Fermipy: An open-source Python package for analysis of Fermi-LAT Data

Fermipy is an open-source python framework that facilitates analysis of data collected by the Fermi Large Area Telescope (LAT). Fermipy is built on the Fermi Science Tools, the publicly available software suite provided by NASA for the LAT mission. Fermipy provides a high-level interface for analyzing LAT data in a simple and reproducible way. The current feature set includes methods for extracting spectral energy distributions and lightcurves, generating test statistic maps, finding new source candidates, and fitting source position and extension. Fermipy leverages functionality from other scientific python packages including NumPy, SciPy, Matplotlib, and Astropy and is organized as a community-developed package following an open-source development model. We review the current functionality of Fermipy and plans for future development.Comment: Proc. 35th ICRC, Busan, South Korea, PoS(ICRC2017)82

Knowledge Domains, Technological Strategies and Open Innovation

This study provides a patent-based framework, investigating the relationship among the relevance of the technological domains, the exploitation vs. exploration strategies and the choice of open innovation practices. Specifically, this work presents five levels of open innovation adoption and analyses the reason why firms open up their innovation boundaries. The methodology is tested on a sample of 240 companies belonging to the bio-pharmaceutical and the technology hardware & equipment industries, by examining their patents filed in 2011. Results show that the relevance of the knowledge domain affects the choice of the innovation strategy; also, non-equity alliances are preferred in explorative activities and equity alliances in exploitative ones

Single spin-polarised Fermi surface in SrTiO3_3 thin films

The 2D electron gas (2DEG) formed at the surface of SrTiO$_3$(001) has attracted great interest because of its fascinating physical properties and potential as a novel electronic platform, but up to now has eluded a comprehensible way to tune its properties. Using angle-resolved photoemission spectroscopy with and without spin detection we here show that the band filling can be controlled by growing thin SrTiO$_3$ films on Nb doped SrTiO$_3$(001) substrates. This results in a single spin-polarised 2D Fermi surface, which bears potential as platform for Majorana physics. Based on our results it can furthermore be concluded that the 2DEG does not extend more than 2 unit cells into the film and that its properties depend on the amount of SrO$_x$ at the surface and possibly the dielectric response of the system

Relaxing the Forget Constraints in Open World Recognition

In the last few years deep neural networks has significantly improved the state-of-the-art of robotic vision. However, they are mainly trained to recognize only the categories provided in the training set (closed world assumption), being ill equipped to operate in the real world, where new unknown objects may appear over time. In this work, we investigate the open world recognition (OWR) problem that presents two challenges: (i) learn new concepts over time (incremental learning) and (ii) discern between known and unknown categories (open set recognition). Current state-of-the-art OWR methods address incremental learning by employing a knowledge distillation loss. It forces the model to keep the same predictions across training steps, in order to maintain the acquired knowledge. This behaviour may induce the model in mimicking uncertain predictions, preventing it from reaching an optimal representation on the new classes. To overcome this limitation, we propose the Poly loss that penalizes less the changes in the predictions for uncertain samples, while forcing the same output on confident ones. Moreover, we introduce a forget constraint relaxation strategy that allows the model to obtain a better representation of new classes by randomly zeroing the contribution of some old classes from the distillation loss. Finally, while current methods rely on metric learning to detect unknown samples, we propose a new rejection strategy that sidesteps it and directly uses the model classifier to estimate if a sample is known or not. Experiments on three datasets demonstrate that our method outperforms the state of the art

Clinical relevance of silent red blood cell autoantibodies.

