201 research outputs found
Recommended from our members
Development of pulse sequences for hyperpolarized 13C magnetic resonance spectroscopic imaging of tumour metabolism
Metabolic imaging with hyperpolarized 13C-labeled cell substrates is a promising technique for imaging tissue metabolism in vivo. However, the transient nature of the hyperpolarization - and its depletion following excitation - limits the imaging time and the number of excitation pulses that can be used. A single-shot 3D imaging sequence has been developed and it is shown in this thesis to generate 13C MR images in tumour-bearing mice injected with hyperpolarized [1-13C]pyruvate. The pulse sequence acquires a stack-of-spirals at two spin echoes after a single excitation pulse and encodes the kz-dimension in an interleaved manner to enhance robustness to B0 inhomogeneity. Spectral-spatial pulses are used to acquire dynamic 3D images from selected hyperpolarized 13C-labeled metabolites. A nominal spatial/temporal resolution of 1.25 x 1.25 x 2.5 x 2 s was achieved in tumour images of hyperpolarized [1-13C]pyruvate and [1-13C]lactate acquired in vivo. An advanced sequence is also described in this thesis in a later study to acquire higher resolution images with isotropic voxels (1.25 x 1.25 x 1.25 ) at no cost of temporal resolution.
EPI is a sequence widely used in hyperpolarized 13C MRI because images can be acquired rapidly with limited RF exposure. However, EPI suffers from Nyquist ghosting, which is normally corrected for by acquiring a reference scan. In this thesis a workflow for hyperpolarized 13C EPI is proposed that requires no reference scan and, therefore, that does not sacrifice a time point in the dynamic monitoring of tissue metabolism.
To date, most of the hyperpolarized MRI on metabolism are based on 13C imaging, while 1H is a better imaging target for its 4 times higher gyromagnetic ratio and hence 16 times signal. In this thesis the world’s first dynamic 1H imaging in vivo of hyperpolarized [1-13C]lactate is presented, via a novel double-dual-spin-echo INEPT sequence that transfers the hyperpolarization from 13C to 1H, achieving a spatial resolution of 1.25 x 1.25
Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition
Spatial and temporal modeling is one of the most core aspects of few-shot
action recognition. Most previous works mainly focus on long-term temporal
relation modeling based on high-level spatial representations, without
considering the crucial low-level spatial features and short-term temporal
relations. Actually, the former feature could bring rich local semantic
information, and the latter feature could represent motion characteristics of
adjacent frames, respectively. In this paper, we propose SloshNet, a new
framework that revisits the spatial and temporal modeling for few-shot action
recognition in a finer manner. First, to exploit the low-level spatial
features, we design a feature fusion architecture search module to
automatically search for the best combination of the low-level and high-level
spatial features. Next, inspired by the recent transformer, we introduce a
long-term temporal modeling module to model the global temporal relations based
on the extracted spatial appearance features. Meanwhile, we design another
short-term temporal modeling module to encode the motion characteristics
between adjacent frame representations. After that, the final predictions can
be obtained by feeding the embedded rich spatial-temporal features to a common
frame-level class prototype matcher. We extensively validate the proposed
SloshNet on four few-shot action recognition datasets, including
Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable
results against state-of-the-art methods in all datasets
Blind2Sound: Self-Supervised Image Denoising without Residual Noise
Self-supervised blind denoising for Poisson-Gaussian noise remains a
challenging task. Pseudo-supervised pairs constructed from single noisy images
re-corrupt the signal and degrade the performance. The visible blindspots solve
the information loss in masked inputs. However, without explicitly noise
sensing, mean square error as an objective function cannot adjust denoising
intensities for dynamic noise levels, leading to noticeable residual noise. In
this paper, we propose Blind2Sound, a simple yet effective approach to overcome
residual noise in denoised images. The proposed adaptive re-visible loss senses
noise levels and performs personalized denoising without noise residues while
retaining the signal lossless. The theoretical analysis of intermediate medium
gradients guarantees stable training, while the Cramer Gaussian loss acts as a
regularization to facilitate the accurate perception of noise levels and
improve the performance of the denoiser. Experiments on synthetic and
real-world datasets show the superior performance of our method, especially for
single-channel images
Two Candidate Obscured Tidal Disruption Events Coincident with High-energy Neutrinos
Recently, three optical tidal disruption event (TDE) candidates discovered by
the Zwicky Transient Facility (ZTF) have been suggested to be coincident with
high-energy neutrinos. They all exhibit unusually strong dust infrared (IR)
echoes, with their peak times matching the neutrino arrival time even better
than the optical peaks. We hereby report on two new TDE candidates that are
spatially and temporally coincident with neutrinos by matching our sample of
mid-infrared outbursts in nearby galaxies (MIRONG) with Gold alerts of IceCube
high-energy neutrino events up to June 2022. The two candidates show negligible
optical variability according to their ZTF light curves and can therefore be
classified as part of the growing population of obscured TDE candidates. The
chance probability of finding two such candidates about by
redistributing the MIRONG sources randomly in the SDSS footprint, which will be
as low as (or ) if we limit to sources with increased
fluxes (or variability amplitudes) comparable with the matched two sources. Our
findings further support the potential connection between high-energy neutrinos
and TDEs in dusty environments by increasing the total number of
neutrino-associated TDE and TDE candidates to five, although the underlying
physics remains poorly understood.Comment: Published, ApJL, 953, L1
AT2018dyk Revisited: a Tidal Disruption Event Candidate with Prominent Infrared Echo and Delayed X-ray Emission in a LINER Galaxy
The multiwavelength data of nuclear transient AT2018dyk, initially discovered
as a changing-look low-ionization nuclear emission-line region (LINER) galaxy,
has been revisited by us and found being in agreement with a tidal disruption
event (TDE) scenario. The optical light curve of AT2018dyk declines as a
power-law form approximately with index -5/3 yet its X-ray emission lags behind
the optical peak by days, both of which are typical characteristics
for TDEs. The X-ray spectra are softer than normal active galactic nuclei
(AGNs) although they show a slight trend of hardening. Interestingly, its
rising time scale belongs to the longest among TDEs while it is nicely
consistent with the theoretical prediction from its relatively large
supermassive black hole (SMBH) mass (). Moreover, a
prominent infrared echo with peak luminosity
has been also detected in
AT2018dyk, implying an unusually dusty subparsec nuclear environment in
contrast with other TDEs. In our sample, LINERs share similar covering factors
with AGNs, which indicates the existence of the dusty torus in these objects.
