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Fei Gao

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preprint2026arXiv

Pervasive Vulnerability Analysis and Defense for QKD-based Quantum Private Query

Quantum Private Query (QPQ) based on Quantum Key Distribution (QKD) is among the most practically viable quantum communication protocols, with application value second only to QKD itself. However, prevalent security vulnerabilities in the post-processing stages of most existing QKD-based QPQ protocols have been severely overlooked. This study focuses on hidden information extraction under undetermined signal bits, revealing that most such QPQ protocols face severe security threats even without complex quantum resources. Specifically, direct observation attack causes incremental information leakage, while the minimum error discrimination attack efficiently steals additional database inforamtion. To address these critical flaws, the proposed multi-encryption defense scheme is compatible with existing QPQ protocols. The study demonstrates the necessity of the multi-encryption strategy for the security of databases in QPQ, providing key theoretical and technical support for constructing practical QPQ protocols resistant to real-world attacks.

preprint2026arXiv

VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving

End-to-end autonomous driving requires models to understand traffic scenes, infer driving intent, and generate executable motion plans. Recent vision-language-action (VLA) models inherit semantic priors from large-scale vision-language pretraining, yet still face a coupling trade-off: fully shared backbones preserve multimodal interaction but may entangle language reasoning and trajectory prediction, whereas decou pled reasoning-action pipelines reduce task conflict but weaken semantic-motion coupling. We propose VECTOR-DRIVE, a tightly coupled VLA framework built on Qwen2.5-VL-3B. VECTOR-DRIVE keeps all tokens coupled through shared self attention and routes feed-forward computation according to token semantics. Vision and language tokens are processed by a Vision-Language Expert to preserve semantic priors, while target-point, ego-state, and noisy action tokens are routed to a Trajectory Expert for motion-specific computation. On the action-token pathway, a flow-matching planner refines noisy action tokens into future waypoints and speed profiles. This design couples semantic reasoning and motion planning within a single multimodal Transformer while separating task-specific FFN computation. On Bench2Drive, VECTOR-DRIVE achieves 88.91 Driving Score and outperforms representative end-to end and VLA-based baselines. Qualitative results and ablations further validate the benefits of shared attention, semantic-aware expert routing, progressive training, and flow-based action de coding.

preprint2025arXiv

CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System

Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot measurements such as bearings, distances, and inertials for tens of robots remains challenging. We present CREPES-X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. CREPES-X starts with a compact hardware design: InfraRed (IR) LEDs, an IR camera, an ultra-wideband module, and an IMU housed in a cube no larger than 6cm on each side. Then CREPES-X implements a two-stage hierarchical estimator to meet different requirements, considering speed, accuracy, and robustness. First, we propose a single-frame relative estimator that provides instant relative poses for multi-robot setups through a closed-form solution and robust bearing outlier rejection. Then a multi-frame relative estimator is designed to offer accurate and robust relative states by exploring IMU pre-integration via robocentric relative kinematics with loosely- and tightly-coupled optimization. Extensive simulations and real-world experiments validate the effectiveness of CREPES-X, showing robustness to up to 90% bearing outliers, proving resilience in challenging conditions, and achieving RMSE of 0.073m and 1.817° in real-world datasets.

preprint2023arXiv

A Linear and Exact Algorithm for Whole-Body Collision Evaluation via Scale Optimization

Collision evaluation is of vital importance in various applications. However, existing methods are either cumbersome to calculate or have a gap with the actual value. In this paper, we propose a zero-gap whole-body collision evaluation which can be formulated as a low dimensional linear program. This evaluation can be solved analytically in O(m) computational time, where m is the total number of the linear inequalities in this linear program. Moreover, the proposed method is efficient in obtaining its gradient, making it easy to apply to optimization-based applications.

preprint2023arXiv

Generalizing the intention-to-treat effect of an active control against placebo from historical placebo-controlled trials to an active-controlled trial: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study

In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate plus emtricitabine (TDF/FTC) in a population of heterosexual women in 7 African countries. One key complication of interpreting study results in an active-controlled trial like HPTN 084 is that the placebo arm is not present and the efficacy of the active control (and hence the experimental drug) compared to the placebo can only be inferred by leveraging other data sources. \bz{In this article, we study statistical inference for the intention-to-treat (ITT) effect of the active control using relevant historical placebo-controlled trials data under the potential outcomes (PO) framework}. We highlight the role of adherence and unmeasured confounding, discuss in detail identification assumptions and two modes of inference (point versus partial identification), propose estimators under identification assumptions permitting point identification, and lay out sensitivity analyses needed to relax identification assumptions. We applied our framework to estimating the intention-to-treat effect of daily oral TDF/FTC versus placebo in HPTN 084 using data from an earlier Phase 3, placebo-controlled trial of daily oral TDF/FTC (Partners PrEP).

preprint2022arXiv

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.

preprint2022arXiv

ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving. The platform provides a complete closed-loop pipeline with training, simulation, and evaluation components. It comes with 300 hours of real-world data in driving scenarios and popular baselines such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents. We elaborate in this paper on the architecture and the environment defined in the platform. In addition, we discuss the performance of the baseline agents in the ApolloRL environment.

preprint2022arXiv

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.

preprint2022arXiv

Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles

Terrestrial-aerial bimodal vehicles bloom in both academia and industry because they incorporate both the high mobility of aerial vehicles and the long endurance of ground vehicles. In this work, we present an autonomous and adaptive navigation framework to bring complete autonomy to this class of vehicles. The framework mainly includes 1) a hierarchical motion planner that generates safe and low-power terrestrial-aerial trajectories in unknown environments and 2) a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive real-world experiments and benchmark comparisons are conducted on a customized robot platform to validate the proposed framework's robustness and performance. During the tests, the robot safely traverses complex environments with terrestrial-aerial integrated mobility, and achieves $7\times$ energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration for the reference of the community.

preprint2022arXiv

Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors

Quadrotors are agile platforms. With human experts, they can perform extremely high-speed flights in cluttered environments. However, fully autonomous flight at high speed remains a significant challenge. In this work, we propose a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework. Specifically, we use a series of overlapping spheres to represent the free space of the environment and propose two novel designs that enable the algorithm to plan high-speed quadrotor trajectories in real-time. One is a sampling-based corridor generation method that generates spheres with large overlapped areas (hence overall corridor size) between two neighboring spheres. The second is a Receding Horizon Corridors (RHC) strategy, where part of the previously generated corridor is reused in each replan. Together, these two designs enlarge the corridor spaces in accordance with the quadrotor's current state and hence allow the quadrotor to maneuver at high speeds. We benchmark our algorithm against other state-of-the-art planning methods to show its superiority in simulation. Comprehensive ablation studies are also conducted to show the necessity of the two designs. The proposed method is finally evaluated on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving flight speeds over 13.7 m/s without any prior map of the environment or external localization facility.

preprint2022arXiv

Certifiably Optimal Mutual Localization with Anonymous Bearing Measurements

Mutual localization is essential for coordination and cooperation in multi-robot systems. Previous works have tackled this problem by assuming available correspondences between measurements and received odometry estimations, which are difficult to acquire, especially for unified robot teams. Furthermore, most local optimization methods ask for initial guesses and are sensitive to their quality. In this paper, we present a certifiably optimal algorithm that uses only anonymous bearing measurements to formulate a novel mixed-integer quadratically constrained quadratic problem (MIQCQP). Then, we relax the original nonconvex problem into a semidefinite programming (SDP) problem and obtain a certifiably global optimum using with off-the-shelf solvers. As a result, our method can determine bearing-pose correspondences and furthermore recover the initial relative poses between robots under a certain condition. We compare the performance with local optimization methods on extensive simulations under different noise levels to show our advantage in global optimality and robustness. Real-world experiments are conducted to show the practicality and robustness.

preprint2022arXiv

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.

preprint2022arXiv

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.

preprint2022arXiv

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.

preprint2022arXiv

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.

