-ray diffraction measurements. Experimental evidence for splitting of the GaN E1(TO), E1(LO), and E2 phonons under anisotropic strain in the basal plane is presented, and their phonon deformation potentials cE1(TO) , cE1(LO) , and cE2 are determined. A distinct correlation between anisotropic strain and the A1(TO) and E1(LO) frequencies of a-plane GaN films reveals theaA1TO, bA1TO, aE1LO, andbE1LO phonon deformation potentials. The aA1TO and bA1TOaA1TO and aE1LO phonon deformation potentials agree well with recently reported theoretical estimations [J.-M. Wagner and F. Bechstedt, Phys. Rev. B 66, 115202 (2002)], while bA1TO and bE1LO are found to be significantly larger than the theoretical values. A discussion of the observed differences is presented