Opposite effect of acute and subchronic treatments with Ferula hermonis on copulatory behavior of male rats

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Electron-phonon coupling in potassium-doped graphene: Angle-resolved photoemission spectroscopy

The electron-phonon coupling in potassium-doped graphene on Ir(111) is studied via the renormalization of the pi* band near the Fermi level, using angle-resolved photoemission spectroscopy. The renormalization is found to be fairly weak and almost isotropic, with a mass enhancement parameter of lambda= 0.28(6) for both the K-M and the K-G direction. These results are found to agree well with recent first principles calculations.Comment: 5 pages, 3 figure

Surface Core Level Shifts of Clean and Oxygen Covered Ru(0001)

We have performed high resolution XPS experiments of the Ru(0001) surface, both clean and covered with well-defined amounts of oxygen up to 1 ML coverage. For the clean surface we detected two distinct components in the Ru 3d_{5/2} core level spectra, for which a definite assignment was made using the high resolution Angle-Scan Photoelectron Diffraction approach. For the p(2x2), p(2x1), (2x2)-3O and (1x1)-O oxygen structures we found Ru 3d_{5/2} core level peaks which are shifted up to 1 eV to higher binding energies. Very good agreement with density functional theory calculations of these Surface Core Level Shifts (SCLS) is reported. The overriding parameter for the resulting Ru SCLSs turns out to be the number of directly coordinated O atoms. Since the calculations permit the separation of initial and final state effects, our results give valuable information for the understanding of bonding and screening at the surface, otherwise not accessible in the measurement of the core level energies alone.Comment: 16 pages including 10 figures. Submitted to Phys. Rev. B. Related publications can be found at http://www.fhi-berlin.mpg.de/th/paper.htm

Modeling the Effects of Maintenance on the degradation of a Water-feeding Turbo-pump of a Nuclear Power Plant

International audienceThis work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation. The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts

Predicting human eye fixations via an LSTM-Based saliency attentive model

Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional neural networks for predicting gaze fixations. In this paper, we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a convolutional long short-term memory that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. In addition, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state-of-the-art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios

Understanding decentralization: deconcentration and devolution processes in the French and Italian cultural sectors

none3noPurpose – Decentralization is a widespread and international phenomenon in public administration. Despite the interest of public management scholars, an in-depth analysis of the interrelationship between two of its forms – deconcentration and devolution – and its impact on policy and management capacities at the local level is seldom investigated. Design/methodology/approach – This article addresses this gap by examining the implementation of deconcentration and devolution processes in France and Italy in the cultural field, combining the analysis of national reform processes with in-depth analyses of two regional cases. The research is the result of document analysis, participatory observation and semi-structured interviews. Findings – The article reconstructs the impacts of devolution and deconcentration processes on the emergence of policy and management capacity in two regions (Rhone-Alpes and Piedmont) in the cultural sector. The article shows that decentralization in the cultural sector in France and Italy is the result of different combinations of devolution and deconcentration processes, that the two processes mutually affect their effectiveness, and that this effectiveness is deeply linked to the previous policy and management capacity of the central state in a specific field/country. Originality/value – The article investigates decentralization as a result of the combination of deconcentration and devolution in comparative terms and in a specific sector of implementation, highlighting the usefulness of this approach also for other sectors/countries.mixedSantagati, Maria Elena; Bonini Baraldi, Sara; Zan, LucaSantagati, Maria Elena; Bonini Baraldi, Sara; Zan, Luc

Analyzing How BERT Performs Entity Matching

State-of-the-art Entity Matching (EM) approaches rely on transformer architectures, such as BERT, for generating highly contextualized embeddings of terms. The embeddings are then used to predict whether pairs of entity descriptions refer to the same real-world entity. BERT-based EM models demonstrated to be effective, but act as black-boxes for the users, who have limited insight into the motivations behind their decisions. In this paper, we perform a multi-facet analysis of the components of pre-trained and fine-tuned BERT architectures applied to an EM task. The main findings resulting from our extensive experimental evaluation are (1) the fine-tuning process applied to the EM task mainly modifies the last layers of the BERT components, but in a different way on tokens belonging to descriptions of matching / non-matching entities; (2) the special structure of the EM datasets, where records are pairs of entity descriptions is recognized by BERT; (3) the pair-wise semantic similarity of tokens is not a key knowledge exploited by BERT-based EM models