The single currency and European citizenship

We could expect that the introduction of the single currency had been accompanied by a significant share of studies and researches about the implications and impacts of such a watershed event on European citizenship. On the contrary, we soon discover to be facing a paradox, which could be phrased as follows: while the purpose of building European citizenship is the very rationale for the project of the single currency, the Scholars – but also the policy community – have mostly underestimated if not neglected this relation, both in terms of public policy making and discourse and of interpretation and forecasting. As a consequence of all of that, relevant features of the single currency happened to remain hidden, poorly considered and almost not thematized. In order to fill this gap, the first part of this article will present the main findings emerged from a documentary research conducted by FONDACA between 2010 and 2011, aimed at mapping the existing academic and policy thematizations about the hidden dimensions of the euro. The second part will be devoted to define “the other side of the coin” as an empirical phenomenon

Align-then-abstract representation learning for low-resource summarization

Generative transformer-based models have achieved state-of-the-art performance in text summarization. Nevertheless, they still struggle in real-world scenarios with long documents when trained in low-resource settings of a few dozen labeled training instances, namely in low-resource summarization (LRS). This paper bridges the gap by addressing two key research challenges when summarizing long documents, i.e., long-input processing and document representation, in one coherent model trained for LRS. Specifically, our novel align-then-abstract representation learning model (ATHENA) jointly trains a segmenter and a summarizer by maximizing the alignment between the chunk-target pairs in output from the text segmentation. Extensive experiments reveal that ATHENA outperforms the current state-of-the-art approaches in LRS on multiple long document summarization datasets from different domains

Efficient self-supervised metric information retrieval: A bibliography based method applied to covid literature

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised train sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our

Transcriptome of the deep-sea black scabbardfish, Aphanopus carbo (Perciformes: Trichiuridae) : tissue-specific expression patterns and candidate genes associated to depth adaptation

Deep-sea fishes provide a unique opportunity to study the physiology and evolutionary adaptation to extreme environments. We carried out a high throughput sequencing analysis on a 454 GS-FLX titanium plate using unnormalized cDNA libraries from six tissues of A. carbo. Assemblage and annotations were performed by Newbler and InterPro/Pfam analyses, respectively. The assembly of 544,491 high quality reads provided 8,319 contigs, 55.6% of which retrieved blast hits against the NCBI nonredundant database or were annotated with ESTscan. Comparison of functional genes at both the protein sequences and protein stability levels, associated with adaptations to depth, revealed similarities between A. carbo and other bathypelagic fishes. A selection of putative genes was standardized to evaluate the correlation between number of contigs and their normalized expression, as determined by qPCR amplification. The screening of the libraries contributed to the identification of new EST simple-sequence repeats (SSRs) and to the design of primer pairs suitable for population genetic studies as well as for tagging and mapping of genes. The characterization of the deep-sea fish A. carbo first transcriptome is expected to provide abundant resources for genetic, evolutionary, and ecological studies of this species and the basis for further investigation of depth-related adaptation processes in fishes.Publisher PDFPeer reviewe

Occurrence and persistence of magnetic elements in the quiet Sun

Turbulent convection efficiently transports energy up to the solar photosphere, but its multi-scale nature and dynamic properties are still not fully understood. Several works in the literature have investigated the emergence of patterns of convective and magnetic nature in the quiet Sun at spatial and temporal scales from granular to global. Aims. To shed light on the scales of organisation at which turbulent convection operates, and its relationship with the magnetic flux therein, we studied characteristic spatial and temporal scales of magnetic features in the quiet Sun. Methods. Thanks to an unprecedented data set entirely enclosing a supergranule, occurrence and persistence analysis of magnetogram time series were used to detect spatial and long-lived temporal correlations in the quiet Sun and to investigate their nature. Results. A relation between occurrence and persistence representative for the quiet Sun was found. In particular, highly recurrent and persistent patterns were detected especially in the boundary of the supergranular cell. These are due to moving magnetic elements undergoing motion that behaves like a random walk together with longer decorrelations ($\sim2$ h) with respect to regions inside the supergranule. In the vertices of the supegranular cell the maximum observed occurrence is not associated with the maximum persistence, suggesting that there are different dynamic regimes affecting the magnetic elements

Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines. © Springer-Verlag 2007

Efficient Memory-Enhanced Transformer for Long-Document Summarization in Low-Resource Regimes

Long document summarization poses obstacles to current generative transformer-based models because of the broad context to process and understand. Indeed, detecting long-range dependencies is still challenging for today’s state-of-the-art solutions, usually requiring model expansion at the cost of an unsustainable demand for computing and memory capacities. This paper introduces Emma, a novel efficient memory-enhanced transformer-based architecture. By segmenting a lengthy input into multiple text fragments, our model stores and compares the current chunk with previous ones, gaining the capability to read and comprehend the entire context over the whole document with a fixed amount of GPU memory. This method enables the model to deal with theoretically infinitely long documents, using less than 18 and 13 GB of memory for training and inference, respectively. We conducted extensive performance analyses and demonstrate that Emma achieved competitive results on two datasets of different domains while consuming significantly less GPU memory than competitors do, even in low-resource settings

Variation of Creep Resistance in Ferritic Steels by a Heat Treatment

AbstractIn the power plants, boiler pipes and heaters, are made with ferritic steels low alloy. These steels have a microstructure with fine stable alloy carbides that impede the movement of the dislocations, however it is inevitable that during long periods of service or very critical conditions, microstructural changes occur that are responsible for the loss of material strength. In the past decades the 1Cr-0.5Mo steel was used, but it has been replaced by ferritc steels containing higher amounts of Cr and Mo, with the addition of other micro alloying elements such as niobium, titanium and vanadium to increase their mechanical strength. The objective of this work is to study the creep behavior of 1Cr-0.5Mo steel and to compare its strength when prior to service it is subjected to different heat treatments that improve its conditions of service, as that is beneficial from the economical point of view. Tensile creep tests were performed at a temperature range between 843 and 893K, and applied stresses between 131 and 205MPa in the material reception conditions comparing its behavior with others that previously has undergone different heat treatments. From experimental data the characteristic parameters were calculated such as the creep coefficient of stress and activation energy. The microstructural variation of the original material was also analyzed, after heat treatment and creep samples were characterized by optical microscopy, scanning electron microscopy and analysis by dispersive X- ray spectroscopy, to evaluate the effects of kinetics changes occurred in the precipitated phases and the presence of microstructural damage, such as nucleation, growth and coalescence of micro cavities. The microhardness of the phases present in the different samples were also measured

Riscos genéticos da produção de híbridos de peixes nativos.

A produção de peixes nativos e seus híbridos chama a atenção daqueles que observam o Brasil como alvo de investimentos agropecuários e futura potência da aquicultura mundial. Dada a competitividade e dinamismo, o mercado aquícola nacional tem motivado a produção de híbridos em maior escala nos últimos anos, buscando neles características favoráveis de ganho de peso, resistência ao frio, rusticidade e adaptação à alimentação artificial. Por outro lado, a mistura de animais híbridos sobre as linhagens puras e o seu escape inadvertido para os ambientes naturais certamente expõe a sustentabilidade da aquicultura. Sendo assim, este documento buscou reunir informações sobre a produção, impacto genético, riscos e desafios do cultivo de híbridos frente ao desafio de equilibrar a conservação da biodiversidade com a produção sustentável de alimentos.bitstream/item/131431/1/cnpasadoc3.pd