386 research outputs found
Structural Polymorphism Kinetics Promoted by Charged Oxygen Vacancy in HfO
Defects such as oxygen vacancy are widely considered to be critical for the
performance of HfO2-based devices, and yet atomistic mechanisms underlying
various exotic effects such as wake-up and fluid imprint remain elusive. Here,
guided by a lattice-mode-matching criterion, we systematically study the phase
transitions between different polymorphs of hafnia under the influences of
neutral and positively charged oxygen vacancies by mapping out the minimum
energy pathways using a first-principles-based variable-cell nudged elastic
band technique. We find that the positively charged oxygen vacancy can
substantially promote the transition of various nonpolar phases to the polar
phase kinetically, enabled by a transient high-energy tetragonal phase and
extreme charge-carrier-inert ferroelectricity of the polar phase. The
intricate coupling between structural polymorphism kinetics and the charge
state of the oxygen vacancy has important implications for the origin of
ferroelectricity in HfO-based thin films as well as wake-up, fluid imprint,
and inertial switching
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Structural Evaluation of Steel-Concrete Joint with UHPC Grout in Single Cable-Plane Hybrid Cable-Stayed Bridges
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Current knowledge distillation approaches in semantic segmentation tend to
adopt a holistic approach that treats all spatial locations equally. However,
for dense prediction, students' predictions on edge regions are highly
uncertain due to contextual information leakage, requiring higher spatial
sensitivity knowledge than the body regions. To address this challenge, this
paper proposes a novel approach called boundary-privileged knowledge
distillation (BPKD). BPKD distills the knowledge of the teacher model's body
and edges separately to the compact student model. Specifically, we employ two
distinct loss functions: (i) edge loss, which aims to distinguish between
ambiguous classes at the pixel level in edge regions; (ii) body loss, which
utilizes shape constraints and selectively attends to the inner-semantic
regions. Our experiments demonstrate that the proposed BPKD method provides
extensive refinements and aggregation for edge and body regions. Additionally,
the method achieves state-of-the-art distillation performance for semantic
segmentation on three popular benchmark datasets, highlighting its
effectiveness and generalization ability. BPKD shows consistent improvements
across a diverse array of lightweight segmentation structures, including both
CNNs and transformers, underscoring its architecture-agnostic adaptability. The
code is available at \url{https://github.com/AkideLiu/BPKD}.Comment: 17 pages, 9 figures, 9 table
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
This paper investigates the capability of plain Vision Transformers (ViTs)
for semantic segmentation using the encoder-decoder framework and introduces
\textbf{SegViTv2}. In this study, we introduce a novel Attention-to-Mask (\atm)
module to design a lightweight decoder effective for plain ViT. The proposed
ATM converts the global attention map into semantic masks for high-quality
segmentation results. Our decoder outperforms the popular decoder UPerNet using
various ViT backbones while consuming only about of the computational
cost. For the encoder, we address the concern of the relatively high
computational cost in the ViT-based encoders and propose a \emph{Shrunk++}
structure that incorporates edge-aware query-based down-sampling (EQD) and
query-based upsampling (QU) modules. The Shrunk++ structure reduces the
computational cost of the encoder by up to while maintaining competitive
performance. Furthermore, we propose to adapt SegViT for continual semantic
segmentation, demonstrating nearly zero forgetting of previously learned
knowledge. Experiments show that our proposed SegViTv2 surpasses recent
segmentation methods on three popular benchmarks including ADE20k,
COCO-Stuff-10k and PASCAL-Context datasets. The code is available through the
following link: \url{https://github.com/zbwxp/SegVit}.Comment: IJCV 2023 accepted, 21 pages, 8 figures, 12 table
Modular development of deep potential for complex solid solutions
The multicomponent oxide solid solution is a versatile platform to tune the
delicate balance between competing spin, charge, orbital, and lattice degrees
of freedom for materials design and discovery. The development of
compositionally complex oxides with superior functional properties has been
largely empirical and serendipitous, in part due to the exceedingly complex
chemistry and structure of solid solutions that span a range of length scales.
