4,936 research outputs found
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
We propose a deep autoencoder with graph topology inference and filtering to
achieve compact representations of unorganized 3D point clouds in an
unsupervised manner. Many previous works discretize 3D points to voxels and
then use lattice-based methods to process and learn 3D spatial information;
however, this leads to inevitable discretization errors. In this work, we
handle raw 3D points without such compromise. The proposed networks follow the
autoencoder framework with a focus on designing the decoder. The encoder adopts
similar architectures as in PointNet. The decoder involves three novel modules.
The folding module folds a canonical 2D lattice to the underlying surface of a
3D point cloud, achieving coarse reconstruction; the graph-topology-inference
module learns a graph topology to represent pairwise relationships between 3D
points, pushing the latent code to preserve both coordinates and pairwise
relationships of points in 3D point clouds; and the graph-filtering module
couples the above two modules, refining the coarse reconstruction through a
learnt graph topology to obtain the final reconstruction. The proposed decoder
leverages a learnable graph topology to push the codeword to preserve
representative features and further improve the unsupervised-learning
performance. We further provide theoretical analyses of the proposed
architecture. In the experiments, we validate the proposed networks in three
tasks, including 3D point cloud reconstruction, visualization, and transfer
classification. The experimental results show that (1) the proposed networks
outperform the state-of-the-art methods in various tasks; (2) a graph topology
can be inferred as auxiliary information without specific supervision on graph
topology inference; and (3) graph filtering refines the reconstruction, leading
to better performances.Comment: To appear in IEEE Transactions on Image Processin
Fast event-based epidemiological simulations on national scales
We present a computational modeling framework for data-driven simulations and
analysis of infectious disease spread in large populations. For the purpose of
efficient simulations, we devise a parallel solution algorithm targeting
multi-socket shared memory architectures. The model integrates infectious
dynamics as continuous-time Markov chains and available data such as animal
movements or aging are incorporated as externally defined events. To bring out
parallelism and accelerate the computations, we decompose the spatial domain
and optimize cross-boundary communication using dependency-aware task
scheduling. Using registered livestock data at a high spatio-temporal
resolution, we demonstrate that our approach not only is resilient to varying
model configurations, but also scales on all physical cores at realistic work
loads. Finally, we show that these very features enable the solution of inverse
problems on national scales.Comment: 27 pages, 5 figure
Rare-Event Sampling of Epigenetic Landscapes and Phenotype Transitions
Stochastic simulation has been a powerful tool for studying the dynamics of
gene regulatory networks, particularly in terms of understanding how
cell-phenotype stability and fate-transitions are impacted by noisy gene
expression. However, gene networks often have dynamics characterized by
multiple attractors. Stochastic simulation is often inefficient for such
systems, because most of the simulation time is spent waiting for rare,
barrier-crossing events to occur. We present a rare-event simulation-based
method for computing epigenetic landscapes and phenotype-transitions in
metastable gene networks. Our computational pipeline was inspired by studies of
metastability and barrier-crossing in protein folding, and provides an
automated means of computing and visualizing essential stationary and dynamic
information that is generally inaccessible to conventional simulation. Applied
to a network model of pluripotency in Embryonic Stem Cells, our simulations
revealed rare phenotypes and approximately Markovian transitions among
phenotype-states, occurring with a broad range of timescales. The relative
probabilities of phenotypes and the transition paths linking pluripotency and
differentiation are sensitive to global kinetic parameters governing
transcription factor-DNA binding kinetics. Our approach significantly expands
the capability of stochastic simulation to investigate gene regulatory network
dynamics, which may help guide rational cell reprogramming strategies. Our
approach is also generalizable to other types of molecular networks and
stochastic dynamics frameworks
Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network
Human bodies exhibit various shapes for different identities or poses, but
the body shape has certain similarities in structure and thus can be embedded
in a low-dimensional space. This paper presents an autoencoder-like network
architecture to learn disentangled shape and pose embedding specifically for
the 3D human body. This is inspired by recent progress of deformation-based
latent representation learning. To improve the reconstruction accuracy, we
propose a hierarchical reconstruction pipeline for the disentangling process
and construct a large dataset of human body models with consistent connectivity
for the learning of the neural network. Our learned embedding can not only
achieve superior reconstruction accuracy but also provide great flexibility in
3D human body generation via interpolation, bilinear interpolation, and latent
space sampling. The results from extensive experiments demonstrate the
powerfulness of our learned 3D human body embedding in various applications.Comment: This manuscript is accepted for publication in the IEEE Transactions
on Visualization and Computer Graphics Journal (IEEE TVCG). The Code is
available at https://github.com/Juyong/DHNN_BodyRepresentatio
Topology, Landscapes, and Biomolecular Energy Transport
While ubiquitous, energy redistribution remains a poorly understood facet of
the nonequilibrium thermodynamics of biomolecules. At the molecular level,
finite-size effects, pronounced nonlinearities, and ballistic processes produce
behavior that diverges from the macroscale. Here, we show that transient
thermal transport reflects macromolecular energy landscape architecture through
the topological characteristics of molecular contacts and the nonlinear
processes that mediate dynamics. While the former determines transport pathways
via pairwise interactions, the latter reflects frustration within the landscape
for local conformational rearrangements. Unlike transport through
small-molecule systems, such as alkanes, nonlinearity dominates over coherent
processes at even quite short time- and length-scales. Our exhaustive all-atom
simulations and novel local-in-time and space analysis, applicable to both
theory and experiment, permit dissection of energy migration in biomolecules.
