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Universal Fourier Attack for Time Series
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering defenses. We demonstrate the effectiveness of the attack on two different classification tasks through both digital and real world experiments, and show that the attack is robust against common transform-and-compare defense pipelines.
Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence
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Local Fourier Analysis of p-Multigrid for High-Order Finite Element Operators
Not provided.
Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library
ABSTRACT In this paper, we present an early version of a SYCL-based FFT library, capable of running on all major vendor hardware, including CPUs and GPUs from AMD, ARM, Intel and NVIDIA. The current limitations of our library is it supports single-dimension FFTs up to 211 in length and base-2 input sequences. Although preliminary, the aim of this work is to seed further developments for a rich set of features for calculating FFTs. The library has the advantage over existing portable FFT libraries in that it is single-source, and there- fore removes the complexities that arise due to abundant use of pre-processor macros and auto-generated kernels to target different architectures. We exercise two SYCL-enabled compilers, Codeplay ComputeCpp and Intel's open-source LLVM project, to evaluate performance portability of our SYCL-based FFT on various hetero- geneous architectures.We provide studies comparing our portable library with highly optimized vendor-specific FFT libraries, and discuss potential sources hindering performance.
Single-shot large field of view Fourier transform holography with a picosecond plasma-based soft X-ray laser
Not Available
2D Reconstruction from Parallel Beam Projections, the Radon Transform, and the Fourier Transform.
Abstract not provided.
Universal Fourier Attack for Time Series
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.
Accelerating Phase Field Simulations Through Time Extrapolation Using Adaptive Fourier Neural Operators and UNets
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Deep Learning without Global Optimization by Random Fourier Features
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Accelerating Phase Field Simulations for Liquid Metal De-alloying Using Adaptive Fourier Neural Operators and U-Nets
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Phase field dislocation dynamics formulation coupled with Fourier based micromechanics solver: Application to grain boundary-dislocation interactions
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Accelerating Phase Field Modeling Through Hybrid Simulations using Fourier Neural Operators and UNets
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Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
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Accelerating Phase Field Simulations Using Adaptive Fourier Neural Operators and U-Nets
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Multifidelity Random Fourier Neural Networks (MF-rFNNs)
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