Transductive Distributional Correspondence Indexing for Cross-Domain Topic Classification

Abstract. Obtaining high-quality annotated data for training a classifier for a new domain is often costly. Domain Adaptation (DA) aims at leveraging the annotated data available from a different but related source domain in order to deploy a classification model for the target domain of interest, thus alleviating the aforementioned costs. To that aim, the learning model is typically given access to a set of unlabelled documents collected from the target domain. These documents might consist of a representative sample of the target distribution, and they could thus be used to infer a general classification model for the domain (inductive inference). Alternatively, these documents could be the entire set of documents to be classified; this happens when there is only one set of documents we are interested in classifying (transductive inference). Many of the DA methods proposed so far have focused on transductive classification by topic, i.e., the task of assigning class labels to a specific set of documents based on the topics they are about. In this work, we report on new experiments we have conducted in transductive classification by topic using Distributional Correspondence Indexing method, a DA method we have recently developed that delivered state-of-the-art results in inductive classification by sentiment. The results we have obtained on three popular datasets show DCI to be competitive with the state of the art also in this scenario, and to be superior to all compared methods in many cases

Laboratory and telescope demonstration of the TP3-WFS for the adaptive optics segment of AOLI

AOLI (Adaptive Optics Lucky Imager) is a state-of-art instrument that combines adaptive optics (AO) and lucky imaging (LI) with the objective of obtaining diffraction limited images in visible wavelength at mid- and big-size ground-based telescopes. The key innovation of AOLI is the development and use of the new TP3-WFS (Two Pupil Plane PositionsWavefront Sensor). The TP3-WFS, working in visible band, represents an advance over classical wavefront sensors such as the Shack-Hartmann WFS (SH-WFS) because it can theoretically use fainter natural reference stars, which would ultimately provide better sky coverages to AO instruments using this newer sensor. This paper describes the software, algorithms and procedures that enabled AOLI to become the first astronomical instrument performing real-time adaptive optics corrections in a telescope with this new type of WFS, including the first control-related results at the William Herschel Telescope (WHT)This work was supported by the Spanish Ministry of Economy under the projects AYA2011-29024, ESP2014-56869-C2-2-P, ESP2015-69020-C2-2-R and DPI2015-66458-C2-2-R, by project 15345/PI/10 from the Fundación Séneca, by the Spanish Ministry of Education under the grant FPU12/05573, by project ST/K002368/1 from the Science and Technology Facilities Council and by ERDF funds from the European Commission. The results presented in this paper are based on observations made with the William Herschel Telescope operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. Special thanks go to Lara Monteagudo and Marcos Pellejero for their timely contributions

Closed-domain natural language approaches: methods and applications

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialEsta tesis doctoral ha sido financiada por medio de la Beca FPU referencia AP2008-02464 del Ministerio de Educación y Ciencia