Our work suggests that the nature of nuclear transients in LINERs needs to be
carefully identified and their infrared echoes offer us a unique opportunity
for exploring the environment of SMBHs at low accretion rate, which has been so
far poorly explored but is crucial for understanding the SMBH activity.Comment: 9 pages, 6figures, 1 table. Accepted for publication in MNRA
LLaMA Rider: Spurring Large Language Models to Explore the Open World
Recently, various studies have leveraged Large Language Models (LLMs) to help
decision-making and planning in environments, and try to align the LLMs'
knowledge with the world conditions. Nonetheless, the capacity of LLMs to
continuously acquire environmental knowledge and adapt in an open world remains
uncertain. In this paper, we propose an approach to spur LLMs to explore the
open world, gather experiences, and learn to improve their task-solving
capabilities. In this approach, a multi-round feedback-revision mechanism is
utilized to encourage LLMs to actively select appropriate revision actions
guided by feedback information from the environment. This facilitates
exploration and enhances the model's performance. Besides, we integrate
sub-task relabeling to assist LLMs in maintaining consistency in sub-task
planning and help the model learn the combinatorial nature between tasks,
enabling it to complete a wider range of tasks through training based on the
acquired exploration experiences. By evaluation in Minecraft, an open-ended
sandbox world, we demonstrate that our approach LLaMA-Rider enhances the
efficiency of the LLM in exploring the environment, and effectively improves
the LLM's ability to accomplish more tasks through fine-tuning with merely 1.3k
instances of collected data, showing minimal training costs compared to the
baseline using reinforcement learning.Comment: 18 page
AT 2023clx: the Faintest and Closest Optical Tidal Disruption Event Discovered in Nearby Star-forming Galaxy NGC 3799
We report the discovery of a faint optical tidal disruption event (TDE) in
the nearby star-forming galaxy NGC 3799. Identification of the TDE is based on
its position at the galaxy nucleus, a light curve declining as t^-5/3, a blue
continuum with an almost constant blackbody temperature of ~12,000K, and broad
(~15,000kms^-1) Balmer lines and characteristic He~II 4686A emission. The light
curve of AT 2023clx peaked at an absolute magnitude of -17.16mag in the g-band
and a maximum blackbody bolometric luminosity of 4.56*10^42 ergs^-1, making it
the faintest TDE discovered to date. With a redshift of 0.01107 and a
corresponding luminosity distance of 47.8Mpc, it is also the closest optical
TDE ever discovered to our best knowledge. Furthermore, our analysis of
Swift/XRT observations of AT 2023clx yields a very tight 3 sigma upper limit of
9.53*10^39 ergs^-1 in the range 0.3--10keV. AT 2023clx, together with very few
other faint TDEs such as AT 2020wey, prove that there are probably a large
number of faint TDEs yet to be discovered at higher redshifts, which is
consistent with the prediction of luminosity functions (LFs). The upcoming
deeper optical time-domain surveys, such as the Legacy Survey of Space and Time
(LSST) and the Wide-Field Survey Telescope (WFST) will discover more TDEs at
even lower luminosities, allowing for a more precise constraint of the low-end
of the LF.Comment: 9 pages, 6 figures; Accepted for ApJL (July, 2023
Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made
great progress. However, these methods are sensitive to outliers, which lead to
more incorrect correspondences. In this paper, we propose a novel deep graph
matching-based framework for point cloud registration. Specifically, we first
transform point clouds into graphs and extract deep features for each point.
Then, we develop a module based on deep graph matching to calculate a soft
correspondence matrix. By using graph matching, not only the local geometry of
each point but also its structure and topology in a larger range are considered
in establishing correspondences, so that more correct correspondences are
found. We train the network with a loss directly defined on the
correspondences, and in the test stage the soft correspondences are transformed
into hard one-to-one correspondences so that registration can be performed by a
correspondence-based solver. Furthermore, we introduce a transformer-based
method to generate edges for graph construction, which further improves the
quality of the correspondences. Extensive experiments on object-level and
scene-level benchmark datasets show that the proposed method achieves
state-of-the-art performance. The code is available at:
\href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.Comment: accepted by TPAMI 2022. arXiv admin note: substantial text overlap
with arXiv:2103.0425
- …