89 research outputs found
A geometrically converging dual method for distributed optimization over time-varying graphs
In this paper we consider a distributed convex optimization problem over
time-varying undirected networks. We propose a dual method, primarily averaged
network dual ascent (PANDA), that is proven to converge R-linearly to the
optimal point given that the agents objective functions are strongly convex and
have Lipschitz continuous gradients. Like dual decomposition, PANDA requires
half the amount of variable exchanges per iterate of methods based on DIGing,
and can provide with practical improved performance as empirically
demonstrated.Comment: Submitted to Transactions on Automatic Contro
Cell Detection by Functional Inverse Diffusion and Non-negative Group SparsityPart I: Modeling and Inverse Problems
In this two-part paper, we present a novel framework and methodology to
analyze data from certain image-based biochemical assays, e.g., ELISPOT and
Fluorospot assays. In this first part, we start by presenting a physical
partial differential equations (PDE) model up to image acquisition for these
biochemical assays. Then, we use the PDEs' Green function to derive a novel
parametrization of the acquired images. This parametrization allows us to
propose a functional optimization problem to address inverse diffusion. In
particular, we propose a non-negative group-sparsity regularized optimization
problem with the goal of localizing and characterizing the biological cells
involved in the said assays. We continue by proposing a suitable discretization
scheme that enables both the generation of synthetic data and implementable
algorithms to address inverse diffusion. We end Part I by providing a
preliminary comparison between the results of our methodology and an expert
human labeler on real data. Part II is devoted to providing an accelerated
proximal gradient algorithm to solve the proposed problem and to the empirical
validation of our methodology.Comment: published, 15 page
Cell Detection by Functional Inverse Diffusion and Non-negative Group SparsityPart II: Proximal Optimization and Performance Evaluation
In this two-part paper, we present a novel framework and methodology to
analyze data from certain image-based biochemical assays, e.g., ELISPOT and
Fluorospot assays. In this second part, we focus on our algorithmic
contributions. We provide an algorithm for functional inverse diffusion that
solves the variational problem we posed in Part I. As part of the derivation of
this algorithm, we present the proximal operator for the non-negative
group-sparsity regularizer, which is a novel result that is of interest in
itself, also in comparison to previous results on the proximal operator of a
sum of functions. We then present a discretized approximated implementation of
our algorithm and evaluate it both in terms of operational cell-detection
metrics and in terms of distributional optimal-transport metrics.Comment: published, 16 page
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Recently proposed methodology
matches human accuracy by leveraging knowledge of the underlying physical
process of these assays and using proximal optimization methods to solve an
inverse problem. Nonetheless, thousands of computationally expensive iterations
are often needed to reach a near-optimal solution. In this paper, we exploit
the structure of the iterations to design a parameterized computation graph,
SpotNet, that learns the patterns embedded within several training images and
their respective cell information. Further, we compare SpotNet to a
convolutional neural network layout customized for cell detection. We show
empirical evidence that, while both designs obtain a detection performance on
synthetic data far beyond that of a human expert, SpotNet is easier to train
and obtains better estimates of particle secretion for each cell.Comment: 5 pages, 4 figures, 2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 201
Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems
In the context of wireless communications, we propose a deep learning
approach to learn the mapping from the instantaneous state of a frequency
selective fading channel to the corresponding frame error probability (FEP) for
an arbitrary set of transmission parameters. We propose an abstract model of a
bit interleaved coded modulation (BICM) orthogonal frequency division
multiplexing (OFDM) link chain and show that the maximum likelihood (ML)
estimator of the model parameters estimates the true FEP distribution. Further,
we exploit deep neural networks as a general purpose tool to implement our
model and propose a training scheme for which, even while training with the
binary frame error events (i.e., ACKs / NACKs), the network outputs converge to
the FEP conditioned on the input channel state. We provide simulation results
that demonstrate gains in the FEP prediction accuracy with our approach as
compared to the traditional effective exponential SIR metric (EESM) approach
for a range of channel code rates, and show that these gains can be exploited
to increase the link throughput.Comment: Submitted to 2018 IEEE International Conference on Acoustics, Speech
and Signal Processin
- …