1,286 research outputs found
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode
(SPAD) array has been widely applied in various fields such as fluorescence
lifetime imaging and quantum computing. However, large-scale high-fidelity
single-photon imaging remains a big challenge, due to the complex hardware
manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we
introduce deep learning into SPAD, enabling super-resolution single-photon
imaging over an order of magnitude, with significant enhancement of bit depth
and imaging quality. We first studied the complex photon flow model of SPAD
electronics to accurately characterize multiple physical noise sources, and
collected a real SPAD image dataset (64 32 pixels, 90 scenes, 10
different bit depth, 3 different illumination flux, 2790 images in total) to
calibrate noise model parameters. With this real-world physical noise model, we
for the first time synthesized a large-scale realistic single-photon image
dataset (image pairs of 5 different resolutions with maximum megapixels, 17250
scenes, 10 different bit depth, 3 different illumination flux, 2.6 million
images in total) for subsequent network training. To tackle the severe
super-resolution challenge of SPAD inputs with low bit depth, low resolution,
and heavy noise, we further built a deep transformer network with a
content-adaptive self-attention mechanism and gated fusion modules, which can
dig global contextual features to remove multi-source noise and extract
full-frequency details. We applied the technique on a series of experiments
including macroscopic and microscopic imaging, microfluidic inspection, and
Fourier ptychography. The experiments validate the technique's state-of-the-art
super-resolution SPAD imaging performance, with more than 5 dB superiority on
PSNR compared to the existing methods
Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection
Millimeter-wave (MMW) imaging is emerging as a promising technique for safe
security inspection. It achieves a delicate balance between imaging resolution,
penetrability and human safety, resulting in higher resolution compared to
low-frequency microwave, stronger penetrability compared to visible light, and
stronger safety compared to X ray. Despite of recent advance in the last
decades, the high cost of requisite large-scale antenna array hinders
widespread adoption of MMW imaging in practice. To tackle this challenge, we
report a large-scale single-shot MMW imaging framework using sparse antenna
array, achieving low-cost but high-fidelity security inspection under an
interpretable learning scheme. We first collected extensive full-sampled MMW
echoes to study the statistical ranking of each element in the large-scale
array. These elements are then sampled based on the ranking, building the
experimentally optimal sparse sampling strategy that reduces the cost of
antenna array by up to one order of magnitude. Additionally, we derived an
untrained interpretable learning scheme, which realizes robust and accurate
image reconstruction from sparsely sampled echoes. Last, we developed a neural
network for automatic object detection, and experimentally demonstrated
successful detection of concealed centimeter-sized targets using 10% sparse
array, whereas all the other contemporary approaches failed at the same sample
sampling ratio. The performance of the reported technique presents higher than
50% superiority over the existing MMW imaging schemes on various metrics
including precision, recall, and mAP50. With such strong detection ability and
order-of-magnitude cost reduction, we anticipate that this technique provides a
practical way for large-scale single-shot MMW imaging, and could advocate its
further practical applications
A missense mutation in Pitx2 leads to early-onset glaucoma via NRF2-YAP1 axis.
Glaucoma is a leading cause of blindness, affecting 70 million people worldwide. Owing to the similarity in anatomy and physiology between human and mouse eyes and the ability to genetically manipulate mice, mouse models are an invaluable resource for studying mechanisms underlying disease phenotypes and for developing therapeutic strategies. Here, we report the discovery of a new mouse model of early-onset glaucoma that bears a transversion substitution c. G344T, which results in a missense mutation, p. R115L in PITX2. The mutation causes an elevation in intraocular pressure (IOP) and progressive death of retinal ganglion cells (RGC). These ocular phenotypes recapitulate features of pathologies observed in human glaucoma. Increased oxidative stress was evident in the inner retina. We demonstrate that the mutant PITX2 protein was not capable of binding to Nuclear factor-like 2 (NRF2), which regulates Pitx2 expression and nuclear localization, and to YAP1, which is necessary for co-initiation of transcription of downstream targets. PITX2-mediated transcription of several antioxidant genes were also impaired. Treatment with N-Acetyl-L-cysteine exerted a profound neuroprotective effect on glaucoma-associated neuropathies, presumably through inhibition of oxidative stress. Our study demonstrates that a disruption of PITX2 leads to glaucoma optic pathogenesis and provides a novel early-onset glaucoma model that will enable elucidation of mechanisms underlying the disease as well as to serve as a resource to test new therapeutic strategies
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions
info:eu-repo/semantics/publishe
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)
Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV
info:eu-repo/semantics/publishe
Evidence for the Higgs boson decay to a bottom quark–antiquark pair
info:eu-repo/semantics/publishe
Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV
Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.
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