About Relics and miracles: from the theological explanation to the authority of pontifical truth

1 archivo PDF (18 páginas). fhquinquagintatresEl artículo intenta explicar el milagro desde la novedad del cristianismo: el santo como intercesor y hacedor de milagros. La explicación del milagro en Occidente se apoyó en la teología de Agustín de Hipona y, posteriormente, en la escolástica tomista. En el siglo XII comenzó a darse el monopolio de la certificación de lo milagroso. La Santa Sede inventó nuevas prácticas para decir qué era milagroso y qué no. La validación de los testimonios para la santificación sólo se puede hacer desde la “verdad retórica” y no desde la “verdad científica”. Para explicar las evidencias de estos procesos recurro al “giro lingüístico”. Abstract: This article seeks to explain “miracles” in a world in which a new character, one who performs miracles, appears: the saint. The explanation for miracles in the Western World was initially based on Augustine of Hippo’s theology, and soon after that, on Thomist Scholastics. During the XIIth century, the Holy See began to monopolize the canonization process through the invention of new practices. Since the validation of testimonies needed for sanctification can only be achieved on the basis of “rhetorical truth”, not on “scientifical truth”, to explain the evidence that these processes use, I will recur to the “Linguistic Turn”. PALABRAS CLAVE: Ciencia, retórica, santos, canonización, giro lingüístico. KEY WORDS: Science, rhetoric, saints, canonization, linguistic turn

Convolutional Neural Networks Applied to House Numbers Digit Classification

We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.Comment: 4 pages, 6 figures, 2 table

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The accurate characterization of collections of bacterial strains is a major scientific challenge, since bacteria are indeed responsible of significant plant diseases and thus subjected to official control procedures (e.g., in Europe, Directive 2000/29/EC). The development of diagnostic tests is therefore an important issue in order to routinely identify strains of these species

SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures

Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend to react to feedback with defensive or oblivious responses. This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future. This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures. We collect a dataset of 10k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.Comment: Accepted at ACL 202