preprint2022arXiv

Elastic Tracker: A Spatio-temporal Trajectory Planner Flexible Aerial Tracking

This paper proposes Elastic Tracker, a flexible trajectory planning framework that can deal with challenging tracking tasks with guaranteed safety and visibility. Firstly, an object detection and intension-free motion prediction method is designed. Then an occlusion-aware path finding method is proposed to provide a proper topology. A smart safe flight corridor generation strategy is designed with the guiding path. An analytical occlusion cost is evaluated. Finally, an effective trajectory optimization approach enables to generate a spatio-temporal optimal trajectory within the resultant flight corridor. Particular formulations are designed to guarantee both safety and visibility, with all the above requirements optimized jointly. The experimental results show that our method works more robustly but with less computation than the existing methods, even in some challenging tracking tasks.

preprint2022arXiv

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.

preprint2022arXiv

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.

preprint2022arXiv

Geometrically Constrained Trajectory Optimization for Multicopters

We present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial-temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization, and backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude. The source code of our framework is available at: https://github.com/ZJU-FAST-Lab/GCOPTER

preprint2022arXiv

Global boundedness and Allee effect for a nonlocal time fractional p-Laplacian reaction-diffusion equation

The global boundedness and asymptotic behavior are investigated for the solutions of a nonlocal time fractional p-Laplacian reaction-diffusion equation (NTFPLRDE) $$ \frac{\partial^{α}u}{\partial t^{α}}=Δ_{p} u+μu^{2}(1-kJ*u) -γu, \qquad(x,t)\in\mathbb{R}^{N}\times(0,+\infty)$$ with $0<α<1,β, μ,k>0,N\leq 2$ and $Δ_{p}u =div(\left| \bigtriangledown u \right|^{p-2}\bigtriangledown u)$. Under appropriate assumptions on $J$ and the conditions of $1<p<2$, it is proved that for any nonnegative and bounded initial conditions, the problem has a global bounded classical solution if $k^{*}=0$ for $N=1$ or $k^{*}=(μC^{2}_{GN}+1)η^{-1}$ for $N=2$, where $C_{GN}$ is the constant in Gagliardo-Nirenberg 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 $t \rightarrow \infty$, which is referred as the Allee effect in sense of Caputo derivative. Moreover, under the condition of $J \equiv 1$, it is proved that the nonlinear NTFPLRDE has a global bounded solution in any dimensional space with the nonlinear p-Laplacian diffusion terms $Δ_{p} u^{m}\, (2-\frac{2}{N}< m\leq 3)$.

preprint2022arXiv

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 $-(-Δ)^{s} u^{m}\, (2-\frac{2}{N}<m<1)$.

preprint2022arXiv

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) $0<b\leq 1-\fracα{n}$, for small initial value and small birth rate. The existence result is obtained by verifying the existence of a solution to the constructed regularization equation and incorporate the generalized compactness criterion of time fractional partial differential equation. At the same time, we get the $L^{\infty}$-bound of weak solutions by establishing the fractional differential inequality and using the Moser iterative method. Furthermore, we prove the uniqueness of weak solutions by using the hyper-contractive estimates when the damping coefficient is strong. Finally, we also propose a blow-up criterion for weak solutions, that is, if a weak solution blows up in finite time, then for all $h>q$, the $L^{h}$-norms of the weak solution blow up at the same time.

preprint2022arXiv

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is inaccurate, unstable, and unfriendly to non-professional operators. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment operators are interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches.

preprint2022arXiv

Hand-held 3D Photoacoustic Imager with GPS

As an emerging medical diagnostic technology, photoacoustic imaging has been implemented for both preclinical and clinical applications. For clinical convenience, a handheld free scan photoacoustic tomography (PAT) system providing 3D imaging capability is essentially needed, which has potential for surgical navigation and disease diagnosis. In this paper, we proposed a free scan 3D PAT (fsPAT) system based on a handheld linear array ultrasound probe. A global positioning system (GPS) is applied for ultrasound probes coordinate acquisition. The proposed fsPAT can simultaneously realize real time 2D imaging, and large field of view 3D volumetric imaging, which is reconstructed from the multiple 2D images with coordinate information acquired by the GPS. To form a high quality 3D image, a dedicated space transformation method and reconstruction algorithm are used and validated by the proposed system. Both simulation and experimental studies have been performed to prove the feasibility of the proposed fsPAT. To explore its clinical potential, in vivo 3D imaging of human wrist vessels is also conducted, showing clear subcutaneous vessel network with high image contrast.

preprint2022arXiv

LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u,v,1]^T to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360°x(40°-120°) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360°x(0°-93.5°). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://github.com/flysoaryun/LF-VIO

preprint2022arXiv

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms.

preprint2022arXiv

Mode-selective Single-dipole Excitation and Controlled Routing of Guided Waves in a Multi-mode Topological Waveguide

Topology-linked binary degrees of freedom of guided waves have been used to expand the channel capacity of and to ensure robust transmission through photonic waveguides. However, selectively exciting optical modes associated with the desired degree of freedom is challenging and typically requires spatially extended sources or filters. Both approaches are incompatible with the ultimate objective of developing compact mode-selective sources powered by single emitters. In addition, the implementation of highly desirable functionalities, such as controllable distribution of guided modes between multiple detectors, becomes challenging in highly-compact devices due to photon loss to reflections. Here, we demonstrate that a linearly-polarized dipole-like source can selectively excite a topologically robust edge mode with the desired valley degree of freedom. Reflection-free routing of valley-polarized edge modes into two spatially-separated detectors with reconfigurable splitting ratios is also presented. An optical implementation of such a source will have the potential to broaden the applications of topological photonic devices.

preprint2022arXiv

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.

preprint2022arXiv

NEDMP: Neural Enhanced Dynamic Message Passing

Predicting stochastic spreading processes on complex networks is critical in epidemic control, opinion propagation, and viral marketing. We focus on the problem of inferring the time-dependent marginal probabilities of states for each node which collectively quantifies the spreading results. Dynamic Message Passing (DMP) has been developed as an efficient inference algorithm for several spreading models, and it is asymptotically exact on locally tree-like networks. However, DMP can struggle in diffusion networks with lots of local loops. We address this limitation by using Graph Neural Networks (GNN) to learn the dependency amongst messages implicitly. Specifically, we propose a hybrid model in which the GNN module runs jointly with DMP equations. The GNN module refines the aggregated messages in DMP iterations by learning from simulation data. We demonstrate numerically that after training, our model's inference accuracy substantially outperforms DMP in conditions of various network structure and dynamics parameters. Moreover, compared to pure data-driven models, the proposed hybrid model has a better generalization ability for out-of-training cases, profiting from the explicitly utilized dynamics priors in the hybrid model. A PyTorch implementation of our model is at https://github.com/FeiGSSS/NEDMP.

preprint2022arXiv

Parton distributions of light quarks and antiquarks in the proton

An algebraic Ansatz for the proton's Poincaré-covariant wave function, which includes both scalar and pseudovector diquark correlations, is used to calculate proton valence, sea, and glue distribution functions (DFs). Regarding contemporary data, a material pseudovector diquark component in the proton is necessary for an explanation of the neutron-proton structure function ratio; and a modest Pauli blocking effect in the gluon splitting function is sufficient to explain the proton's light-quark antimatter asymmetry. In comparison with pion DFs, the light-front momentum fractions carried by all identifiable parton classes are the same; on the other hand, the higher moments are different. Understanding these features may provide insights that explain distinctions between Nambu-Goldstone bosons and seemingly less complex hadrons.

preprint2022arXiv

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.

preprint2022arXiv

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.

preprint2022arXiv

Photoacoustic Imaging Based on AlN MF-PMUT with Broadened Bandwidth

This paper reports an aluminum nitride (AlN) multi-frequency piezoelectric micromachined ultrasound transducers (MF-PMUT) array for photoacoustic (PA) imaging, where the broadened bandwidth is beneficial to improve imaging resolution. Specifically, PMUT based on micro-electromechanical systems (MEMS) technology is suitable for PA endoscopic imaging of blood vessels and bronchi due to its miniature size. More importantly, AlN is a non-toxic material, which makes it harmless for biomedical applications. In this work, a MF-PMUT array are designed and fabricated for PAI. The device's vibration mode impedance and bandwidth are analyzed. The MF-PMUT sensor provides a wider bandwidth (65%) signal detection, which increases the resolution of PAI compared with traditional PMUT. We conduct an experiment on agar sample to present sensor's performance in images' axial resolution.

