A coalgebraic semantics for causality in Petri nets

In this paper we revisit some pioneering efforts to equip Petri nets with compact operational models for expressing causality. The models we propose have a bisimilarity relation and a minimal representative for each equivalence class, and they can be fully explained as coalgebras on a presheaf category on an index category of partial orders. First, we provide a set-theoretic model in the form of a a causal case graph, that is a labeled transition system where states and transitions represent markings and firings of the net, respectively, and are equipped with causal information. Most importantly, each state has a poset representing causal dependencies among past events. Our first result shows the correspondence with behavior structure semantics as proposed by Trakhtenbrot and Rabinovich. Causal case graphs may be infinitely-branching and have infinitely many states, but we show how they can be refined to get an equivalent finitely-branching model. In it, states are equipped with symmetries, which are essential for the existence of a minimal, often finite-state, model. The next step is constructing a coalgebraic model. We exploit the fact that events can be represented as names, and event generation as name generation. Thus we can apply the Fiore-Turi framework: we model causal relations as a suitable category of posets with action labels, and generation of new events with causal dependencies as an endofunctor on this category. Then we define a well-behaved category of coalgebras. Our coalgebraic model is still infinite-state, but we exploit the equivalence between coalgebras over a class of presheaves and History Dependent automata to derive a compact representation, which is equivalent to our set-theoretical compact model. Remarkably, state reduction is automatically performed along the equivalence.Comment: Accepted by Journal of Logical and Algebraic Methods in Programmin

Eliciting the Demand for Long Term Care Coverage: A Discrete Choice Modelling Analysis

We evaluate the demand for long term care (LTC) insurance prospects in a stated preference context, by means of the results of a choice experiment carried out on a representative sample of the Emilia-Romagna population. Choice modelling techniques have not been used yet for studying the demand for LTC services. In this paper these methods are first of all used in order to assess the relative importance of the characteristics which define some hypothetical insurance programmes and to elicit the willingness to pay for some LTC coverage prospects. Moreover, thanks to the application of a nested logit specification with ‘partial degeneracy’, we are able to model the determinants of the preference for status quo situations where no systematic cover for LTC exists. On the basis of this empirical model, we test for the effects of a series of socio-demographic variables as well as personal and household health state indicators.Health Insurance, Long Term Care, Choice Experiments, Nested Logit Models

Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

Latest evidence for a late time vacuum -- geodesic CDM interaction

We perform a reconstruction of the coupling function between vacuum energy and geodesic cold dark matter using the latest observational data. We bin the interaction in seventeen redshift bins but use a correlation prior to prevent rapid, unphysical oscillations in the coupling function. This prior also serves to eliminate any dependence of the reconstruction on the binning method. We use two different forms of the correlation prior, finding that both give similar results for the reconstruction of the dark matter -- dark energy interaction. Calculating the Bayes factor for each case, we find no meaningful evidence for deviation from the null interacting case, i.e. $\Lambda$CDM, in our reconstruction.Comment: 14 pages, 7 figures. Version 2 matches published version in Physics of the Dark Universe (Figure 2 updated to better show H0 and sigma 8 tensions, additional discussion of results added in section 4.1

Delegating home care for the elderly to external caregivers? An empirical study on Italian data

We study care arrangement decisions in Italy, where families are increasingly delegating the role of primary caregiver to external (paid) people also for the provision of home care. We consider a sample of households with a dependent elderly person cared for either at home or in a residential home, extracted from a survey representative of the population of Italy’s Emilia-Romagna region. We investigate the determinants of a household’s decision to opt for one of the following three alternatives: the institutionalisation of elderly family members, informal home care, or paid home care. We estimate two model specifications, based on a simultaneous and a sequential decision process respectively, the results of which are fairly consistent. Disability related variables, rather than family characteristics, emerge as the main determinants of institutionalisation. On the other hand, household characteristics and socio-economic variables are more influential when it comes to choosing between informal and formal home care provisions

Spatial effects in hospital expenditures: a district level analysis

Geographical clusters in health expenditures are well documented and accounting for spatial interactions may contribute to properly identify the factors affecting the use of health services the most. As for hospital care, spillovers may derive from strategic behaviour of hospitals and from patients’ preferences that may induce mobility across jurisdictions, as well as from geographically-concentrated risk factors, knowledge transfer and interactions between different layers of care. Our paper focuses on a largely overlooked potential source of spillovers in hospital expenditure: the heterogeneity of primary care providers’ behaviour. To do so, we analyse expenditures associated to avoidable hospitalisations separately from expenditures for highly complex treatments, as the former are most likely affected by General Practitioners, while the latter are not. We use administrative data for Italy’s Region Emilia Romagna between 2007 and 2010. Since neighbouring districts may belong to different Local Health Authorities (LHAs), we employ a spatial contiguity matrix that allows to investigate the effects of geographical and institutional proximity and use it to estimate Spatial Autoregressive and Spatial Durbin Models

Red-channel (6000-8000 {\AA}) nuclear spectra of 376 local galaxies

We obtained long-slit optical spectra of the nuclear regions of 376 galaxies in the local Universe using the 1.5m Cassini telescope of Bologna Observatory. Of these spectra, 164 were either never taken before by the Sloan Digital Sky Survey (SDSS), or given by the Nasa Extragalactic Database (NED). With these new spectra, we contribute investigating the occurrence of active galactic nuclei (AGNs). Nevertheless, we stress that the present sample is by no means complete, thus, it cannot be used to perform any demographic study. Following the method presented in Gavazzi et al. (2011), we classify the nuclear spectra using a six bin scheme: SEY (Seyfert), sAGN (strong AGN), and wAGN (weak AGN) represent active galactic nuclei of different levels of activity; HII accounts for star-forming nuclei; RET (retired) and PAS (passive) refer to nuclei with poor or no star-formation activity. The spectral classification is performed using the ratio of 6584 {\lambda} [NII] to H{\alpha} lines and the equivalent width (EW) of H{\alpha} versus [NII]/H{\alpha} (WHAN diagnostic introduced by Cid Fernandes and collaborators) after correcting H{\alpha} for underlying absorption. The obtained spectra are made available in machine readable format via the Strasbourg Astronomical Data Center (CDS) and NED.Comment: 8 pages, 6 Figures, 4 Tables, accepted for publication in Astronomy & Astrophysic

Disentangling the effect of waiting times on hospital choice: Evidence from a panel data analysis

This study examines the effect of waiting times on hospital choice by using patient-level data on elective Percutaneous Transluminal Coronary Angioplasty (PTCA) procedures in the Italian NHS over the years 2008-2011. We perform a multinomial logit analysis including conditional logit and mixed logit specifications. Our findings show the importance of jointly controlling for time-invariant and time varying dimensions of hospital quality in order to disentangle the effect of waiting times on hospital choice. We provide evidence that patients are responsive to changes in waiting times and aspects of clinical quality within hospitals over time, and estimate the trade-off that patients make between different hospital attributes. The results convey important policy implications for highly regulated health care markets