arXiv subjects

Fei Gao

Publications and source records attributed to Fei Gao.

At least 343 records · Page 19Linked to original sources

arXiv2022

Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation

Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down frameworks suffer from high computational redundancy with an additional detection stage. By contrast, the bottom-up methods enjoy low computational costs as they are less affected by the number of humans. However, most existing bottom-up methods treat camera-centric 3D human pose estimation as two unrelated subtasks: 2.5D pose estimation and camera-centric depth estimation. In this paper, we propose a unified model that leverages the mutual benefits of both these subtasks. Within the framework, a robust structured 2.5D pose estimation is designed to recognize inter-person occlusion based on depth relationships. Additionally, we develop an end-to-end geometry-aware depth reasoning method that exploits the mutual benefits of both 2.5D pose and camera-centric root depths. This method first uses 2.5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2.5D pose estimation in a backward pass. Further, we designed an adaptive fusion scheme that leverages both visual perception and body geometry to alleviate inherent depth ambiguity issues. Extensive experiments demonstrate the superiority of our proposed model over a wide range of bottom-up methods. Our accuracy is even competitive with top-down counterparts. Notably, our model runs much faster than existing bottom-up and top-down methods.

cs.CV↗

arXiv2022

Automatic Parameter Adaptation for Quadrotor Trajectory Planning

Online trajectory planners enable quadrotors to safely and smoothly navigate in unknown cluttered environments. However, tuning parameters is challenging since modern planners have become too complex to mathematically model and predict their interaction with unstructured environments. This work takes humans out of the loop by proposing a planner parameter adaptation framework that formulates objectives into two complementary categories and optimizes them asynchronously. Objectives evaluated with and without trajectory execution are optimized using Bayesian Optimization (BayesOpt) and Particle Swarm Optimization (PSO), respectively. By combining two kinds of objectives, the total convergence rate of the black-box optimization is accelerated while the dimension of optimized parameters can be increased. Benchmark comparisons demonstrate its superior performance over other strategies. Tests with changing obstacle densities validate its real-time environment adaption, which is difficult for prior manual tuning. Real-world flights with different drone platforms, environments, and planners show the proposed framework's scalability and effectiveness.

cs.RO↗

arXiv2022

Characterization and manipulation of intervalley scattering induced by an individual monovacancy in graphene

Intervalley scattering involves microscopic processes that electrons are scattered by atomic-scale defects on nanometer length scales. Although central to our understanding of electronic properties of materials, direct characterization and manipulation of range and strength of the intervalley scattering induced by an individual atomic defect have so far been elusive. Using scanning tunneling microscope, we visualized and controlled intervalley scattering from an individual monovacancy in graphene. By directly imaging the affected range of intervalley scattering of the monovacancy, we demonstrated that it is inversely proportional to the energy, i.e., it is proportional to the wavelength of massless Dirac Fermions. A giant electron-hole asymmetry of the intervalley scattering is observed because that the monovacancy is charged. By further charging the monovacancy, the bended electronic potential around the monovacancy softened the scattering potential, which, consequently, suppressed the intervalley scattering of the monovacancy.

cond-mat.mes-hall↗

arXiv2022

Quantum discriminative canonical correlation analysis

Discriminative Canonical Correlation Analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists of two parts: (i) mean-centering that subtracts the sample mean from the sample; (ii) solving the generalized eigenvalue problem. The cost of DCCA is expensive when dealing with a large number of high-dimensional samples. To solve this problem, here we propose a quantum DCCA algorithm. Specifically, we devise an efficient method to compute the mean of all samples, then use block-Hamiltonian simulation and quantum phase estimation to solve the generalized eigenvalue problem. Our algorithm achieves a polynomial speedup in the dimension of samples under certain conditions over its classical counterpart.

