What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets

In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists. This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words. To prove it, we describe and evaluate APSyn, a variant of Average Precision that, independently of the adopted parameters, outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50%) and several state-of-the-art approaches.Comment: in LREC 201

Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs

In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. To prove it, we describe and evaluate APSyn, a variant of the Average Precision that, without any optimization, outperforms the vector cosine and the co-occurrence on the standard ESL test set, with an improvement ranging between +9.00% and +17.98%, depending on the number of chosen top contexts.Comment: in AAAI 2016. arXiv admin note: substantial text overlap with arXiv:1603.0870

Inference of interaction kernels in mean-field models of opinion dynamics

In models of opinion dynamics, many parameters -- either in the form of constants or in the form of functions -- play a critical role in describing, calibrating, and forecasting how opinions change with time. When examining a model of opinion dynamics, it is beneficial to infer its parameters using empirical data. In this paper, we study an example of such an inference problem. We consider a mean-field bounded-confidence model with an unknown interaction kernel between individuals. This interaction kernel encodes how individuals with different opinions interact and affect each other's opinions. Because it is often difficult to quantitatively measure opinions as empirical data from observations or experiments, we assume that the available data takes the form of partial observations of a cumulative distribution function of opinions. We prove that certain measurements guarantee a precise and unique inference of the interaction kernel and propose a numerical method to reconstruct an interaction kernel from a limited number of data points. Our numerical results suggest that the error of the inferred interaction kernel decays exponentially as we strategically enlarge the data set.Comment: 20 pages, 3 figure

A dynamic and stochastic model for distribution of empty containers

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1994.Includes bibliographical references (leaves 85-88).by Qin Chu.M.S

Light-Directed Growth of Semiconductor Nanomaterials by Photoelectrodeposition

In this work, the chemical and physical properties of semiconductor microcrystals and metastable polymorphs were transformed through photoexcitation during electrochemical synthesis. Interfacial charge transfer using photogenerated carriers were used to successfully control the growth of metal oxide semiconductors and change their morphology, composition improve their catalytic activity. The main material systems studied in this thesis are electrodeposited copper oxide (Cu2O) and manganese oxide (MnOx) semiconductor films. Illumination can direct the shape transformation of Cu2O crystals at the nanoscale by facet-selective charge transfer. During photoelectrodeposition on Cu2O microcrystals with well-developed facets, light mediates the deposition of copper metal selectively on the {100} facets while the crystal interior is etched at {111} facets to form a shell structure. This process reveals that the combination of an applied electrochemical bias and illumination can control the facet-dependence of photochemical reactions on the surface of Cu2O. Illumination can also enhance the catalytic activity of MnOx films through transforming their composition and morphology during growth. MnOx films synthesized under illumination display a significantly higher activity for the oxygen evolution reaction (OER), better stability, and a lower onset potential compared to MnOx films synthesized in the dark. Structural and electrochemical characterization reveal that MnOx films formed under illumination undergo a phase change to develop structural features that increase their stability and activity for the OER. This thesis provides insights into the spatial separation of photo-induced charge carriers on the surface of semiconductors during photoelectrochemical synthesis. Photoelectrodeposition has been shown as a novel method to control redox reactions at preferred surfaces of the nanomaterials. It can tailor the morphology and control the oxidation states of transition metal ions in metal oxide semiconductors and enhance desirable properties including catalytic activity. Thus, photoelectrochemical synthesis helps us design functional materials at the nanoscale