6,767 research outputs found
Customisable arithmetic hardware designs
Imperial Users onl
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators
This paper presents a diffusion based probabilistic interpretation of
spectral clustering and dimensionality reduction algorithms that use the
eigenvectors of the normalized graph Laplacian. Given the pairwise adjacency
matrix of all points, we define a diffusion distance between any two data
points and show that the low dimensional representation of the data by the
first few eigenvectors of the corresponding Markov matrix is optimal under a
certain mean squared error criterion. Furthermore, assuming that data points
are random samples from a density p(\x) = e^{-U(\x)} we identify these
eigenvectors as discrete approximations of eigenfunctions of a Fokker-Planck
operator in a potential 2U(\x) with reflecting boundary conditions. Finally,
applying known results regarding the eigenvalues and eigenfunctions of the
continuous Fokker-Planck operator, we provide a mathematical justification for
the success of spectral clustering and dimensional reduction algorithms based
on these first few eigenvectors. This analysis elucidates, in terms of the
characteristics of diffusion processes, many empirical findings regarding
spectral clustering algorithms.Comment: submitted to NIPS 200
Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networks
Three-dimensional solid computational representations of natural heterogeneous materials are challenging to generate due to their high degree of randomness and varying scales of patterns, such as veins and cracks, in different sizes and directions. In this regard, this paper introduces a new architecture to synthesise 3D solid material models by using encoding deep convolutional generative adversarial networks (EDCGANs). DCGANs have been useful in generative tasks in relation to image processing by successfully recreating similar results based on adequate training. While concentrating on natural heterogeneous materials, this paper uses an encoding and a decoding DCGAN combined in a similar way to auto-encoders to convert a given image into marble, based on patches. Additionally, the method creates an input dataset from a single 2D high-resolution exemplar. Further, it translates of 2D data, used as a seed, into 3D data to create material blocks. While the results on the Z-axis do not have size restrictions, the X- and Y-axis are constrained by the given image. Using the method, the paper explores possible ways to present 3D solid textures. The modelling potentials of the developed approach as a design tool is explored to synthesise a 3D solid texture of leaf-like material from an exemplar of a leaf image
Stochastic model for the 3D microstructure of pristine and cyclically aged cathodes in Li-ion batteries
It is well-known that the microstructure of electrodes in lithium-ion
batteries strongly affects their performance. Vice versa, the microstructure
can exhibit strong changes during the usage of the battery due to aging
effects. For a better understanding of these effects, mathematical analysis and
modeling has turned out to be of great help. In particular, stochastic 3D
microstructure models have proven to be a powerful and very flexible tool to
generate various kinds of particle-based structures. Recently, such models have
been proposed for the microstructure of anodes in lithium-ion energy and power
cells. In the present paper, we describe a stochastic modeling approach for the
3D microstructure of cathodes in a lithium-ion energy cell, which differs
significantly from the one observed in anodes. The model for the cathode data
enhances the ideas of the anode models, which have been developed so far. It is
calibrated using 3D tomographic image data from pristine as well as two aged
cathodes. A validation based on morphological image characteristics shows that
the model is able to realistically describe both, the microstructure of
pristine and aged cathodes. Thus, we conclude that the model is suitable to
generate virtual, but realistic microstructures of lithium-ion cathodes
-MLE: A fast algorithm for learning statistical mixture models
We describe -MLE, a fast and efficient local search algorithm for learning
finite statistical mixtures of exponential families such as Gaussian mixture
models. Mixture models are traditionally learned using the
expectation-maximization (EM) soft clustering technique that monotonically
increases the incomplete (expected complete) likelihood. Given prescribed
mixture weights, the hard clustering -MLE algorithm iteratively assigns data
to the most likely weighted component and update the component models using
Maximum Likelihood Estimators (MLEs). Using the duality between exponential
families and Bregman divergences, we prove that the local convergence of the
complete likelihood of -MLE follows directly from the convergence of a dual
additively weighted Bregman hard clustering. The inner loop of -MLE can be
implemented using any -means heuristic like the celebrated Lloyd's batched
or Hartigan's greedy swap updates. We then show how to update the mixture
weights by minimizing a cross-entropy criterion that implies to update weights
by taking the relative proportion of cluster points, and reiterate the mixture
parameter update and mixture weight update processes until convergence. Hard EM
is interpreted as a special case of -MLE when both the component update and
the weight update are performed successively in the inner loop. To initialize
-MLE, we propose -MLE++, a careful initialization of -MLE guaranteeing
probabilistically a global bound on the best possible complete likelihood.Comment: 31 pages, Extend preliminary paper presented at IEEE ICASSP 201
High Level Trigger Using ALICE ITS Detector
The high trigger capabilities of the ALICE inner tracking system (ITS)
as a standalone detector have been investigated. Since the high charged
particles mostly lead to the linear trajectories within this ITS sector, it is
possible to select tracks of of the order of 2 GeV and above by confining
to a narrow search window in the () space. Also shown that by
performing a principal component transformation, it is possible to rotate from
a 12 dimensional (-) space (in this space, a good ITS track has 6
pairs of hit co-ordinates) into a parametric space characterized by only two
independent components when the track momentum exceeds a particular limit. This
independent component analysis (ICA) has been uitilised further to reduce the
false track contribution to an acceptable level particularly when the charged
multiplicity is large. Finally, it is shown that with a narrow bin width of
radian and with PCA or ICA cut, the
ITS can be used to trigger the jet particles with GeV. Apart from
triggering these high particles, this method can also be used to estimate
the initial momentum of the high tracks for seeding which can be further
prolonged into the TPC detector both for offline and online Kalman tracking or
even to detect those high tracks of rare events which might get lost in
the TPC-TRD dead zone.Comment: 20 pages, 10 figure
Unsupervised Superpixel Generation using Edge-Sparse Embedding
Partitioning an image into superpixels based on the similarity of pixels with
respect to features such as colour or spatial location can significantly reduce
data complexity and improve subsequent image processing tasks. Initial
algorithms for unsupervised superpixel generation solely relied on local cues
without prioritizing significant edges over arbitrary ones. On the other hand,
more recent methods based on unsupervised deep learning either fail to properly
address the trade-off between superpixel edge adherence and compactness or lack
control over the generated number of superpixels. By using random images with
strong spatial correlation as input, \ie, blurred noise images, in a
non-convolutional image decoder we can reduce the expected number of contrasts
and enforce smooth, connected edges in the reconstructed image. We generate
edge-sparse pixel embeddings by encoding additional spatial information into
the piece-wise smooth activation maps from the decoder's last hidden layer and
use a standard clustering algorithm to extract high quality superpixels. Our
proposed method reaches state-of-the-art performance on the BSDS500,
PASCAL-Context and a microscopy dataset
A Hardware Efficient Random Number Generator for Nonuniform Distributions with Arbitrary Precision
Nonuniform random numbers are key for many technical applications, and designing efficient hardware implementations of non-uniform random
number generators is a very active research field. However, most state-of-the-art architectures are either tailored to specific distributions or use up a lot of hardware resources. At ReConFig 2010, we have presented a new design that saves up to 48% of area compared to state-of-the-art inversion-based implementation, usable for arbitrary distributions and precision. In this paper, we introduce a more flexible version together with a refined segmentation scheme that allows to further reduce the approximation error significantly. We provide a free software tool allowing users to implement their own distributions easily, and we have tested our random number generator thoroughly by statistic analysis and two application tests
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