This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response...
We present the results obtained through the various ISO extragalactic deep surveys. While IRAS revealed the existence of galaxies forming stars at a rate of a few tens (LIRGs) or even hundreds (ULIRGs) solar masses in the local universe, ISO not on...
License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate...
Billions of X-ray images are taken worldwide each year. Machine learning, and deep learning in particular, has shown potential to help radiologists triage and diagnose images. However, deep learning requires large datasets with reliable labels. The C...
Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification...
Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with over a hundred models deve...
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May 15, 2023 · Most vaginas are roughly as deep as the length of your hand, but they can change shape in certain situations. They cannot be permanently stretched out, but the muscles inside your vagina …
In preparation for deep extragalactic imaging with the James Webb Space Telescope, we explore the clustering of massive halos at $z=8$ and $10$ using a large N-body simulation. We find that halos with masses $10^9$ to $10^{11}$ $h^{-1}\;M_\odot$, whi...
An introduction and summary is given of the main results achieved by working group 1: Structure Functions in Deep Inelastic Scattering at HERA. The prospects were discussed of future measurements of the structure functions $F_{2}, F_{L}, xG_{3}, F_...
We present a brief description of the recently developed Fortran code TERAD91 for semi-analytical calculations of the double differential cross sections of NC and CC deep inelastic electron-proton scattering and of some related observables. The cod...
Jun 18, 2025 · The official Deep Purple website with all the latest news, tour dates, media, official merchandise and more.
With a body of work spanning seven decades, Deep Purple has helped pioneer and define the hard rock genre while progressively moving into new areas, both keeping their sound fresh and attracting new …
We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of st...
Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient recalled echo (mGRE) MRI data. Methods: RoAR trains a convolutional neur...
Many recent approaches of passage retrieval are using dense embeddings generated from deep neural models, called "dense passage retrieval". The state-of-the-art end-to-end dense passage retrieval systems normally deploy a deep neural model followed b...
Commercial iterative reconstruction techniques on modern CT scanners target radiation dose reduction but there are lingering concerns over their impact on image appearance and low contrast detectability. Recently, machine learning, especially deep le...
This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible...
The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machi...
Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids...