Das Treibhausgas‐Schnüffelnetz Arnold, Sabrina; Lindauer, Matthias; Muller, Jennifer ...
Physik in unserer Zeit,
01/2020, Volume:
51, Issue:
1
Journal Article
Zusammenfassung
Das Integrated Carbon Observation System (ICOS) ist eine auf mindestens 20 Jahre ausgelegte europäische Forschungsinfrastruktur. Fertig ausgebaut, sollen mehr als 130 über Europa ...verteilte Stationen hochpräzise und zeitlich hochaufgelöste Treibhausgasmessungen bereitstellen. Ermöglicht wird dies zum einen durch extrem empfindliche Messtechniken, zum Beispiel Laserspektroskopie, zum anderen durch eine hohe Standardisierung der Messsysteme, der Erfassung und Auswertung der Daten sowie einer strengen Qualitätskontrolle und Qualitätssicherung. Basierend auf ICOS‐Daten und mit geeigneten Inversionsmodellen sollen Veränderungen bei den Treibhausgasflüssen in Deutschland verfolgt werden. Damit wird der Erfolg von Maßnahmen zur Emissionsminderung von Treibhausgasen verifizierbar.
With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to ...multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method and to motivate new multimodal data fusion techniques of deep learning. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.
Data Fusion by Matrix Factorization Zitnik, Marinka; Zupan, Blaz
IEEE transactions on pattern analysis and machine intelligence,
2015-Jan.-1, 2015-Jan, 2015-1-1, 20150101, Volume:
37, Issue:
1
Journal Article
Peer reviewed
Open access
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous ...data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system's constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.
In January 2024, a targeted conference, ‘CellVis2’, was held at Scripps Research in La Jolla, USA, the second in a series designed to explore the promise, practices, roadblocks, and prospects of ...creating, visualizing, sharing, and communicating physical representations of entire biological cells at scales down to the atom.
In January 2024, a targeted conference, ‘CellVis2’, was held at Scripps Research in La Jolla, USA, the second in a series designed to explore the promise, practices, roadblocks, and prospects of creating, visualizing, sharing, and communicating physical representations of entire biological cells at scales down to the atom.
•Methods of integrating ground and aerial meta-data for localization and reconstruction are reviewed.•Localization methods are reviewed in terms of image based methods and structure based ...methods.•Reconstruction methods are reviewed in terms of image based methods and laser based methods.
Localization and reconstruction are two highly related research areas. Both of them have developed rapidly in recent years. Apparently, with the help of ground and aerial meta-data integration, the performance of both localization and reconstruction can go a step further. For localization, aerial meta-data provides a global reference, by which the ground query can achieve a cumulative error free absolute localization. As for reconstruction, a complete and detailed model can be reconstructed by integrating ground and aerial meta-data. Though with many advantages, the integration itself is non-trivial. It is difficult to obtain ground-to-aerial correspondences neither in 2D manner nor in 3D manner. That is because: (1) The differences between the ground and aerial images in viewpoint, scale, illumination, etc. are notable; (2) The discrepancies between the ground and aerial point clouds in terms of point density, accuracy, noise level, etc. are very large. To deal with these problems, lots of methods have been proposed recently. In this paper, the methods of integrating ground and aerial meta-data for localization and reconstruction are reviewed respectively. Though many intermediate results with high quality have been achieved, we hope that inspired by the reviewed methods in this paper, more thorough methods and impressive results would emerge.
A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell ...correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.
Identifying cell-to-cell correspondences between unpaired datasets from different single cell protocols promises to provide a more comprehensive view of cellular states.Integration of unpaired data from multiple modalities is more complicated than single-omics integration due to a lack of feature correspondence across modalities and ground truth information about biological differences between modalities.Retention of biological variation during multi-omic data integration has been insufficiently addressed to date, but is essential to leverage complementary information from different omics layers.Ground truth data can now be provided by new paired multi-omics assays. This will inform robust associations between features of different modalities and reveal modality-specific biological patterns that may also help to improve methods for multimodal integration of unpaired data.
Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined ...object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.
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