Radarinterferometrische Untersuchungen mit ERS-1/2 auf der Antarktischen Halbinsel

Synthetic Aperture Radar Interferometry (InSAR) ermöglicht effiziente Untersuchungen von polaren Regionen, mit denen ein Beitrag zur Bestimmung von Massenbilanzen geleistet werden kann. Dieser Parameter hat entscheidenden Einfluss auf den globalen Meeresspiegel. Die Antarktische Halbinsel ist ein Raum, der besonders empfindlich auf Änderungen der klimatischen Bedingungen reagiert und für den von einem positiven Beitrag zum Meeresspiegelanstieg ausgegangen wird. Zur Quantifizierung dessen, sind präzise Kenntnisse des Verhaltens von Eisströmen, Gletschern und Schelfeisen erforderlich. Die eingesetzte Methodik kann dafür glaziologische Schlüsselparameter wie Fließgeschwindigkeiten und Höheninformationen bestimmen. Diese werden in der vorliegenden Arbeit für zwei Untersuchungsgebiete – King George Island, mit einer auf Klimaänderungen empfindlich reagierenden temperierten Eiskappe und Wilkins Schelfeis, das mit am nördlichsten verbleibende Schelfeis auf der Westseite der Antarktischen Halbinsel, das bereits teilweise zerfällt – abgeleitet. Aufgrund der sich schnell ändernden Oberflächenbedingungen in diesen Regionen, können nur Aufnahmen in zeitlich sehr kurzen Abständen verwendet werden. Daher kommen ausschließlich Radardaten der European Remote Sensing Satellites (ERS) aus bestimmten Missions Phasen, die dies erfüllen, zum Einsatz. Dadurch ermöglicht sich eine Bestimmung von Ergebnissen, die ausschließlich aus Mitte der 1990’er Jahre stammen. Nachdem in dieser Arbeit die Grundlagen der differentiellen Radarinterferometrie erläutert werden, wird die komplette Prozessierungskette der Verarbeitung der Radardaten vorgelegt. Zudem wird auf die Besonderheiten, die sich speziell bei diesen Untersuchungen ergeben, eingegangen. Die im Folgenden genannten, wesentlichen Ergebnisse, erlauben Aussagen mit hoher Genauigkeit, hoher räumlicher Auflösung und gleichzeitig flächenhafter Abdeckung für die Untersuchungsgebiete. Für King George Island wird ein Geschwindigkeitsfeld bestimmt, das die gesamte Insel abdeckt und Geschwindigkeiten von bis zu 120 m/a im Bereich von Auslassgletschern aufweist. Es wird zusätzlich für ein Teilgebiet der Eiskappe ein Höhenmodell mit einer vertikalen Genauigkeit von ± 18 m erstellt. Für das Untersuchungsgebiet Wilkins Schelfeis wird zum ersten Mal ein Geschwindigkeitsfeld, das einen Teil des Schelfeises abdeckt, bestimmt. Dies weist eine Genauigkeit von ± 10 m/a auf und enthüllt differenzierte Fließstrukturen in räumlich hoher Auflösung von 50 m. Es werden erstmalig Geschwindigkeiten für einen Teil der Zuflussgletscher zum Schelfeis mit gleicher Genauigkeit hergeleitet. Die Geschwindigkeiten erreichen dabei Werte von bis zu 265 m/a. Für das Schelfeis werden größtenteils Werte zwischen 35 m/a – 105 m/a gemessen. Für einen Teil der Zuflussgletscher auf dem Gebiet von Alexander Island wird ein Höhenmodell mit 50 m Auflösung und einer Genauigkeit von ± 18 m erstellt. Dies bedeutet eine enorme Verbesserung zu den bisher für diesen Raum verfügbaren Höhenmodellen, sowohl was die vertikale Genauigkeit als auch die räumliche Auflösung betrifft. Zusätzlich werden Untersuchungen zum Einsatz von Tidenmodellen zu InSAR Zwecken angestellt und die Schwimmgrenze des Schelfeises in einem Teilgebiet neu bestimmt. Die aus der Arbeit gewonnenen Ergebnisse sind zum Teil bereits in geophysikalische Modelle (King George Island) eingeflossen bzw. werden in solchen zum Einsatz kommen (Wilkins Schelfeis). Des Weiteren stellen sie eine Basis für Vergleiche mit Daten aus anderen Zeitepochen, wenn verfügbar, zur möglichen Bestimmung von Änderungen im Fließverhalten dar. Damit würde eine Aussage über Reaktionen auf Klimaänderungen und einen potentiellen Beitrag zum Meeresspiegelanstieg möglich

