9,591 research outputs found
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time
This paper studies the dynamic generator model for spatial-temporal processes
such as dynamic textures and action sequences in video data. In this model,
each time frame of the video sequence is generated by a generator model, which
is a non-linear transformation of a latent state vector, where the non-linear
transformation is parametrized by a top-down neural network. The sequence of
latent state vectors follows a non-linear auto-regressive model, where the
state vector of the next frame is a non-linear transformation of the state
vector of the current frame as well as an independent noise vector that
provides randomness in the transition. The non-linear transformation of this
transition model can be parametrized by a feedforward neural network. We show
that this model can be learned by an alternating back-propagation through time
algorithm that iteratively samples the noise vectors and updates the parameters
in the transition model and the generator model. We show that our training
method can learn realistic models for dynamic textures and action patterns.Comment: 10 page
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Computational cytometer based on magnetically modulated coherent imaging and deep learning.
Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
Dynamic Variational Autoencoders for Visual Process Modeling
This work studies the problem of modeling visual processes by leveraging deep
generative architectures for learning linear, Gaussian representations from
observed sequences. We propose a joint learning framework, combining a vector
autoregressive model and Variational Autoencoders. This results in an
architecture that allows Variational Autoencoders to simultaneously learn a
non-linear observation as well as a linear state model from sequences of
frames. We validate our approach on artificial sequences and dynamic textures
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning,
this paper proposes a novel GAN-based disentanglement framework with One-Hot
Sampling and Orthogonal Regularization (OOGAN). While previous works mostly
attempt to tackle disentanglement learning through VAE and seek to implicitly
minimize the Total Correlation (TC) objective with various sorts of
approximation methods, we show that GANs have a natural advantage in
disentangling with an alternating latent variable (noise) sampling method that
is straightforward and robust. Furthermore, we provide a brand-new perspective
on designing the structure of the generator and discriminator, demonstrating
that a minor structural change and an orthogonal regularization on model
weights entails an improved disentanglement. Instead of experimenting on simple
toy datasets, we conduct experiments on higher-resolution images and show that
OOGAN greatly pushes the boundary of unsupervised disentanglement.Comment: AAAI 202
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Challenges to the Integration of Renewable Resources at High System Penetration
Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches
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