On the Lp-Norm Regression Models for Estimating Value-at-Risk

Analysis of risk measures associated with price series data movements and its predictions are of strategic importance in the financial markets as well as to policy makers in particular for short- and longterm planning for setting up economic growth targets. For example, oilprice risk-management focuses primarily on when and how an organization can best prevent the costly exposure to price risk. Value-at-Risk (VaR) is the commonly practised instrument to measure risk and is evaluated by analysing the negative/positive tail of the probability distributions of the returns (profit or loss). In modelling applications, least-squares estimation (LSE)-based linear regression models are often employed for modeling and analyzing correlated data. These linear models are optimal and perform relatively well under conditions such as errors following normal or approximately normal distributions, being free of large size outliers and satisfying the Gauss-Markov assumptions. However, often in practical situations, the LSE-based linear regression models fail to provide optimal results, for instance, in non-Gaussian situations especially when the errors follow distributions with fat tails and error terms possess a finite variance. This is the situation in case of risk analysis which involves analyzing tail distributions. Thus, applications of the LSE-based regression models may be questioned for appropriateness and may have limited applicability. We have carried out the risk analysis of Iranian crude oil price data based on the Lp-norm regression models and have noted that the LSE-based models do not always perform the best. We discuss results from the L1, L2 and L∞-norm based linear regression models. ACM Computing Classification System (1998): B.1.2, F.1.3, F.2.3, G.3, J.2.

Classification of Texts' Authorship Using a Regression Model on Compressed Data

2010 Mathematics Subject Classification: 68T50,62H30,62J05.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.

Potapov-Ginsburg Transformation and Functional Models of Non-Dissipative Operators

2000 Mathematics Subject Classification: Primary 47A20, 47A45; Secondary 47A48.

Age models, accumulation rates, and grain size distributions of sediment cores from the northern subtropical Atlantic

The terrigenous sediment proportion of the deep sea sediments from off Northwest Africa has been studied in order to distinguish between the aeolian and the fluvial sediment supply. The present and fossil Saharan dust trajectories were recognized from the distribution patterns of the aeolian sediment. The following timeslices have been investigated: Present, 6,000, 12,000 and 18,000 y. B. P. Furthermore, the quantity of dust deposited off the Saharan coast has been estimated. For this purpose, 80 surface sediment samples and 34 sediment cores have been analysed. The stratigraphy of the cores has been achieved from oxygen isotopic curves, 14C-dating, foraminiferal transfer temperatures, and carbonate contents. Silt sized biogenic opal generally accounts for less than 2 % of the total insoluble sediment proportion. Only under productive upwelling waters and off river mouths, the opal proportion exceeds 2 % significantly. The modern terrigenous sediment from off the Saharan coast is generally characterized by intensely stained quartz grains. They indicate an origin from southern Saharan and Sahelian laterites, and a zonal aeolian transport in midtropospheric levels, between 1.5 an 5.5 km, by 'Harmattan' Winds. The dust particles follow large outbreaks of Saharan air across the African coast between 15° and 21° N. Their trajectories are centered at about 18° N and continue further into a clockwise gyre situated south of the Canary Islands. This course is indicated by a sickle-shaped tongue of coarser grain sizes in the deep-sea sediment. Such loess-sized terrigenous particles only settle within a zone extending to 700 km offshore. Fine silt and clay sized particles, with grain sizes smaller than 10- 15 µm, drift still further west and can be traced up to more than 4,000 km distance from their source areas. Additional terrigenous silt which is poor in stained quartz occurs within a narrow zone off the western Sahara between 20° and 27° N only. It depicts the present dust supply by the trade winds close to the surface. The dust load originates from the northwestern Sahara, the Atlas Mountains and coastal areas, which contain a particularly low amount of stained quartz. The distribution pattern of these pale quartz sediments reveals a SSW-dispersal of dust being consistent with the present trade wind direction from the NNE. In comparison to the sediments from off the Sahara and the deeper subtropical Atlantic, the sediments off river mouths, in particular off the Senegal river, are characterized by an additional input of fine grained terrigenous particles (< 6 µm). This is due to fluvial suspension load. The fluvial discharge leads to a relative excess of fine grained particles and is observed in a correlation diagram of the modal grain sizes of terrigenous silt with the proportion of fine fraction (< 6 µm). The aeolian sediment contribution by the Harmattan Winds strongly decreased during the Climatic Optimum at 6,000 y. B. P. The dust discharge of the trade winds is hardly detectable in the deep-sea sediments. This probably indicates a weakened atmospheric circulation. In contrast, the fluvial sediment supply reached a maximum, and can be traced to beyond Cape Blanc. Thus, the Saharan climate was more humid at 6,000 y B. P. A latitudinal shift of the Harmattan driven dust outbreaks cannot be observed. Also during the Glacial, 18,000 y. B. P., Harmattan dust transport crossed the African coast at latitudes of 15°-20° N. Its sediment load increased intensively, and markedly coarser grains spread further into the Atlantic Ocean. An expanded zone of pale-quart sediments indicates an enhanced dust supply by the trade winds blowing from the NE. No synglacial fluvial sediment contribution can be recognized between 12° and 30° N. This indicates a dry glacial climate and a strengthened stmospheric circulation over the Sahelian and Saharan region. The climatic transition pahes, at 12, 000 y. B. P., between the last Glacial and the Intergalcial, which is compareable to the Alerod in Europe, is characterized by an intermediate supply of terrigenous particles. The Harmattan dust transport wa weaker than during the Glacial. The northeasterly trade winds were still intensive. River supply reached a first postglacial maximum seaward of the Senegal river mouth. This indicates increasing humidity over the southern Sahara and a weaker atmospheric circulation as compared to the glacial. The accumulation rates of the terrigenous silt proportion (> 6 µm) decrcase exponentially with increasing distance from the Saharan coast. Those of the terrigenous fine fraction (< 6 µm) follow the same trend and show almost similar gradients. Accordingly, also the terrigenous fine fraction is believed to result predominantly from aeolian transport. In the Atlantic deep-sea sediments, the annual terrigenous sediment accumulation has fluctuated, from about 60 million tons p. a. during the Late Glacial (13,500-18,000 y. B. P, aeolian supply only) to about 33 million tons p. a. during the Holocene Climatic Optimum (6,000-9,000 y. B. P, mainly fluvial supply), when the river supply has reached a maximum, and to about 45 million tons p. a. during the last 4,000 years B. P. (fluvial supply only south of 18° N).

