This paper presents a registration framework based on the polynomial expansion transform. The idea of polynomial expansion is that the image is locally approximated by polynomials at each pixel. Starting with observations of how the coefficients of ideal linear and quadratic polynomials change under translation and affine transformation, algorithms are developed to estimate translation and compute affine and deformable registration between a fixed and a moving image, from the polynomial expansion coefficients. All algorithms can be used for signals of any dimensionality. The algorithms are evaluated on medical data.
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Neural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
View Article and Find Full Text PDFBioinspir Biomim
January 2025
Mathematics and Statistics, College of New Jersey, 2000 Pennington Road, Ewing, New Jersey, 08628, UNITED STATES.
Tomopterids are mesmerizing holopelagic swimmers. They use two modes of locomotion simultaneously: drag-based metachronal paddling and bodily undulation.has two rows of flexible legs (parapodia) positioned on opposite sides of its body.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
IBM Research-Europe, 8803 Rüschlikon, Zurich, Switzerland.
Encryption and decryption of data with very low latency and high energy efficiency is desirable in almost every application that deals with sensitive data. The advanced encryption standard (AES) is a widely adopted algorithm in symmetric key cryptography with numerous efficient implementations. Nonetheless, in scenarios involving extensive data processing, the primary limitations on performance and efficiency arise from data movement between memory and the processor, rather than data processing itself.
View Article and Find Full Text PDFNat Commun
January 2025
Neuromorphic Computing Lab, Intel, Santa Clara, CA, USA.
Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit-the reservoir-can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and expands them into nonlinear features has been challenged. A representation scheme in which memory buffer and expansion into higher-order polynomial features can be configured separately has been shown to significantly outperform traditional reservoir computing in prediction of multivariate time-series.
View Article and Find Full Text PDFBiol Imaging
November 2024
Institut de Recherche en Informatique de Toulouse (IRIT), CNRS & Université de Toulouse, Toulouse, France.
We propose a neural network architecture and a training procedure to estimate blurring operators and deblur images from a single degraded image. Our key assumption is that the forward operators can be parameterized by a low-dimensional vector. The models we consider include a description of the point spread function with Zernike polynomials in the pupil plane or product-convolution expansions, which incorporate space-varying operators.
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