A Generalized Quasi-Likelihood Estimator for Nonstationary Stochastic Processes−Asymptotic Properties and Examples

2010 Mathematics Subject Classification: 62F12, 62M05, 62M09, 62M10, 60G42.

The Asymptotic Behaviour of the First Eigenvalue of Linear Second-Order Elliptic Equations in Divergente Form

2000 Mathematics Subject Classification: 35J70, 35P15.

Integral Representations of Functional Series with Members Containing Jacobi Polynomials

MSC 2010: Primary 33C45, 40A30; Secondary 26D07, 40C10

Inequalities and Asymptotic Formulae for the Three Parametric Mittag-Leffler Functions

MSC 2010: 33E12, 30A10, 30D15, 30E15

Asymptotic Analysis of a Schrödinger-Poisson System with Quantum Wells and Macroscopic Nonlinearities in Dimension 1

2000 Mathematics Subject Classification: 35Q02, 35Q05, 35Q10, 35B40.

On the Asymptotic Distribution of Closed Orbits for a Class of Open Billiards

2000 Mathematics Subject Classification: 37D40.

To move or not to move, that is the question! Body schema and non-action oriented body representations:an fMRI meta-analytic study

Many studies have attempted to identify the different cognitive components of body representation (BR). Due to methodological issues, the data reported in these studies are often confusing. Here we summarize the fMRI data from previous studies and explore the possibility of a neural segregation between BR supporting actions (body-schema, BS) or not (non-oriented-to-action-body-representation, NA). We performed a general activation likelihood estimation meta-analysis of 59 fMRI experiments and two individual meta-analyses to identify the neural substrates of different BR. Body processing involves a wide network of areas in occipital, parietal, frontal and temporal lobes. NA selectively activates the somatosensory primary cortex and the supramarginal gyrus. BS involves the primary motor area and the right extrastriate body area. Our data suggest that motor information and recognition of body parts are fundamental to build BS. Instead, sensory information and processing of the egocentric perspective are more important for NA. In conclusion, our results strongly support the idea that different and segregated neural substrates are involved in body representations orient or not to actions.

Stokes waves revisited:exact solutions in the asymptotic limit

The Stokes perturbative solution of the nonlinear (boundary value dependent) surface gravity wave problem is known to provide results of reasonable accuracy to engineers in estimating the phase speed and amplitudes of such nonlinear waves. The weakling in this structure though is the presence of aperiodic “secular variation” in the solution that does not agree with the known periodic propagation of surface waves. This has historically necessitated increasingly higher-ordered (perturbative) approximations in the representation of the velocity profile. The present article ameliorates this long-standing theoretical insufficiency by invoking a compact exact n-ordered solution in the asymptotic infinite depth limit, primarily based on a representation structured around the third-ordered perturbative solution, that leads to a seamless extension to higher-order (e.g., fifth-order) forms existing in the literature. The result from this study is expected to improve phenomenological engineering estimates, now that any desired higher-ordered expansion may be compacted within the same representation, but without any aperiodicity in the spectral pattern of the wave guides.

Resisting the final word:challenging stale media and policy representations of students’ performative technological encounters in university education

This article explores powerful, constraining representations of encounters between digital technologies and the bodies of students and teachers, using corpus-based Critical Discourse Analysis (CDA). It discusses examples from a corpus of UK Higher Education (HE) policy documents, and considers how confronting such documents may strengthen arguments from educators against narrow representations of an automatically enhanced learning. Examples reveal that a promise of enhanced ‘student experience’ through information and communication technologies internalizes the ideological constructs of technology and policy makers, to reinforce a primary logic of exchange value. The identified dominant discursive patterns are closely linked to the Californian ideology. By exposing these texts, they provide a form of ‘linguistic resistance’ for educators to disrupt powerful processes that serve the interests of a neoliberal social imaginary. To mine this current crisis of education, the authors introduce productive links between a Networked Learning approach and a posthumanist perspective. The Networked Learning approach emphasises conscious choices between political alternatives, which in turn could help us reconsider ways we write about digital technologies in policy. Then, based on the works of Haraway, Hayles, and Wark, a posthumanist perspective places human digital learning encounters at the juncture of non-humans and politics. Connections between the Networked Learning approach and the posthumanist perspective are necessary in order to replace a discourse of (mis)representations with a more performative view towards the digital human body, which then becomes situated at the centre of teaching and learning. In practice, however, establishing these connections is much more complex than resorting to the typically straightforward common sense discourse encountered in the Critical Discourse Analysis, and this may yet limit practical applications of this research in policy making.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.

An asymptotic test for the Conditional Value-at-Risk

Conditional Value-at-Risk (equivalent to the Expected Shortfall, Tail Value-at-Risk and Tail Conditional Expectation in the case of continuous probability distributions) is an increasingly popular risk measure in the fields of actuarial science, banking and finance, and arguably a more suitable alternative to the currently widespread Value-at-Risk. In my paper, I present a brief literature survey, and propose a statistical test of the location of the CVaR, which may be applied by practising actuaries to test whether CVaR-based capital levels are in line with observed data. Finally, I conclude with numerical experiments and some questions for future research.

Representations of Violence in the Theater

Center for Humanities in an Urban Environment presents a forum featuring several individuals from the areas of Academics, journalism and theater, on the subject of violence in the Theater. Event held at GableStage, Coral Gables on September 12, 2012.

Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.