Results 31 to 40 of about 17,196 (205)
Bootstraping financial time series [PDF]
It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations ...
Pascual, Lorenzo +4 more
core +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
The liposome nanoreactor method enables uniform metal nanoparticle synthesis using the interior as a reaction environment. We demonstrate that peptide sequences and lipid compositions act as a functional molecular switch to control reaction environments in liposome nanoreactors.
Yuya Abe +6 more
wiley +1 more source
Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong +12 more
wiley +1 more source
Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes [PDF]
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck processes. These processes represent an alternative to the previously considered discrete superpositions.
Griffin, Jim, Steel, Mark F.J.
core
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Properties of the sample autocorrelations of non-linear transformations in long memory stochastic volatility models [PDF]
The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility.
Ruiz Ortega, Esther +2 more
core +1 more source
Resonant Domain Wall Dynamics in a Three‐Dimensional Magnetic Nano Double Helix
3D magnetic nanostructures promise exciting possibilities for magnetization dynamics. However, experimental realizations remain scarce. In nanoprinted cobalt double helices, time‐resolved X‐ray microscopy reveals harmonic domain wall dynamics. Simulations identify the mode and additional higher‐frequency resonances, revealing a rich dynamic landscape ...
Pamela Morales‐Fernández +15 more
wiley +1 more source

