A new model for the X-ray continuum of the magnetized accreting pulsars

Accreting highly magnetized pulsars in binary systems are among the brightest X-ray emitters in our Galaxy. Although a number of high statistical quality broad-band (0.1-100 keV) X-ray observations are available, the spectral energy distribution of these sources is usually investigated by adopting pure phenomenological models, rather than models linked to the physics of accretion. In this paper, a detailed spectral study of the X-ray emission recorded from the high-mass X-ray binary pulsars Cen X-3, 4U 0115+63, and Her X-1 is carried out by using BeppoSAX and joined Suzaku+NuStar data, together with an advanced version of the compmag model. The latter provides a physical description of the high energy emission from accreting pulsars, including the thermal and bulk Comptonization of cyclotron and bremsstrahlung seed photons along the neutron star accretion column. The compmag model is based on an iterative method for solving second-order partial differential equations, whose convergence algorithm has been improved and consolidated during the preparation of this paper. Our analysis shows that the broad-band X-ray continuum of all considered sources can be self-consistently described by the compmag model. The cyclotron absorption features, not included in the model, can be accounted for by using Gaussian components. From the fits of the compmag model to the data we inferred the physical properties of the accretion columns in all sources, finding values reasonably close to those theoretically expected according to our current understanding of accretion in highly magnetized neutron stars. The updated version of the compmag model has been tailored to the physical processes that are known to occur in the columns of highly magnetized accreting neutron stars and it can thus provide a better understanding of the high energy radiation from these sources.Comment: 19 pages, 10 figures, accepted for publication in A&

RX J0440.9+4431: a persistent Be/X-ray binary in outburst

The persistent Be/X-ray binary RX J0440.9+4431 flared in 2010 and 2011 and has been followed by various X-ray facilities Swift, RXTE, XMM-Newton, and INTEGRAL. We studied the source timing and spectral properties as a function of its X-ray luminosity to investigate the transition from normal to flaring activity and the dynamical properties of the system. We have determined the orbital period from the long-term Swift/BAT light curve, but our determinations of the spin period are not precise enough to constrain any orbital solution. The source spectrum can always be described by a bulk-motion Comptonization model of black body seed photons attenuated by a moderate photoelectric absorption. At the highest luminosity, we measured a curvature of the spectrum, which we attribute to a significant contribution of the radiation pressure in the accretion process. This allows us to estimate that the transition from a bulk-motion-dominated flow to a radiatively dominated one happens at a luminosity of ~2e36 erg/s. The luminosity dependency of the size of the black body emission region is found to be $r_{BB} \propto L_X^{0.39\pm0.02}$. This suggests that either matter accreting onto the neutron star hosted in RX J0440.9+4431 penetrates through closed magnetic field lines at the border of the compact object magnetosphere or that the structure of the neutron star magnetic field is more complicated than a simple dipole close to the surfaceComment: Accepted for publication by A&

Spectral evolution of bright NS LMXBs with INTEGRAL: an application of the thermal plus bulk Comptonization model

The aim of this work is to investigate in a physical and quantitative way the spectral evolution of bright Neutron Star Low-Mass X-ray Binaries (NS LMXBs), with special regard to the transient hard X-ray tails. We analyzed INTEGRAL data for five sources (GX 5-1, GX 349+2, GX 13+1, GX 3+1, GX 9+1) and built broad-band X-ray spectra from JEM-X1 and IBIS/ISGRI data. For each source, X-ray spectra from different states were fitted with the recently proposed model compTB. The spectra have been fit with a two-compTB model. In all cases the first compTB describes the dominant part of the spectrum that we interpret as thermal Comptonization of soft seed photons (< 1 keV), likely from the accretion disk, by a 3-5 keV corona. In all cases, this component does not evolve much in terms of Comptonization efficiency, with the system converging to thermal equilibrium for increasing accretion rate. The second compTB varies more dramatically spanning from bulk plus thermal Comptonization of blackbody seed photons to the blackbody emission alone. These seed photons (R < 12 km, kT_s > 1 keV), likely from the neutron star and the innermost part of the system, the Transition Layer, are Comptonized by matter in a converging flow. The presence and nature of this second compTB component (be it a pure blackbody or Comptonized) are related to the inner local accretion rate which can influence the transient behaviour of the hard tail: high values of accretion rates correspond to an efficient Bulk Comptonization process (bulk parameter delta > 0) while even higher values of accretion rates suppress the Comptonization, resulting in simple blackbody emission (delta=0).Comment: 12 pages, 10 figures, accepted for publication in A&

