132,132 research outputs found
Tumor microenvironment: a main actor in the metastasis process
No full textOver recent decades, various studies have argued that the metastatic tissue microenvironment is fully controlled by the intrinsic properties of the cancer cells (growth, motility and invasion, angiogenesis, extracellular matrix remodeling, immune escape) and additional cells types. Overall, the extrinsic factors and determinants medi- ate the contribution of the host microenvironment to metastasis formation. The tumor microenvironment carries out these functions by secretion of molecules that can influence and modulate its phenotype, making these complex interactions the basis for support for the progression of a cancer. Here, we undertake a summary of the ‘‘state of the art’’ of the functions and actions of these cells, as the main actors in the promotion of the formation of the microenvi- ronment of the metastatic niche, and the associated network of interactions. The unraveling of the relationships between tumorigenic cells and their microenvironment represents an important issue for the development of new therapeutic agents that can fight both initiation and recurrence of cancer
Understanding each-other: Engineering challenges and opportunities for users and systems in the deep learning era
No full textIn this paper, we discuss the impact of Deep Learning (DL) techniques in the present and future of the interactive system engineering. On the one hand, the support for more complex vocabularies offers opportunities in better shaping the communication between the user and the system. On the other hand, we identify challenges related to the lack of transparency and explainability in the trained models, which have a negative impact on system understanding for both developers and users
Lo stato dell'arte del federalismo fiscale nel 2010: agenda, iter attuativo e questioni aperte
No full textA che punto è l’attuazione del federalismo fiscale? Si può parlare di autonomia finanziaria? Negli ultimi anni, sono state emanate una serie di riforme con l’obiettivo di attuare il federalismo fiscale e rendere operativa l’autonomia finanziaria. Le riforme ispirate al federalismofiscale affondano le loro radici, da un lato, nell’accresciuta sensibilità dei contribuential peso delle imposte richieste per finanziare il settore pubblico e, dall’altro,nella sempre minore disponibilità a sostenere sistemi redistributivi "pesanti" provocata dalla competizione su scala globale tra i territori.In linea con tale finalità, i Quaderni del NETCAP (Network Conti&Controlli nelle AmministrazioniPubbliche), rappresentano l’output del primo e del secondo Laboratoriosul Federalismo Fiscale e si pongono l’obiettivo di valutare l’impatto delle riforme sul livellodi autonomia finanziaria delle Amministrazioni Pubbliche, facendo riferimento allaricerca di una definizione condivisa di autonomia, a elementi operativi per la determinazionedei costi standard e suggerendo strumenti e comportamenti da adottare perrendere effettiva l’attuazione del federalismo fiscale
Integrating declarative models and HMMs for online gesture recognition
No full textIn the last years, the introduction of new, precise and pervasive tracking devices has contributed to the popularity of gestural interaction. In general, the effectiveness of such interfaces depends on two components: the algorithm used for accurately recognizing the user movements and the guidance provided to users while executing gestures. In this paper, we discuss a work in progress research for connecting these two components and increasing their effectiveness: the recognition algorithm supports the implementation of feedback the and feed-forward mechanisms, providing information on the identified gesture parts in real time, while developers define complex gestures starting from simple primitives
Alpine Complex Landscape Environment
No full textNell'ambito del programma triennale Torino e le Alpi (2014/2016), la Compagnia di San Paolo ha promosso un Bando di ricerca applicata per lo sviluppo economico e sociale dei territori alpini di Piemonte, Liguria e Valle d'Aosta.Il bando ha inteso sostenere ricerche su politiche, modelli, progetti in Italia e all'estero e studiarne le condizioni per la trasferibilità/fattibilità in territorio montano, con soluzioni sperimentali, attraverso la selezione di buone pratiche, modelli operativi e idee originali, con forte carattere innovativo. Dei 153 progetti presentati 20 sono stati vincitori e finanziati tra cui La ricerca Alpine Landscape Environment (referente e coordinatore scientifico Daniele Regis) . La pubblicazione contiene l'abstract dei progetti presentati con link del report completo. La ricerca Alpine Cle ha inteso costituire un sistema transdisciplinare come modello per un'applicazione puntuale degli indirizzi del piano paesistico regionale Le discipline della Progettazione del paesaggio e architettonica (D. Regis) della cartografia e GIS (N. Spano) della valutazione economica dei progetti (C. Coscia) sono state interpellate attraverso processi congiunti di analisi per costruire scenari disviluppo sostenibile in particolare per la mobilità e infrastrutture per lo sviluppo con innovativi metodi di ricerca scientifica e modelli ad alta trasferibilità
Applying Long-Short Term Memory Recurrent Neural Networks for Real-Time Stroke Recognition
No full textThis note discusses how to build a real-Time recognizer for stroke gestures based on Long Short Term Memory Recurrent Neural Networks. The recognizer provides both the gesture class prediction and the completion percentage estimation for each point in the stroke while the user is performing it. We considered the stroke vocabulary of the N datasets, and we defined four different architectures. We trained them using synthetic data, and we assessed the recognition accuracy on the original N datasets. The results show an accuracy comparable with state of the art approaches classifying the stroke when completed, and a good precision in the completion percentage estimation
Optimizing genetic parameters of CSM-CERES wheat and CSM-CERES maize for durum wheat, common wheat, and maize in Italy
No full textThe expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10- year (2001-2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha-1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions
DG3: Exploiting Gesture Declarative Models for Sample Generation and Online Recognition
No full textIn this paper, we introduce DG3, an end-to-end method for exploiting gesture interaction in user interfaces. The method allows to declaratively model stroke gestures and their sub-parts, generating the training samples for the recognition algorithm. In addition, we extend the algorithms of the $-family for supporting the online (i.e., real-time ) stroke recognition and their parts, as declared in the models. Finally, we show that the method outperforms existing approaches for online recognition and has comparable accuracy with offline methods after a few gesture segments
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