999 resultados para collection kernel


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Articles by Fuchs; letters to his son; obituaries.

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Letters from Max Warburg (May 1938) and Otto Hirsch (March 1939), thanking him for his contributions to Reichsvertretung and Hilfsverein. Also included are pages from a Jewish calendar for Wuerttemberg 1932/33.

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Epstein, Felix, 1972

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Documents pertaining to the protected Jew Lion Eppenstein in Berlin; 1798-1813

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Article about fate of Jews from Singen during Third Reich (by Reinhild Kappes); book review about Jews in Singen during Third Reich (in Suedkurier, August 1990).

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Copies of photos and documents pertaining to Regina Teitelbaum's sports activities in Jewish sports events during the 1920s and 1930s, including clippings about these events (from Der Makkabi, 1935), and certificates for Teitelbaum's accomplishments.

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Excerpts from Salzmann's autobiography "Divertimento". Book review in Die Welt (September 1991).

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Family trees reaching back to the 1700s and other materials pertaining to the extended Bronne family. Also included is a typescript about the Bronne Shirt Company in Glens Falls, New York.

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Various photographs of sculptures with explanatory notes, all connected with experience during WW II

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Letter to Aufbau with a series of poems entitled: "Ich moechte wieder beten lernen" (1984); another collection of poetry entitled: Seele im Spiegel (Mirror of the Soul)

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Eight German poems by Karl Seemann in Bad Bentheim

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M.A. Pach, 1995

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Printed materials about the first Jewish museum in Vienna, Austria, which existed 1895 to 1938. Also included is a biography about Maurice (Moritz) Bronner, curator at the museum 1910-1914, by his son, Felix Bronner. Folder 2 holds copies of 2 photographs of Maurice Bronner, circa 1910 and 1965.

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Correspondence and clipping referring to contribution of 'Action Atonement' to build a synagogue in Villeurbanne.

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Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.