Prolegomena to the Study of Early Modern Commentators on Johannes de Sacrobosco’s Tractatus de sphaera

Valleriani, Matteo

doi:10.1007/978-3-030-30833-9_1

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…This commentary was republished 23 times from 1549 (hdl.handle.net/21.11103/sphaera.100137) to 1620 (hdl.handle.net/21.11103/sphaera.100358). As shown by other research, not only did the text of Élie Vinet have considerable success over time, but he, as a scientist, was also a very central figure in the formation of scientific university textbook content 25 .…”

Section: Discussionmentioning

confidence: 83%

“…The last treatise collected in the corpus analyzed here was printed in 1650, meaning the corpus covers a period of 178 years and as such is the time span of our study, a temporal interval that includes the advent of heliocentrism, the calendar reform and closes at the beginning of the emergence of classical mechanics with the publication of Galileo’s Two new sciences (1642). Finally, though with very different rates of production, the textbooks were printed in 40 European cities 25 .…”

Section: Methodsmentioning

confidence: 99%

“…The last treatise collected in the corpus analyzed here was printed in 1650, meaning the corpus covers a period of 178 years and as such is the time span of our study. Finally, though with very different rates of production, the textbooks were printed in 40 European cities [25]. We collected all relevant pieces of information concerning all the texts that were printed together with the original treatise of Sacrobosco.…”

Section: Data and Networkmentioning

confidence: 99%

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Evolution and transformation of early modern cosmological knowledge: a network study

Zamani

Tejedor

Vogl

et al. 2020

Sci Rep

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We investigated the evolution and transformation of scientific knowledge in the early modern period, analyzing more than 350 different editions of textbooks used for teaching astronomy in European universities from the late fifteenth century to mid-seventeenth century. These historical sources constitute the Sphaera Corpus. By examining different semantic relations among individual parts of each edition on record, we built a multiplex network consisting of six layers, as well as the aggregated network built from the superposition of all the layers. The network analysis reveals the emergence of five different communities. The contribution of each layer in shaping the communities and the properties of each community are studied. The most influential books in the corpus are found by calculating the average age of all the out-going and in-coming links for each book. A small group of editions is identified as a transmitter of knowledge as they bridge past knowledge to the future through a long temporal interval. Our analysis, moreover, identifies the most impactful editions. These books introduce new knowledge that is then adopted by almost all the books published afterwards until the end of the whole period of study. The historical research on the content of the identified books, as an empirical test, finally corroborates the results of all our analyses.

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Section: Discussionmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Data and Networkmentioning

confidence: 99%

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Evolution and transformation of early modern cosmological knowledge: a network study

Zamani

Tejedor

Vogl

et al. 2020

Sci Rep

Self Cite

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“…-'Unconstrained Facial Images' (UFI) [71], -'Labeled Faces in the Wild' (LFW) [72], -'The Sphaera Corpus' [29], [73].…”

Section: How Transferable Is the Similarity Model?mentioning

confidence: 99%

Building and Interpreting Deep Similarity Models

Eberle

Büttner

Kräutli

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on distances or similarities. Before similarities are used for training an actual machine learning model, we would like to verify that they are bound to meaningful patterns in the data. In this paper, we propose to make similarities interpretable by augmenting them with an explanation. We develop BiLRP, a scalable and theoretically founded method to systematically decompose the output of an already trained deep similarity model on pairs of input features. Our method can be expressed as a composition of LRP explanations, which were shown in previous works to scale to highly nonlinear models. Through an extensive set of experiments, we demonstrate that BiLRP robustly explains complex similarity models, e.g. built on VGG-16 deep neural network features. Additionally, we apply our method to an open problem in digital humanities: detailed assessment of similarity between historical documents such as astronomical tables. Here again, BiLRP provides insight and brings verifiability into a highly engineered and problem-specific similarity model.

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“…-'Unconstrained Facial Images' (UFI) [72], -'Labeled Faces in the Wild' (LFW) [73], -'The Sphaera Corpus' [29], [74].…”

Section: How Transferable Is the Similarity Model?mentioning

confidence: 99%

Building and Interpreting Deep Similarity Models

Eberle,

Büttner,

Kräutli

et al. 2020

Preprint

Self Cite

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Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'. Before similarities are used for training an actual machine learning model, we would like to verify that they are bound to meaningful patterns in the data. In this paper, we propose to make similarities interpretable by augmenting them with an explanation in terms of input features. We develop BiLRP, a scalable and theoretically founded method to systematically decompose similarity scores on pairs of input features. Our method can be expressed as a composition of LRP explanations, which were shown in previous works to scale to highly nonlinear functions. Through an extensive set of experiments, we demonstrate that BiLRP robustly explains complex similarity models, e.g. built on VGG-16 deep neural network features. Additionally, we apply our method to an open problem in digital humanities: detailed assessment of similarity between historical documents such as astronomical tables. Here again, BiLRP provides insight and brings verifiability into a highly engineered and problem-specific similarity model.

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Prolegomena to the Study of Early Modern Commentators on Johannes de Sacrobosco’s Tractatus de sphaera

Cited by 13 publications

References 9 publications

Evolution and transformation of early modern cosmological knowledge: a network study

Evolution and transformation of early modern cosmological knowledge: a network study

Building and Interpreting Deep Similarity Models

Building and Interpreting Deep Similarity Models

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