Related papers: Accelerated Coupled Mode Model for Fiber Laser Amp…
The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and…
Phase-sensitive amplification of squeezed states is a technique to mitigate high detection loss, e.g. at 2-micrometre wavelengths. Our analytical model of amplified squeezed states expands on the effect of phase noise and derives two…
Optical I/O plays a crucial role in the lifespan of lab-on-a-chip systems, from preliminary testing to operation in the target environment. However, due to the precise alignments required, efficient and reliable fiber-to-chip connections…
Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with…
A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm adopts the federated architecture, and…
In recent times, with the exception of sporadic cases, the trend in Computer Vision is to achieve minor improvements compared to considerable increases in complexity. To reverse this trend, we propose a novel method to boost image…
Optical parametric amplifiers (OPAs) are promising to overcome the wavelength coverage and noise limitations in conventional optical amplifiers based on rare-earth doping and semiconductor gain. However, the high power requirement remains a…
Prompt Tuning has emerged as a prominent research paradigm for adapting vision-language models to various downstream tasks. However, recent research indicates that prompt tuning methods often lead to overfitting due to limited training…
We report on a methodology for the evaluation of the DC characteristics, small-signal frequency response and large-signal dynamic response of carrier and photon density responses in semiconductor laser diodes. A single mode laser is…
Optical parametric amplification generates squeezed light in device-specific sets of time-frequency eigenmodes, and it has been widely accepted that detection and utilization of squeezing must comply with this modal constraint. We show that…
Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the computational time of inference methods such as Markov chain…
Recent hardware acceleration advances have enabled powerful specialized accelerators for finite element computations, spiking neural network inference, and sparse tensor operations. However, existing approaches face fundamental limitations:…
We have developed a coupled-mode analysis framework for superconducting travelling-wave parametric amplifiers using the full Telegrapher's equations to incorporate loss-related behaviour. Our model provides an explanation of previous…
Iterative methods are ubiquitous in large-scale scientific computing applications, and a number of approaches based on meta-learning have been recently proposed to accelerate them. However, a systematic study of these approaches and how…
To analyze a homogeneous weakly coupled multicore fiber (WC-MCF) based transmission system via simulation, we propose an efficient (fast and accurate) WC-MCF's channel model, which can describe the propagation effects including attenuation,…
As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in accelerating the training of LIC methods by modeling the neural…
Learned iterative reconstructions hold great promise to accelerate tomographic imaging with empirical robustness to model perturbations. Nevertheless, an adoption for photoacoustic tomography is hindered by the need to repeatedly evaluate…
Providing efficient and accurate parametrizations for model reduction is a key goal in many areas of science and technology. Here we present a strong link between data-driven and theoretical approaches to achieving this goal. Formal…
We present an on-line tuning strategy for the ISAC post-accelerator that pre-sets machine optics with a digital twin and then performs Bayesian optimization for steering under online operation with beam. The model computes end-to-end tunes…
A contactless control of mean values and fluctuations of position and velocity of a nanoobject belongs among the key methods needed for ultra-precise nanotechnology and the upcoming quantum technology of macroscopic systems. An analysis of…