quant-ph↗

arXiv2022

Gate-Tunable Spin-Orbit Coupling in a Germanium Hole Double Quantum Dot

Hole spins confined in semiconductor quantum dot systems have gained considerable interest for their strong spin-orbit interactions (SOIs) and relatively weak hyperfine interactions. Here we experimentally demonstrate a tunable SOI in a double quantum dot in a Germanium (Ge) hut wire (HW), which could help enable fast all-electric spin manipulations while suppressing unwanted decoherence. Specifically, we measure the transport spectra in the Pauli spin blockade regime in the double quantum dot device.By adjusting the interdot tunnel coupling, we obtain an electric field tuned spin-orbit length lso = 2.0 - 48.9 nm. This tunability of the SOI could pave the way toward the realization of high-fidelity qubits in Ge HW systems.

cond-mat.mes-hall↗

arXiv2022

Passive Photoacoustic Effect

Photoacoustic effect refers to the acoustic generation induced by laser irradiation, where nanosecond pulsed laser source is normally used to provide instantaneous heating and thermoelastic expansion of the sample. More generally, photoacoustic generation requires active intensity modulation of laser source to produce sharp temperature gradient, which is key to generate acoustic wave. In this paper, we propose a novel photoacoustic effect from moving droplet, which generates photoacoustic wave by a non-modulated continuous-wave laser. When the droplet moves through the laser spot area, it can be heated up and cooled down instantaneously, generating photoacoustic waves. We name it passive photoacoustic effect. Theoretical analysis and simulation study validated the existence of passive photoacoustic effect. This phenomenon may find potential application in high-throughput photoacoustic cytometry.

physics.app-ph↗

arXiv2022

Proposal for all-electrical spin manipulation and detection for a single molecule on boron-substituted graphene

All-electrical writing and reading of spin states attract considerable attention for their promising applications in energy-efficient spintronics devices. Here we show, based on rigorous first-principles calculations, that the spin properties can be manipulated and detected in molecular spinterfaces, where an iron tetraphenyl porphyrin (FeTPP) molecule is deposited on boron-substituted graphene (B-G). Notably, a reversible spin switching between the $S=1$ and $S=3/2$ states is achieved by a gate electrode. We can trace the origin to a strong hybridization between the Fe-$d_{{z}^2}$ and B-$p_z$ orbitals. Combining density functional theory with nonequilibrium Green's function formalism, we propose an experimentally feasible 3-terminal setup to probe the spin state. Furthermore, we show how the in-plane quantum transport for the B-G, which is non-spin polarized, can be modified by FeTPP, yielding a significant transport spin polarization near the Fermi energy ($>10\%$ for typical coverage). Our work paves the way to realize all-electrical spintronics devices using molecular spinterfaces.

cond-mat.mes-hall↗

arXiv2022

A quantum algorithm for solving eigenproblem of the Laplacian matrix of a fully connected weighted graph

Solving eigenproblem of the Laplacian matrix of a fully connected weighted graph has wide applications in data science, machine learning, and image processing, etc. However, this is very challenging because it involves expensive matrix operations. Here, we propose an efficient quantum algorithm to solve it based on a assumption that the element of each vertex and its norms can be effectively accessed via a quantum random access memory data structure. Specifically, we adopt the optimal Hamiltonian simulation technique based on the block-encoding framework to implement the quantum simulation of the Laplacian matrix. Then, the eigenvalues and eigenvectors of the Laplacian matrix are extracted by the quantum phase estimation algorithm. The core of our entire algorithm is to construct the block-encoding of the Laplacian matrix. To achieve this, we propose in detail how to construct the block-encodings of operators containing the information of the weight matrix and the degree matrix respectively, and further obtain the block-encoding of the Laplacian matrix. Compared with its classical counterpart, our algorithm has a polynomial speedup on the number of vertices and an exponential speedup on the dimension of each vertex. We also show that our algorithm can be extended to solve the eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.

quant-ph↗

arXiv2022

Valence quark ratio in the proton

Beginning with precise data on the ratio of structure functions in deep inelastic scattering (DIS) from $^3$He and $^3$H, collected on the domain $0.19 \leq x_B \leq 0.83$, where $x_B$ is the Bjorken scaling variable, we employ a robust method for extrapolating such data to arrive at a model-independent result for the $x_B=1$ value of the ratio of neutron and proton structure functions. Combining this with information obtained in analyses of DIS from nuclei, corrected for target-structure dependence, we arrive at a prediction for the proton's valence-quark ratio: $\left. d_v/u_v \right|_{x_B\to 1} = 0.230 (57)$. Requiring consistency with this result presents a challenge to many descriptions of proton structure.

