206 research outputs found
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on different levels of detail end-to-end. We propose a series of architectural
design contributions that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study.
Qualitative and quantitative experiments show that our method significantly
outperforms the state-of-the-art learning-based and optimazation-based
approaches, both in terms of handling low-resolution inputs and revealing
high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3
Geometric Structure Extraction and Reconstruction
Geometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Partial differential equations (PDEs) are widely used for the description of
physical and engineering phenomena. Some key parameters involved in PDEs, which
represent certain physical properties with important scientific
interpretations, are difficult or even impossible to measure directly.
Estimating these parameters from noisy and sparse experimental data of related
physical quantities is an important task. Many methods for PDE parameter
inference involve a large number of evaluations for numerical solutions to PDE
through algorithms such as the finite element method, which can be
time-consuming, especially for nonlinear PDEs. In this paper, we propose a
novel method for the inference of unknown parameters in PDEs, called the
PDE-Informed Gaussian Process (PIGP) based parameter inference method. Through
modeling the PDE solution as a Gaussian process (GP), we derive the manifold
constraints induced by the (linear) PDE structure such that, under the
constraints, the GP satisfies the PDE. For nonlinear PDEs, we propose an
augmentation method that transforms the nonlinear PDE into an equivalent PDE
system linear in all derivatives, which our PIGP-based method can handle. The
proposed method can be applied to a broad spectrum of nonlinear PDEs. The
PIGP-based method can be applied to multi-dimensional PDE systems and PDE
systems with unobserved components. Like conventional Bayesian approaches, the
method can provide uncertainty quantification for both the unknown parameters
and the PDE solution. The PIGP-based method also completely bypasses the
numerical solver for PDEs. The proposed method is demonstrated through several
application examples from different areas
Characterization, synthesis, and optimization of quantum circuits over multiple-control -rotation gates: A systematic study
We conduct a systematic study of quantum circuits composed of
multiple-control -rotation (MCZR) gates as primitives, since they are
widely-used components in quantum algorithms and also have attracted much
experimental interest in recent years. Herein, we establish a
circuit-polynomial correspondence to characterize the functionality of quantum
circuits over the MCZR gate set with continuous parameters. An analytic method
for exactly synthesizing such quantum circuit to implement any given diagonal
unitary matrix with an optimal gate count is proposed, which also enables the
circuit depth optimal for specific cases with pairs of complementary gates.
Furthermore, we present a gate-exchange strategy together with a flexible
iterative algorithm for effectively optimizing the depth of any MCZR circuit,
which can also be applied to quantum circuits over any other commuting gate
set.
Besides the theoretical analysis, the practical performances of our circuit
synthesis and optimization techniques are further evaluated by numerical
experiments on two typical examples in quantum computing, including diagonal
Hermitian operators and Quantum Approximate Optimization Algorithm (QAOA)
circuits with tens of qubits, which can demonstrate a reduction in circuit
depth by 33.40\% and 15.55\% on average over relevant prior works,
respectively. Therefore, our methods and results provide a pathway for
implementing quantum circuits and algorithms on recently developed devices.Comment: Comments are welcom
Potential solution to strong CP violation: X±
The strong charge parity (CP) violation has been an open problem for many years. Expanding
the current standard model (SM) to include new physics particles is a potential
approach to explain it. To do so, X± was introduced with X⁺ coupling to anti-ferimion
current and X⁻ to fermion current. As possible channels for searches for X±, we have
considered X⁺ in e⁺−e⁺ scattering and X⁻ in e⁻−e⁻ scattering. The difference between
the cross sections was calculated using Mathematica with packages FeynArts, FeynCalc,
Form and LoopTools at one loop level accuracy. The results were displayed in the form
of exclusion plots. In the exclusion plots, the possible range of physical parameters of the
new particles were tested, such as masses, couplings and phase factors.
Feynman rules, amplitude calculation and different renormalization methods at one loop
level were also discussed to demonstrate the algorithmic potential for cross section calculation.
In addition, new models for FeynArts and FormCalc were programmed to include
the new particles.
However, in order to further test the new physics particles influence on strong CP violation,
more research is needed. More specifically, one must test the hadronic interactions
for X±
Population and allelic variation of A-to-I RNA editing in human transcriptomes.
BackgroundA-to-I RNA editing is an important step in RNA processing in which specific adenosines in some RNA molecules are post-transcriptionally modified to inosines. RNA editing has emerged as a widespread mechanism for generating transcriptome diversity. However, there remain significant knowledge gaps about the variation and function of RNA editing.ResultsIn order to determine the influence of genetic variation on A-to-I RNA editing, we integrate genomic and transcriptomic data from 445 human lymphoblastoid cell lines by combining an RNA editing QTL (edQTL) analysis with an allele-specific RNA editing (ASED) analysis. We identify 1054 RNA editing events associated with cis genetic polymorphisms. Additionally, we find that a subset of these polymorphisms is linked to genome-wide association study signals of complex traits or diseases. Finally, compared to random cis polymorphisms, polymorphisms associated with RNA editing variation are located closer spatially to their respective editing sites and have a more pronounced impact on RNA secondary structure.ConclusionsOur study reveals widespread cis variation in RNA editing among genetically distinct individuals and sheds light on possible phenotypic consequences of such variation on complex traits and diseases
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