Deformations of algebraic schemes via Reedy-Palamodov cofibrant resolutions

Let $X$ be a Noetherian separated and finite dimensional scheme over a field $\mathbb{K}$ of characteristic zero. The goal of this paper is to study deformations of $X$ over a differential graded local Artin $\mathbb{K}$-algebra by using local Tate-Quillen resolutions, i.e., the algebraic analog of the Palamodov's resolvent of a complex space. The above goal is achieved by describing the DG-Lie algebra controlling deformation theory of a diagram of differential graded commutative algebras, indexed by a direct Reedy category.Comment: Final version. To appear in Indagationes Mathematica

The numerical duplication of a numerical semigroup

In this paper we present and study the numerical duplication of a numerical semigroup, a construction that, starting with a numerical semigroup $S$ and a semigroup ideal $E\subseteq S$, produces a new numerical semigroup, denoted by S\Join^b\E (where $b$ is any odd integer belonging to $S$), such that S=(S\Join^b\E)/2. In particular, we characterize the ideals $E$ such that $S\Join^bE$ is almost symmetric and we determine its type.Comment: 17 pages. Accepted for publication on: Semigroup Foru

Can Severe Fiscal Contractions be Expansionary? Tales of Two Small European Countries

According to conventional wisdom, a fiscal consolidation is likely to contract real aggregate demand. It has often been argued, however, that this conclusion is misleading as it neglects the role of expectations of future policy: if the fiscal consolidation is read by the private sector as a signal that the share of government spending in GDP is being permanently reduced, households will revise upwards their estimate of their permanent income, and will raise current and planned consumption. Only the empirical evidence can sort out which of these two contending views about fiscal policy is more appropriate -- i.e how often the contractionary effect of a fiscal consolidation prevails on its expansionary expectational effect. This paper brings new evidence to bear on this issue drawing on the European exercise in fiscal rectitude of the 1980s, and focusing, in particulars on its two most extreme cases -- Denmark and Ireland. We find that at least in the experience of these two countries the expectations' view has a serious claim to empirical relevance.

The job creation effect of R&D expenditures

In this study we use a unique database covering 25 manufacturing and service sectors for 16 European countries over the period 1996-2005, for a total of 2,295 observations, and apply GMM-SYS panel estimations of a demand-for-labour equation augmented with technology. We find that R&D expenditures have a job-creating effect, in accordance with the previous theoretical and empirical literature discussed in the paper. Interestingly enough, the labour-friendly nature of R&D emerges in both the flow and the stock specifications. These findings provide further justification for the European Lisbon-Barcelona targets.Technological change, corporate R&D, employment, product innovation, GMMSYS

Prone Positioning for ARDS. still misunderstood and misused

Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by a non-cardiogenic pulmonary edema with bilateral chest X-ray opacities and hypoxemia refractory to oxygen therapy and low level of positive end-expiratory pressure (1). Recently, a large observational study reported an ARDS prevalence of 10.4% of all ICU admissions and of 23.4% of all subjects receiving mechanical ventilation (2). Despite these alarming numbers, according to the most recent literature, ARDS is still under-recognized, undertreated, and associated with a mortality rate that in the most severe forms is close to 50% (2)

Multiclass latent locally linear support vector machines

Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach

Computing the Shapley value in allocation problems: approximations and bounds, with an application to the Italian VQR research assessment program

In allocation problems, a given set of goods are assigned to agents in such a way that the social welfare is maximised, that is, the largest possible global worth is achieved. When goods are indivisible, it is possible to use money compensation to perform a fair allocation taking into account the actual contribution of all agents to the social welfare. Coalitional games provide a formal mathematical framework to model such problems, in particular the Shapley value is a solution concept widely used for assigning worths to agents in a fair way. Unfortunately, computing this value is a #P-hard problem, so that applying this good theoretical notion is often quite difficult in real-world problems. We describe useful properties that allow us to greatly simplify the instances of allocation problems, without affecting the Shapley value of any player. Moreover, we propose algorithms for computing lower bounds and upper bounds of the Shapley value, which in some cases provide the exact result and that can be combined with approximation algorithms. The proposed techniques have been implemented and tested on a real-world application of allocation problems, namely, the Italian research assessment program known as VQR (Verifica della Qualità della Ricerca, or Research Quality Assessment)1. For the large university considered in the experiments, the problem involves thousands of agents and goods (here, researchers and their research products). The algorithms described in the paper are able to compute the Shapley value for most of those agents, and to get a good approximation of the Shapley value for all of the