4,078 research outputs found
Romana 1.ª y 2.ª con Romana negra ancha, novedad : muy llamativo y propio para reclamos y anuncios
No full textRelación pegada en el interior de la portada posterior sobre las características y tipología de la letra RomanaSegún datos del librero, esta obra fue publicada ca. 1920Catálogo plegado con el muestrario de la tipografía Romana 1.ª y 2.ª creada por la Fundición Richard Gans, con distintos ejemplos de estas tipografías en tinta negra y rojaPortada con orla tipográfica a varias tintas, emblema de la Fundición Gans y tipografía en tinta marrón y verde en tipo de letra romanaEn p. [1] viñeta con escudo de la Fundición con el texto: "Pidan tipos orlas Gans" en tinta negra y roj
Landsberger Gans Family Collection 1887-2008 Bulk dates: 1934-1972
No full textThis collection is arranged in four series and includes family correspondence, family trees, memoirs, official documents, photographs, printed materials, and professional correspondence belonging to Carl Heinz Gans, Ruth Gans, and their respective extended families.The collection documents the lives and work of the members of the Gans and Landsberger families, with especial emphasis on Carl Heinz Gans and Ruth Gans née Landsberger. There is an abundance of materials related to Carl Gans's professional experience in the lumber industry and conservation interests, including many photographs of his travels to South America in the early 1970s (Series III). There is also a large amount of printed material relating to the family's genealogy (Series II) and emigration from Germany to North America. The Gans's experiences returning to Germany after many decades are particularly well covered (Series II). The collection also includes several letters from Robert F. Kennedy and a framed autograph of his brother, John F. Kennedy (Series IV).A typescript, 448 p., of a novel ‘Den Abgrund entlang’ von Alter Ego [Leopold Landsberger] has been removed to the LBI Manuscript Collection.Portrait photographs of Ruth Landsberger at age 6 and of her mother Elly Stiasny Landsberger, as well as other photographs of Ruth Landsberger; Elly Stiasny Landsberger and her family; and of the Gans family have been transferred to the LBI Photograph CollectionA book by Mettay, Joël : Die verlorene Spur. Auf der Suche nach Otto Freundlich , has been removed to the LBI LibraryA commemorative silver tray for Carl H. Gans, 1970 and a birthday cup for Leopold Landsberger have been transferred to the LBI Art and Objects CollectionVideo tapes removed to the LBI LibraryRuth Gans (née Landsberger) was born in 1920 in Berlin, the daughter of the lawyer Leopold Landsberger and his wife Hedwig Elly, née Stiasny. She left Germany in 1937 for Switzerland, the Netherlands, and France, eventually arriving in the United States. Ruth Gans was the wife of Carl Heinz Gans, who was active for many years in the lumber and veneer business.Processeddigitize
Demographic forecasting in the Netherlands 1895-1945. The analysis and implications of a paradigm shift
No full textChildren and the urban environment learning experience. Evaluation of the WGBH-TV educational project.
No full textGans (Sheldon P) & Kahn (Howard M
Using autoencoders on differentially private federated learning GANs
No full textMachine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning, differential privacy and GANs can be used to work with private data without giving away a users' privacy. Recently, two implementations of such combinations have been published: DP-Fed-Avg GAN and GS-WGAN. This paper compares their performance and introduces an alternative version of DP-Fed-Avg GAN that makes use of denoising techniques to combat the loss in accuracy that generally occurs when applying differential privacy and federated learning to GANs. We also compare the novel adaptation of denoised DP-Fed-Avg GAN to the state-of-the-art implementations in this field.CSE3000 Research ProjectComputer Science and Engineerin
Sefer ṣemaḥ Dawid
No full textTh. 2 mit besd. Titel u. spec. Pag.Jüdische Chronik von d. Erschaffung der Welt bis z. J. 1592Ersch.-jahr n. jüd. Zeitrechnung: [5]352Steinschneider, Cat. bibl. Bodl. p. 862 N 4805
Cercolophia steindachneri Strauch
