Fourier series approximation of separable models

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Norms, Norms, and Norms: Validity, Existence and Referents of the Term Norm in Alexy, Conte, and Guastini

In this paper we examine the interplay between validity and existence of a norm. We compare Amedeo Giovanni Conte\u2019s five-folded conception of norm with the \u201csemantic\u201d conception of Robert Alexy\u2019s and Riccardo Guastini\u2019s idea of existence-as-legal-membership. We show how Alexy\u2019s model encompasses all the referents of Conte. We investigate the interplay between different theses on the relationships between validity and existence of norms and the referents for norm that a theory is able to admit. In particular, we show that if we want to encompass all five Contean referents we have to give up the (Kelsenian) validity-as-existence thesis

A Network-Constrain Weibull AFT Model for Biomarkers Discovery

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples

Technical note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data.

A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysi

The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy

The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm−1 with a sampling of 0.25 cm−1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index’s time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts

Cosmonet: An r package for survival analysis using screening-network methods

Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample size significantly increase the complexity of cancer survival analysis, demanding the development of specific statistical methods and software. Here, we present a novel R package, COSMONET (COx Survival Methods based On NETworks), that provides a complete workflow from the pre-processing of omics data to the selection of gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) three different screening approaches to reduce the initial dimension of the data from a high-dimensional space p to a moderate scale d, (ii) a network-penalized Cox regression algorithm to identify the gene signature, (iii) several approaches to determine an optimal cut-off on the prognostic index (PI) to separate high- and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of PIs. Moreover, COSMONET provides functions for data pre-processing, visualization, survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step R vignette using two cancer datasets.</jats:p

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset

Violent and non-violent crimes against sex workers : the influence of the sex market on reporting practices in the United Kingdom

Previous research has shown that sex workers experience extremely high rates of victimization but are often reluctant to report their experiences to the police. This paper explores how the markets in which sex workers operate in the United Kingdom impact upon the violent and non-violent crimes they report to a national support organization and their willingness to report victimization to the police. We use a secondary quantitative data analysis of 2,056 crime reports submitted to the UK National Ugly Mugs (NUM) scheme between 2012 and 2016. The findings indicate that although violence is the most common crime type reported to NUM, sex workers operating in different markets report varying relative proportions of different types of victimization. We also argue that there is some variation in the level of willingness to share reports with the police across the different sex markets, even when the type crime, presence of violence, and other variables are taken into account. Our finding that street sex workers are most likely to report victimization directly to the police challenges previously held assumptions that criminalization is the key factor preventing sex workers from engaging with the police. Key words: sex work; violence; policing; reported victimizatio

Publisher correction:Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites

[This corrects the article DOI: 10.1186/s40462-018-0142-4.].</p