hep-ph↗

arXiv2022

Global boundedness and asymptotic behavior of time-space fractional nonlocal reaction-diffusion equation

The global boundedness and asymptotic behavior are investigate for the solution of time-space fractional non-local reaction-diffusion equation (TSFNRDE) $$ \frac{\partial^{α}u}{\partial t^{α}}=-(-Δ)^{s} u+μu^{2}(1-kJ*u)-γu, \qquad(x,t)\in\mathbb{R}^{N}\times(0,+\infty),$$ where $s\in(0,1),α\in(0,1), N \leq 2$. The operator $\partial_{t}^{α}$ is the Caputo fractional derivative, which $-(-Δ)^{s}$ is the fractional Laplacian operator. For appropriate assumptions on $J$, it is proved that for homogeneous Dirichlet boundary condition, this problem admits a global bounded weak solution for $N=1$, while for $N=2$, global bounded weak solution exists for large $k$ values by Gagliardo-Nirenberg inequality and fractional differential inequality. With further assumptions on the initial datum, for small $μ$ values, the solution is shown to converge to $0$ exponentially or locally uniformly as $t \rightarrow \infty$. Furthermore, under the condition of $J \equiv 1$, it is proved that the nonlinear TSFNRDE has a unique weak solution which is global bounded in fractional Sobolev space with the nonlinear fractional diffusion terms $-(-Δ)^{s} u^{m}\, (2-\frac{2}{N}<m<1)$.

math.AP↗

arXiv2022

Global existence, uniqueness and $L^{\infty}$-bound of weak solutions of fractional time-space Keller-Segel system

This paper studies the properties of weak solutions to a class of space-time fractional parabolic-elliptic Keller-Segel equations with logistic source terms in $\mathbb{R}^{n}$, $n\geq 2$. The global existence and $L^{\infty}$-bound of weak solutions are established. We mainly divide the damping coefficient into two cases: (i) $b>1-\fracα{n}$, for any initial value and birth rate; (ii) $0 q$, the $L^{h}$-norms of the weak solution blow up at the same time.

math.AP↗

arXiv2022

Photoacoustic Digital Skin: Generation and Simulation of Human Skin Vascular for Quantitative Image Analysis

Photoacoustic computed tomography (PACT) is a hybrid imaging modality, which combines the high optical contrast of pure optical imaging and the high penetration depth of ultrasound imaging. However, photoacoustic image dataset with good quality and large quantity is lacking. In this paper, we mainly talk about how to generate a practical photoacoustic dataset. Firstly, we extracted 389 3D vessel volumes from CT whole-lung scan database, and enhanced the blood vessel structures. Then for each 3D vessel volume, we embedded it into a three-layer cubic phantom to formulate a skin tissue model, which includes epidermis, dermis, and hypodermis. The vessel volume was placed randomly in dermis layer in 10 different ways. Thus, 3890 3D skin tissue phantoms were generated. Then we assigned optical properties for the four kinds of tissue types. Monte-Carlo optical simulations were deployed to obtain the optical fluence distribution. Then acoustic propagation simulations were deployed to obtain the photoacoustic initial pressure. Universal back-projection algorithm was used to reconstruct the photoacoustic images. This dataset could be used for deep learning-based photoacoustic image reconstruction, classification, registration, quantitative image analysis.

physics.med-ph↗

arXiv2022

Cosmology meets functional QCD: First-order cosmic QCD transition induced by large lepton asymmetries

The lepton flavour asymmetries of the Universe are observationally almost unconstrained before the onset of neutrino oscillations. We calculate the cosmic trajectory during the cosmic QCD epoch in the presence of large lepton flavour asymmetries. By including QCD thermodynamic quantities derived from functional QCD methods in our calculation our work reveals for the first time the possibility of a first-order cosmic QCD transition. We specify the required values of the lepton flavour asymmetries for which a first-order transition occurs for a number of different benchmark scenarios.

