1,003 research outputs found
A non-local inequality and global existence
In this article we prove a collection of new non-linear and non-local
integral inequalities. As an example for and we
obtain \int_{\threed} dx ~ u^{p+1}(x) \le (\frac{p+1}{p})^2 \int_{\threed}
dx ~ \{(-\triangle)^{-1} u(x) \} \nsm \nabla u^{\frac{p}{2}}(x)\nsm^2. We
use these inequalities to deduce global existence of solutions to a non-local
heat equation with a quadratic non-linearity for large radial monotonic
positive initial conditions. Specifically, we improve \cite{ksLM} to include
all .Comment: 6 pages, to appear in Advances in Mathematic
Bistatic Experiment Using TerraSAR-X and DLRâs new F-SAR System
A bistatic X-band experiment was successfully performed early November 2007. TerraSAR-X was used as transmitter and DLRâs new airborne radar system F-SAR, which was programmed to acquire data in a quasi-continuous mode to avoid echo window synchronization issues, was used as bistatic receiver. Precise phase and time referencing between both systems, which is essential for obtaining high resolution SAR images, was derived during the bistatic processing. Hardware setup and performance analyses of the bistatic configuration are pre-sented together with first processing results that verify the predicted synchronization and imaging performance
Föderales maschinelles Lernen
Unter dem Begriff föderales Lernen (Federated Learning â FL) wird eine Alternative zu zentralen AnsĂ€tzen des maschinellen Lernens (Machine Learning â ML) verstanden. Zentrale ML-Architekturen fĂŒhren Daten von Nutzer/innen zu einem groĂen Datenpool zusammen und trainieren auf dieser Grundlage KI-Modelle. Bei FL werden die Rohdaten der Nutzer/innen erst gar nicht an einen zentralen Server ĂŒbertragen. Vielmehr wird das KI-Modell dezentral auf den jeweiligen EndgerĂ€ten der Nutzer/innen trainiert. Lediglich die Ergebnisse des lokal ausgefĂŒhrten Trainingsprogramms werden anschlieĂend zusammengefĂŒhrt und fĂŒr das Training eines zentralen KI-Modells verwendet. Unternehmen wie auch DatenschĂŒtzer erhoffen sich davon eine höhere Akzeptanz bei Nutzer/innen, womit erhebliche gesellschaftliche und ökonomische Potenziale verbunden wĂ€ren. Bei Smartphones und Sprachassistenten kommt FL bereits heute zum Einsatz. Im Zusammenhang mit industriellen Services, wie der vorausschauenden Instandhaltung (Predictive Maintenance), sind erste Anbieter am Markt. FĂŒr sensible Anwendungskontexte, wie das Gesundheitswesen und die Strafverfolgung, sind entsprechende Systeme in der Entwicklung. JĂŒngste Forschungsergebnisse deuten darauf hin, dass trotz zusĂ€tzlich implementierter Privacymechanismen (z.B. Differential Privacy und homomorphe VerschlĂŒsselung) der Datenschutz durch FL nicht ohne Weiteres zu garantieren ist. Dies stellt Leistungsversprechen von Anbietern und bislang angenommene Vorteile beim Datenschutz grundsĂ€tzlich infrage. Sowohl fĂŒr Unternehmen als auch fĂŒr politische Entscheider/innen erwĂ€chst daraus unmittelbarer Handlungsbedarf
People Analytics â Technologien zur Auswertung von BeschĂ€ftigtendaten
People Analytics (PA) bezeichnet Analyseverfahren, die auf Basis von BeschĂ€ftigtendaten evidenzbasierte EntscheidungsunterstĂŒtzung ermöglichen. Neben einfachen statistischen Methoden kommen dabei auch Verfahren des maschinellen Lernens zur Anwendung. Die verwendeten Daten können aus Personalinformationssystemen (Human Resources Information SystemsâŻâ HRIS und anderen Datenquellen mit Unternehmens- und BeschĂ€ftigtenbezug stammen. Anwendungsmöglichkeiten fĂŒr PA finden sich in allen personalbezogenen Prozessen: von der Personalgewinnung (z.âB. Active Sourcing, also die gezielte Suche nach möglichen ArbeitskrĂ€ften) ĂŒber das Personalmanagement â etwa Learning Analytics, d.âh. die Verbesserung von Lehr- und Lernprozessen durch kĂŒnstliche Intelligenz (KI) â, das Performance Management (z.âB. die Steuerung der Leistungserbringung von Mitarbeiter/innen, Teams und Unternehmen) bis zum Austrittsmanagement, z.âB. Fluktuationsanalyse. WĂ€hrend auch in Deutschland Unternehmen damit beginnen, PA einzusetzen, steht die Nutzung von PA-Anwendungen insgesamt noch am Anfang. Neben ökonomischen PotenÂzialen, z.âB. die Steigerung der WettbewerbsfĂ€higkeit, und sozialen Potenzialen, wie Transparenz fĂŒr BeschĂ€ftigte, gibt es insbesondere bezogen auf den Datenschutz Risiken, die mit der zunehmenden Verbreitung von PA an Relevanz gewinnen können. Auch die Gefahr einer zunehmenden Ăberwachung von BeschĂ€ftigten wird kritisch diskutiert
