A numerical method to calculate the muon relaxation function in the presence of diffusion

We present an accurate and efficient method to calculate the effect of random fluctuations of the local field at the muon, for instance in the case muon diffusion, within the framework of the strong collision approximation. The method is based on a reformulation of the Markovian process over a discretized time base, leading to a summation equation for the muon polarization function which is solved by discrete Fourier transform. The latter is formally analogous, though not identical, to the integral equation of the original continuous-time model, solved by Laplace transform. With real-case parameter values, the solution of the discrete-time strong collision model is found to approximate the continuous-time solution with excellent accuracy even with a coarse-grained time sampling. Its calculation by the fast Fourier transform algorithm is very efficient and suitable for real time fitting of experimental data even on a slow computer.Comment: 7 pages, 3 figures. Submitted to Journal of Physics: Condensed Matte

Minimal Path Length of Trees with Known Fringe

In this paper we continue the study of the path length of trees with known fringe as initiated by [1] and [2]. We compute the path length of the minimal tree with given number of leaves N and fringe ∆ for the case ∆ ≥ N/2. This complements the result of [2] that studied the case ∆ ≤ N/2. Our methods also yields a linear time algorithm for constructing the minimal tree when ∆ ≥ N/2

The perception of psychosocial risks and work-related stress in relation to job insecurity and gender differences: a cross-sectional study

Introduction.The perception of psychosocial risks exposesworkers to developwork-related stress. Recently the attention of scientific research has focused on a psychosocial risk already identified as “job insecurity” that regards the “overall concern about the continued existence of the job in the future” and that also depends onworker’s perception, different for each gender. Aimof the Study. The aim of this cross sectional study is to show if job insecurity, in the formof temporary contracts, can influence the perception of psychosocial risks and therefore increase worker’s vulnerability to work-related stress and how the magnitude of this effect differs between genders. Materials and Methods. 338 administrative technical workers (113 males and 225 females) were administered a questionnaire, enquiring contract typology (permanent or temporary contracts), and the Health Safety Executive questionnaire to assess work-related stress. The Health Safety Executive Analysis Tool software was used to process collected questionnaires and theWilcoxon rank-sum test was used to evaluate the statistical significance of the differences obtained. Results. Workers with temporary contracts obtained lower scores than workers with permanent contracts in all the domains explored by theHealth Safety Executive Analysis questionnaire, statistically significant (P<0,05). The female workers obtained lower scores than male workers in all domains explored by the Health Safety Executive questionnaire. Conclusions. Authors conclude that perception of psychosocial risks can be influenced by job insecurity, in the form of temporary contracts, and increases worker’s vulnerability to work-related stress and differs between genders

Merger of compact stars in the two-families scenario

We analyse the phenomenological implications of the two-families scenario on the merger of compact stars. That scenario is based on the coexistence of both hadronic stars and strange quark stars. After discussing the classification of the possible mergers, we turn to detailed numerical simulations of the merger of two hadronic stars, i.e., "first family" stars in which delta resonances and hyperons are present, and we show results for the threshold mass of such binaries, for the mass dynamically ejected and the mass of the disk surrounding the post-merger object. We compare these results with those obtained within the one-family scenario and we conclude that relevant signatures of the two-families scenario can be suggested, in particular: the possibility of a rapid collapse to a black hole for masses even smaller than the ones associated to GW170817; during the first milliseconds, oscillations of the postmerger remnant at frequencies higher than the ones obtained in the one-family scenario; a large value of the mass dynamically ejected and a small mass of the disk, for binaries of low total mass. Finally, based on a population synthesis analysis, we present estimates of the number of mergers for: two hadronic stars; hadronic star - strange quark star; two strange quark stars. We show that for unequal mass systems and intermediate values of the total mass, the merger of a hadronic star and a strange quark star is very likely (GW170817 has a possible interpretation into this category of mergers). On the other hand, mergers of two strange quark stars are strongly suppressed.Comment: 18 pages, 16 figure

Grounding LTLf specifications in images

A critical challenge in neurosymbolic approaches is to handle the symbol grounding problem without direct supervision. That is mapping high-dimensional raw data into an interpretation over a finite set of abstract concepts with a known meaning, without using labels. In this work, we ground symbols into sequences of images by exploiting symbolic logical knowledge in the form of Linear Temporal Logic over finite traces (LTLf) formulas, and sequence-level labels expressing if a sequence of images is compliant or not with the given formula. Our approach is based on translating the LTLf formula into an equivalent deterministic finite automaton (DFA) and interpreting the latter in fuzzy logic. Experiments show that our system outperforms recurrent neural networks in sequence classification and can reach high image classification accuracy without being trained with any single-image label

Grounding LTLf specifications in image sequences

A critical challenge in neuro-symbolic (NeSy) approaches is to handle the symbol grounding problem without direct supervision. That is mapping high-dimensional raw data into an interpretation over a finite set of abstract concepts with a known meaning, without using labels. In this work, we ground symbols into sequences of images by exploiting symbolic logical knowledge in the form of Linear Temporal Logic over finite traces (LTLf) formulas, and sequence-level labels expressing if a sequence of images is compliant or not with the given formula. Our approach is based on translating the LTLf formula into an equivalent deterministic finite automaton (DFA) and interpreting the latter in fuzzy logic. Experiments show that our system outperforms recurrent neural networks in sequence classification and can reach high image classification accuracy without being trained with any single-image label

Relationship and predictive role of the dual expression of FGFR and IL-8 in metastatic renal cell carcinoma treated with targeted agents

Background/Aim: The expression of IL-8 and FGFR has been related to prognosis and pathological features in renal cell carcinoma. We investigated the relationship between IL-8 and FGFR and the outcome in metastatic renal cell carcinoma (mRCC) patients. Materials and Methods: Clinical data and histological samples of patients affected by mRCC and treated with targeted agents were reviewed. The expression of proteins was assessed using immunohistochemistry. Results: FGFR1, FGFR2, and IL-8 were found to be expressed in 16%, 30%, and 50% of cases, respectively. Significant correlations were found between selected proteins. A lack of expression of FGFR2 and IL8 was found to be correlated with increased progression-free survival (PFS). The survival rate at 24 months was 44%, 38%, and 79% of those expressing both, one, or none of the evaluated proteins, respectively (p=0.047). Conclusion: This analysis found a relationship between the expression of IL-8 and FGFR2 in mRCC patients treated with targeted agents

A dynamic Bayesian network model for predicting organ failure associations without predefining outcomes

Critical care medicine has been a field for Bayesian networks (BNs) application for investigating relationships among failing organs. Criticisms have been raised on using mortality as the only outcome to determine the treatment efficacy. We aimed to develop a dynamic BN model for detecting interrelationships among failing organs and their progression, not predefining outcomes and omitting hierarchization of organ interactions. We collected data from 850 critically ill patients from the national database used in many intensive care units. We considered as nodes the organ failure assessed by a score as recorded daily. We tested several possible DBNs and used the best bootstrapping results for calculating the strength of arcs and directions. The network structure was learned using a hill climbing method. The parameters of the local distributions were fitted with a maximum of the likelihood algorithm. The network that best satisfied the accuracy requirements included 15 nodes, corresponding to 5 variables measured at three times: ICU admission, second and seventh day of ICU stay. From our findings some organ associations had probabilities higher than 50% to arise at ICU admittance or in the following days persisting over time. Our study provided a network model predicting organ failure associations and their evolution over time. This approach has the potential advantage of detecting and comparing the effects of treatments on organ function