1,645 research outputs found
Evolution and control of the phase competition morphology in a manganite film
The competition among different phases in perovskite manganites is pronounced
since their energies are very close under the interplay of charge, spin,
orbital and lattice degrees of freedom. To reveal the roles of underlying
interactions, many efforts have been devoted towards directly imaging phase
transitions at microscopic scales. Here we show images of the charge-ordered
insulator (COI) phase transition from a pure ferromagnetic metal with reducing
field or increasing temperature in a strained phase-separated manganite film,
using a home-built magnetic force microscope. Compared with the COI melting
transition, this reverse transition is sharp, cooperative and martensitic-like
with astonishingly unique yet diverse morphologies. The COI domains show
variable-dimensional growth at different temperatures and their distribution
can illustrate the delicate balance of the underlying interactions in
manganites. Our findings also display how phase domain engineering is possible
and how the phase competition can be tuned in a controllable manner.Comment: Published versio
Tidal disruption rate suppression by the event horizon of spinning black holes
The rate of observable tidal disruption events (TDEs) by the most massive
black holes (BHs) is suppressed due to direct capture of stars by the event
horizon. This suppression effect depends on the shape of the horizon and holds
the promise of probing the spin distribution of dormant BHs at the centers of
galaxies. By extending the frozen-in approximation commonly used in the
Newtonian limit, we propose a general relativistic criterion for the tidal
disruption of a star of given interior structure. The rate suppression factor
is then calculated for different BH masses, spins, and realistic stellar
populations. We find that either a high BH spin (> 0.5) or a young stellar
population (< 1 Gyr) allows TDEs to be observed from BHs significantly more
massive than 10^8 solar masses. We call this spin-age degeneracy (SAD). This
limits our utility of the TDE rate to constrain the BH spin distribution,
unless additional constraints on the age of the stellar population or the mass
of the disrupted star can be obtained by modeling the TDE radiation or the
stellar spectral energy distribution near the galactic nuclei.Comment: 19 pages, 14 figures, 3 tables; submitted to MNRA
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Characterization of Laser-Resistant Port Wine Stain Blood Vessels Using In Vivo Reflectance Confocal Microscopy.
Background and objectivesPort wine stain (PWS) is a congenital vascular malformation of the human skin. Laser is the treatment of choice for PWS. Laser-resistant PWS is one crucial factor accounting for inadequate treatment outcome, which needs to be fully characterized. This study aims to quantitatively characterize the morphology of laser-resistant PWS blood vessels in the upper papillary dermis using in vivo reflectance confocal microscopy (RCM).Study design/materials and methodsA total of 42 PWS subjects receiving laser treatment from August 2016 through July 2018 were enrolled into this study. Thirty-three subjects had facial PWS; nine had extremity PWS. All subject's PWS received multiplex 585/1,064 nm laser treatment. RCM images were taken before and after treatment. The density, diameter, blood flow, and depth of PWS blood vessels were analyzed.ResultsWe found 44.4% PWS on the extremities (four out of nine subjects) were laser-resistant, which was significantly higher (P < 0.001) when compared with those PWS on the face (15.2%, 5 out of 33 subjects). The laser-resistant facial PWS blood vessels had significantly higher blood flow (1.35 ± 0.26 U vs. 0.89 ± 0.22 U, P < 0.001), larger blood vessel diameters (109.60 ± 18.24 µm vs. 84.36 ± 24.04 µm, P = 0.033) and were located deeper in the skin (106.01 ± 13.87 µm vs. 87.82 ± 12.57 µm, P < 0.001) in the skin when compared with laser-responsive PWS on the face. The average PWS blood vessel density (17.01 ± 4.63/mm2 vs. 16.61 ± 4.44/mm2 , P = 0.857) was not correlated to the laser resistance.ConclusionsLaser-resistant PWS blood vessels had significantly higher blood flow, larger diameters, and were located deeper in the skin. RCM can be a valuable tool for a prognostic evaluation on laser-resistant lesions before treatment, thereby providing guidance for tailored laser treatment protocols, which may improve the therapeutic outcome. The limitations for this study include relative small sample size and acquisitions of different blood vessels before and after 2 months of treatment. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc
MaturePred: Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAs
MicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates.A miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method.The superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/
Quasinormal modes and stability of higher dimensional rotating black holes under massive scalar perturbations
We consider the stability of six-dimensional singly rotating Myers-Perry
black holes under massive scalar perturbations. Using Leaver's continued
fraction method, we compute the quasinormal modes of the massive scalar fields.
All modes found are damped under the quasinormal boundary conditions. It is
also found that long-living modes called quasiresonances exist for large scalar
masses as in the four-dimensional Kerr black hole case. Our numerical results
provide a direct and complement evidence for the stability of six-dimensional
MP black holes under massive scalar perturbation.Comment: 11 pages,9 figure
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
In this paper, we mainly focus on the problem of how to learn additional
feature representations for few-shot image classification through pretext tasks
(e.g., rotation or color permutation and so on). This additional knowledge
generated by pretext tasks can further improve the performance of few-shot
learning (FSL) as it differs from human-annotated supervision (i.e., class
labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical
Tree Structure-aware (HTS) method, which not only learns the relationship of
FSL and pretext tasks, but more importantly, can adaptively select and
aggregate feature representations generated by pretext tasks to maximize the
performance of FSL tasks. A hierarchical tree constructing component and a
gated selection aggregating component is introduced to construct the tree
structure and find richer transferable knowledge that can rapidly adapt to
novel classes with a few labeled images. Extensive experiments show that our
HTS can significantly enhance multiple few-shot methods to achieve new
state-of-the-art performance on four benchmark datasets. The code is available
at: https://github.com/remiMZ/HTS-ECCV22.Comment: 22 pages, 9 figures and 4 tables Accepted by ECCV 202
Giant spin-vorticity coupling excited by shear-horizontal surface acoustic waves
A non-magnetic layer can inject spin-polarized currents into an adjacent
ferromagnetic layer via spin vorticity coupling (SVC), inducing spin wave
resonance (SWR). In this work, we present the theoretical model of SWR
generated by shear-horizontal surface acoustic wave (SH-SAW) via SVC, which
contains distinct vorticities from well-studied Rayleigh SAW. Both Rayleigh-
and SH-SAW delay lines have been designed and fabricated with a Ni81Fe19/Cu
bilayer integrated on ST-cut quartz. Given the same wavelength, the measured
power absorption of SH-SAW is four orders of magnitudes higher than that of the
Rayleigh SAW. In addition, a high-order frequency dependence of the SWR is
observed in the SH-SAW, indicating SVC can be strong enough to compare with
magnetoelastic coupling
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models
Prompt-based learning has been proved to be an effective way in pre-trained
language models (PLMs), especially in low-resource scenarios like few-shot
settings. However, the trustworthiness of PLMs is of paramount significance and
potential vulnerabilities have been shown in prompt-based templates that could
mislead the predictions of language models, causing serious security concerns.
In this paper, we will shed light on some vulnerabilities of PLMs, by proposing
a prompt-based adversarial attack on manual templates in black box scenarios.
First of all, we design character-level and word-level heuristic approaches to
break manual templates separately. Then we present a greedy algorithm for the
attack based on the above heuristic destructive approaches. Finally, we
evaluate our approach with the classification tasks on three variants of BERT
series models and eight datasets. And comprehensive experimental results
justify the effectiveness of our approach in terms of attack success rate and
attack speed. Further experimental studies indicate that our proposed method
also displays good capabilities in scenarios with varying shot counts, template
lengths and query counts, exhibiting good generalizability
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