102 research outputs found
Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to learn recognition models for recognizing new
classes without labeled data. In this work, we propose a novel approach dubbed
Transferrable Semantic-Visual Relation (TSVR) to facilitate the cross-category
transfer in transductive ZSL. Our approach draws on an intriguing insight
connecting two challenging problems, i.e. domain adaptation and zero-shot
learning. Domain adaptation aims to transfer knowledge across two different
domains (i.e., source domain and target domain) that share the identical
task/label space. For ZSL, the source and target domains have different
tasks/label spaces. Hence, ZSL is usually considered as a more difficult
transfer setting compared with domain adaptation. Although the existing ZSL
approaches use semantic attributes of categories to bridge the source and
target domains, their performances are far from satisfactory due to the large
domain gap between different categories. In contrast, our method directly
transforms ZSL into a domain adaptation task through redrawing ZSL as
predicting the similarity/dissimilarity labels for the pairs of semantic
attributes and visual features. For this redrawn domain adaptation problem, we
propose to use a domain-specific batch normalization component to reduce the
domain discrepancy of semantic-visual pairs. Experimental results over diverse
ZSL benchmarks clearly demonstrate the superiority of our method
Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography
Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong projection artifacts, which restrict the performance of depth-resolved 3D analysis. In this paper, we propose a novel 2D-to-3D vessel reconstruction framework based on the 2D en face OCTA images. This framework takes advantage of the detailed 2D OCTA depth map for prediction and thus does not rely on any 3D volumetric data. Based on the data with available vessel depth labels, we first introduce a network with structure constraint blocks to estimate the depth map of blood vessels in other cross-domain en face OCTA data with unavailable labels. Afterwards, a depth adversarial adaptation module is proposed for better unsupervised cross-domain training, since images captured using different devices may suffer from varying image contrast and noise levels. Finally, vessels are reconstructed in 3D space by utilizing the estimated depth map and 2D vascular information. Experimental results demonstrate the effectiveness of our method and its potential to guide subsequent vascular analysis in 3D domain
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential
challenge in the structure-based drug design. Despite recent advance in
data-driven methods in affinity prediction, their accuracy is still limited,
partially because they only take advantage of static crystal structures while
the actual binding affinities are generally depicted by the thermodynamic
ensembles between proteins and ligands. One effective way to approximate such a
thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, we
curated an MD dataset containing 3,218 different protein-ligand complexes, and
further developed Dynaformer, which is a graph-based deep learning model.
Dynaformer was able to accurately predict the binding affinities by learning
the geometric characteristics of the protein-ligand interactions from the MD
trajectories. In silico experiments demonstrated that our model exhibits
state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset,
outperforming the methods hitherto reported. Moreover, we performed a virtual
screening on the heat shock protein 90 (HSP90) using Dynaformer that identified
20 candidates and further experimentally validated their binding affinities. We
demonstrated that our approach is more efficient, which can identify 12 hit
compounds (two were in the submicromolar range), including several newly
discovered scaffolds. We anticipate this new synergy between large-scale MD
datasets and deep learning models will provide a new route toward accelerating
the early drug discovery process.Comment: totally reorganize the texts and figure
The Ginger-shaped Asteroid 4179 Toutatis: New Observations from a Successful Flyby of Chang'e-2
On 13 December 2012, Chang'e-2 conducted a successful flyby of the near-Earth
asteroid 4179 Toutatis at a closest distance of 770 120 meters from the
asteroid's surface. The highest-resolution image, with a resolution of better
than 3 meters, reveals new discoveries on the asteroid, e.g., a giant basin at
the big end, a sharply perpendicular silhouette near the neck region, and
direct evidence of boulders and regolith, which suggests that Toutatis may bear
a rubble-pile structure. Toutatis' maximum physical length and width are (4.75
1.95 km) 10, respectively, and the direction of the + axis
is estimated to be (2505, 635) with respect to the
J2000 ecliptic coordinate system. The bifurcated configuration is indicative of
a contact binary origin for Toutatis, which is composed of two lobes (head and
body). Chang'e-2 observations have significantly improved our understanding of
the characteristics, formation, and evolution of asteroids in general.Comment: 21 pages, 3 figures, 1 tabl
3D VESSEL RECONSTRUCTION IN OCT-ANGIOGRAPHY VIA DEPTH MAP ESTIMATION
Optical Coherence Tomography Angiography (OCTA) has been increasingly used in
the management of eye and systemic diseases in recent years. Manual or
automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is
commonly used in clinical practice, however it may lose rich 3D spatial
distribution information of blood vessels or capillaries that are useful for
clinical decision-making. In this paper, we introduce a novel 3D vessel
reconstruction framework based on the estimation of vessel depth maps from OCTA
images. First, we design a network with structural constraints to predict the
depth of blood vessels in OCTA images. In order to promote the accuracy of the
predicted depth map at both the overall structure- and pixel- level, we combine
MSE and SSIM loss as the training loss function. Finally, the 3D vessel
reconstruction is achieved by utilizing the estimated depth map and 2D vessel
segmentation results. Experimental results demonstrate that our method is
effective in the depth prediction and 3D vessel reconstruction for OCTA
images.% results may be used to guide subsequent vascular analysi
Observation of nonrelativistic plaid-like spin splitting in a noncoplanar antiferromagnet
Spatial, momentum and energy separation of electronic spins in condensed
matter systems guides the development of novel devices where spin-polarized
current is generated and manipulated. Recent attention on a set of previously
overlooked symmetry operations in magnetic materials leads to the emergence of
a new type of spin splitting besides the well-studied Zeeman, Rashba and
Dresselhaus effects, enabling giant and momentum dependent spin polarization of
energy bands on selected antiferromagnets independent of relativistic
spin-orbit interaction. Despite the ever-growing theoretical predictions, the
direct spectroscopic proof of such spin splitting is still lacking. Here, we
provide solid spectroscopic and computational evidence for the existence of
such materials. In the noncoplanar antiferromagnet MnTe, the in-plane
components of spin are found to be antisymmetric about the high-symmetry planes
of the Brillouin zone, comprising a plaid-like spin texture in the
antiferromagnetic ground state. Such an unconventional spin pattern, further
found to diminish at the high-temperature paramagnetic state, stems from the
intrinsic antiferromagnetic order instead of the relativistic spin-orbit
coupling. Our finding demonstrates a new type of spin-momentum locking with a
nonrelativistic origin, placing antiferromagnetic spintronics on a firm basis
and paving the way for studying exotic quantum phenomena in related materials.Comment: Version 2, 30 pages, 4 main figures and 8 supporting figure
Crowdsourced mapping of unexplored target space of kinase inhibitors
Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts
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