Case study on mathematics pre-service teachers' difficulties in problem posing

This research is presented in a way that provides useful knowledge for successful problem posing by mathematics pre-service teachers. We present a review of the concept of mathematical creativity (by different authors) and review studies that underline the relevance of problem posing in teaching mathematics, studies that consider problem posing a way to identify students' learning patterns and to test them, and studies that relate mathematical competences to problem posing. Participants in the study were 10 pre-service teachers who were successful in problem solving. Data were gathered through qualitative techniques: classroom observations, sequences of tasks, questionnaires, student focus groups and discussion. The case study illustrated some of pre-service teachers' difficulties in problem posing: creating problems that students recognize as relevant to their everyday lives, problems adapted to the school curriculum at a specific educational level, and problems that can be self-corrected

D-NeRF: neural radiance fields for dynamic scenes

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksNeural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions.This work is supported in part by a Google Daydream Research award and by the Spanish government with the project HuMoUR TIN2017-90086-R, the ERA-Net Chistera project IPALM PCI2019-103386 and María de Maeztu Seal of Excellence MDM-2016- 0656. Gerard Pons-Moll is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 409792180 (Emmy Noether Programme, project: Real Virtual Humans).Peer ReviewedPostprint (author's final draft

SMPLicit: Topology-aware Generative Model for Clothed People

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.Comment: Accepted at CVPR 202

Fast TeV variability from misaligned minijets in the jet of M87

The jet of the radio galaxy M87 is misaligned, resulting in a Doppler factor delta~1 for emission of plasma moving parallel to the jet. This makes the observed fast TeV flares on timescales of t_v~5R_g/c harder to understand as emission from the jet. In previous work, we have proposed a jets-in-a-jet model for the ultra-fast TeV flares with t_v<<R_g/c seen in Mrk 501 and PKS 2155-304. Here, we show that about half of the minijets beam their emission outside the jet cone. Minijets emitting off the jet axis result in rapidly evolving TeV (and maybe lower energy) flares that can be observed in nearby radio galaxies. The TeV flaring from M87 fits well into this picture, if M87 is a misaligned blazar.Comment: 9 pages, 5 figures, minor changes, MNRAS, accepte

SMPLicit: Topology-aware generative model for clothed people

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (eg from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.his work is supported in part by an Amazon Research Award and by the Spanish government with the projects HuMoUR TIN2017-90086-R and María de Maeztu Seal of Excellence MDM2016-0656. Gerard Pons-Moll is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 409792180 (Emmy Noether Programme, project: Real Virtual Humans)Peer ReviewedPostprint (published version

G protein variation in respiratory syncytial virus group A does not correlate with clinical severity

Respiratory syncytial virus group A strain variations of 28 isolates from The Netherlands collected during three consecutive seasons were studied by ana

EPI-001, A Compound Active against Castration-Resistant Prostate Cancer, Targets Transactivation Unit 5 of the Androgen Receptor

ACKNOWLEDGEMENTS We thank J. M. Valverde (IRB) as well as the NMR facilities of the University of Barcelona (CCiT UB) and the Instituto de Química Física Rocasolano (IQFR, CSIC) for their assistance in, respectively, protein production and NMR. This work was supported by IRB, ICREA (X.S.), Obra Social “la Caixa” (Fellowship to E.D.M. and CancerTec grants to X.S.) MICINN (CTQ2009-08850 to X.S.), MINECO (BIO2012-31043 to X.S.; CTQ2014-56361-P to A.R), Marató de TV3 (102030 to X.S. and 102031 to E.E.P) the COFUND programme of the European Commission (C.T.W.P., A. R. and X.S.), the European Research Council (CONCERT, contract number 648201, to X.S.), the Ramón y Cajal program of MICINN (RYC-2011-07873 to C.W.B.) the Serra Hunter Programme (E.E.P.) and AGAUR (SGR-2014-56RR14 to E.E.P). IRB Barcelona is the recipient of a Severo Ochoa Award of Excellence from MINECO (Government of Spain)Peer reviewedPostprin