Explaining the influence of prior knowledge on POMCP policies

Partially Observable Monte Carlo Planning is a recently proposed online planning algorithm which makes use of Monte Carlo Tree Search to solve Partially Observable Monte Carlo Decision Processes. This solver is very successful because of its capability to scale to large uncertain environments, a very important property for current real-world planning problems. In this work we propose three main contributions related to POMCP usage and interpretability. First, we introduce a new planning problem related to mobile robot collision avoidance in paths with uncertain segment difficulties, and we show how POMCP performance in this context can take advantage of prior knowledge about segment difficulty relationships. This problem has direct real-world applications, such as, safety management in industrial environments where human-robot interaction is a crucial issue. Then, we present an experimental analysis about the relationships between prior knowledge provided to the algorithm and performance improvement, showing that in our case study prior knowledge affects two main properties, namely, the distance between the belief and the real state, and the mutual information between segment difficulty and action taken in the segment. This analysis aims to improve POMCP explainability, following the line of recently proposed eXplainable AI and, in particular, eXplainable planning. Finally, we analyze results on a synthetic case study and show how the proposed measures can improve the understanding about internal planning mechanisms

Learning environment properties in Partially Observable Monte Carlo Planning

We tackle the problem of learning state-variable relationships in Partially Observable Markov Decision Processes to improve planning performance on mobile robots. The proposed approach extends Partially Observable Monte Carlo Planning (POMCP) and represents state-variable relationships with Markov Random Fields. A ROS-based implementation of the approach is proposed and evaluated in rocksample, a standard benchmark for probabilistic planning under uncertainty. Experiments have been performed in simulation with Gazebo. Results show that the proposed approach allows to effectively learn state- variable probabilistic constraints on ROS-based robotic platforms and to use them in subsequent episodes to outperform standard POMC

Occupational risk of overweight and obesity: an analysis of the Australian Health Survey

<p>Abstract</p> <p>Background</p> <p>Adults spend about one third of their day at work and occupation may be a risk factor for obesity because of associated socioeconomic and behavioral factors such as physical activity and sedentary time. The aim of this study was to examine body mass index (BMI) and prevalence of overweight and obesity by occupation and explore the contributions of socioeconomic factors and lifestyle behaviors (including leisure time and commuting physical activity, diet, smoking, and alcohol) to occupational risk.</p> <p>Methods</p> <p>Secondary analyses of the National Health Survey in Australia (2005) were conducted for working age adults (20 to 64 years). Linear and logistic regression models using BMI as either dichotomous or continuous response were computed for occupation type. Model 1 was age-adjusted, Model 2 adjusted for age and socioeconomic variables and Model 3 adjusted for age, socioeconomic variables and lifestyle behaviours. All models were stratified by gender.</p> <p>Results</p> <p>Age-adjusted data indicated that men in associate professional (OR 1.34, 95% CI 1.10-1.63) and intermediate production and transport (OR 1.24 95% CI 1.03-1.50) occupations had a higher risk of BMI ≥ 25 kg/m²than those without occupation, and women in professional (OR 0.71, 95% CI 0.61-0.82), management (OR 0.72, 95% CI 0.56-0.92) and advanced clerical and service occupations (OR 0.73 95% CI 0.58-0.93) had a lower risk. After adjustment for socioeconomic factors no occupational group had an increased risk but for males, professionals, tradesmen, laborers and elementary clerical workers had a lower risk as did female associate professionals and intermediate clerical workers. Adjustment for lifestyle factors explained the lower risk in the female professional and associate professionals but failed to account for the lower odds ratios in the other occupations.</p> <p>Conclusions</p> <p>The pattern of overweight and obesity among occupations differs by gender. Healthy lifestyle behaviors appear to protect females in professional and associate professional occupations from overweight. For high-risk occupations lifestyle modification could be included in workplace health promotion programs. Further investigation of gender-specific occupational behaviors and additional lifestyle behaviors to those assessed in the current Australian Health Survey, is indicated.</p

Scalable Safe Policy Improvement via Monte Carlo Tree Search

Algorithms for safely improving policies are important to deploy reinforcement learning approaches in real-world scenarios. In this work, we propose an algorithm, called MCTS-SPIBB, that computes safe policy improvement online using a Monte Carlo Tree Search based strategy. We theoretically prove that the policy generated by MCTS-SPIBB converges, as the number of simulations grows, to the optimal safely improved policy generated by Safe Policy Improvement with Baseline Bootstrapping (SPIBB), a popular algorithm based on policy iteration. Moreover, our empirical analysis performed on three standard benchmark domains shows that MCTS-SPIBB scales to significantly larger problems than SPIBB because it computes the policy online and locally, i.e., only in the states actually visited by the agent