Reward circuitry dysfunction in psychiatric and neurodevelopmental disorders and genetic syndromes: animal models and clinical findings.

This review summarizes evidence of dysregulated reward circuitry function in a range of neurodevelopmental and psychiatric disorders and genetic syndromes. First, the contribution of identifying a core mechanistic process across disparate disorders to disease classification is discussed, followed by a review of the neurobiology of reward circuitry. We next consider preclinical animal models and clinical evidence of reward-pathway dysfunction in a range of disorders, including psychiatric disorders (i.e., substance-use disorders, affective disorders, eating disorders, and obsessive compulsive disorders), neurodevelopmental disorders (i.e., schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorders, Tourette's syndrome, conduct disorder/oppositional defiant disorder), and genetic syndromes (i.e., Fragile X syndrome, Prader-Willi syndrome, Williams syndrome, Angelman syndrome, and Rett syndrome). We also provide brief overviews of effective psychopharmacologic agents that have an effect on the dopamine system in these disorders. This review concludes with methodological considerations for future research designed to more clearly probe reward-circuitry dysfunction, with the ultimate goal of improved intervention strategies.

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

The difficulty of measuring culture and diversity. Comparison of three international models of cultural measurement: FCS-2009, ESSnet-2012 and CAB-2015

The growing interest in quantifying the cultural and creative industries, visualize the economic contribution of activities related to culture demands first of all the construction of internationally comparable analysis frameworks. Currently there are three major bodies which address this issue and whose comparative study is the focus of this article: the UNESCO Framework for Cultural Statistics (FCS-2009), the European Framework for Cultural Statistics (ESSnet-Culture 2012) and the methodological resource of the “Convenio Andrés Bello” group for working with the Satellite Accounts on Culture in Ibero-America (CAB-2015). Cultural sector measurements provide the information necessary for correct planning of cultural policies which in turn leads to sustaining industries and promoting cultural diversity. The text identifies the existing differences in the three models and three levels of analysis, the sectors, the cultural activities and the criteria that each one uses in order to determine the distribution of the activities by sector. The end result leaves the impossibility of comparing cultural statistics of countries that implement different frameworks.