preprint2022arXiv

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.

preprint2022arXiv

Real-Time Trajectory Planning for Aerial Perching

This paper presents a novel trajectory planning method for aerial perching. Compared with the existing work, the terminal states and the trajectory durations can be adjusted adaptively, instead of being determined in advance. Furthermore, our planner is able to minimize the tangential relative speed on the premise of safety and dynamic feasibility. This feature is especially notable on micro aerial robots with low maneuverability or scenarios where the space is not enough. Moreover, we design a flexible transformation strategy to eliminate terminal constraints along with reducing optimization variables. Besides, we take precise SE(3) motion planning into account to ensure that the drone would not touch the landing platform until the last moment. The proposed method is validated onboard by a palm-sized micro aerial robot with quite limited thrust and moment (thrust-to-weight ratio 1.7) perching on a mobile inclined surface. Sufficient experimental results show that our planner generates an optimal trajectory within 20ms, and replans with warm start in 2ms.

preprint2022arXiv

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.

preprint2022arXiv

Solution of integrals with fractional Brownian motion for different Hurst indices

In this paper, we will evaluate integrals that define the conditional expectation, variance and characteristic function of stochastic processes with respect to fractional Brownian motion (fBm) for all relevant Hurst indices, i.e. $H \in (0,1)$. The fractional Ornstein-Uhlenbeck (fOU) process, for example, gives rise to highly nontrivial integration formulas that need careful analysis when considering the whole range of Hurst indices. We will show that the classical technique of analytic continuation, from complex analysis, provides a way of extending the domain of validity of an integral, from $H\in(1/2,1)$, to the larger domain, $H\in(0,1)$. Numerical experiments for different Hurst indices confirm the robustness and efficiency of the integral formulations presented here. Moreover, we provide accurate and highly efficient financial option pricing results for processes that are related to the fOU process, with the help of Fourier cosine expansions.

preprint2022arXiv

Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future.

preprint2022arXiv

Surface engineering for cellulose as a boosted Layer-by-Layer assembly: excellent flame retardancy and improved durability with introduction of bio-based "molecular glue"

Layer-by-Layer (LbL) assembly was attractive as a versatile tool to address the flammability of cotton, while the washing fastness of LbL coating stayed an issue. Aiming to tackle this issue, LbL layers consisted of phenylphosphonic acid (PHA) and 3-aminopropyltriethoxysilane (APTES) was deposited on polydopamine (PDA)-coated cotton. The prepared cotton reached 31.4% of limiting oxygen index (LOI), and extinguished immediately after removing the ignitor. Peak of heat release rate (pHRR) attenuated around 36 % compared with pure cotton. A combined barrier and quenching mechanisms were proposed. Moreover, enhanced washing durability (24.1% of LOI) was achieved even after 50 detergent laundering cycles. A facile, boosted LbL approach with proposed π-π stacking interactions between PDA abundant aromatic structures and benzene ring in PHA from LbL layers, is first to put forward to construct durable efficient flame retardant (FR) cotton. This work attempted to enlighten more thoughts and design for durable FR cotton fabrics.

preprint2022arXiv

The Visual-Inertial-Dynamical Multirotor Dataset

Recently, the community has witnessed numerous datasets built for developing and testing state estimators. However, for some applications such as aerial transportation or search-and-rescue, the contact force or other disturbance must be perceived for robust planning and control, which is beyond the capacity of these datasets. This paper introduces a Visual-Inertial-Dynamical (VID) dataset, not only focusing on traditional six degrees of freedom (6-DOF) pose estimation but also providing dynamical characteristics of the flight platform for external force perception or dynamics-aided estimation. The VID dataset contains hardware synchronized imagery and inertial measurements, with accurate ground truth trajectories for evaluating common visual-inertial estimators. Moreover, the proposed dataset highlights rotor speed and motor current measurements, control inputs, and ground truth 6-axis force data to evaluate external force estimation. To the best of our knowledge, the proposed VID dataset is the first public dataset containing visual-inertial and complete dynamical information in the real world for pose and external force evaluation. The dataset: https://github.com/ZJU-FAST-Lab/VID-Dataset and related files: https://github.com/ZJU-FAST-Lab/VID-Flight-Platform are open-sourced.

preprint2022arXiv

Topological metasurface: From passive toward active and beyond

Metasurfaces are subwavelength structured thin films consisting of arrays of units that allow the controls of polarization, phase and amplitude of light over a subwavelength thickness. The recent developments in topological photonics have greatly broadened the horizon in designing the metasurfaces for novel functional applications. In this review, we summarize recent progress in the research field of topological metasurfaces, firstly from the perspectives of passive and active in the classical regime, and then in the quantum regime. More specifically, we begin by examining the passive topological phenomena in two-dimensional photonic systems, including both time-reversal broken systems and time-reversal preserved systems. Subsequently, we move to discuss the cutting-edge studies of the active topological metasurfaces, including nonlinear topological metasurfaces and reconfigurable topological metasurfaces. After overviewing the topological metasurfaces in the classical regime, we show how the topological metasurfaces could provide a new platform for quantum information and quantum many-body physics. Finally, we conclude and describe some challenges and future directions of this fast-evolving field.

preprint2022arXiv

Ultrafast coherent control of a hole spin qubit in a germanium quantum dot

Operation speed and coherence time are two core measures for the viability of a qubit. Strong spin-orbit interaction (SOI) and relatively weak hyperfine interaction make holes in germanium (Ge) intriguing candidates for spin qubits with rapid, all-electrical coherent control. Here we report ultrafast single-spin manipulation in a hole-based double quantum dot in a germanium hut wire (GHW). Mediated by the strong SOI, a Rabi frequency exceeding 540 MHz is observed at a magnetic field of 100 mT, setting a record for ultrafast spin qubit control in semiconductor systems. We demonstrate that the strong SOI of heavy holes (HHs) in our GHW, characterized by a very short spin-orbit length of 1.5 nm, enables the rapid gate operations we accomplish. Our results demonstrate the potential of ultrafast coherent control of hole spin qubits to meet the requirement of DiVincenzo's criteria for a scalable quantum information processor.

preprint2022arXiv

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.

preprint2021arXiv

Bridging Unpaired Facial Photos And Sketches By Line-drawings

In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain $\mathcal{X}$ and the sketch domain $Y$ by using the line-drawing domain $\mathcal{Z}$. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. $F: \mathcal{X}/\mathcal{Y} \mapsto \mathcal{Z}$. Consequently, we obtain \textit{pseudo paired data} $(\mathcal{Z}, \mathcal{Y})$, and can learn the mapping $G:\mathcal{Z} \mapsto \mathcal{Y}$ in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by $G \circ F$. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists' rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.

preprint2021arXiv

CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight

In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system's robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, and will be released as a low-level plugin for a large variety of quadrotor systems.

preprint2021arXiv

Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography

Photoacoustic imaging (PAI) is a non-invasive imaging modality that detects the ultrasound signal generated from tissue with light excitation. Photoacoustic computed tomography (PACT) uses unfocused large-area light to illuminate the target with ultrasound transducer array for PA signal detection. Limited-view issue could cause a low-quality image in PACT due to the limitation of geometric condition. The model-based method is used to resolve this problem, which contains different regularization. To adapt fast and high-quality reconstruction of limited-view PA data, in this paper, a model-based method that combines the mathematical variational model with deep learning is proposed to speed up and regularize the unrolled procedure of reconstruction. A deep neural network is designed to adapt the step of the gradient updated term of data consistency in the gradient descent procedure, which can obtain a high-quality PA image only with a few iterations. Note that all parameters and priors are automatically learned during the offline training stage. In experiments, we show that this method outperforms the other methods with half-view (180 degrees) simulation and real data. The comparison of different model-based methods show that our proposed scheme has superior performances (over 0.05 for SSIM) with same iteration (3 times) steps. Furthermore, an unseen data is used to validate the generalization of different methods. Finally, we find that our method obtains superior results (0.94 value of SSIM for in vivo) with a high robustness and accelerated reconstruction.