No full text<i>Cercolophia steindachneri</i> Strauch <p> <i>Amphisbaena steindachneri</i> Strauch, 1881, col. 81. TYPE LOCALITY: ‘‘Brasilien (Caigara; Mattogrosso).’’ Lectotype: NMW 12343 (Mato Grosso) (Gans, 1964f: 389). Lectoparatypes: NMW 12342 (Caigara); ZIL 312 (Brazil).</p> <p> <i>Rhinoblanus oxyrhynchus</i> Strauch, 1881: col. 84. Manuscript name for types of <i>A. steindachneri</i>. Not available as published in synonymy (International Code of Zoological Nomenclature, 1961: art. 11d.).</p> <p>DISCUSSION OF FORM: Mertens (1930: 164) and Gans (1964d: 391). See also Aquino et al. (1996, distribution), Dirksen and de la Riva (1999, distribution), Fugler (1989), Gans (1971c), Goeldi (1902), Griffin (1917), Montero and Terol (1999, distribution), Tiedemann and Häupl (1980, variation), and Vanzolini (2000).</p> <p>RANGE: Southwestern Brazil; possibly northwestern Paraguay.</p>Published as part of <i>GANS, CARL, 2005, Checklist And Bibliography Of The Amphisbaenia Of The World, pp. 1-130 in Bulletin of the American Museum of Natural History 2005 (289)</i> on page 26, DOI: 10.1206/0003-0090(2005)289<0001:CABOTA>2.0.CO;2, <a href="http://zenodo.org/record/5361528">http://zenodo.org/record/5361528</a>
Agamodon anguliceps W. C. H. Peters
No full text<i>Agamodon anguliceps</i> W.C.H. Peters <p> <i>Agamodon anguliceps</i> W.C.H. Peters, 1882c: 580. TYPE LOCALITY: ‘‘Barava (Africa orientalis)’’ <b>=</b> Brava, Somali Republic. Syntypes: ZMB 10189, 10190.</p> <p>DISCUSSION OF FORM: Gans (1960: 179), Gans and Pandit (1965: 82). See also Anderson (1901, list), Boettger (1893c, distribution), Bonin (1965, anatomy), Boulenger (1890a, anatomy), Gans and Pandit (1965, distribution), Guibé (1954, list), Joger (1987, distribution), Lanza (1983, distribution), Obst and Wranik (1998, distribution), Peters (1882c, distribution), and Scortecci (1929, distribution).</p> <p>RANGE: South­central coast of Somali Republic, eastern Ethiopia.</p>Published as part of <i>GANS, CARL, 2005, Checklist And Bibliography Of The Amphisbaenia Of The World, pp. 1-130 in Bulletin of the American Museum of Natural History 2005 (289)</i> on page 42, DOI: 10.1206/0003-0090(2005)289<0001:CABOTA>2.0.CO;2, <a href="http://zenodo.org/record/5361528">http://zenodo.org/record/5361528</a>
Monte Carlo simulation of SDEs using GANs
No full textGenerative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can also be used to approximate one-dimensional Ito ^ stochastic differential equations (SDEs). We propose a scheme that approximates the path-wise conditional distribution of SDEs for large time steps. Standard GANs are only able to approximate processes in distribution, yielding a weak approximation to the SDE. A conditional GAN architecture is proposed that enables strong approximation. We inform the discriminator of this GAN with the map between the prior input to the generator and the corresponding output samples, i.e. we introduce a ‘supervised GAN’. We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation. The GAN is trained on a dataset obtained with exact simulation. The architecture was tested on geometric Brownian motion (GBM) and the Cox–Ingersoll–Ross (CIR) process. The supervised GAN outperformed the Euler and Milstein schemes in strong error on a discretisation with large time steps. It also outperformed the standard conditional GAN when approximating the conditional distribution. We also demonstrate how standard GANs may give rise to non-parsimonious input-output maps that are sensitive to perturbations, which motivates the need for constraints and regularisation on GAN generators.ImPhys/Practicum supportNumerical Analysi
Synesthesia-inspired cross-modal learning of common representation using GANs
No full textSynesthesia is a phenomenon in which the stimulation of one sensory
modality simultaneously leads to the sensation in one another. A well-known
type of synesthesia is grapheme-color synesthesia, i.e., letters and digits are
consistently associated with speci c colors. Understanding the way crossmodal
perception in synesthesia works has broadened the research in the eld
of arti cial intelligence (AI) and its applications in dealing with multimodal
data. Here, we describe a novel application of the cross-modal generative
adversarial networks (CM-GANs) approach in order to learn the cross-modal
common representation enforced by the shared semantic classes between the
visual letter grapheme modality and the color modality, as in grapheme-color
synesthesia. In order to evaluate the e ectiveness of the model, we perform
two cross-modal retrieval tasks: bi-modal retrieval (i.e., retrieving the correct
matching color instances using letters as queries) and all-modal retrieval
(i.e., retrieving the correct matching letter and color instances using letters as
queries). The experimental results, obtained from the cross-modal retrieval
tasks, are shown to be relatively high, indicating that the shared semantics
between two modalities have a cross-modal e ect in common representation
learning. Regarding multimodal representation learning, we investigate the effectiveness
of the CM-GANs network and discuss the approaches to overcome
its shortcomings. As for grapheme-color synesthesia, we assess the applicability
of the model in mathematically modeling the cross-modal perceptual
association experience
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