13,617 research outputs found
TLib: A Flexible C++ Tensor Framework for Numerical Tensor Calculus
Numerical tensor calculus comprise basic tensor operations such as the
entrywise addition and contraction of higher-order tensors. We present, TLib,
flexible tensor framework with generic tensor functions and tensor classes that
assists users to implement generic and flexible tensor algorithms in C++. The
number of dimensions, the extents of the dimensions of the tensors and the
contraction modes of the tensor operations can be runtime variable. Our
framework provides tensor classes that simplify the management of
multidimensional data and utilization of tensor operations using
object-oriented and generic programming techniques. Additional stream classes
help the user to verify and compare of numerical results with MATLAB. Tensor
operations are implemented with generic tensor functions and in terms of
multidimensional iterator types only, decoupling data storage representation
and computation. The user can combine tensor functions with different tensor
types and extend the framework without further modification of the classes or
functions. We discuss the design and implementation of the framework and
demonstrate its usage with examples that have been discussed in the literature.Comment: 29 page
A compression scheme for radio data in high performance computing
We present a procedure for efficiently compressing astronomical radio data
for high performance applications. Integrated, post-correlation data are first
passed through a nearly lossless rounding step which compares the precision of
the data to a generalized and calibration-independent form of the radiometer
equation. This allows the precision of the data to be reduced in a way that has
an insignificant impact on the data. The newly developed Bitshuffle lossless
compression algorithm is subsequently applied. When the algorithm is used in
conjunction with the HDF5 library and data format, data produced by the CHIME
Pathfinder telescope is compressed to 28% of its original size and
decompression throughputs in excess of 1 GB/s are obtained on a single core.Comment: 12 pages, 3 figures, 2 tables. As published in Astronomy and
Computing special issue on "The future of astronomical data formats
Homotopic Group ICA for Multi-Subject Brain Imaging Data
Independent Component Analysis (ICA) is a computational technique for
revealing latent factors that underlie sets of measurements or signals. It has
become a standard technique in functional neuroimaging. In functional
neuroimaging, so called group ICA (gICA) seeks to identify and quantify
networks of correlated regions across subjects. This paper reports on the
development of a new group ICA approach, Homotopic Group ICA (H-gICA), for
blind source separation of resting state functional magnetic resonance imaging
(fMRI) data. Resting state brain functional homotopy is the similarity of
spontaneous fluctuations between bilaterally symmetrically opposing regions
(i.e. those symmetric with respect to the mid-sagittal plane) (Zuo et al.,
2010). The approach we proposed improves network estimates by leveraging this
known brain functional homotopy. H-gICA increases the potential for network
discovery, effectively by averaging information across hemispheres. It is
theoretically proven to be identical to standard group ICA when the true
sources are both perfectly homotopic and noise-free, while simulation studies
and data explorations demonstrate its benefits in the presence of noise.
Moreover, compared to commonly applied group ICA algorithms, the structure of
the H-gICA input data leads to significant improvement in computational
efficiency. A simulation study comfirms its effectiveness in homotopic,
non-homotopic and mixed settings, as well as on the landmark ADHD-200 dataset.
From a relatively small subset of data, several brain networks were found
including: the visual, the default mode and auditory networks, as well as
others. These were shown to be more contiguous and clearly delineated than the
corresponding ordinary group ICA. Finally, in addition to improving network
estimation, H-gICA facilitates the investigation of functional homotopy via
ICA-based networks.Comment: 35 pages, 13 figure
Efficient cache oblivious algorithms for randomized divide-and-conquer on the multicore model
In this paper we present randomized algorithms for sorting and convex hull
that achieves optimal performance (for speed-up and cache misses) on the
multicore model with private cache model. Our algorithms are cache oblivious
and generalize the randomized divide and conquer strategy given by Reischuk and
Reif and Sen. Although the approach yielded optimal speed-up in the PRAM model,
we require additional techniques to optimize cache-misses in an oblivious
setting. Under a mild assumption on input and number of processors our
algorithm will have optimal time and cache misses with high probability.
Although similar results have been obtained recently for sorting, we feel that
our approach is simpler and general and we apply it to obtain an optimal
parallel algorithm for 3D convex hulls with similar bounds. We also present a
simple randomized processor allocation technique without the explicit knowledge
of the number of processors that is likely to find additional applications in
resource oblivious environments
Submatrix Maximum Queries in Monge Matrices are Equivalent to Predecessor Search
We present an optimal data structure for submatrix maximum queries in n x n
Monge matrices. Our result is a two-way reduction showing that the problem is
equivalent to the classical predecessor problem in a universe of polynomial
size. This gives a data structure of O(n) space that answers submatrix maximum
queries in O(loglogn) time. It also gives a matching lower bound, showing that
O(loglogn) query-time is optimal for any data structure of size O(n
polylog(n)). Our result concludes a line of improvements that started in
SODA'12 with O(log^2 n) query-time and continued in ICALP'14 with O(log n)
query-time. Finally, we show that partial Monge matrices can be handled in the
same bounds as full Monge matrices. In both previous results, partial Monge
matrices incurred additional inverse-Ackerman factors
SoftNull: Many-Antenna Full-Duplex Wireless via Digital Beamforming
In this paper, we present and study a digital-controlled method, called
SoftNull, to enable full-duplex in many-antenna systems. Unlike most designs
that rely on analog cancelers to suppress self-interference, SoftNull relies on
digital transmit beamforming to reduce self-interference. SoftNull does not
attempt to perfectly null self-interference, but instead seeks to reduce
self-interference sufficiently to prevent swamping the receiver's dynamic
range. Residual self-interference is then cancelled digitally by the receiver.