preprint2021arXiv

Defining and Estimating Subgroup Mediation Effects with Semi-Competing Risks Data

In many medical studies, an ultimate failure event such as death is likely to be affected by the occurrence and timing of other intermediate clinical events. Both event times are subject to censoring by loss-to-follow-up but the nonterminal event may further be censored by the occurrence of the primary outcome, but not vice versa. To study the effect of an intervention on both events, the intermediate event may be viewed as a mediator, but conventional definition of direct and indirect effects is not applicable due to semi-competing risks data structure. We define three principal strata based on whether the potential intermediate event occurs before the potential failure event, which allow proper definition of direct and indirect effects in one stratum whereas total effects are defined for all strata. We discuss the identification conditions for stratum-specific effects, and proposed a semiparametric estimator based on a multivariate logistic stratum membership model and within-stratum proportional hazards models for the event times. By treating the unobserved stratum membership as a latent variable, we propose an EM algorithm for computation. We study the asymptotic properties of the estimators by the modern empirical process theory and examine the performance of the estimators in numerical studies.

preprint2021arXiv

Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight

For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a global sampling process to ensure faster convergence. The incorporation of local optimization on different sampling-based methods shows significantly improved success rates and less planning time in various types of challenging environments. We also present a refinement module that fully investigates the resulting trajectory of the global sampling and greatly improves its smoothness with negligible computation effort. Benchmark results illustrate that compared to the state-of-the-art ones, our proposed method can better exploit a previous trajectory. The planning methods are applied to generate trajectories for a simulated quadrotor system, and its capability is validated in real-time applications.

preprint2021arXiv

Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use \emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off. Besides, we use the convolutional block attention module (CBAM) in the networks, which helps us understand the reasoning process. During training, we use A-Softmax loss to learn angularly discriminative representations. In the inference stage, we employ an ensemble of diverse neural networks to improve the prediction accuracy and robustness. We conduct extensive experiments on the LIDC-IDRI database. Compared with previous state-of-the-art, our model shows highly comparable performance by using less than 1/40 parameters. Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis. Related code and results have been released at: https://github.com/fei-hdu/NAS-Lung.

preprint2021arXiv

Phase structure of 2+1-flavour QCD and the magnetic equation of state

We determine the chiral phase structure of $2+1$-flavour QCD in dependence of temperature and the light flavour quark mass with Dyson-Schwinger equations. Specifically, we compute the renormalised chiral condensate and its susceptibility. The latter is used to determine the (pseudo)critical temperature for general light current quark masses. In the chiral limit we obtain a critical temperature of about 141\,MeV. This result is in quantitative agreement with recent functional renormalisation group results in QCD, and is compatible with the respective lattice results. We also compute the order parameter potential of the light chiral condensate and map out the regime in the phase diagram which exhibits quasi-massless modes, and discuss the respective chiral dynamics.

preprint2021arXiv

Quantum algorithm for Neighborhood Preserving Embedding

Neighborhood Preserving Embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e., finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al. proposed a variational quantum algorithm (VQA) for NPE [Phys. Rev. A 101, 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality $n$. However, the algorithm has two disadvantages: (1) It is incomplete in the sense that the input of the third sub-algorithm cannot be obtained by the second sub-algorithm. (2) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points $m$ and an exponential speedup on the dimensionality $n$ under certain conditions over the classical NPE algorithm, and achieve significant speedup compared to Liang et al.'s algorithm even without considering the complexity of the VQA.

preprint2021arXiv

Quantum algorithms for anomaly detection using amplitude estimation

Anomaly detection plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. Anomaly detection algorithm based on density estimation (called ADDE algorithm) is one of widely used algorithms. Liang et al. proposed a quantum version of the ADDE algorithm [Phys. Rev. A 99, 052310 (2019)] and it is believed that the algorithm has exponential speedups on both the number and the dimension of training data point over the classical algorithm. In this paper, we find that Liang et al.'s algorithm doesn't actually execute. Then we propose a new quantum ADDE algorithm based on amplitude estimation. It is shown that our algorithm can achieves exponential speedup on the number $M$ of training data points compared with the classical counterpart. Besides, the idea of our algorithm can be applied to optimize the anomaly detection algorithm based on kernel principal component analysis (called ADKPCA algorithm). Different from the quantum ADKPCA proposed by Liu et al. [Phys. Rev. A 97, 042315 (2018)], compared with the classical counterpart, which offer exponential speedup on the dimension $d$ of data points, our algorithm achieves exponential speedup on $M$.

preprint2021arXiv

Radiation-tolerant high-entropy alloys via interstitial-solute-induced chemical heterogeneities

High-entropy alloys (HEAs) composed of multiple principal elements have been shown to offer improved radiation resistance over their elemental or dilute-solution counterparts. Using NiCoFeCrMn HEA as a model, here we introduce carbon and nitrogen interstitial alloying elements to impart chemical heterogeneities in the form of the local chemical order (LCO) and associated compositional variations. Density functional theory simulations predict chemical short-range order (CSRO) (nearest neighbors and the next couple of atomic shells) surrounding C and N, due to the chemical affinity of C with (Co, Fe) and N with (Cr, Mn). Atomic-resolution chemical mapping of the elemental distribution confirms marked compositional variations well beyond statistical fluctuations. Ni+ irradiation experiments at elevated temperatures demonstrate a remarkable reduction in void swelling by at least one order of magnitude compared to the base HEA without C and N alloying. The underlying mechanism is that the interstitial-solute-induced chemical heterogeneities roughen the lattice as well as the energy landscape, impeding the movements of, and constraining the path lanes for, the normally fast-moving self-interstitials and their clusters. The irradiation-produced interstitials and vacancies therefore recombine more readily, delaying void formation. Our findings thus open a promising avenue towards highly radiation-tolerant alloys.

preprint2021arXiv

Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform

Speech enhancement algorithms based on deep learning have been improved in terms of speech intelligibility and perceptual quality greatly. Many methods focus on enhancing the amplitude spectrum while reconstructing speech using the mixture phase. Since the clean phase is very important and difficult to predict, the performance of these methods will be limited. Some researchers attempted to estimate the phase spectrum directly or indirectly, but the effect is not ideal. Recently, some studies proposed the complex-valued model and achieved state-of-the-art performance, such as deep complex convolution recurrent network (DCCRN). However, the computation of the model is huge. To reduce the complexity and further improve the performance, we propose a novel method using discrete cosine transform as the input in this paper, called deep cosine transform convolutional recurrent network (DCTCRN). Experimental results show that DCTCRN achieves state-of-the-art performance both on objective and subjective metrics. Compared with noisy mixtures, the mean opinion score (MOS) increased by 0.46 (2.86 to 3.32) absolute processed by the proposed model with only 2.86M parameters.

preprint2021arXiv

Statistical Considerations for Cross-Sectional HIV Incidence Estimation Based on Recency Test

Longitudinal cohorts to determine the incidence of HIV infection are logistically challenging, so researchers have sought alternative strategies. Recency test methods use biomarker profiles of HIV-infected subjects in a cross-sectional sample to infer whether they are "recently" infected and to estimate incidence in the population. Two main estimators have been used in practice: one that assumes a recency test is perfectly specific, and another that allows for false-recent results. To date, these commonly used estimators have not been rigorously studied with respect to their assumptions and statistical properties. In this paper, we present a theoretical framework with which to understand these estimators and interrogate their assumptions, and perform a simulation study to assess the performance of these estimators under realistic HIV epidemiological dynamics. We conclude with recommendations for the use of these estimators in practice and a discussion of future methodological developments to improve HIV incidence estimation via recency test.

preprint2020arXiv

Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flights, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the planning problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. In each minimization phase, we leverage the algebraic convenience of the sub-problem to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our proposed method is provided. Moreover, based on polynomial theory, an extremely fast feasibility check method is designed for various kinds of constraints. By incorporating the method into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in a limited space with dense obstacles are presented to demonstrate the performance of the proposed algorithm. We release our implementation as an open-source ros-package.