The classical molecular dynamics (MD), as a powerful statistical method to
investigate materials properties over large spatial and temporal scales, often
plays a secondary role in computer-aided materials discovery because of the
limited availability and accuracy of classical force fields. Here, we introduce
the strategy of ``modular developing deep potential" (ModDP) that enables a
systematic development and improvement of deep neural network-based model
potential, termed as deep potential, for complex solid solutions with minimum
human intervention. The converged training database associated with an
end-member material is treated as an independent module and is reused to train
the deep potential of solid solutions via a concurrent learning procedure. We
apply ModDP to obtain classical force fields of two technologically important
solid solutions, PbSrTiO and HfZrO. For both
materials systems, a single model potential is capable of predicting various
properties of solid solutions including temperature-driven and
composition-driven phase transitions over a wide range of compositions. In
particular, the deep potential of PbSrTiO reproduces a few
known topological textures such as polar vortex lattice and electric dipole
waves in PbTiO/SrTiO superlattices, paving the way for MD
investigations on the dynamics of topological structures in response to
external stimuli.Comment: 32 pages, 9 figure
Universal digital filtering for denoising volumetric retinal OCT and OCT angiography in 3D shearlet domain
Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer
from the degeneration of image quality due to speckle noise and bulk-motion
noise, respectively. Because the cross-sectional retina has distinct features
in OCT and OCTA B-scans, existing digital filters that can denoise OCT
efficiently are unable to handle the bulk-motion noise in OCTA. In this Letter,
we propose a universal digital filtering approach that is capable of minimizing
both types of noise. Considering the retinal capillaries in OCTA are hard to
differentiate in B-scans while having distinct curvilinear structures in 3D
volumes, we decompose the volumetric OCT and OCTA data with 3D shearlets thus
efficiently separate the retinal tissue and vessels from the noise in this
transform domain. Compared with wavelets and curvelets, the shearlets provide
better representation of the layer edges in OCT and the vasculature in OCTA.
Qualitative and quantitative results show the proposed method outperforms the
state-of-the-art OCT and OCTA denoising methods. Besides, the superiority of 3D
denoising is demonstrated by comparing the 3D shearlet filtering with its 2D
counterpart.Comment: This version has been accepted for publication in Opt. Let
Novel Microfiber Sensor and Its Biosensing Application for Detection of hCG Based on a Singlemode-Tapered Hollow Core-Singlemode Fiber Structure
A novel microfiber sensor is proposed and demonstrated based on a singlemode-tapered hollow core -singlemode (STHS) fiber structure. Experimentally a STHS with taper waist diameter of 26.5 μm has been fabricated and RI sensitivity of 816, 1601.86, and 4775.5 nm/RIU has been achieved with RI ranges from 1.3335 to 1.3395 , from 1.369 to 1.378, and from 1.409 to 1.4175 respectively, which agrees very well with simulated RI sensitivity of 885, 1517, and 4540 nm/RIU at RI ranges from 1.3335 to 1.337, from 1.37 to 1.374, and from 1.41 to 1.414 . The taper waist diameter has impact on both temperature and strain sensitivity of the sensor structure: (1) the smaller the waist diameter, the higher the temperature sensitivity, and experimentally 26.82 pm/°C has been achieved with a taper waist diameter of 21.4 μm; (2) as waist diameter decrease, strain sensitivity increase and 7.62 pm/με has been achieved with a taper diameter of 20.3 μm. The developed sensor was then functionalized for human chorionic gonadotropin (hCG) detection as an example for biosensing application. Experimentally for hCG concentration of 5 mIU/ml, the sensor has 0.5 nm wavelength shift, equivalent to limit of detection (LOD) of 0.6 mIU/ml by defining 3 times of the wavelength variation (0.06 nm) as measurement limit. The biosensor demonstrated relatively good reproducibility and specificity, which has potential for real medical diagnostics and other applications
Semiconducting nonperovskite ferroelectric oxynitride designed ab initio
Recent discovery of HfO2-based and nitride-based ferroelectrics that are
compatible to the semiconductor manufacturing process have revitalized the
field of ferroelectric-based nanoelectronics. Guided by a simple design
principle of charge compensation and density functional theory calculations, we
discover HfO2-like mixed-anion materials, TaON and NbON, can crystallize in the
polar Pca21 phase with a strong thermodynamic driving force to adopt anion
ordering spontaneously. Both oxynitrides possess large remnant polarization,
low switching barriers, and unconventional negative piezoelectric effect,
making them promising piezoelectrics and ferroelectrics. Distinct from HfO2
that has a wide band gap, both TaON and NbON can absorb visible light and have
high charge carrier mobilities, suitable for ferroelectric photovoltaic and
photocatalytic applications. This new class of multifunctional nonperovskite
oxynitride containing economical and environmentally benign elements offer a
platform to design and optimize high-performing ferroelectric semiconductors
for integrated systems
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