220 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Diffusion models have emerged as potential tools to tackle the challenge of
sparse-view CT reconstruction, displaying superior performance compared to
conventional methods. Nevertheless, these prevailing diffusion models
predominantly focus on the sinogram or image domains, which can lead to
instability during model training, potentially culminating in convergence
towards local minimal solutions. The wavelet trans-form serves to disentangle
image contents and features into distinct frequency-component bands at varying
scales, adeptly capturing diverse directional structures. Employing the Wavelet
transform as a guiding sparsity prior significantly enhances the robustness of
diffusion models. In this study, we present an innovative approach named the
Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for
sparse-view CT reconstruction. Specifically, we establish a unified
mathematical model integrating low-frequency and high-frequency generative
models, achieving the solution with optimization procedure. Furthermore, we
perform the low-frequency and high-frequency generative models on wavelet's
decomposed components rather than sinogram or image domains, ensuring the
stability of model training. Our method rooted in established optimization
theory, comprising three distinct stages, including low-frequency generation,
high-frequency refinement and domain transform. Our experimental results
demonstrate that the proposed method outperforms existing state-of-the-art
methods both quantitatively and qualitatively
High efficiency uniform positron beam loading in a hollow channel plasma wakefield accelerator
We propose a novel positron beam loading regime in a hollow plasma channel
that can efficiently accelerate beam with high gradient and narrow energy
spread. In this regime, the beam coincides with the drive beam in
time and space and their net current distribution determines the plasma
wakefields. By precisely shaping the beam current profile and loading phase
according to explicit expressions, three-dimensional Particle-in-Cell (PIC)
simulations show that the acceleration for beam of nC charge with
GV/m gradient, 0.5% induced energy spread and 50% energy
transfer efficiency can be achieved simultaneously. Besides, only tailoring the
current profile of the more tunable beam instead of the beam is
enough to obtain such favorable results. A theoretical analysis considering
both linear and nonlinear plasma responses in hollow plasma channels is
proposed to quantify the beam loading effects. This theory agrees very well
with the simulation results and verifies the robustness of this beam loading
regime over a wide range of parameters
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Graph Neural Network (GNN) has demonstrated extraordinary performance in
classifying graph properties. However, due to the selection bias of training
and testing data (e.g., training on small graphs and testing on large graphs,
or training on dense graphs and testing on sparse graphs), distribution
deviation is widespread. More importantly, we often observe \emph{hybrid
structure distribution shift} of both scale and density, despite of one-sided
biased data partition. The spurious correlations over hybrid distribution
deviation degrade the performance of previous GNN methods and show large
instability among different datasets. To alleviate this problem, we propose
\texttt{OOD-GMixup} to jointly manipulate the training distribution with
\emph{controllable data augmentation} in metric space. Specifically, we first
extract the graph rationales to eliminate the spurious correlations due to
irrelevant information. Secondly, we generate virtual samples with perturbation
on graph rationale representation domain to obtain potential OOD training
samples. Finally, we propose OOD calibration to measure the distribution
deviation of virtual samples by leveraging Extreme Value Theory, and further
actively control the training distribution by emphasizing the impact of virtual
OOD samples. Extensive studies on several real-world datasets on graph
classification demonstrate the superiority of our proposed method over
state-of-the-art baselines.Comment: Under revie
Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis: IR to improve IQ for DBT
Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data
Element Replacement Approach by Reaction with Lewis Acidic Molten Salts to Synthesize Nanolaminated MAX Phases and MXenes
Nanolaminated materials are important because of their exceptional properties
and wide range of applications. Here, we demonstrate a general approach to
synthesize a series of Zn-based MAX phases and Cl-terminated MXenes originating
from the replacement reaction between the MAX phase and the late transition
metal halides. The approach is a top-down route that enables the late
transitional element atom (Zn in the present case) to occupy the A site in the
pre-existing MAX phase structure. Using this replacement reaction between Zn
element from molten ZnCl2 and Al element in MAX phase precursors (Ti3AlC2,
Ti2AlC, Ti2AlN, and V2AlC), novel MAX phases Ti3ZnC2, Ti2ZnC, Ti2ZnN, and V2ZnC
were synthesized. When employing excess ZnCl2, Cl terminated MXenes (such as
Ti3C2Cl2 and Ti2CCl2) were derived by a subsequent exfoliation of Ti3ZnC2 and
Ti2ZnC due to the strong Lewis acidity of molten ZnCl2. These results indicate
that A-site element replacement in traditional MAX phases by late transition
metal halides opens the door to explore MAX phases that are not
thermodynamically stable at high temperature and would be difficult to
synthesize through the commonly employed powder metallurgy approach. In
addition, this is the first time that exclusively Cl-terminated MXenes were
obtained, and the etching effect of Lewis acid in molten salts provides a green
and viable route to prepare MXenes through an HF-free chemical approach.Comment: Title changed; experimental section and discussion revise
Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor
Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional
quasiparticles with non-Abelian braiding statistics that hold a great promise
for topological quantum computing. Due to its particle-antiparticle
equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h
quantized conductance at low temperature. By utilizing variable-tunnel-coupled
scanning tunneling spectroscopy, we study tunneling conductance of vortex bound
states on FeTe0.55Se0.45 superconductors. We report observations of conductance
plateaus as a function of tunnel coupling for zero-energy vortex bound states
with values close to or even reaching the 2e2/h quantum conductance. In
contrast, no such plateau behaviors were observed on either finite energy
Caroli-de Genne-Matricon bound states or in the continuum of electronic states
outside the superconducting gap. This unique behavior of the zero-mode
conductance reaching a plateau strongly supports the existence of MZMs in this
iron-based superconductor, which serves as a promising single-material platform
for Majorana braiding at a relatively high temperature
Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor
Adiabatic quantum computing enables the preparation of many-body ground
states. This is key for applications in chemistry, materials science, and
beyond. Realisation poses major experimental challenges: Direct analog
implementation requires complex Hamiltonian engineering, while the digitised
version needs deep quantum gate circuits. To bypass these obstacles, we suggest
an adiabatic variational hybrid algorithm, which employs short quantum circuits
and provides a systematic quantum adiabatic optimisation of the circuit
parameters. The quantum adiabatic theorem promises not only the ground state
but also that the excited eigenstates can be found. We report the first
experimental demonstration that many-body eigenstates can be efficiently
prepared by an adiabatic variational algorithm assisted with a multi-qubit
superconducting coprocessor. We track the real-time evolution of the ground and
exited states of transverse-field Ising spins with a fidelity up that can reach
about 99%.Comment: 12 pages, 4 figure
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