262 research outputs found
Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach
Popular methods in compressed sensing (CS) are dependent on deep learning
(DL), where large amounts of data are used to train non-linear reconstruction
models. However, ensuring generalisability over and access to multiple datasets
is challenging to realise for real-world applications. To address these
concerns, this paper proposes a single image, self-supervised (SS) CS-MRI
framework that enables a joint deep and sparse regularisation of CS artefacts.
The approach effectively dampens structured CS artefacts, which can be
difficult to remove assuming sparse reconstruction, or relying solely on the
inductive biases of CNN to produce noise-free images. Image quality is thereby
improved compared to either approach alone. Metrics are evaluated using
Cartesian 1D masks on a brain and knee dataset, with PSNR improving by 2-4dB on
average.Comment: 5 pages, 4 figures, 2 tables, conferenc
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI
Sparse reconstruction is an important aspect of MRI, helping to reduce
acquisition time and improve spatial-temporal resolution. Popular methods are
based mostly on compressed sensing (CS), which relies on the random sampling of
k-space to produce incoherent (noise-like) artefacts. Due to hardware
constraints, 1D Cartesian phase-encode under-sampling schemes are popular for
2D CS-MRI. However, 1D under-sampling limits 2D incoherence between
measurements, yielding structured aliasing artefacts (ghosts) that may be
difficult to remove assuming a 2D sparsity model. Reconstruction algorithms
typically deploy direction-insensitive 2D regularisation for these
direction-associated artefacts. Recognising that phase-encode artefacts can be
separated into contiguous 1D signals, we develop two decoupling techniques that
enable explicit 1D regularisation and leverage the excellent 1D incoherence
characteristics. We also derive a combined 1D + 2D reconstruction technique
that takes advantage of spatial relationships within the image. Experiments
conducted on retrospectively under-sampled brain and knee data demonstrate that
combination of the proposed 1D AliasNet modules with existing 2D deep learned
(DL) recovery techniques leads to an improvement in image quality. We also find
AliasNet enables a superior scaling of performance compared to increasing the
size of the original 2D network layers. AliasNet therefore improves the
regularisation of aliasing artefacts arising from phase-encode under-sampling,
by tailoring the network architecture to account for their expected appearance.
The proposed 1D + 2D approach is compatible with any existing 2D DL recovery
technique deployed for this application
The Wisdom of Older Technology (Non-)Users
Older adults consistently reject digital technology even when designed to be accessible and trustworthy
Plasmonic coupling in closed-packed ordered gallium nanoparticles
Plasmonic gallium (Ga) nanoparticles (NPs) are well known to exhibit good performance in numerous applications such as surface enhanced fluorescence and Raman spectroscopy or biosensing. However, to reach the optimal optical performance, the strength of the localized surface plasmon resonances (LSPRs) must be enhanced particularly by suitable narrowing the NP size distribution among other factors. With this purpose, our last work demonstrated the production of hexagonal ordered arrays of Ga NPs by using templates of aluminium (Al) shallow pit arrays, whose LSPRs were observed in the VIS region. The quantitative analysis of the optical properties by spectroscopic ellipsometry confirmed an outstanding improvement of the LSPR intensity and full width at half maximum (FWHM) due to the imposed ordering. Here, by engineering the template dimensions, and therefore by tuning Ga NPs size, we expand the LSPRs of the Ga NPs to cover a wider range of the electromagnetic spectrum from the UV to the IR regions. More interestingly, the factors that cause this optical performance improvement are studied with the universal plasmon ruler equation, supported with discrete dipole approximation simulations. The results allow us to conclude that the plasmonic coupling between NPs originated in the ordered systems is the main cause for the optimized optical responseThe research is supported by the MINECO (CTQ2014-53334-C2-2-R, CTQ2017-84309-C2-2-R and MAT201676824-C3-1-R) and Comunidad de Madrid (P2018/NMT4349 and S2018/NMT-4321 NANOMAGCOST) projects. ARC acknowledges Ramón y Cajal program (under contract number RYC-2015-18047
ICT under constraint: exposing tensions in collaboratively prioritising ICT innovation for climate targets
The international treaty known as the Paris Agreement requires global greenhouse gas emissions to decrease at a pace that will limit global warming to 1.5 degrees Celsius. Given the pressure on all sectors to reduce their emissions to meet this target, the ICT sector must begin to explore how to innovate under constraint for the first time. This could mean facing the unprecedented dilemma of having to choose between innovations, in which case the community will need to develop processes for making collective decisions regarding which innovations are most deserving of their carbon costs. In this paper, we expose tensions in collaboratively prioritising ICT innovation under constraints, and discuss the considerations and approaches the ICT sector may require to make such decisions effectively across the sector. This opens up a new area of research where we envision HCI expertise can inform and resolve such tensions for values-based and target-led ICT innovation towards a sustainable future
ICT Under Constraint : Exposing Tensions in Collaboratively Prioritising ICT Innovation for Climate Targets
The international treaty known as the Paris Agreement requires global greenhouse gas emissions to decrease at a pace that will limit global warming to 1.5 degrees Celsius. Given the pressure on all sectors to reduce their emissions to meet this target, the ICT sector must begin to explore how to innovate under constraint for the first time. This could mean facing the unprecedented dilemma of having to choose between innovations, in which case the community will need to develop processes for making collective decisions regarding which innovations are most deserving of their carbon costs. In this paper, we expose tensions in collaboratively prioritising ICT innovation under constraints, and discuss the considerations and approaches the ICT sector may require to make such decisions effectively across the sector. This opens up a new area of research where we envision HCI expertise can inform and resolve such tensions for values-based and target-led ICT innovation towards a sustainable future
Proposals for Innovation and Improvement of the Quality of Life in Caprine Pastoralist Communities of Subsistence in the Monte Desert, Argentina
In a satisfactory alliance between the main environmental policy organizations and the academy, the National Observatory on Land Degradation and Desertification (ONDTyT) is created. The ONDTyD provides information regarding status and trends of land degradation/desertification in order to promote prevention and mitigation measures used for advising public and private decision-makers in Argentina. It is based in the development of 17 Pilot Sites that constitutes the local level network, providing bio-physical and socio-economic indicators of land degradation.
