Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification

Fire History from Life-History: Determining the Fire Regime that a Plant Community Is Adapted Using Life-Histories

Wildfire is a fundamental disturbance process in many ecological communities, and is critical in maintaining the structure of some plant communities. In the past century, changes in global land use practices have led to changes in fire regimes that have radically altered the composition of many plant communities. As the severe biodiversity impacts of inappropriate fire management regimes are recognized, attempts are being made to manage fires within a more ‘natural’ regime. In this aim, the focus has typically been on determining the fire regime to which the community has adapted. Here we take a subtly different approach and focus on the probability of a patch being burnt. We hypothesize that competing sympatric taxa from different plant functional groups are able to coexist due to the stochasticity of the fire regime, which creates opportunities in both time and space that are exploited differentially by each group. We exploit this situation to find the fire probability at which three sympatric grasses, from different functional groups, are able to co-exist. We do this by parameterizing a spatio-temporal simulation model with the life-history strategies of the three species and then search for the fire frequency and scale at which they are able to coexist when in competition. The simulation gives a clear result that these species only coexist across a very narrow range of fire probabilities centred at 0.2. Conversely, fire scale was found only to be important at very large scales. Our work demonstrates the efficacy of using competing sympatric species with different regeneration niches to determine the probability of fire in any given patch. Estimating this probability allows us to construct an expected historical distribution of fire return intervals for the community; a critical resource for managing fire-driven biodiversity in the face of a growing carbon economy and ongoing climate change

Limits on active to sterile neutrino oscillations from disappearance searches in the MINOS, Daya Bay, and bugey-3 experiments

Searches for a light sterile neutrino have been performed independently by the MINOS and the Daya Bay experiments using the muon (anti)neutrino and electron antineutrino disappearance channels, respectively. In this Letter, results from both experiments are combined with those from the Bugey-3 reactor neutrino experiment to constrain oscillations into light sterile neutrinos. The three experiments are sensitive to complementary regions of parameter space, enabling the combined analysis to probe regions allowed by the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE experiments in a minimally extended four-neutrino flavor framework. Stringent limits on sin^2 2θμe are set over 6 orders of magnitude in the sterile mass-squared splitting Δm^2 41. The sterile-neutrino mixing phase space allowed by the LSND and MiniBooNE experiments is excluded for Δm^2 41 < 0.8 eV^2 at 95% CLs

Keep off the grass?:Cannabis, cognition and addiction

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.In an increasing number of states and countries, cannabis now stands poised to join alcohol and tobacco as a legal drug. Quantifying the relative adverse and beneficial effects of cannabis and its constituent cannabinoids should therefore be prioritized. Whereas newspaper headlines have focused on links between cannabis and psychosis, less attention has been paid to the much more common problem of cannabis addiction. Certain cognitive changes have also been attributed to cannabis use, although their causality and longevity are fiercely debated. Identifying why some individuals are more vulnerable than others to the adverse effects of cannabis is now of paramount importance to public health. Here, we review the current state of knowledge about such vulnerability factors, the variations in types of cannabis, and the relationship between these and cognition and addiction.This work was supported by grants from the US National Institutes of Health to L.H.P. (AA020404, AA006420, AA022249 and AA017447) and by grants from the UK Medical Research Council to H.V.C. and C.J.A.M. (G0800268; MR/K015524/1)

A next-generation liquid xenon observatory for dark matter and neutrino physics

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector

The Single-Phase ProtoDUNE Technical Design Report

ProtoDUNE-SP is the single-phase DUNE Far Detector prototype that is under construction and will be operated at the CERN Neutrino Platform (NP) starting in 2018. ProtoDUNE-SP, a crucial part of the DUNE effort towards the construction of the first DUNE 10-kt fiducial mass far detector module (17 kt total LAr mass), is a significant experiment in its own right. With a total liquid argon (LAr) mass of 0.77 kt, it represents the largest monolithic single-phase LArTPC detector to be built to date. It's technical design is given in this report

The Single-Phase ProtoDUNE Technical Design Report

ProtoDUNE-SP is the single-phase DUNE Far Detector prototype that is under construction and will be operated at the CERN Neutrino Platform (NP) starting in 2018. ProtoDUNE-SP, a crucial part of the DUNE effort towards the construction of the first DUNE 10-kt fiducial mass far detector module (17 kt total LAr mass), is a significant experiment in its own right. With a total liquid argon (LAr) mass of 0.77 kt, it represents the largest monolithic single-phase LArTPC detector to be built to date. It's technical design is given in this report

Initialization of Physical Parameter Estimates

Grey box models of dynamical systems contain designated parameters with physical interpretation to be estimated from input-output data. This often gives distinct advantages over black-box models in terms of fewer parameters to estimate and hence better statistical accuracy. The basic theory for how this can be done is well established. The main practical obstacle may however be how the search for the estimates should be initialized. In this contribution we review the difficulties and point to a possibility to use Semidefinite Programming via a Sum-Of-Squares formulation to achieve guaranteed consistent initial values for the physical parameters

Prognostic value of estimated glomerular filtration rate in hospitalized elderly patients.

A multicenter observational study, REPOSI (REgistro POliterapie Società Italiana di Medicina Interna), was conducted to assess the prognostic value of glomerular filtration rate (eGFR) on in-hospital mortality, hospital re-admission and death within 3 months, in a sample of elderly patients (n = 1,363) admitted to 66 internal medicine and geriatric wards. Based on eGFR, calculated by the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula, subjects at hospital admission were classified into three groups: group 1 with normal eGFR (≥60 ml/min/1.73 m2, reference group), group 2 with moderately reduced eGFR (30-59 ml/min/1.73 m2) and group 3 with severely reduced eGFR (<30 ml/min/1.73 m2). Patients with the lowest eGFR (group 3) on admission were more likely to be older, to have a greater cognitive and functional impairment and a high rate of comorbidities. Multivariable logistic regression analysis showed that severely reduced eGFR at the time of admission was associated with in-hospital mortality (OR 3.00; 95 % CI 1.20-7.39, p = 0.0230), but not with re-hospitalization (OR 0.97; 95 % CI 0.54-1.76, p = 0.9156) or mortality at 3 months after discharge (OR 1.93; 95 % CI 0.92-4.04, p = 0.1582). On the contrary, an increased risk (OR 2.60; 95 % CI 1.13-5.98, p = 0.0813) to die within 3 months after discharge was associated with decreased eGFR measured at the time of discharge. Our study demonstrates that severely reduced eGFRs in elderly patients admitted to hospital are strong predictors of the risk of dying during hospitalization, and that this measurement at the time of discharge helps to predict early death after hospitalization