Related papers: All-optical nonlinear activation function based on…
Nonlinear photonics uses coherent interactions between optical waves to engineer functionality that is not possible with purely linear optics. Traditionally, the function of a nonlinear-optical device is determined during design and fixed…
The ability to coherently store and manipulate optical information across multiple degrees of freedom is a central requirement for scalable quantum information processing and multidimensional quantum computing. While polarization- and…
Nonlinear activation functions are pivotal to the success of deep neural nets, and choosing the appropriate activation function can significantly affect their performance. Most networks use fixed activation functions (e.g., ReLU, GELU,…
We grow accustomed to the notion that optical susceptibilities can be treated as a local property of a medium. In the context of nonlinear optics, both Kerr and Raman processes are considered local, meaning that optical fields at one…
The acousto-optic interaction known as stimulated Brillouin scattering (SBS) has emerged as fundamental principles for realizing crucial components and functionalities in integrated photonics. However, the main challenge of integrated…
Many industrial and real life problems exhibit highly nonlinear periodic behaviors and the conventional methods may fall short of finding their analytical or closed form solutions. Such problems demand some cutting edge computational tools…
A photonics-enabled wavelet-like transform system, characterized by multi-resolution time-frequency analysis, is proposed based on a typical stimulated Brillouin scattering (SBS) pump-probe setup using an optical nonlinear frequency-sweep…
A wide variety of activation functions have been proposed for neural networks. The Rectified Linear Unit (ReLU) is especially popular today. There are many practical reasons that motivate the use of the ReLU. This paper provides new…
Neural networks can very effectively perform multidimensional nonlinear classification. However, electronic networks suffer from significant bandwidth limitations due to carrier lifetimes and capacitive coupling. This project investigates…
Stimulated Brillouin scattering (SBS), a coherent nonlinear effect coupling acoustics and optics, can be used in a wide range of applications such as Brillouin lasers and tunable narrowband RF filtering. Wide adoption of such technologies…
The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics…
Analog coprocessors for neural networks are an intensively developing field. They provide approximate results of computations for relatively low energy cost and at high speed. We show that a set of ring resonators with Brillouin interaction…
Dynamic adaptation in single-neuron response plays a fundamental role in neural coding in biological neural networks. Yet, most neural activation functions used in artificial networks are fixed and mostly considered as an inconsequential…
The choice of activation function in deep networks has a significant effect on the training dynamics and task performance. At present, the most effective and widely-used activation function is ReLU. However, because of the non-zero mean,…
Activation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community.…
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a…
The selection rules governing spontaneous Brillouin scattering in crystalline solids are usually taken as intrinsic material properties, locking the relative polarization of excitation and signal in bulk. In this work, we independently…
The fast-growing development of optomechanical applications has motivated advancements in Brillouin scattering research. In particular, the study of high frequency acoustic phonons at the nanoscale is interesting due to large range of…
Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several vision-related domains. However, the performance of INR is…
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but…