UAV Formation Optimization for Communication-assisted InSAR Sensing
Interferometric synthetic aperture radar (InSAR) is an increasingly important
remote sensing technique that enables three-dimensional (3D) sensing
applications such as the generation of accurate digital elevation models
(DEMs). In this paper, we investigate the joint formation and communication
resource allocation optimization for a system comprising two unmanned aerial
vehicles (UAVs) to perform InSAR sensing and to transfer the acquired data to
the ground. To this end, we adopt as sensing performance metrics the
interferometric coherence, i.e., the local correlation between the two
co-registered UAV radar images, and the height of ambiguity (HoA), which
together are a measure for the accuracy with which the InSAR system can
estimate the height of ground objects. In addition, an analytical expression
for the coverage of the considered InSAR sensing system is derived. Our
objective is to maximize the InSAR coverage while satisfying all relevant
InSAR-specific sensing and communication performance metrics. To tackle the
non-convexity of the formulated optimization problem, we employ alternating
optimization (AO) techniques combined with successive convex approximation
(SCA). Our simulation results reveal that the resulting resource allocation
algorithm outperforms two benchmark schemes in terms of InSAR coverage while
satisfying all sensing and real-time communication requirements. Furthermore,
we highlight the importance of efficient communication resource allocation in
facilitating real-time sensing and unveil the trade-off between InSAR height
estimation accuracy and coverage
Global Solutions to a Non-Local Diffusion Equation with Quadratic Non-Linearity
In this paper we prove the global in time well-posedness of the following non-local diffusion equation with : . The initial condition is positive, radial, and non-increasing with u_0 \varepsilon L^1 \cap L^{2+(\mathbb{R^3) for some small . There is no size restriction on . This model problem appears of interest due to its structural similarity with Landauâs equation from plasma physics, and moreover its radically different behavior from the semi-linear Heat equation:
Distinctive and complementary roles of default mode network subsystems in semantic cognition
The default mode network (DMN) typically deactivates to external tasks, yet supports semantic cognition. It comprises medial temporal (MT), core, and fronto-temporal (FT) subsystems, but its functional organisation is unclear: the requirement for perceptual coupling versus decoupling, input modality (visual/verbal), type of information (social/spatial) and control demands all potentially affect its recruitment. We examined the effect of these factors on activation and deactivation of DMN subsystems during semantic cognition, across four task-based human functional magnetic resonance imaging (fMRI) datasets, and localised these responses in whole-brain state space defined by gradients of intrinsic connectivity. FT showed activation consistent with a central role across domains, tasks and modalities, although it was most responsive to abstract, verbal tasks; this subsystem uniquely showed more âtunedâ states characterised by increases in both activation and deactivation when semantic retrieval demands were higher. MT also activated to both perceptually-coupled (scenes) and decoupled (autobiographical memory) tasks, and showed stronger responses to picture associations, consistent with a role in scene construction. Core DMN consistently showed deactivation, especially to externally-oriented tasks. These diverse contributions of DMN subsystems to semantic cognition were related to their location on intrinsic connectivity gradients: activation was closer to sensory-motor cortex than deactivation, particularly for FT and MT, while activation for core DMN was distant from both visual cortex and cognitive control. These results reveal distinctive yet complementary DMN responses: MT and FT support different memory-based representations that are accessed externally and internally, while deactivation in core DMN is associated with demanding, external semantic tasks
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