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Found 17 papers, 12 papers with code
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data.
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
We introduce flying adversarial patches, where multiple images are mounted on at least one other flying robot and therefore can be placed anywhere in the field of view of a victim multirotor.
Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors
We introduce flying adversarial patches, where an image is mounted on another flying robot and therefore can be placed anywhere in the field of view of a victim multirotor.
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research.
Continual learning autoencoder training for a particle-in-cell simulation via streaming
These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible.
InFlow: Robust outlier detection utilizing Normalizing Flows
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode.
Data-Driven Shadowgraph Simulation of a 3D Object
In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium.
Large-scale Neural Solvers for Partial Differential Equations
However, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing.
Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration
Laser-ion acceleration with ultra-short pulse, PW-class lasers is dominated by non-thermal, intra-pulse plasma dynamics.
Plasma Physics Accelerator Physics Computational Physics
Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles
Quantitative predictions from synthetic radiation diagnostics often have to consider all accelerated particles.
Computational Physics Plasma Physics
Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library
On some of these we are able to reach almost 50\% of the peak floating point operation performance using the aforementioned means.
Distributed, Parallel, and Cluster Computing
On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective
We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU.
Performance Computational Physics D.4.8; B.4.3; I.6.6
In situ, steerable, hardware-independent and data-structure agnostic visualization with ISAAC
The computation power of supercomputers grows faster than the bandwidth of their storage and network.
Distributed, Parallel, and Cluster Computing
Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond
With the appearance of the heterogeneous platform OpenPower, many-core accelerator devices have been coupled with Power host processors for the first time.
Distributed, Parallel, and Cluster Computing
Alpaka - An Abstraction Library for Parallel Kernel Acceleration
The model exploits parallelism and memory hierarchies on a node at all levels available in current hardware.
Distributed, Parallel, and Cluster Computing Mathematical Software
Visualizing the Radiation of the Kelvin-Helmholtz Instability
Emerging new technologies in plasma simulations allow tracking billions of particles while computing their radiative spectra.
Plasma Physics Computational Physics 85-08