preprint2020arXiv

Chiral plasmons with twisted atomic bilayers

Van der Waals heterostructures of atomically thin layers with rotational misalignments, such as twisted bilayer graphene, feature interesting structural moiré superlattices. Due to the quantum coupling between the twisted atomic layers, light-matter interaction is inherently chiral; as such, they provide a promising platform for chiral plasmons in the extreme nanoscale. However, while the interlayer quantum coupling can be significant, its influence on chiral plasmons still remains elusive. Here we present the general solutions from full Maxwell equations of chiral plasmons in twisted atomic bilayers, with the consideration of interlayer quantum coupling. We find twisted atomic bilayers have a direct correspondence to the chiral metasurface, which simultaneously possesses chiral and magnetic surface conductivities, besides the common electric surface conductivity. In other words, the interlayer quantum coupling in twisted van der Waals heterostructures may facilitate the construction of various (e.g., bi-anisotropic) atomically-thin metasurfaces. Moreover, the chiral surface conductivity, determined by the interlayer quantum coupling, determines the existence of chiral plasmons and leads to a unique phase relationship (i.e., +/-π/2 phase difference) between their TE and TM wave components. Importantly, such a unique phase relationship for chiral plasmons can be exploited to construct the missing longitudinal spin of plasmons, besides the common transverse spin of plasmons.

preprint2020arXiv

Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs

If light passes through the body tissues, focusing only on areas where treatment needs, such as tumors, will revolutionize many biomedical imaging and therapy technologies. So how to focus light through deep inhomogeneous tissues overcoming scattering is Holy Grail in biomedical areas. In this paper, we use deep learning to learn and accelerate the process of phase pre-compensation using wavefront shaping. We present an approach (LoftGAN, light only focuses on treatment needs) for learning the relationship between phase domain X and speckle domain Y . Our goal is not just to learn an inverse mapping F:Y->X such that we can know the corresponding X needed for imaging Y like most work, but also to make focusing that is susceptible to disturbances more robust and precise by ensuring that the phase obtained can be forward mapped back to speckle. So we introduce different constraints to enforce F(Y)=X and H(F(Y))=Y with the transmission mapping H:X->Y. Both simulation and physical experiments are performed to investigate the effects of light focusing to demonstrate the effectiveness of our method and comparative experiments prove the crucial improvement of robustness and precision. Codes are available at https://github.com/ChangchunYang/LoftGAN.

preprint2020arXiv

Detailed Proofs of Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

This technical report provides detailed theoretical analysis of the algorithm used in \textit{Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight}. An assumption is provided to ensure that settings for the objective function are meaningful. What's more, we explore the structure of the optimization problem and analyze the global/local convergence rate of the employed algorithm.

preprint2020arXiv

Dipole coupling of a tunable hole double quantum dot in germanium hut wire to a microwave resonator

The germanium (Ge) hut wire system has strong spin-orbit coupling, a long coherence time due to a very large heavy-light hole splitting, and the advantage of site-controlled large-scale hut wire positioning. These properties make the Ge hut wire a promising candidate for the realization of strong coupling of spin to superconducting resonators and scalability for multiple qubit coupling. We have coupled a reflection line resonator to a hole double quantum dot (DQD) formed in Ge hut wire. The amplitude and phase responses of the microwave resonator revealed that the charge stability diagrams of the DQD are in good agreement with those obtained from transport measurements. The DQD interdot tunneling rate is shown to be tunable from 6.2 GHz to 8.5 GHz, which demonstrates the ability to adjust the frequency detuning between the qubit and the resonator. Furthermore, we achieved a hole-resonator coupling strength of up to 15 MHz, with a charge qubit decoherence rate of 0.28 GHz. Meanwhile the hole spin-resonator coupling rate was estimated to be 3 MHz. These results suggest that holes of a DQD in a Ge hut wire are dipole coupled to microwave photons, potentially enabling tunable hole spin-photon interactions in Ge with an inherent spin-orbit coupling.

preprint2020arXiv

Hole spin in tunable Ge hut wire double quantum dot

Holes in germanium (Ge) exhibit strong spin-orbit interaction, which can be exploited for fast and all-electrical manipulation of spin states. Here, we report transport experiments in a tunable Ge hut wire hole double quantum dot. We observe the signatures of Pauli spin blockade (PSB) with a large singlet-triplet energy splitting of ~1.1 meV and extract the g factor. By analyzing the the PSB leakage current, we obtain a spin-orbit length l_so of ~ 40-100 nm. Furthermore, we demonstrate the electric dipole spin resonance. These results lay a solid foundation for implementing high quality tunable hole spin-orbit qubits.

preprint2020arXiv

Making Robots Draw A Vivid Portrait In Two Minutes

Significant progress has been made with artistic robots. However, existing robots fail to produce high-quality portraits in a short time. In this work, we present a drawing robot, which can automatically transfer a facial picture to a vivid portrait, and then draw it on paper within two minutes averagely. At the heart of our system is a novel portrait synthesis algorithm based on deep learning. Innovatively, we employ a self-consistency loss, which makes the algorithm capable of generating continuous and smooth brush-strokes. Besides, we propose a componential sparsity constraint to reduce the number of brush-strokes over insignificant areas. We also implement a local sketch synthesis algorithm, and several pre- and post-processing techniques to deal with the background and details. The portrait produced by our algorithm successfully captures individual characteristics by using a sparse set of continuous brush-strokes. Finally, the portrait is converted to a sequence of trajectories and reproduced by a 3-degree-of-freedom robotic arm. The whole portrait drawing robotic system is named AiSketcher. Extensive experiments show that AiSketcher can produce considerably high-quality sketches for a wide range of pictures, including faces in-the-wild and universal images of arbitrary content. To our best knowledge, AiSketcher is the first portrait drawing robot that uses neural style transfer techniques. AiSketcher has attended a quite number of exhibitions and shown remarkable performance under diverse circumstances.

preprint2020arXiv

Portable probe design for photoacoustic imaging in vivo

A low-cost adjustable illumination scheme for hand-held photoacoustic imaging probe is presented, manufactured and tested in this paper. Compared with traditional photoacoustic probe design, it has the following advantages: (1) Different excitation modes can be selected as needed. By tuning control parameters, it can achieve bright-field, dark-field, and hybrid field light illumination schemes. (2) The spot-adjustable unit (SAU) specifically designed for beam expansion, together with a water tank for transmitting ultrasonic waves, enable the device to break through the constraints of the transfer medium and is more widely used. The beam-expansion experiment is conducted to verify the function of SAU. After that, we built a PAT system based on our newly designed apparatus. Phantom and in vivo experimental results show different performance in different illumination schemes

preprint2020arXiv

Quantum restricted Boltzmann machine universal for quantum computation

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the many-body wave functions with high complexity. Quantum neural network provides a powerful tool to represent the large-scale wave function, which has aroused widespread concern in the quantum superiority era. A significant open problem is what exactly the representational power boundary of the single-layer quantum neural network is. In this paper, we design a 2-local Hamiltonian and then give a kind of Quantum Restricted Boltzmann Machine (QRBM, i.e. single-layer quantum neural network) based on it. The proposed QRBM has the following two salient features. (1) It is proved universal for implementing quantum computation tasks. (2) It can be efficiently implemented on the Noisy Intermediate-Scale Quantum (NISQ) devices. We successfully utilize the proposed QRBM to compute the wave functions for the notable cases of physical interest including the ground state as well as the Gibbs state (thermal state) of molecules on the superconducting quantum chip. The experimental results illustrate the proposed QRBM can compute the above wave functions with an acceptable error.

preprint2020arXiv

RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively. We will release our implementation as an open-source package for the community.

preprint2020arXiv

Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths

Gradient-based trajectory optimization (GTO) has gained wide popularity for quadrotor trajectory replanning. However, it suffers from local minima, which is not only fatal to safety but also unfavorable for smooth navigation. In this paper, we propose a replanning method based on GTO addressing this issue systematically. A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly. A topological path searching algorithm is developed to capture a collection of distinct useful paths in 3-D environments, each of which then guides an independent trajectory optimization. It activates a more comprehensive exploration of the solution space and output superior replanned trajectories. Benchmark evaluation shows that our method outplays state-of-the-art methods regarding replanning success rate and optimality. Challenging experiments of aggressive autonomous flight are presented to demonstrate the robustness of our method. We will release our implementation as an open-source package.

