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Related papers: Decoupling P-NARX models using filtered CPD

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We derive a topological decoupling of the equations of modified nodal analysis (MNA) to a semi-explicit index one differential-algebraic equation. The decoupling explicitly allows for controlled sources, which play a crucial role in…

Numerical Analysis · Mathematics 2026-04-23 Idoia Cortes Garcia , Peter F. Förster , Lennart Jansen , Wil Schilders , Sebastian Schöps

This paper addresses the problem of inferring a hybrid automaton from a set of input-output traces of a hybrid system exhibiting discrete mode switching between continuously evolving dynamics. Existing approaches mainly adopt a…

Systems and Control · Electrical Eng. & Systems 2026-04-02 Hengzhi Yu , Bohan Ma , Mingshuai Chen , Huangying Dong , Jie An , Bin Gu , Naijun Zhan , Jianwei Yin

Time-variant reliability analysis is a critical task for ensuring the safety of engineering dynamical systems subjected to stochastic excitations. However, assessing failure probability for realistic systems with Monte-Carlo…

Methodology · Statistics 2026-05-13 Stefano Marelli , Styfen Schär , Bruno Sudret

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large, which has presented a barrier to routine open sharing of…

For massive multiple-input multiple-output (MIMO) systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse…

Signal Processing · Electrical Eng. & Systems 2022-03-29 Pengxia Wu , Julian Cheng

During recent years, dynamical decoupling (DD) has gained relevance as a tool for manipulating quantum systems and extracting information from them. This is particularly relevant for spins involved in nuclear magnetic resonance (NMR), where…

Quantum Physics · Physics 2014-09-09 Gonzalo A. Alvarez , Noam Shemesh , Lucio Frydman

Decomposing discrete signals such as images into components is vital in many applications, and this paper propose a framework to produce filtering banks to accomplish this task. The framework is an equation set which is ill-posed, and thus…

Image and Video Processing · Electrical Eng. & Systems 2018-04-05 Yiguang Liu

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal…

Computation and Language · Computer Science 2022-12-27 Wei Chen , Cheng Zhong , Jiajie Peng , Zhongyu Wei

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced…

Machine Learning · Computer Science 2021-06-09 P. H. O. Silva , A. S. Cerqueira , E. G. Nepomuceno

Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal algorithms, for example, the alternating direction method of multipliers (ADMM), with advanced denoising priors. Over the past few years, great empirical…

Computer Vision and Pattern Recognition · Computer Science 2021-09-21 Kaixuan Wei , Angelica Aviles-Rivero , Jingwei Liang , Ying Fu , Hua Huang , Carola-Bibiane Schönlieb

We present a general procedure to introduce electronic polarization into classical Molecular Dynamics (MD) force-fields using a Neural Network (NN) model. We apply this framework to the simulation of a solid-liquid interface where the…

Computational Physics · Physics 2021-03-31 Nicodemo Di Pasquale , Joshua D. Elliott , Panagiotis Hadjidoukas , Paola Carbone

Deep learning has made significant advances in computer vision, particularly in image classification tasks. Despite their high accuracy on training data, deep learning models often face challenges related to complexity and overfitting. One…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Minsoo Kang , Minkoo Kang , Suhyun Kim

Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators, many contain parameters which need to…

Computer Vision and Pattern Recognition · Computer Science 2018-08-10 Qingnan Fan , Dongdong Chen , Lu Yuan , Gang Hua , Nenghai Yu , Baoquan Chen

A cell dynamics method for domain separation of diblock copolymers (DBCPs) interacting with nanoparticles (NPs) whose diffusion coefficients depend on chain configuration is proposed for self-assembly of DBCP/NP composites. Increasing NP…

Materials Science · Physics 2009-04-02 Fabio D. A. Aarao Reis

The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the exploitation of their full potential. Towards network…

Computer Vision and Pattern Recognition · Computer Science 2020-08-26 Yuchao Li , Rongrong Ji , Shaohui Lin , Baochang Zhang , Chenqian Yan , Yongjian Wu , Feiyue Huang , Ling Shao

This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear…

Systems and Control · Electrical Eng. & Systems 2026-01-27 Corrado Sgadari , Alessio La Bella , Marcello Farina

We report a comparative analysis of three dynamic neural network (NN) architectures -- NAR, NARX, and NIO -- to evaluate their efficiency in estimating the critical-measurement-strength parameter ($c_{crit}$) characterizing topological…

Quantum Physics · Physics 2026-05-27 Graciana Puentes

Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large language models, yet generation often remains sequential, relying on autoregressive…

Machine Learning · Computer Science 2026-05-11 Fred Zhangzhi Peng , Avishek Joey Bose , Anru R. Zhang , Alexander Tong

We extend the index-aware model-order reduction method to systems of nonlinear differential-algebraic equations with a special nonlinear term f(Ex), where E is a singular matrix. Such nonlinear differential-algebraic equations arise, for…

Numerical Analysis · Mathematics 2020-02-25 Nicodemus Banagaaya , Giuseppe Ali , Sara Grundel , Peter Benner

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images. Usually the success of these methods comes with a cost…

Computer Vision and Pattern Recognition · Computer Science 2019-12-24 Yawei Li , Shuhang Gu , Luc Van Gool , Radu Timofte