scholarly journals Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1322
Author(s):  
Nanyan Zhu ◽  
Chen Liu ◽  
Andrew F. Laine ◽  
Jia Guo
Keyword(s):  
Global Warming ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Climate Impacts ◽  
Shortwave Radiation ◽  
Present Information ◽  
System Memory ◽  
Long Short Term Memory ◽  
Photosynthetic Activities ◽  
Evolution Of Photosynthesis

Global warming, which largely results from excessive carbon emission, has become an increasingly heated international issue due to its ever-detereorating trend and the profound consequences. Plants sequester a large amount of atmospheric CO 2 via photosynthesis, thus greatly mediating global warming. In this study, we aim to model the temporal dynamics of photosynthesis for two different vegetation types to further understand the controlling factors of photosynthesis machinery. We experimented with a feedforward neural network that does not utilize past histories, as well as two networks that integrate past and present information, long short-term memory and transformer. Our results showed that one single climate driver, shortwave radiation, carries the most information with respect to prediction of upcoming photosynthetic activities. We also demonstrated that photosynthesis and its interactions with climate drivers, such as temperature, precipitation, radiation, and vapor pressure deficit, has an internal system memory of about two weeks. Thus, the predictive model could be best trained with historical data over the past two weeks and could best predict temporal evolution of photosynthesis two weeks into the future.

scholarly journals Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 203
Author(s):  
Benjamin Plaster ◽  
Gautam Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Brain Dynamics ◽  
Short Term ◽  
Term Memory ◽  
Brain Functions ◽  
Long Short Term Memory

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

scholarly journals Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory

2019 ◽  
Author(s):  
Benjamin Plaster ◽  
Gautam Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Brain Dynamics ◽  
Short Term ◽  
Term Memory ◽  
Brain Functions ◽  
Long Short Term Memory

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

scholarly journals Deep spatiotemporal models for robust proprioceptive terrain classification

2017 ◽  
Vol 36 (13-14) ◽  
pp. 1521-1539 ◽  
Author(s):  
Abhinav Valada ◽  
Wolfram Burgard
Keyword(s):  
Real World ◽  
Network Architecture ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Terrain Classification ◽  
Short Term ◽  
Term Memory ◽  
Training Scheme ◽  
Long Short Term Memory ◽  
Spatiotemporal Models

Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new convolution neural network architecture that learns deep spatial features, complemented with long-short term memory units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate state-of-the-art performance compared to existing techniques. We additionally evaluate the performance in adverse acoustic conditions with high-ambient noise and propose a noise-aware training scheme that enables learning of more generalizable models that are essential for robust real-world deployments.

scholarly journals Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory

2019 ◽  
Author(s):  
Benjamin Plaster ◽  
Gautam Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Brain Dynamics ◽  
Short Term ◽  
Term Memory ◽  
Brain Functions ◽  
Long Short Term Memory

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

scholarly journals Passive Shallow Water Automated Target Recognition using Deep Convolutional Bi directional Long Short Term Memory

2021 ◽  
Vol 71 (1) ◽  
pp. 117-123
Author(s):  
Suraj Kamal ◽  
C. Satheesh Chandran ◽  
H.M. Supriya
Keyword(s):  
Short Term Memory ◽  
Temporal Dynamics ◽  
Target Recognition ◽  
Computational Cost ◽  
Short Term ◽  
Term Memory ◽  
Model Search ◽  
Acoustic Target ◽  
Long Short Term Memory ◽  
Passive Acoustic

The extremely challenging nature of passive acoustic surveillance makes it a key area of research in NavalNon-Co-operative Target Recognition especially in Anti-Submarine Warfare systems. In shallow waters, thecomplex acoustics due to the highly varying ambient background noise as well as the multi-modal propagation in the surface-bottom bounded channel makes surveillance even difficult. In this work, an ensemble of Convolutional Neural Networks and Bidirectional Long Short Term Memory stages employing soft attention is used to effectively capture the spectro-temporal dynamics of the target signature. In order to alleviate the overall computational cost associated with the optimal model search in the extensive hyperparameter space, a recursive model elimination scheme, making frugal use of the available resources, is also proposed. Experimental analysis on acoustic target records, collected from the shallows of Arabian Sea, has yielded encouraging results in terms of model accuracy, precision and recall.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
De Novo ◽  
De Novo Drug Design ◽  
Generative Process ◽  
New Methods ◽  
Multiparameter Optimization ◽  
Long Short Term Memory ◽  
New Compounds

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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