9,167 research outputs found
Searching Previous Configurations in Membrane Computing
Searching all the configurations C′ which produce a given configuration C is an extremely hard task. The current approximations are based on heavy hand-made calculus by considering the specific features of the given configuration. In this paper we present a general method for characterizing all the configurations C′ which produce a given configuration C in the framework of transition P systems without cooperation and without dissolution.Ministerio de Educación y Ciencia TIN2006- 13425Junta de Andalucía P08-TIC-0420
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Exact goodness-of-fit testing for the Ising model
The Ising model is one of the simplest and most famous models of interacting
systems. It was originally proposed to model ferromagnetic interactions in
statistical physics and is now widely used to model spatial processes in many
areas such as ecology, sociology, and genetics, usually without testing its
goodness of fit. Here, we propose various test statistics and an exact
goodness-of-fit test for the finite-lattice Ising model. The theory of Markov
bases has been developed in algebraic statistics for exact goodness-of-fit
testing using a Monte Carlo approach. However, finding a Markov basis is often
computationally intractable. Thus, we develop a Monte Carlo method for exact
goodness-of-fit testing for the Ising model which avoids computing a Markov
basis and also leads to a better connectivity of the Markov chain and hence to
a faster convergence. We show how this method can be applied to analyze the
spatial organization of receptors on the cell membrane.Comment: 20 page
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
The molecular dynamics simulation package GROMACS runs efficiently on a wide
variety of hardware from commodity workstations to high performance computing
clusters. Hardware features are well exploited with a combination of SIMD,
multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as
accelerators to compute interactions offloaded from the CPU. Here we evaluate
which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most
economical way. We have assembled and benchmarked compute nodes with various
CPU/GPU combinations to identify optimal compositions in terms of raw
trajectory production rate, performance-to-price ratio, energy efficiency, and
several other criteria. Though hardware prices are naturally subject to trends
and fluctuations, general tendencies are clearly visible. Adding any type of
GPU significantly boosts a node's simulation performance. For inexpensive
consumer-class GPUs this improvement equally reflects in the
performance-to-price ratio. Although memory issues in consumer-class GPUs could
pass unnoticed since these cards do not support ECC memory, unreliable GPUs can
be sorted out with memory checking tools. Apart from the obvious determinants
for cost-efficiency like hardware expenses and raw performance, the energy
consumption of a node is a major cost factor. Over the typical hardware
lifetime until replacement of a few years, the costs for electrical power and
cooling can become larger than the costs of the hardware itself. Taking that
into account, nodes with a well-balanced ratio of CPU and consumer-class GPU
resources produce the maximum amount of GROMACS trajectory over their lifetime
Coarse-Grained Simulations of Membranes under Tension
We investigate the properties of membranes under tension by Monte-Carlo
simulations of a generic coarse-grained model for lipid bilayers. We give a
comprising overview of the behavior of several membrane characteristics, such
as the area per lipid, the monolayer overlap, the nematic order, and pressure
profiles. Both the low-temperature regime, where the membranes are in a gel
phase, and the high-temperature regime, where they are in the fluid phase, are
considered. In the gel state, the membrane is hardly influenced by tension. In
the fluid state, high tensions lead to structural changes in the membrane,
which result in different compressibility regimes. The ripple state, which is
found at tension zero in the transition regime between the fluid and the gel
phase, disappears under tension and gives way to an interdigitated phase. We
also study the membrane fluctuations in the fluid phase. In the low tension
regime the data can be fitted nicely to a suitably extended elastic theory. At
higher tensions the elastic fit consistently underestimates the strength of
long-wavelength fluctuations. Finally, we investigate the influence of tension
on the effective interaction between simple transmembrane inclusions and show
that tension can be used to tune the hydrophobic mismatch interaction between
membrane proteins.Comment: 14 pages, 14 figures, accepted for publication in The Journal of
Chemical Physic
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D
views and precise distance measures under highly uncertain driving conditions.
Its practical implementation, however, remains costly. This paper investigates
the optimal LiDAR configuration problem to achieve utility maximization. We use
the perception area and non-detectable subspace to construct the design
procedure as solving a min-max optimization problem and propose a bio-inspired
measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost
function representing the notion of the size of the non-detectable subspaces of
a given configuration. We then adopt a cuboid-based approach to show that the
proposed VSR-based measure is a well-suited proxy for object detection rate. It
is found that the Artificial Bee Colony evolutionary algorithm yields a
tractable cost function computation. Our experiments highlight the
effectiveness of our proposed VSR measure in terms of cost-effectiveness
configuration as well as providing insightful analyses that can improve the
design of AV systems.Comment: 7 pages including the references, accepted by International
Conference on Robotics and Automation (ICRA), 201
- …