The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
This book breaks new ground towards an understanding of the mental processes involved in presupposition, the comprehension of information taken for granted. Various psycholinguistic experiments are discussed to support the idea that involved in ordinary language comprehension are complex and demanding cognitive processes. The author demonstrates that these processes exist not only at the explicit level of an utterance but also at a deeper level of computing, where the background information taken for granted as already known and shared between interlocutors is processed. The author shows that experimental research can suggest new theoretical models for presupposition, thus this book will be of interest to researchers and students of psycholinguistics, the philosophy of language and experimental pragmatics.
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
In fourteenth-century Italy, literacy became accessible to a significantly larger portion of the lay population (allegedly between 60 and 80 percent in Florence) and provided a crucial means for the vernacularization and secularization of learning, and for the democratization of citizenship. Dante Alighieri's education and oeuvre sit squarely at the heart of this historical and cultural transition and provide an ideal case study for investigating the impact of Latin education on the consolidation of autonomous vernacular literature in the Middle Ages, a fascinating and still largely unexamined phenomenon. On the basis of manuscript and archival evidence, Gianferrari reconstructs the contents, practice, and readings of Latin instruction in the urban schools of fourteenth-century Florence. It also shows Dante's continuous engagement with this culture of teaching in his poetics, thus revealing his contribution to the expansion of vernacular literacy and education. The book argues that to achieve his unprecedented position of authority as a vernacular intellectual, Dante conceived his poetic works as an alternative educational program for laypeople, who could read and write in the vernacular but had little or no proficiency in Latin. By reconstructing the culture of literacy shared by Dante and his lay readers, Dante's Education shifts critical attention from his legacy as Italy's national poet, and a "great books" author in the Western canon, to his experience as a marginal intellectual engaged in advancing a marginal culture.
Widely admired for his paintings of exquisitely beautiful Madonnas, Florentine Renaissance friar-artist Fra Filippo Lippi (c. 1406-69) gained renown also for his love affair with the nun Lucrezia who bore their son, Filippino Lippi, later a well-known painter himself. In this beautiful and compelling book, Megan Holmes shines new light on Lippi's life and career, from the first paintings he created while a friar in Santa Maria del Carmine to the later works he painted when living outside the monastery for the Medici family, their supporters, and other patrons. Focusing especially on the fascinating conjunction of Lippi's work as a painter and his experiences as a Carmelite friar, Holmes transforms our understanding of Filippo Lippi and of the way art was produced and viewed in fifteenth-century Florence. Unlike most monastic artists, Fra Filippo learned to paint only after joining a religious order. In the first section of the book, the author considers how the doctrines, rules, rituals, and practices of the Carmelites shaped Lippi's art and manner of envisioning sacred subjects. In the second section, Holmes discusses Lippi's life and painting after he left the monastery, demonstrating how his mature work broke new ground but continued to draw upon Carmelite influences. The final section of the book looks closely at three altarpieces Fra Filippo painted for monastic institutions and sets them in a broader social and religious context.
We analyze a union of financially-integrated yet politically-sovereign countries, where households in the Northern core of the union lend to those in the Southern periphery in a unified debt market subject to a borrowing constraint. This constraint generates sudden stops throughout the South, depresses the intra-union interest rate, and reduces Northern welfare below its unconstrained level, while having ambiguous effects on Southern welfare. During sudden stops, Pareto improvements can be achieved using North-to-South governmental loans if Southern governments have the capacity to commit to repay, or using a combination of Southern debt relief and budget-neutral taxes and subsidies if they do not. From the pre-crisis perspective, it is Pareto-improving to allow loans and debt relief to be negotiated in later sudden-stop periods as long as the regions in the union are sufficiently heterogeneous to begin with. We show that our results are robust to production and to limited financial openness of the union.
The re-creation of classically inspired armor is invariably associated with Filippo Negroli, the most innovative and celebrated of the renowned armorers of Milan.
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