803 research outputs found
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
Audio-based music classification with a pretrained convolutional network
Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all available data in a supervised fashion, so we use unsupervised pretraining to be able to harness the entire dataset: we train a convolutional deep belief network on all data, and then use the learnt parameters to initialize a convolutional multilayer perceptron with the same architecture. The MLP is then trained on a labeled subset of the data for each task. We also train the same MLP with randomly initialized weights. We find that our convolutional approach improves accuracy for the genre recognition and artist recognition tasks. Unsupervised pretraining improves convergence speed in all cases. For artist recognition it improves accuracy as well
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
The bacterial strains characterization problem
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
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