The approach demonstrates that vibrational energy transport can probe otherwise
inaccessible aspects of macromolecular dynamics and interactions that underly
biological function.Comment: Final published version + supplementary inf
An equation-free approach to coarse-graining the dynamics of networks
We propose and illustrate an approach to coarse-graining the dynamics of
evolving networks (networks whose connectivity changes dynamically). The
approach is based on the equation-free framework: short bursts of detailed
network evolution simulations are coupled with lifting and restriction
operators that translate between actual network realizations and their
(appropriately chosen) coarse observables. This framework is used here to
accelerate temporal simulations (through coarse projective integration), and to
implement coarsegrained fixed point algorithms (through matrix-free
Newton-Krylov GMRES). The approach is illustrated through a simple network
evolution example, for which analytical approximations to the coarse-grained
dynamics can be independently obtained, so as to validate the computational
results. The scope and applicability of the approach, as well as the issue of
selection of good coarse observables are discussed.Comment: 28 pages, 8 figure
Distance measures and evolution of polymer chains in their topological space
Conformational transitions are ubiquitous in biomolecular systems, have
significant functional roles and are subject to evolutionary pressures. Here we
provide a first theoretical framework for topological transition, i.e.
conformational transitions that are associated with changes in molecular
topology. For folded linear biomolecules, arrangement of intramolecular
contacts is identified as a key topological property, termed as circuit
topology. Distance measures are proposed as reaction coordinates to represent
progress along a pathway from initial topology to final topology. Certain
topological classes are shown to be more accessible from a random topology. We
study dynamic stability and pathway degeneracy associated with a topological
reaction and found that off-pathways might seriously hamper evolution to
desired topologies. Finally we present an algorithm for estimating the number
of intermediate topologies visited during a topological reaction. The results
of this study are relevant to, among others, structural studies of RNA and
proteins, analysis of topologically associated domains in chromosomes, and
molecular evolution.Comment: 21 pages, 7 figures, 4 appendixe
Application of Quantum Annealing to Training of Deep Neural Networks
In Deep Learning, a well-known approach for training a Deep Neural Network
starts by training a generative Deep Belief Network model, typically using
Contrastive Divergence (CD), then fine-tuning the weights using backpropagation
or other discriminative techniques. However, the generative training can be
time-consuming due to the slow mixing of Gibbs sampling. We investigated an
alternative approach that estimates model expectations of Restricted Boltzmann
Machines using samples from a D-Wave quantum annealing machine. We tested this
method on a coarse-grained version of the MNIST data set. In our tests we found
that the quantum sampling-based training approach achieves comparable or better
accuracy with significantly fewer iterations of generative training than
conventional CD-based training. Further investigation is needed to determine
whether similar improvements can be achieved for other data sets, and to what
extent these improvements can be attributed to quantum effects.Comment: 18 page
Single- and Multi-level Network Sparsification by Algebraic Distance
Network sparsification methods play an important role in modern network
analysis when fast estimation of computationally expensive properties (such as
the diameter, centrality indices, and paths) is required. We propose a method
of network sparsification that preserves a wide range of structural properties.
Depending on the analysis goals, the method allows to distinguish between local
and global range edges that can be filtered out during the sparsification.
First we rank edges by their algebraic distances and then we sample them. We
also introduce a multilevel framework for sparsification that can be used to
control the sparsification process at various coarse-grained resolutions. Based
primarily on the matrix-vector multiplications, our method is easily
parallelized for different architectures
Memory efficient RNA energy landscape exploration
Energy landscapes provide a valuable means for studying the folding dynamics
of short RNA molecules in detail by modeling all possible structures and their
transitions. Higher abstraction levels based on a macro-state decomposition of
the landscape enable the study of larger systems, however they are still
restricted by huge memory requirements of exact approaches.
We present a highly parallelizable local enumeration scheme that enables the
computation of exact macro-state transition models with highly reduced memory
requirements. The approach is evaluated on RNA secondary structure landscapes
using a gradient basin definition for macro-states. Furthermore, we demonstrate
the need for exact transition models by comparing two barrier-based appoaches
and perform a detailed investigation of gradient basins in RNA energy
landscapes.
Source code is part of the C++ Energy Landscape Library available at
http://www.bioinf.uni-freiburg.de/Software/
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