Eine effiziente Offline-Sensorplatzierungsmethode zur Durchflussschätzung
We present an efficient method to optimize sensor placement for flow estimation using sensors with time-delay embedding in advection-dominated flows. Our solution allows identifying promising candidates for sensor positions using solely preliminary flow field measurements with non-time-resolved Particle Image Velocimetry (PIV), without...
Supermassive Binärdateien in ultraleichten Solitonen aus Dunkler Materie
Ultraleicht (oder unscharf) dunkle Materie (ULDM) is an alternative to cold dark matter. A key feature of ULDM is the presence of solitonic cores at the centers of collapsed halos. These would potentially increase the drag experienced by supermassive black hole (SMBH)...
Property-Preserving Hashing for $\ell_1$-Distance Predicates: Anwendungen zur Abwehr gegnerischer Eingabeangriffe
Perceptual hashing is used to detect whether an input image is similar to a reference image with a variety of security applications. Kürzlich, they have been shown to succumb to adversarial input attacks which make small imperceptible changes to the input image...
Exakte stationäre Zustände und fragmentierungsinduzierte Entspannung im No-Passing-asymmetrischer einfacher Ausschlussprozess
We introduce a multi-species generalization of the asymmetric simple exclusion process (ASEP) mit einem “Überholverbot” Zwang, forbidding overtaking, auf einer eindimensionalen offenen Kette. This no-passing rule fragments the Hilbert space into an exponential number of disjoint sectors labeled by the particle sequence,...
Hamiltonsche Quantisierung der komplexen Chern-Simons-Theorie auf der Ebene-$k$
This paper develops a framework for the Hamiltonian quantization of complex Chern-Simons theory with gauge group $\mathrm{Sl}(2,\mathbb{C})$ at an even level $k\in\mathbb{Z}_+$. Our approach follows the procedure of combinatorial quantization to construct the operator algebras of quantum holonomies on 2-surfaces and develop...
Überprüfung der Radarkameraausrichtung durch kontrastives Lernen für 3D-Objekte Erkennung
Kürzlich, 3D object detection algorithms based on radar and camera fusion have shown excellent performance, setting the stage for their application in autonomous driving perception tasks. Existing methods have focused on dealing with feature misalignment caused by the domain gap between radar...
Schnelles adaptives neuronales Online-MPC über Meta-Learning
Datengesteuerte modellprädiktive Steuerung (MPC) has demonstrated significant potential for improving robot control performance in the presence of model uncertainties. Jedoch, existing approaches often require extensive offline data collection and computationally intensive training, limiting their ability to adapt online. To address these...
DAS AUGE: Ein subjektives, absichtsbasiertes Framework mit geringer Latenz für mehrere Vorhersage der gemeinsamen Flugbahn der Verkehrsteilnehmer
Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety and...
PINN-MdEP: Kontinuierliche neuronale Darstellungen für den Pfad mit minimaler Energie Entdeckung in molekularen Systemen
Characterizing conformational transitions in physical systems remains a fundamental challenge in the computational sciences. Traditional sampling methods like molecular dynamics (MD) or MCMC often struggle with the high-dimensional nature of molecular systems and the high energy barriers of transitions between stable states....
Vollständig skalierbare MPC-Algorithmen für das euklidische k-Center
The $k$-center problem is a fundamental optimization problem with numerous applications in machine learning, data analysis, data mining, and communication networks. The $k$-center problem has been extensively studied in the classical sequential setting for several decades, and more recently there have been...