103,489 research outputs found
GAMBA C., Le conseguenze del mancato espletamento del tentativo obbligatorio di conciliazione per i contratti certificati (Nota a T. Bari, 2 marzo 2017)
No full textArgomento relativo ad istituti di diritto processuale civile del lavoro
Il convivio. Tra una politica globale dell'ospitalità e un'etica individuale dell'amicizia ospitale
No full textPreferences-dependent learning in the centipede game: The persistence of mistrust
No full textA candidate explanation for the persistence of heterogeneous behavior in a sequential social dilemma played many times is the existence of heterogeneous preferences. Preferences-dependent conjectures about opponents’ behavior are an additional source of heterogeneity. By behaving differently, different preference types acquire different information. Thus, when observing only outcomes of own past interactions heterogeneous and possibly wrong conjectures about opponents’ strategies may endogenously arise and persist. In a Centipede game experiment played for forty rounds, we manipulate the type of ex post information and the method of play. We find that, when the game is played in its reduced normal form and subjects have only access to personal statistics, heterogeneity of behavior across preference types persists in the long run. In this case, behavior resembles a self-confirming equilibrium: selfish subjects take at earlier nodes due to an unjustified lack of trust. When subjects have also access to public statistics, heterogeneity disappears: selfish subjects tend to pass more often and play moves towards Bayes Nash equilibrium
Guest Recital by Grace Feldman, viola da gamba, March 17, 1965
No full textThis is the concert program of the Guest Recital by Grace Feldman, viola da gamba on Wednesday, March 17, 1965 at 8:30 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were Il Lamento di Tristano - Italian, xiv c., Ducta royale - French, xiii c., Trotto - English, xiv c., La Caccia by Thomas Morley, Il Doloroso by T. Morley, Il Grillo by T. Morley, Sonata in D major by Georg Philipp Telemann, Treizième concert à deux instrumens à l'unisson by François Couperin, Woodycock by Anonymous, La Polonaise by Marin Marais, and Sonata No. 1 in G major by Johann Sebastian Bach. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund
Letter, [Author unclear] to Paulina T. Merritt
No full textHandwritten letter to Paulina Merritt from an unknown author, October 1, 1876.
Scherzi musicali per la viola da gamba con basso continuo ad libitum
No full textScore: viola da gamba and figured bass; realization of bass for keyboard instrument below.With facsimile of original t.-p.Mode of access: Internet.Deacidified
Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia
No full textFlood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management
Escaping the "tortoise shell paradox": Digitalization and servitization in the green building construction industry-the case of Marlegno
No full textMultisource Urban Classification: Joint Processing of Optical and SAR Data for Land Cover Mapping
No full textIn this paper we present and compare different techniques for the fusion of multitemporal SAR and multiband optical images. We consider both neuro-fuzzy and statistical approaches for the exploitation of the contextual information and the classification, and different schemes for the multisensor fusion. The proposed techniques are applied to a set of two multitemporal SAR and a Landsat multiband image of an urban area. Results show that it is possible to fully exploit the potentialities of the two sensors, by appropriately fusing their information. In particular, the proposed schemes are useful to retain at the same time the change detection capability and the best possible classification accuracy, thus they are of practical interest for civil protection applications
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