In this network the pilot site of the Monte, the largest dry region of Argentina (Lavalle desert, Mendoza), aims to improve the living conditions of native communities dedicated to subsistence goat farming, located below the poverty line. Precipitation ranges from 80-100 mm/year, strongly affecting productive activities. The proposal includes innovative traits in an area whose natural resources have been devastated. It is framed within a conception of rural territory development generating sustainable development strategies of rural indigenous communities, improve the status of the ecosystem through an integral management of natural and cultural resources, and improve socioeconomic conditions of inhabitants, compatibilizing ecosystem regeneration with investment in infrastructure and services, diversification of productive activities and generation of employment.
An interdisciplinary group designed the proposal and the integrated desertification assessment in the fields with active community participation through their knowledge, land and livestock. The pilot case can be replicated throughout the territory. The work combines participatory and integrated methodologies, showing that the Observatory is a successful example of partnership building between the political and scientific-technological sectors in Argentina
Multi-contrast MRI Super-resolution via Implicit Neural Representations
Clinical routine and retrospective cohorts commonly include multi-parametric
Magnetic Resonance Imaging; however, they are mostly acquired in different
anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.
Thus acquired views suffer from poor out-of-plane resolution and affect
downstream volumetric image analysis that typically requires isotropic 3D
scans. Combining different views of multi-contrast scans into high-resolution
isotropic 3D scans is challenging due to the lack of a large training cohort,
which calls for a subject-specific framework.This work proposes a novel
solution to this problem leveraging Implicit Neural Representations (INR). Our
proposed INR jointly learns two different contrasts of complementary views in a
continuous spatial function and benefits from exchanging anatomical information
between them. Trained within minutes on a single commodity GPU, our model
provides realistic super-resolution across different pairs of contrasts in our
experiments with three datasets. Using Mutual Information (MI) as a metric, we
find that our model converges to an optimum MI amongst sequences, achieving
anatomically faithful reconstruction. Code is available at:
https://github.com/jqmcginnis/multi_contrast_inr
Radial Star Formation Histories in 32 Nearby Galaxies
The spatially resolved star formation histories are studied for 32 normal
star-forming galaxies drawn from the the Spitzer Extended Disk Galaxy
Exploration Science survey. At surface brightness sensitivities fainter than 28
mag arcsec, the new optical photometry is deep enough to complement
archival ultraviolet and infrared imaging and to explore the properties of the
emission well beyond the traditional optical extents of these nearby galaxies.
Fits to the spectral energy distributions using a delayed star formation
history model indicate a subtle but interesting average radial trend for the
spiral galaxies: the inner stellar systems decrease in age with increasing
radius, consistent with inside-out disk formation, but the trend reverses in
the outermost regions with the stellar age nearly as old as the innermost
stars. These results suggest an old stellar outer disk population formed
through radial migration and/or the cumulative history of minor mergers and
accretions of satellite dwarf galaxies. The subset of S0 galaxies studied here
show the opposite trend compared to what is inferred for spirals:
characteristic stellar ages that are increasingly older with radius for the
inner portions of the galaxies, and increasingly younger stellar ages for the
outer portions. This result suggests that either S0 galaxies are not well
modeled by a delayed- model, and/or that S0 galaxies have a more
complicated formation history than spiral galaxies.Comment: Accepted for publication in the Astronomical Journal. arXiv admin
note: text overlap with arXiv:1511.0328
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