We evaluate the performance of SoftNull using measurements from a 72-element
antenna array in both indoor and outdoor environments. We find that SoftNull
can significantly outperform half-duplex for small cells operating in the
many-antenna regime, where the number of antennas is many more than the number
of users served simultaneously
Resolving Weak Sources within a Dense Array using a Network Approach
A non-parametric technique to identify weak sources within dense sensor
arrays is developed using a network approach. No knowledge about the
propagation medium is needed except that signal strengths decay to
insignificant levels within a scale that is shorter than the aperture. We then
reinterpret the spatial covariance matrix of a wave field as a matrix whose
support is a connectivity matrix of a network of vertices (sensors) connected
into communities. These communities correspond to sensor clusters associated
with individual sources. We estimate the support of the covariance matrix from
limited-time data using a robust hypothesis test combined with a physical
distance criterion. The latter ensures sufficient network sparsity to prevent
vertex communities from forming by chance. We verify the approach on simulated
data and quantify its reliability. The method is then applied to data from a
dense 5200 element geophone array that blanketed 710 km of the city of
Long Beach (CA). The analysis exposes a helicopter traversing the array, oil
production facilities, and reveals that low-frequency events tend to occur near
roads
Deep Learning for Bug-Localization in Student Programs
Providing feedback is an integral part of teaching. Most open online courses
on programming make use of automated grading systems to support programming
assignments and give real-time feedback. These systems usually rely on test
results to quantify the programs' functional correctness. They return failing
tests to the students as feedback. However, students may find it difficult to
debug their programs if they receive no hints about where the bug is and how to
fix it. In this work, we present the first deep learning based technique that
can localize bugs in a faulty program w.r.t. a failing test, without even
running the program. At the heart of our technique is a novel tree
convolutional neural network which is trained to predict whether a program
passes or fails a given test. To localize the bugs, we analyze the trained
network using a state-of-the-art neural prediction attribution technique and
see which lines of the programs make it predict the test outcomes. Our
experiments show that the proposed technique is generally more accurate than
two state-of-the-art program-spectrum based and one syntactic difference based
bug-localization baselines
A Unified Approach to Sparse Signal Processing
A unified view of sparse signal processing is presented in tutorial form by
bringing together various fields. For each of these fields, various algorithms
and techniques, which have been developed to leverage sparsity, are described
succinctly. The common benefits of significant reduction in sampling rate and
processing manipulations are revealed.
The key applications of sparse signal processing are sampling, coding,
spectral estimation, array processing, component analysis, and multipath
channel estimation. In terms of reconstruction algorithms, linkages are made
with random sampling, compressed sensing and rate of innovation. The redundancy
introduced by channel coding in finite/real Galois fields is then related to
sampling with similar reconstruction algorithms. The methods of Prony,
Pisarenko, and MUSIC are next discussed for sparse frequency domain
representations. Specifically, the relations of the approach of Prony to an
annihilating filter and Error Locator Polynomials in coding are emphasized; the
Pisarenko and MUSIC methods are further improvements of the Prony method. Such
spectral estimation methods is then related to multi-source location and DOA
estimation in array processing. The notions of sparse array beamforming and
sparse sensor networks are also introduced. Sparsity in unobservable source
signals is also shown to facilitate source separation in SCA; the algorithms
developed in this area are also widely used in compressed sensing. Finally, the
multipath channel estimation problem is shown to have a sparse formulation;
algorithms similar to sampling and coding are used to estimate OFDM channels.Comment: 43 pages, 40 figures, 15 table
A Unified Framework for Identifiability Analysis in Bilinear Inverse Problems with Applications to Subspace and Sparsity Models
Bilinear inverse problems (BIPs), the resolution of two vectors given their
image under a bilinear mapping, arise in many applications. Without further
constraints, BIPs are usually ill-posed. In practice, properties of natural
signals are exploited to solve BIPs. For example, subspace constraints or
sparsity constraints are imposed to reduce the search space. These approaches
have shown some success in practice. However, there are few results on
uniqueness in BIPs. For most BIPs, the fundamental question of under what
condition the problem admits a unique solution, is yet to be answered. For
example, blind gain and phase calibration (BGPC) is a structured bilinear
inverse problem, which arises in many applications, including inverse rendering
in computational relighting (albedo estimation with unknown lighting), blind
phase and gain calibration in sensor array processing, and multichannel blind
deconvolution (MBD). It is interesting to study the uniqueness of such
problems.
In this paper, we define identifiability of a BIP up to a group of
transformations. We derive necessary and sufficient conditions for such
identifiability, i.e., the conditions under which the solutions can be uniquely
determined up to the transformation group. Applying these results to BGPC, we
derive sufficient conditions for unique recovery under several scenarios,
including subspace, joint sparsity, and sparsity models. For BGPC with joint
sparsity or sparsity constraints, we develop a procedure to compute the
relevant transformation groups. We also give necessary conditions in the form
of tight lower bounds on sample complexities, and demonstrate the tightness of
these bounds by numerical experiments. The results for BGPC not only
demonstrate the application of the proposed general framework for
identifiability analysis, but are also of interest in their own right.Comment: 40 pages, 3 figure
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