hep-ph↗

arXiv2022

Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2

COVID-19 vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of SARS-CoV-2 is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine efficacy against the secondary attack rate of SARS-CoV-2 using data available in existing healthcare databases or contact tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. In this manuscript, we highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies including event-prompted and infrequent testing and illustrate their potential biases in estimating vaccine efficacy against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine efficacy against the SARS-CoV-2 SAR, and we provide design and reporting considerations for studies using retrospective databases.

stat.AP↗

arXiv2022

FPGA Acceleration of Image Reconstruction for Real-Time Photoacoustic Tomography

Photoacoustic (PA) imaging has been widely applied in both preclinical and clinical applications. With a significantly increasing number of data acquisition channels, fast and high-quality image reconstruction for real-time PA imaging is an open challenge in this community. In this paper, we propose a FPGA-accelerated method to achieve a much faster image reconstruction speed by 20~60 times compared with using CPU, with much-reduced system cost and power budget, from dozens of Watt (CPU) to 1~2 Watt (FPGA). Equivalently, the energy efficiency ratio (EER) is improved by ~1000 times. This FPGA acceleration method can be easily adapted to the most widely used algorithms, such as delay-and-sum (DAS) and its variants (e.g. DMAS, DAS-CF). We have performed in-vivo human finger experiments to demonstrate the feasibility and potential of the proposed method. To our best knowledge, this is the first study of accelerating PA image reconstruction based on FPGA platform.

physics.med-ph↗

arXiv2022

Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments

For aerial swarms, navigation in a prescribed formation is widely practiced in various scenarios. However, the associated planning strategies typically lack the capability of avoiding obstacles in cluttered environments. To address this deficiency, we present an optimization-based method that ensures collision-free trajectory generation for formation flight. In this paper, a novel differentiable metric is proposed to quantify the overall similarity distance between formations. We then formulate this metric into an optimization framework, which achieves spatial-temporal planning using polynomial trajectories. Minimization over collision penalty is also incorporated into the framework, so that formation preservation and obstacle avoidance can be handled simultaneously. To validate the efficiency of our method, we conduct benchmark comparisons with other cutting-edge works. Integrated with an autonomous distributed aerial swarm system, the proposed method demonstrates its efficiency and robustness in real-world experiments with obstacle-rich surroundings. We will release the source code for the reference of the community.

cs.RO↗

arXiv2022

Generating Large-Scale Trajectories Efficiently using Double Descriptions of Polynomials

For quadrotor trajectory planning, describing a polynomial trajectory through coefficients and end-derivatives both enjoy their own convenience in energy minimization. We name them double descriptions of polynomial trajectories. The transformation between them, causing most of the inefficiency and instability, is formally analyzed in this paper. Leveraging its analytic structure, we design a linear-complexity scheme for both jerk/snap minimization and parameter gradient evaluation, which possesses efficiency, stability, flexibility, and scalability. With the help of our scheme, generating an energy optimal (minimum snap) trajectory only costs 1 $μs$ per piece at the scale up to 1,000,000 pieces. Moreover, generating large-scale energy-time optimal trajectories is also accelerated by an order of magnitude against conventional methods.

cs.RO↗

arXiv2022

Dynamic Free-Space Roadmap for Safe Quadrotor Motion Planning

Free-space-oriented roadmaps typically generate a series of convex geometric primitives, which constitute the safe region for motion planning. However, a static environment is assumed for this kind of roadmap. This assumption makes it unable to deal with dynamic obstacles and limits its applications. In this paper, we present a dynamic free-space roadmap, which provides feasible spaces and a navigation graph for safe quadrotor motion planning. Our roadmap is constructed by continuously seeding and extracting free regions in the environment. In order to adapt our map to environments with dynamic obstacles, we incrementally decompose the polyhedra intersecting with obstacles into obstacle-free regions, while the graph is also updated by our well-designed mechanism. Extensive simulations and real-world experiments demonstrate that our method is practically applicable and efficient.

cs.RO↗