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  1. Book: Motorisches Lernen in der Neuroreha

    Huber, Martin / Janssen, Christina / Erzer Lüscher, Florian / Cox Steck, Gail

    2023  

    Author's details Martin Huber, Christina Janssen, Florian Erzer Lüscher, Gail Andrea Cox Steck
    Keywords Neurologie ; Neurorehabilitation ; Ergotherapie ; Physiotherapie ; Multiple Sklerose ; ICF ; Schlaganfall ; Querschnittlähmung ; 105 ; Physikalische Therapie ; Rehabilitation ; Nervenkrankheit ; Motorisches Lernen
    Subject Bewegungslernen ; Psychomotorisches Lernen ; Sensomotorisches Lernen ; Sensumotorisches Lernen ; Nervenkrankheiten ; Neurologische Erkrankung ; Neurologische Krankheit ; Nervensystem ; Nervensystemkrankheit ; Nervous disease ; Rehabilitierung ; Physiotherapie
    Language German
    Size 232 Seiten, Illustrationen, 24 cm x 17 cm
    Publisher Georg Thieme Verlag
    Publishing place Stuttgart
    Publishing country Germany
    Document type Book
    HBZ-ID HT021292423
    ISBN 978-3-13-244278-8 ; 3-13-244278-X ; 9783132442795 ; 9783132442801 ; 3132442798 ; 3132442801
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2022 A 1193 on loan 0 Vormerkungen

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  2. Book ; Thesis: Control of protein abundance by stable unannotated antisense transcripts in Saccharomyces cerevisiae

    Huber, Florian

    2017  

    Author's details presented by Florian Huber, B.Sc., M.Sc
    Subject code 570
    Language English
    Size 111 Seiten, Illustrationen, Diagramme
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruperto-Carola University of Heidelberg, 2017
    HBZ-ID HT019376017
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2017 E 1110 order for loan Magazin

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  3. Book ; Online: Consumer data protection in Brazil, China and Germany : a comparative study

    Metz, Rainer / Binding, Jörg / Haifeng, Pan / Huber, Florian

    2016  

    Keywords Law ; consumer data protection ; enforcement ; regulations
    Size 1 Online-Ressource
    Publisher Universitätsverlag Göttingen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021028714
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  4. Book ; Online: Durch Lesen sich selbst verstehen : Zum Verhältnis von Literatur und Identitätsbildung

    Huber, Florian

    2008  

    Keywords Education ; Identität ; Narrative Identität ; Literatur ; Bibliotherapie ; Lesen ; Sozialität ; Sozialpsychologie ; Bildungssoziologie ; Sozialpädagogik ; Psychoanalyse ; Psychologie ; Literature ; Social Relations ; Social Psychology ; Sociology of Education ; Social Pedagogy ; Psychoanalysis ; Psychology
    Size 1 electronic resource (246 pages)
    Publisher transcript Verlag
    Publishing place Bielefeld
    Document type Book ; Online
    Note German ; Open Access
    HBZ-ID HT021029763
    ISBN 9783899428278 ; 3899428277
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Article: Subspace shrinkage in conjugate Bayesian vector autoregressions.

    Huber, Florian / Koop, Gary

    Journal of applied econometrics (Chichester, England)

    2023  Volume 38, Issue 4, Page(s) 556–576

    Abstract: Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior ... ...

    Abstract Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical properties of our prior, we show that it successfully detects the number of factors in simulations and that it leads to forecast improvements using US macroeconomic data.
    Language English
    Publishing date 2023-03-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 1500458-2
    ISSN 1099-1255 ; 0883-7252
    ISSN (online) 1099-1255
    ISSN 0883-7252
    DOI 10.1002/jae.2966
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Modelling of material recovery from waste incineration bottom ash.

    Huber, Florian

    Waste management (New York, N.Y.)

    2020  Volume 105, Page(s) 61–72

    Abstract: Bottom ash from municipal solid waste incineration is usually treated in order to recover valuable materials like metals and to generate a mineral material for utilisation in construction industry or disposal. At present, different technologies and ... ...

    Abstract Bottom ash from municipal solid waste incineration is usually treated in order to recover valuable materials like metals and to generate a mineral material for utilisation in construction industry or disposal. At present, different technologies and combinations thereof are used for bottom ash treatment resulting in different quantities and qualities of the final products (metals and minerals). So far, a comparison of these technologies is hardly possible based on the available literature. Hence, the present paper presents and applies a modelling approach that allows predicting the quantities and qualities (in terms of composition) of the final outputs of bottom ash treatment plants. In particular, material flow analysis models of five different bottom ash treatment plants were established on goods, material and element level and the mass and composition of the output flows of these plants were calculated based on an input of 118,000 Mg/a of bottom ash dry matter. The highest recovery of metals (up to 8640 ± 820 Mg/a iron, 1530 ± 220 Mg/a aluminium, 627 ± 73 Mg/a stainless steel and 608 ± 70 Mg/a heavy non-ferrous metals) can be achieved in plants that apply comminution before any ageing processes and are equipped with jiggers, inductive sorting systems and/or a high number of eddy current separators. The iron scrap fractions separated from bottom ash are contaminated by up to 114 ± 44 mg/kg Cd and up to 9900 ± 3300 mg/kg Cu, which might impair their suitability for recycling. Only minor differences in the composition of mineral material generated by different treatment plants could be observed.
    MeSH term(s) Coal Ash ; Incineration ; Metals ; Metals, Heavy ; Recycling ; Refuse Disposal ; Solid Waste
    Chemical Substances Coal Ash ; Metals ; Metals, Heavy ; Solid Waste
    Language English
    Publishing date 2020-02-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2001471-5
    ISSN 1879-2456 ; 0956-053X
    ISSN (online) 1879-2456
    ISSN 0956-053X
    DOI 10.1016/j.wasman.2020.01.034
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning.

    Huber, Florian / Inderka, Alvin / Steinhage, Volker

    Sensors (Basel, Switzerland)

    2024  Volume 24, Issue 3

    Abstract: Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield ... ...

    Abstract Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.
    Language English
    Publishing date 2024-01-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s24030770
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

    Huber, Florian / Schulz, Stefan / Steinhage, Volker

    Sensors (Basel, Switzerland)

    2024  Volume 24, Issue 5

    Abstract: Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to ... ...

    Abstract Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.
    Language English
    Publishing date 2024-02-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s24051604
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Clinical and Molecular Aspects Associated with Defects in the Transcription Factor POU3F4: A Review.

    Bernardinelli, Emanuele / Huber, Florian / Roesch, Sebastian / Dossena, Silvia

    Biomedicines

    2023  Volume 11, Issue 6

    Abstract: X-linked deafness (DFNX) is estimated to account for up to 2% of cases of hereditary hearing loss and occurs in both syndromic and non-syndromic forms. ...

    Abstract X-linked deafness (DFNX) is estimated to account for up to 2% of cases of hereditary hearing loss and occurs in both syndromic and non-syndromic forms.
    Language English
    Publishing date 2023-06-12
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11061695
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Corrigendum

    Franz K. Huber / Florian P. Schiestl

    Frontiers in Ecology and Evolution, Vol

    Scent releasing silicone septa: a versatile method for bioassays with volatiles

    2023  Volume 11

    Keywords floral scent ; pollination ; volatile ; chemical ecology ; bioassay ; herbivore ; Evolution ; QH359-425 ; Ecology ; QH540-549.5
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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