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Using the Oxford cognitive screen to detect cognitive impairment in stroke patients. A comparison with the Mini-Mental State Examination

Background: The Oxford Cognitive Screen (OCS) was recently developed with the aim of describing the cognitive de cits after stroke. The scale consists of 10 tasks encom- passing ve cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive de cits of widely used instruments, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment, which were originally devised for demented patients. Objective: The present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine perfor- mance on the OCS as a function of subtypes of cerebral infarction and clinical severity. Methods: 325 rst stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classi cation and NIHSS, were given according to standard procedures. results: About a third of patients (35.3%) had a performance lower than the cutoff (<22) on the MMSE, whereas 91.6% were impaired in at least one OCS domain, indicating higher incidences of impairment for the OCS. More than 80% of patients showed an impairment in two or more cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories. conclusion: Overall, the results showed a much higher incidence of cognitive impairment with the OCS than with the MMSE and demonstrated no false negatives for OCS vs MMSE. It is concluded that OCS is a sensitive screen tool for cognitive de cits after stroke. In particular, the OCS detects high incidences of stroke-specific cognitive impairments, not detected by the MMSE, demonstrating the importance of cognitive pro ling.Background: The Oxford Cognitive Screen (OCS) was recently developed with the aim of describing the cognitive deficits after stroke. The scale consists of 10 tasks encompassing five cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive deficits of widely used instruments, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment, which were originally devised for demented patients. Objective: The present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine performance on the OCS as a function of subtypes of cerebral infarction and clinical severity. Methods: 325 first stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classification and NIHSS, were given according to standard procedures. Results: About a third of patients (35.3%) had a performance lower than the cutoff(< 22) on the MMSE, whereas 91.6% were impaired in at least one OCS domain, indicating higher incidences of impairment for the OCS. More than 80% of patients showed an impairment in two or more cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories. Conclusion: Overall, the results showed a much higher incidence of cognitive impairment with the OCS than with the MMSE and demonstrated no false negatives for OCS vs MMSE. It is concluded that OCS is a sensitive screen tool for cognitive deficits after stroke. In particular, the OCS detects high incidences of stroke-specific cognitive impairments, not detected by the MMSE, demonstrating the importance of cognitive profiling. © 2018 Mancuso, Demeyere, Abbruzzese, Damora, Varalta, Pirrotta, Antonucci, Matano, Caputo, Caruso, Pontiggia, Coccia, Ciancarelli, Zoccolotti and The Italian OCS Grou

Patent data analysis of innovation behaviours in technology hardware & equipment industry

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Investments and Divestments of Intangibles in an Open Innovation Context

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Business Models for Open Innovation: From Collaboration to Incorporation

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Force-frequency relationship during dobutamine stress echocardiography predicts exercise tolerance and BNP levels in patients with chronic congestive heart failure

Purpose: D obutamine stress echocardiography (DSE) is widely used to evaluate myocardial contractile reserve; it provides prognostic information in patients with chronic congestive heart failure (CHF). The force?frequency relationship (FFR) is a method for evaluate LV contractility during DSE . The aim of our study is to assess the relationship among FFR, BNP levels, and aerobic exercise capacity in CHF patients. Methods and materials: 37 CHF patients (age 67?8 years, 54% with an ischemic etiology), underwent high dose DSE (up to 40 m g/kg/min). FFR was determined as a ratio between systolic cuff pressure and end-systolic volume (biplane using a Simposon rule) assessed at baseline and peak DSE . BNP levels were determined on blood samples withdrawn at baseline. After a few hours, CHF patients underwent cardiopulmonary exercise test with expired gas measurement. Results: Mean ejection fraction was 32?7% and NHYA class 2.5?0.6. FFR was directly related to peak oxygen consumption (Figure Left), LV ejection fraction (r=0.398, p=0.015) and mitral annulus peak systolic velocity (r=0.428, p=0.013). FFR was inversely related to NYHA class (r=-0.43, p=0.013), LV end-diastolic diameter (r=-0.377, p=0.022), LV intraventricular dyssynchrony (r=-0.394, p=0.016), and BNP levels (Figure Right). At multiple regression analysis, FFR (B=0.502, p= 0.004) and E/Ea ratio (B=-0.336, p=0.044) were the best predictors of exercise tolerance. Conclusions: In patients with stable CHF, impaired myocardial contractility during DSE is related to higher BNP levels and poorer exercise tolerance