preprint2020arXiv

Self-controlled growth of highly uniform Ge/Si hut wires for scalable qubit devices

Semiconductor nanowires have been playing a crucial role in the development of nanoscale devices for the realization of spin qubits, Majorana fermions, single photon emitters, nanoprocessors, etc. The monolithic growth of site-controlled nanowires is a prerequisite towards the next generation of devices that will require addressability and scalability. Here, combining top-down nanofabrication and bottom-up self-assembly, we report on the growth of Ge wires on pre-patterned Si (001) substrates with controllable position, distance, length and structure. This is achieved by a novel growth process which uses a SiGe strain-relaxation template and can be generalized to other material combinations. Transport measurements show an electrically tunable spin-orbit coupling, with a spin-orbit length similar to that of III-V materials. Also, capacitive coupling between closely spaced wires is observed, which underlines their potential as a host for implementing two qubit gates. The reported results open a path towards scalable qubit devices with Si compatibility.

preprint2020arXiv

Thermal properties of $π$ and $ρ$ meson

We computed the pole masses and decay constants of $π$ and $ρ$ meson at finite temperature in the framework of Dyson-Schwinger equations and Bethe-Salpeter equations approach. Below transition temperature, pion pole mass increases monotonously, while $ρ$ meson seems to be temperature independent. Above transition temperature, pion mass approaches the free field limit, whereas $ρ$ meson is about twice as large as that limit. Pion and the longitudinal projection of $ρ$ meson decay constants have similar behaviour as the order parameter of chiral symmetry, whereas the transverse projection of $ρ$ meson decay constant rises monotonously as temperature increases. The inflection point of decay constant and the chiral susceptibility get the same phase transition temperature. Though there is no access to the thermal width of mesons within this scheme, it is discussed by analyzing the Gell-Mann-Oakes-Renner (GMOR) relation in medium. These thermal properties of hadron observables will help us understand the QCD phases at finite temperature and can be employed to improve the experimental data analysis and heavy ion collision simulations.

preprint2020arXiv

Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Frechet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: https://fei-hdu.github.io/ca-gan/.

preprint2019arXiv

Pion and Kaon Structure at the Electron-Ion Collider

Understanding the origin and dynamics of hadron structure and in turn that of atomic nuclei is a central goal of nuclear physics. This challenge entails the questions of how does the roughly 1 GeV mass-scale that characterizes atomic nuclei appear; why does it have the observed value; and, enigmatically, why are the composite Nambu-Goldstone (NG) bosons in quantum chromodynamics (QCD) abnormally light in comparison? In this perspective, we provide an analysis of the mass budget of the pion and proton in QCD; discuss the special role of the kaon, which lies near the boundary between dominance of strong and Higgs mass-generation mechanisms; and explain the need for a coherent effort in QCD phenomenology and continuum calculations, in exa-scale computing as provided by lattice QCD, and in experiments to make progress in understanding the origins of hadron masses and the distribution of that mass within them. We compare the unique capabilities foreseen at the electron-ion collider (EIC) with those at the hadron-electron ring accelerator (HERA), the only previous electron-proton collider; and describe five key experimental measurements, enabled by the EIC and aimed at delivering fundamental insights that will generate concrete answers to the questions of how mass and structure arise in the pion and kaon, the Standard Model's NG modes, whose surprisingly low mass is critical to the evolution of our Universe.

preprint2018arXiv

Acoustic higher-order topological insulator on a Kagome lattice

High-order topological insulators (TIs) are a family of recently-predicted topological phases of matter obeying an extended topological bulk-boundary correspondence principle. For example, a two-dimensional (2D) second-order TI does not exhibit gapless one-dimensional (1D) topological edge states, like a standard 2D TI, but instead has topologically-protected zero-dimensional (0D) corner states. So far, higher-order TIs have been demonstrated only in classical mechanical and electromagnetic metamaterials exhibiting quantized quadrupole polarization. Here, we experimentally realize a second-order TI in an acoustic metamaterial. This is the first experimental realization of a new type of higher-order TI, based on a breathing Kagome lattice, that has zero quadrupole polarization but nontrivial bulk topology characterized by quantized Wannier centers (WCs). Unlike previous higher-order TI realizations, the corner states depend not only on the bulk topology but also on the corner shape; we show experimentally that they exist at acute-angled corners of the Kagome lattice, but not at obtuse-angled corners. This shape dependence allows corner states to act as topologically-protected but reconfigurable local resonances.

preprint2016arXiv

Coherence of Superpositions

Quantum coherence is important in quantum mechanics, and its essence is from superposition principle. We study the coherence of any two pure states and that of their arbitrary superposition, and obtain the relationship between them. In the case that the two states have support on orthogonal subspaces, the relationship is simple, that is, the difference between the coherence of their superposition state and the average coherence of them is smaller than 1. In other cases, we obtain different and a little more complicated relationships. Furthermore, we also obtain the lower bound of coherence of superpositions.

preprint2016arXiv

Creating cat states in one-dimensional quantum walks using delocalized initial states

Cat states are coherent quantum superpositions of macroscopically distinct states and are useful for understanding the boundary between the classical and the quantum world. Due to their macroscopic nature, cat states are difficult to prepare in physical systems. We propose a method to create cat states in one-dimensional quantum walks using delocalized initial states of the walker. Since the quantum walks can be performed on any quantum system, our proposal enables a platform-independent realization of the cat states. We further show that the linear dispersion relation of the effective quantum walk Hamiltonian, which governs the dynamics of the delocalized states, is responsible for the formation of the cat states. We analyze the robustness of these states against environmental interactions and present methods to control and manipulate the cat states in the photonic implementation of quantum walks.

preprint2016arXiv

Distribution amplitudes of radially-excited pi- and K-mesons

A symmetry-preserving truncation of the two-body bound-state problem in relativistic quantum field theory is used to compute the leading-twist parton distribution amplitudes (PDAs) for the first radial excitations of the $π$- and $K$-mesons. In common with ground states in these channels, the PDAs are found to be dilated with respect to the relevant conformal-limit form and skewed toward the heavier valence-quark in asymmetric systems. In addition, the PDAs of radially-excited pseudoscalar mesons are not positive definite, owing to the fact that dynamical chiral symmetry breaking (DCSB) forces the leptonic decay constant of such states to vanish in the chiral limit. These results highlight that DCSB is expressed visibly in every pseudoscalar meson constituted from light-quarks. Hence, so long as its impact is empirically evident in the pseudoscalar members of a given spectrum level, it is unlikely that chiral symmetry is restored in any of the hadrons that populate this level.

preprint2016arXiv

Dynamical Mechanism of Two-Dimensional Plasmon Launching by Swift Electrons

Launching of surface plasmons by swift electrons has long been utilized in electron-energy-loss spectroscopy (EELS) to investigate plasmonic properties of ultrathin, or two-dimensional (2D), electron systems. However, its spatio-temporal process has never been revealed. This is because the impact of an electron will generate not only plasmons, but also photons, whose emission cannot be achieved at a single space-time point, as fundamentally determined from the uncertainty principle. Here, we propose that such a space-time limitation also applies to surface plasmon generation in EELS experiment. On the platform of graphene, we demonstrate within the framework of classical electrodynamics that the launching of 2D plasmons by an electron's impact is delayed after a hydrodynamic splashing-like process, which occurs during the plasmonic "formation time" when the electron traverses the "formation zone." Considering this newly revealed process, we show that previous estimates on the yields of graphene plasmons in EELS have been overestimated.

preprint2016arXiv

Generic Quantum Walks with Memory

Quantum walks with memory(QWM) are a type of modified quantum walks that record the walker's latest path. As we know, only two kinds of QWM are presented up to now. It is desired to design more QWM for research, so that we can explore the potential of QWM. In this work, through presenting the one-to-one correspondence between QWM on a regular graph and quantum walks without memory(QWoM) on line digraph of the regular graph, we construct a generic model of QWM on regular graphs. This construction gives a general scheme for building all possible standard QWM on regular graphs and makes it possible to study properties of different kinds of QWM. Here, by taking the simplest example which is QWM with 1 memory on the line, we analyze some properties of QWM, such as variance, occupancy rate and localization.

