Orthogonal Polynomials on the Unit Ball and Fourth-Order Partial Differential Equations

The purpose of this work is to analyse a family of mutually orthogonal polynomials on the unit ball with respect to an inner product which includes an additional term on the sphere. First, we will get connection formulas relating classical multivariate orthogonal polynomials on the ball with our family of orthogonal polynomials. Then, using the representation of these polynomials in terms of spherical harmonics, algebraic and differential properties will be deduced

Squatters in the Capitalist City

To date, there has been no comprehensive analysis of the disperse research on the squatters’ movement in Europe. In Squatters in the Capitalist City, Miguel A. Martínez López presents a critical review of the current research on squatting and of the historical development of the movements in European cities according to their major social, political and spatial dimensions.　 Comparing cities, contexts, and the achievements of the squatters’ movements, this book presents the view that squatting is not simply a set of isolated, illegal and marginal practices, but is a long-lasting urban and transnational movement with significant and broad implications. While intersecting with different housing struggles, squatters face various aspects of urban politics and enhance the content of the movements claiming for a ‘right to the city.’ Squatters in the Capitalist City seeks to understand both the socio-spatial and political conditions favourable to the emergence and development of squatting, and the nature of the interactions between squatters, authorities and property owners by discussing the trajectory, features and limitations of squatting as a potential radicalisation of urban democracy

Universal Indexes for Highly Repetitive Document Collections

Indexing highly repetitive collections has become a relevant problem with the emergence of large repositories of versioned documents, among other applications. These collections may reach huge sizes, but are formed mostly of documents that are near-copies of others. Traditional techniques for indexing these collections fail to properly exploit their regularities in order to reduce space. We introduce new techniques for compressing inverted indexes that exploit this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar compression of the differential inverted lists, instead of the usual practice of gap-encoding them. We show that, in this highly repetitive setting, our compression methods significantly reduce the space obtained with classical techniques, at the price of moderate slowdowns. Moreover, our best methods are universal, that is, they do not need to know the versioning structure of the collection, nor that a clear versioning structure even exists. We also introduce compressed self-indexes in the comparison. These are designed for general strings (not only natural language texts) and represent the text collection plus the index structure (not an inverted index) in integrated form. We show that these techniques can compress much further, using a small fraction of the space required by our new inverted indexes. Yet, they are orders of magnitude slower.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094

RDF-TR: Exploiting structural redundancies to boost RDF compression

The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr (RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques

ASSET PRICING AND SYSTEMATIC LIQUIDITY RISK: AN EMPIRICAL INVESTIGATION OF THE SPANISH STOCK MARKET

It seems reasonable to expect systematic liquidity shocks to affect the optimal behavior of agents in financial markets. Indeed, fluctuations in various measures of liquidity are significantly correlated across common stocks(Chordia, Roll and Subrahmanyam (2000)). Thus, this paper empirically analyzes whether Spanish expected returns during the nineties are associated cross-sectionally to betas estimated relative to two competing liquidity risk factors. On one hand, we propose a new market-wide liquidity factor which is defined as the difference between returns of stocks highly sensitive to changes in the relative bid-ask spread less returns from stocks with low sensitivities to those changes. We argue that stocks with positive covariability between returns and this factor are assets whose returns tend to go down when aggregate liquidity is low, and hence do not hedge a potential liquidity crisis. Consequently, investors will require a premium to hold these assets. Similarly, note that in the case of assets that covary negatively with the liquidity factor, investors may be willing to pay a premium rather than to require an additional compensation. On the other hand, Pastor and Stambaugh (2002) suggest that a reasonable liquidity risk factor should be associated with the strength of volume-related return reversals since order flow induces greater return reversals when liquidity is lower. Our empirical results show that neither of these proxies for systematic liquidity risk carries a premium in the Spanish stock market.

Radiocirugía: pasado, presente y futuro

La radiocirugía (RDC) no es sino un método para radiar un volumen concreto y obedece a un triple principio: localización de la lesión a tratar de forma precisa, (preferentemente submilimétrica), administrar una gran cantidad de radiación a la zona deseada, en una sola sesión (que, eventualmente, podría repetirse tras un período de vigilancia evolutiva) y limitar, en lo posible, la radiación en el tejido circundante, colimando profundamente, hasta una auténtica ultraconformación, el haz de radiación que se adapta exquisitamente a la forma y volumen de la lesión, con una alta concentración de la dosis en el interior de la misma y un rápido decaimiento (“fall-off”) en su periferia

Movies Tags Extraction Using Deep Learning

Retrieving information from movies is becoming increasingly demanding due to the enormous amount of multimedia data generated each day. Not only it helps in efficient search, archiving and classification of movies, but is also instrumental in content censorship and recommendation systems. Extracting key information from a movie and summarizing it in a few tags which best describe the movie presents a dedicated challenge and requires an intelligent approach to automatically analyze the movie. In this paper, we formulate movies tags extraction problem as a machine learning classification problem and train a Convolution Neural Network (CNN) on a carefully constructed tag vocabulary. Our proposed technique first extracts key frames from a movie and applies the trained classifier on the key frames. The predictions from the classifier are assigned scores and are filtered based on their relative strengths to generate a compact set of most relevant key tags. We performed a rigorous subjective evaluation of our proposed technique for a wide variety of movies with different experiments. The evaluation results presented in this paper demonstrate that our proposed approach can efficiently extract the key tags of a movie with a good accuracy

Compressed k2-Triples for Full-In-Memory RDF Engines

Current "data deluge" has flooded the Web of Data with very large RDF datasets. They are hosted and queried through SPARQL endpoints which act as nodes of a semantic net built on the principles of the Linked Data project. Although this is a realistic philosophy for global data publishing, its query performance is diminished when the RDF engines (behind the endpoints) manage these huge datasets. Their indexes cannot be fully loaded in main memory, hence these systems need to perform slow disk accesses to solve SPARQL queries. This paper addresses this problem by a compact indexed RDF structure (called k2-triples) applying compact k2-tree structures to the well-known vertical-partitioning technique. It obtains an ultra-compressed representation of large RDF graphs and allows SPARQL queries to be full-in-memory performed without decompression. We show that k2-triples clearly outperforms state-of-the-art compressibility and traditional vertical-partitioning query resolution, remaining very competitive with multi-index solutions.Comment: In Proc. of AMCIS'201