Opinion: Can coalescent models explain deep divergences in the diatoms and argue for the acceptance of paraphyletic taxa at all taxonomic hierarchies?

Although ancestral polymorphisms and incomplete lineage sorting are commonly used at the population level, increasing reports of these models have been invoked and tested to explain deep radiations. Hypotheses are put forward for ancestral polymorphisms being the likely reason for paraphyletic taxa at the class level in the diatoms based on an ancient rapid radiation of the entire groups. Models for ancestral deep coalescence are invoked to explain paraphyly and molecular evolution at the class level in the diatoms. Other examples at more recent divergences are also documented. Discussion as to whether or not paraphyletic groups seen in the diatoms at all taxonomic levels should be recognized is provided. The continued use of the terms centric and pennate diatoms is substantiated with additional evidence produced to support their use in diatoms both as descriptive terms for both groups and as taxonomic groups for the latter because new morphological evidence from the auxospores justifies the formal classification of the basal and core araphids as new subclasses of pennate diatoms in the Class Bacillariophyceae. Keys for higher levels of the diatoms showing how the terms centrics and araphid diatoms can be defined are provided.

Opinion: Can coalescent models explain deep divergences in the diatoms and argue for the acceptance of paraphyletic taxa at all taxonomic hierarchies?

Although ancestral polymorphisms and incomplete lineage sorting are commonly used at the population level, increasing reports of these models have been invoked and tested to explain deep radiations. Hypotheses are put forward for ancestral polymorphisms being the likely reason for paraphyletic taxa at the class level in the diatoms based on an ancient rapid radiation of the entire groups. Models for ancestral deep coalescence are invoked to explain paraphyly and molecular evolution at the class level in the diatoms. Other examples at more recent divergences are also documented. Discussion as to whether or not paraphyletic groups seen in the diatoms at all taxonomic levels should be recognized is provided. The continued use of the terms centric and pennate diatoms is substantiated with additional evidence produced to support their use in diatoms both as descriptive terms for both groups and as taxonomic groups for the latter because new morphological evidence from the auxospores justifies the formal classification of the basal and core araphids as new subclasses of pennate diatoms in the Class Bacillariophyceae. Keys for higher levels of the diatoms showing how the terms centrics and araphid diatoms can be defined are provided.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

A Phylogenetic Classification of the Video-Game Industry’s Business Model Ecosystem

Part 8: Business Strategies Alignment

Changing family models in Spain: the impact of the sociodemographic factors

Abstract During the last few decades, there has been an increasing international recognition of the studies related to the analysis of the family models change, the focus being the determinants of the female employment and the problems related to the work family balance (Lewis, 2001; Petit & Hook, 2005Saraceno, Crompton & Lyonette, 20062008; Pfau-Effinger, 2012). The majority of these studies have been focused on the analysis of the work-family balance problems as well as the effectiveness of the family and gender policies in order to encourage female employment (Korpi et al., 2013). In Spain, special attention has been given to the family policies implemented, the employability of women and on the role of the father in the family (Flaquer et al., 2015; Meil, 2015); however, there has been far less emphasis on the analysis of the family cultural models (González and Jurado, 2012; Crespi and Moreno, 2016). The purpose of this paper is to present some of the first results on the influence of the socio-demographic factors on the expectations and attitudes about the family models. This study offers an analytical reflection upon the foundation of the determinants of the family ambivalence in Spain from the cultural and the institutional dimension. This study shows the Spanish family models of preferences following the Pfau-Effinger (2004) classification of the famiy living arrangements. The reason for this study is twofold; on the one hand, there is confirmed the scarcity of studies that have focused their attention on this objective in Spain; on the other hand, the studies carried out in the international context have confirmed the analytical effectiveness of researching on the attitude and value changes to explain the meaning and trends of the family changes. There is also presented some preliminary results that have been obtained from the multinomial analysis related to the influence of the socio-demographic factors on the family model chosen by the individuals in Spain (father and mother working full time; mother part-time father full-time; mother not at work father full-time; mother and father part-time). 3 The database used has been the International Social Survey Programme: Family and Changing Gender Roles IV- ISSP 2012-. Spain is the only country of South Europe that has participated in the survey. For this reason it has been considered as a representative case study.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.