preprint2016arXiv

Guiding, bending, and splitting of coupled defect surface modes in a surface-wave photonic crystal

We experimentally demonstrate a type of waveguiding mechanism for coupled surface-wave defect modes in a surface-wave photonic crystal. Unlike conventional spoof surface plasmon waveguides, waveguiding of coupled surface-wave defect modes is achieved through weak coupling between tightly localized defect cavities in an otherwise gapped surface-wave photonic crystal, as a classical wave analogue of tight-binding electronic wavefunctions in solid state lattices.

preprint2016arXiv

Interface Effect in QCD Phase Transitions via Dyson-Schwinger Equation Approach

With the chiral susceptibility criterion we obtain the phase diagram of strong-interaction matter in terms of temperature and chemical potential in the framework of Dyson-Schwinger equations (DSEs) of QCD.After calculating the pressure and some other thermodynamic properties of the matter in the DSE method, we get the phase diagram in terms of temperature and baryon number density. We also obtain the interface tension and the interface entropy density to describe the inhomogeneity of the two phases in the coexistence region of the first order phase transition. After including the interface effect, we find that the total entropy density of the system increases in both the deconfinement (dynamical chiral symmetry restoration) and the hadronization (dynamical chiral symmetry breaking) processes of the first order phase transitions and thus solve the entropy puzzle in the hadronization process.

preprint2016arXiv

Invisibility Dips of Near-Field Energy Transport in a Spoof Plasmonic Metadimer

Invisibility dips, minima in scattering spectrum associated with asymmetric Fano-like line-shapes, have been predicted with transformation optics in studying strong coupling between two plasmonic nanoparticles. This feature of strongly coupled plasmonic nanoparticles holds promise for sensor cloaking. It requires an extremely narrow gap between the two nanoparticles, though, preventing its experimental observation at optical frequencies. Here, the concept of spoof surface plasmons is utilized to facilitate the strong coupling between two spoof-localized-surface-plasmon (SLSP) resonators. Instead of observing in far field, the near-field energy transport is probed through the two SLSP resonators. By virtue of enhanced coupling between the two resonators stacked vertically, a spectral transmission dip with asymmetric Fano-like line-shape, similar to the far-field invisibility dips predicted by transformation optics, is observed. The underlying mode interference mechanism is further demonstrated by directly imaging the field maps of interfered waves that are tightly localized around the resonators. These near-field invisibility dips may find use in near-field sensing, on-chip switching, filters and logical operation elements.

preprint2016arXiv

One laser pulse generates two photoacoustic signals

Photoacoustic sensing and imaging techniques have been studied widely to explore optical absorption contrast based on nanosecond laser illumination. In this paper, we report a long laser pulse induced dual photoacoustic (LDPA) nonlinear effect, which originates from unsatisfied stress and thermal confinements. Being different from conventional short laser pulse illumination, the proposed method utilizes a long square-profile laser pulse to induce dual photoacoustic signals. Without satisfying the stress confinement, the dual photoacoustic signals are generated following the positive and negative edges of the long laser pulse. More interestingly, the first expansion-induced photoacoustic signal exhibits positive waveform due to the initial sharp rising of temperature. On the contrary, the second contraction-induced photoacoustic signal exhibits exactly negative waveform due to the falling of temperature, as well as pulse-width-dependent signal amplitude which is caused by the concurrent heat accumulation and thermal diffusion during the long laser illumination. An analytical model is derived to describe the generation of the dual photoacoustic pulses, incorporating Gruneisen saturation and thermal diffusion effect, which is experimentally proved. Lastly, an alternate of LDPA technique using quasi-CW laser excitation is also introduced and demonstrated for both super-contrast in vitro and in vivo imaging. Compared with existing nonlinear PA techniques, the proposed LDPA nonlinear effect could enable a much broader range of potential applications.

preprint2016arXiv

Phase diagram and thermal properties of strong-interaction matter

We introduce a novel procedure for computing the (mu,T)-dependent pressure in continuum QCD; and therefrom obtain a complex phase diagram and predictions for thermal properties of the system, providing the in-medium behaviour of the trace anomaly, speed of sound, latent heat and heat capacity.

preprint2016arXiv

Probing topological protection using a designer surface plasmon structure

Topological photonic states, inspired by robust chiral edge states in topological insulators, have recently been demonstrated in a few photonic systems, including an array of coupled on-chip ring resonators at communication wavelengths. However, the intrinsic difference between electrons and photons determines that the topological protection in time-reversal-invariant photonic systems does not share the same robustness as its counterpart in electronic topological insulators. Here, in a designer surface plasmon platform consisting of tunable metallic sub-wavelength structures, we construct photonic topological edge states and probe their robustness against a variety of defect classes, including some common time-reversal-invariant photonic defects that can break the topological protection, but do not exist in electronic topological insulators. This is also the first experimental realization of anomalous Floquet topological edge states, whose topological phase cannot be predicted by the usual Chern number topological invariants.

preprint2016arXiv

QCD phase transitions via a refined truncation of Dyson-Schwinger equations

We investigate both the chiral and deconfinement phase transitions of QCD matter in a refined scheme of Dyson-Schwinger equations, which have been shown to be successful in giving the meson mass spectrum and matching the interaction with the results from ab initio computation. We verify the equivalence of the chiral susceptibility criterion with different definitions for the susceptibility and confirm that the chiral susceptibility criterion is efficient to fix not only the chiral phase boundary but also the critical end point (CEP), especially when one could not have the effective thermodynamical potential. We propose a generalized Schwinger function criterion for the confinement. We give the phase diagram of both phase transitions and show that in the refined scheme the position of the CEP shifts to lower chemical potential and higher temperature. Based on our calculation and previous results of the chemical freeze out conditions, we propose that the CEP locates in the states of the matter generated by the Au--Au collisions with $\sqrt{s_{NN}^{}}=9\sim15$ GeV.

preprint2016arXiv

Quantum algorithm for association rules mining

Association rules mining is one of the most important problems in knowledge discovery and data mining. The goal of it is to acquire consumption habits of customers by discovering the relationships between items from a transaction database that has a large number of transactions and items. The most compute intensive process for ARM is to find out the frequent 1-itemsets and 2-itemsets. In this paper, we propose a quantum algorithm for finding out the frequent 1-itemsets and 2-itemsets. In our algorithm, to mine the frequent 1-itemsets efficiently, we use the technique of amplitude amplification. To mine the frequent 2-itemsets efficiently, we propose a new tomography scheme, i.e., pure-state-based quantum state tomography. It is shown that our algorithm is potential to achieve exponential speedup in the number of transactions and polynomial speedup in the number of items over the classical algorithm.

preprint2016arXiv

Sign-Reversal Coupling in Coupled-Resonator Optical Waveguide

Coupled-resonator optical waveguides (CROWs), which play a significant role in modern photonics, achieve waveguiding through near-field coupling between tightly localized resonators. The coupling factor, a critical parameter in CROW theory, determines the coupling strength between two resonators and the waveguiding dispersion of a CROW. However, the original CROW theory proposed by Yariv et al. only demonstrated one value of coupling factor for a multipole resonance mode. Here, by imaging the tight-binding Bloch waves on a CROW consisting of designer-surface-plasmon resonators in the microwave regime, we demonstrate that the coupling factor in the CROW theory can reverse its sign for a multipole resonance mode. This determines two different waveguiding dispersion curves in the same frequency range, experimentally confirmed by matching Bloch wavevectors and frequencies in the CROW. Our study supplements and extends the original CROW theory, and may find novel use in functional photonic systems.

preprint2016arXiv

Spoof surface plasmon Fabry-Perot open resonators in a surface-wave photonic crystal

