8,520 research outputs found
SoFiA: a flexible source finder for 3D spectral line data
We introduce SoFiA, a flexible software application for the detection and
parameterization of sources in 3D spectral-line datasets. SoFiA combines for
the first time in a single piece of software a set of new source-finding and
parameterization algorithms developed on the way to future HI surveys with
ASKAP (WALLABY, DINGO) and APERTIF. It is designed to enable the general use of
these new algorithms by the community on a broad range of datasets. The key
advantages of SoFiA are the ability to: search for line emission on multiple
scales to detect 3D sources in a complete and reliable way, taking into account
noise level variations and the presence of artefacts in a data cube; estimate
the reliability of individual detections; look for signal in arbitrarily large
data cubes using a catalogue of 3D coordinates as a prior; provide a wide range
of source parameters and output products which facilitate further analysis by
the user. We highlight the modularity of SoFiA, which makes it a flexible
package allowing users to select and apply only the algorithms useful for their
data and science questions. This modularity makes it also possible to easily
expand SoFiA in order to include additional methods as they become available.
The full SoFiA distribution, including a dedicated graphical user interface, is
publicly available for download.Comment: MNRAS, accepted. SoFiA is registered at the Astrophysics Source Code
Library with ID ascl:1412.001. Download SoFiA at
https://github.com/SoFiA-Admin/SoFi
Exact reconstruction with directional wavelets on the sphere
A new formalism is derived for the analysis and exact reconstruction of
band-limited signals on the sphere with directional wavelets. It represents an
evolution of the wavelet formalism developed by Antoine & Vandergheynst (1999)
and Wiaux et al. (2005). The translations of the wavelets at any point on the
sphere and their proper rotations are still defined through the continuous
three-dimensional rotations. The dilations of the wavelets are directly defined
in harmonic space through a new kernel dilation, which is a modification of an
existing harmonic dilation. A family of factorized steerable functions with
compact harmonic support which are suitable for this kernel dilation is firstly
identified. A scale discretized wavelet formalism is then derived, relying on
this dilation. The discrete nature of the analysis scales allows the exact
reconstruction of band-limited signals. A corresponding exact multi-resolution
algorithm is finally described and an implementation is tested. The formalism
is of interest notably for the denoising or the deconvolution of signals on the
sphere with a sparse expansion in wavelets. In astrophysics, it finds a
particular application for the identification of localized directional features
in the cosmic microwave background (CMB) data, such as the imprint of
topological defects, in particular cosmic strings, and for their reconstruction
after separation from the other signal components.Comment: 22 pages, 2 figures. Version 2 matches version accepted for
publication in MNRAS. Version 3 (identical to version 2) posted for code
release announcement - "Steerable scale discretised wavelets on the sphere" -
S2DW code available for download at
http://www.mrao.cam.ac.uk/~jdm57/software.htm
Performance and resource modeling for FPGAs using high-level synthesis tools
High-performance computing with FPGAs is gaining momentum with the advent of sophisticated High-Level Synthesis (HLS) tools. The performance of a design is impacted by the input-output bandwidth, the code optimizations and the resource consumption, making the performance estimation a challenge. This paper proposes a performance model which extends the roofline model to take into account the resource consumption and the parameters used in the HLS tools. A strategy is developed which maximizes the performance and the resource utilization within the area of the FPGA. The model is used to optimize the design exploration of a class of window-based image processing application
An optimally concentrated Gabor transform for localized time-frequency components
Gabor analysis is one of the most common instances of time-frequency signal
analysis. Choosing a suitable window for the Gabor transform of a signal is
often a challenge for practical applications, in particular in audio signal
processing. Many time-frequency (TF) patterns of different shapes may be
present in a signal and they can not all be sparsely represented in the same
spectrogram. We propose several algorithms, which provide optimal windows for a
user-selected TF pattern with respect to different concentration criteria. We
base our optimization algorithm on -norms as measure of TF spreading. For
a given number of sampling points in the TF plane we also propose optimal
lattices to be used with the obtained windows. We illustrate the potentiality
of the method on selected numerical examples
Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm
Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear
time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein
profiles from biological samples with the aim of discovering biomarkers for
disease. However, the raw protein profiles suffer from several sources of bias
or systematic variation which need to be removed via pre-processing before
meaningful downstream analysis of the data can be undertaken. Baseline
subtraction, an early pre-processing step that removes the non-peptide signal
from the spectra, is complicated by the following: (i) each spectrum has, on
average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and
(ii) the time-consuming and error-prone trial-and-error process for optimising
the baseline subtraction input arguments. With reference to the aforementioned
complications, we present an automated pipeline that includes (i) a novel
`continuous' line segment algorithm that efficiently operates over data with a
transformed m/z-axis to remove the relationship between peptide mass and peak
width, and (ii) an input-free algorithm to estimate peak widths on the
transformed m/z scale. The automated baseline subtraction method was deployed
on six publicly available proteomic MS datasets using six different m/z-axis
transformations. Optimality of the automated baseline subtraction pipeline was
assessed quantitatively using the mean absolute scaled error (MASE) when
compared to a gold-standard baseline subtracted signal. Near-optimal baseline
subtraction was achieved using the automated pipeline. The advantages of the
proposed pipeline include informed and data specific input arguments for
baseline subtraction methods, the avoidance of time-intensive and subjective
piecewise baseline subtraction, and the ability to automate baseline
subtraction completely. Moreover, individual steps can be adopted as
stand-alone routines.Comment: 50 pages, 19 figure
Performance Evaluation of cuDNN Convolution Algorithms on NVIDIA Volta GPUs
Convolutional neural networks (CNNs) have recently attracted considerable attention due to their outstanding accuracy in applications, such as image recognition and natural language processing. While one advantage of the CNNs over other types of neural networks is their reduced computational cost, faster execution is still desired for both training and inference. Since convolution operations pose most of the execution time, multiple algorithms were and are being developed with the aim of accelerating this type of operations. However, due to the wide range of convolution parameter configurations used in the CNNs and the possible data type representations, it is not straightforward to assess in advance which of the available algorithms will be the best performing in each particular case. In this paper, we present a performance evaluation of the convolution algorithms provided by the cuDNN, the library used by most deep learning frameworks for their GPU operations. In our analysis, we leverage the convolution parameter configurations from widely used the CNNs and discuss which algorithms are better suited depending on the convolution parameters for both 32 and 16-bit floating-point (FP) data representations. Our results show that the filter size and the number of inputs are the most significant parameters when selecting a GPU convolution algorithm for 32-bit FP data. For 16-bit FP, leveraging specialized arithmetic units (NVIDIA Tensor Cores) is key to obtain the best performance.This work was supported by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie under Grant 749516, and in part by the Spanish Juan de la Cierva under Grant IJCI-2017-33511Peer ReviewedPostprint (published version
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