AIMC Topic: Time Factors

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A data-driven framework for learning hybrid dynamical systems.

Chaos (Woodbury, N.Y.)
The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, ...

Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing.

Chaos (Woodbury, N.Y.)
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaot...

Predicting chaotic dynamics from incomplete input via reservoir computing with (D+1)-dimension input and output.

Physical review. E
Predicting future evolution based on incomplete information of the past is still a challenge even though data-driven machine learning approaches have been successfully applied to forecast complex nonlinear dynamics. The widely adopted reservoir compu...

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods.

The Journal of general virology
Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponenti...

Predictors of urinary function recovery after laparoscopic and robot-assisted radical prostatectomy.

International braz j urol : official journal of the Brazilian Society of Urology
INTRODUCTION: Even in the era of laparoscopic radical prostatectomy (LRP) and robot-assisted laparoscopic radical prostatectomy (RALP), we sometimes encounter patients with severe urinary incontinence after surgery. The aim of the present study was t...

Key role of neuronal diversity in structured reservoir computing.

Chaos (Woodbury, N.Y.)
Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homoge...

Physics-informed graph neural networks enhance scalability of variational nonequilibrium optimal control.

The Journal of chemical physics
When a physical system is driven away from equilibrium, the statistical distribution of its dynamical trajectories informs many of its physical properties. Characterizing the nature of the distribution of dynamical observables, such as a current or e...

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.

Nucleic acids research
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields o...

A Deep Learning Based Approach to Synthesize Intelligible Speech with Limited Temporal Envelope Information.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Envelope waveforms can be extracted from multiple frequency bands of a speech signal, and envelope waveforms carry important intelligibility information for human speech communication. This study aimed to investigate whether a deep learning-based mod...