105 research outputs found
Higher superconducting transition temperature by breaking the universal pressure relation
By investigating the bulk superconducting state via dc magnetization
measurements, we have discovered a common resurgence of the superconductive
transition temperatures (Tcs) of the monolayer Bi2Sr2CuO6+{\delta} (Bi2201) and
bilayer Bi2Sr2CaCu2O8+{\delta} (Bi2212) to beyond the maximum Tcs (Tc-maxs)
predicted by the universal relation between Tc and doping (p) or pressure (P)
at higher pressures. The Tc of under-doped Bi2201 initially increases from 9.6
K at ambient to a peak at ~ 23 K at ~ 26 GPa and then drops as expected from
the universal Tc-P relation. However, at pressures above ~ 40 GPa, Tc rises
rapidly without any sign of saturation up to ~ 30 K at ~ 51 GPa. Similarly, the
Tc for the slightly overdoped Bi2212 increases after passing a broad valley
between 20-36 GPa and reaches ~ 90 K without any sign of saturation at ~ 56
GPa. We have therefore attributed this Tc-resurgence to a possible
pressure-induced electronic transition in the cuprate compounds due to a charge
transfer between the Cu 3d_(x^2-y^2 ) and the O 2p bands projected from a
hybrid bonding state, leading to an increase of the density of states at the
Fermi level, in agreement with our density functional theory calculations.
Similar Tc-P behavior has also been reported in the trilayer
Br2Sr2Ca2Cu3O10+{\delta} (Bi2223). These observations suggest that higher Tcs
than those previously reported for the layered cuprate high temperature
superconductors can be achieved by breaking away from the universal Tc-P
relation through the application of higher pressures.Comment: 13 pages, including 5 figure
LncRNA-p21 alters the antiandrogen enzalutamide-induced prostate cancer neuroendocrine differentiation via modulating the EZH2/STAT3 signaling
While the antiandrogen enzalutamide (Enz) extends the castration resistant prostate cancer (CRPC) patients' survival an extra 4.8 months, it might also result in some adverse effects via inducing the neuroendocrine differentiation (NED). Here we found that lncRNA-p21 is highly expressed in the NEPC patients derived xenograft tissues (NEPC-PDX). Results from cell lines and human clinical sample surveys also revealed that lncRNA-p21 expression is up-regulated in NEPC and Enz treatment could increase the lncRNA-p21 to induce the NED. Mechanism dissection revealed that Enz could promote the lncRNA-p21 transcription via altering the androgen receptor (AR) binding to different androgen-response-elements, which switch the EZH2 function from histone-methyltransferase to non-histone methyltransferase, consequently methylating the STAT3 to promote the NED. Preclinical studies using the PDX mouse model proved that EZH2 inhibitor could block the Enz-induced NED. Together, these results suggest targeting the Enz/AR/lncRNA-p21/EZH2/STAT3 signaling may help urologists to develop a treatment for better suppression of the human CRPC progression
Implicit Temporal Modeling with Learnable Alignment for Video Recognition
Contrastive language-image pretraining (CLIP) has demonstrated remarkable
success in various image tasks. However, how to extend CLIP with effective
temporal modeling is still an open and crucial problem. Existing factorized or
joint spatial-temporal modeling trades off between the efficiency and
performance. While modeling temporal information within straight through tube
is widely adopted in literature, we find that simple frame alignment already
provides enough essence without temporal attention. To this end, in this paper,
we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes
the temporal modeling effort while achieving incredibly high performance.
Specifically, for a frame pair, an interactive point is predicted in each
frame, serving as a mutual information rich region. By enhancing the features
around the interactive point, two frames are implicitly aligned. The aligned
features are then pooled into a single token, which is leveraged in the
subsequent spatial self-attention. Our method allows eliminating the costly or
insufficient temporal self-attention in video. Extensive experiments on
benchmarks demonstrate the superiority and generality of our module.
Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on
Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is
released at https://github.com/Francis-Rings/ILA .Comment: ICCV 2023 oral. 14 pages, 7 figures. Code released at
https://github.com/Francis-Rings/IL
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Existing diffusion-based video editing models have made gorgeous advances for
editing attributes of a source video over time but struggle to manipulate the
motion information while preserving the original protagonist's appearance and
background. To address this, we propose MotionEditor, a diffusion model for
video motion editing. MotionEditor incorporates a novel content-aware motion
adapter into ControlNet to capture temporal motion correspondence. While
ControlNet enables direct generation based on skeleton poses, it encounters
challenges when modifying the source motion in the inverted noise due to
contradictory signals between the noise (source) and the condition (reference).
Our adapter complements ControlNet by involving source content to transfer
adapted control signals seamlessly. Further, we build up a two-branch
architecture (a reconstruction branch and an editing branch) with a
high-fidelity attention injection mechanism facilitating branch interaction.
This mechanism enables the editing branch to query the key and value from the
reconstruction branch in a decoupled manner, making the editing branch retain
the original background and protagonist appearance. We also propose a skeleton
alignment algorithm to address the discrepancies in pose size and position.
Experiments demonstrate the promising motion editing ability of MotionEditor,
both qualitatively and quantitatively.Comment: 18 pages, 15 figures. Project page at
https://francis-rings.github.io/MotionEditor
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