We report on the proposal and experimental realization of a spoof surface plasmon Fabry-Perot (FP) open resonator in a surface-wave photonic crystal. This surface-wave FP open resonator is formed by introducing a finite line defect in a surface-wave photonic crystal. The resonance frequencies of the surface-wave FP open resonator lie exactly within the forbidden band gap of the surface-wave photonic crystal and the FP open resonator uses this complete forbidden band gap to concentrate surface waves within a subwavelength cavity. Due to the complete forbidden band gap of the surface-wave photonic crystal, a new FP plasmonic resonance mode that exhibits monopolar features which is missing in traditional FP resonators and plasmonic resonators is demonstrated. Near-field response spectra and mode profiles are presented in the microwave regime to characterize properties of the proposed FP open resonator for spoof surface plasmons.

preprint2016arXiv

Surface-wave photonic quasicrystal

In developing strategies of manipulating surface electromagnetic waves, it has been recently recognized that a complete forbidden band gap can exist in a periodic surface-wave photonic crystal, which has subsequently produced various surface-wave photonic devices. However, it is not obvious whether such a concept can be extended to a non-periodic surface-wave system that lacks translational symmetry. Here we experimentally demonstrate that a surface-wave photonic quasicrystal that lacks periodicity can also exhibit a forbidden band gap for surface electromagnetic waves. The lower cutoff of this forbidden band gap is mainly determined by the maximum separation between nearest neighboring pillars. Point defects within this band gap show distinct properties compared to a periodic photonic crystal for the absence of translational symmetry. A line-defect waveguide, which is crafted out of this surface-wave photonic quasicrystal by shortening a random row of metallic rods, is also demonstrated to guide and bend surface waves around sharp corners along an irregular waveguiding path.

preprint2016arXiv

Topological water wave state in a one-dimensional structure

Topological concepts have been introduced into electronic, photonic, and phononic systems, but have not been studied in surface-water-wave systems. Here we study a one-dimensional periodic resonant surface-water-wave system and demonstrate its topological transition. By selecting three different water depths, we can construct different types of water waves - shallow, intermediate and deep water waves. The periodic surface-water-wave system consists of an array of cylindrical water tanks connected with narrow water channels. As the width of connecting channel varies, the band diagram undergoes a topological transition which can be further characterized by Zak phase. This topological transition holds true for shallow, intermediate and deep water waves. However, the interface state at the boundary separating two topologically distinct arrays of water tanks can exhibit different bands for shallow, intermediate and deep water waves. Our work studies for the first time topological properties of water wave systems, and paves the way to potential management of water waves.

preprint2016arXiv

Translation and Rotation of Transformation Media under Electromagnetic Pulse

It is well known that optical media create artificial geometry for light, and curved geometry acts as an effective optical medium. This correspondence originates from the form invariance of Maxwells equations, which recently has spawned a booming field called transformation optics. Here we investigate responses of three transformation media under electromagnetic pulses, and find that pulse radiation can induce unbalanced net force on transformation media, which will cause translation and rotation of transformation media although their final momentum can still be zero. Therefore, the transformation media do not necessarily stay the same after an electromagnetic wave passes through.

preprint2016arXiv

Tunable band-notched line-defect waveguide in a surface-wave photonic crystal

We propose and experimentally demonstrate a tunable band-notched line-defect waveguide in a surface-wave photonic crystal, which consists of a straight line-defect waveguide and side-coupled defect cavities. A tunable narrow stopband can be observed in the broadband transmission spectra. We also demonstrate that both the filtering levels and filtering frequencies of the band-notched line-defect waveguide can be conveniently tuned through changing the total number and the pillar height of the side-coupled defect cavities. The band-notch function is based on the idea that the propagating surface modes with the resonance frequencies of the side-coupled defect cavities will be tightly localized around the defect sites, being filtered from the waveguide output. Transmission spectra measurements and direct near-field profiles imaging are performed at microwave frequencies to verify our idea and design. These results may enable new band-notched devices design and provide routes for the realization of tunable surface-wave filters on a single metal surface.

preprint2015arXiv

A Novel Paradigm for Calculating Ramsey Number via Artificial Bee Colony Algorithm

The Ramsey number is of vital importance in Ramsey's theorem. This paper proposed a novel methodology for constructing Ramsey graphs about R(3,10), which uses Artificial Bee Colony optimization(ABC) to raise the lower bound of Ramsey number R(3,10). The r(3,10)-graph contains two limitations, that is, neither complete graphs of order 3 nor independent sets of order 10. To resolve these limitations, a special mathematical model is put in the paradigm to convert the problems into discrete optimization whose smaller minimizers are correspondent to bigger lower bound as approximation of inf R(3,10). To demonstrate the potential of the proposed method, simulations are done to to minimize the amount of these two types of graphs. For the first time, four r(3,9,39) graphs with best approximation for inf R(3,10) are reported in simulations to support the current lower bound for R(3,10). The experiments' results show that the proposed paradigm for Ramsey number's calculation driven by ABC is a successful method with the advantages of high precision and robustness.

preprint2015arXiv

Asymptotic formula on average path length of fractal networks modelled on Sierpinski Gasket

In this paper, we introduce a new method to construct evolving networks based on the construction of the Sierpinski gasket. Using self-similarity and renewal theorem, we obtain the asymptotic formula for average path length of our evolving networks.

preprint2015arXiv

Atomic Conversion Reaction Mechanism of WO3 in Secondary Ion Batteries

Conversion reaction is one of the most important chemical processes in energy storage such as lithium ion batteries. While it is generally assumed that the conversion reaction is initiated by ion intercalation into the electrode material, solid evidence of intercalation and the subsequent transition mechanism to conversion remain elusive. Here, using well-defined WO3 single crystalline thin films grown on Nb doped SrTiO3(001) as a model electrode, we elucidate the conversion reaction mechanisms during Li+, Na+ and Ca2+ insertion into WO3 by in situ transmission electron microscopy studies. Intercalation reactions are explicitly revealed for all ion insertions. With corroboration from first principle molecular simulations, it is found that, beyond intercalation, ion-oxygen bonding destabilize the W framework, which gradually collapses to pseudo-amorphous structure. In addition, we show the interfacial tensile strain imposed by the SrTiO3 substrate can preserve the structure of an ultra-thin layer of WO3, offering a possible engineering solution to improve the cyclability of electrode materials. This study provides a detailed atomistic picture on the conversion-type electrodes in secondary ion batteries.

preprint2015arXiv

Caustic graphene plasmons with Kelvin angle

A century-long argument made by Lord Kelvin that all swimming objects have an effective Mach number of 3, corresponding to the Kelvin angle of 19.5 degree for ship waves, has been recently challenged with the conclusion that the Kelvin angle should gradually transit to the Mach angle as the ship velocity increases. Here we show that a similar phenomenon can happen for graphene plasmons. By analyzing the caustic wave pattern of graphene plasmons stimulated by a swift charged particle moving uniformly above graphene, we show that at low velocities of the charged particle, the caustics of graphene plasmons form the Kelvin angle. At large velocities of the particle, the caustics disappear and the effective semi-angle of the wave pattern approaches the Mach angle. Our study introduces caustic wave theory to the field of graphene plasmonics, and reveals a novel physical picture of graphene plasmon excitation during electron energy-loss spectroscopy measurement.

preprint2015arXiv

Chemical Freeze-out Parameters via a Non-perturbative QCD Approach

By analyzing the calculated baryon number susceptibility ratios ${χ_{1}^{B}}/{χ_{2}^{B}}$ and ${χ_{3}^{B}}/{χ_{1}^{B}}$ in two-flavor system via the Dyson-Schwinger equation approach of QCD, we determine the chemical freeze-out temperature and baryon chemical potential in cases of both thermodynamic limit and finite size. We calculate the center-of-mass energy dependence of the ${χ_{4}^{B}}/{χ_{2}^{B}}\, (κσ^{2})$ at the freeze-out line and find an excellent agreement with experimental data in $\sqrt{S_{NN}^{}} \geq 19.6\,$GeV region when taking into account the finite size effect. Our calculations indicate that the $κσ^{2}$ exhibits a non-monotonic behavior in lower collision energy region.

preprint2015arXiv

Complementary magnetic localized surface plasmons

Magnetic localized surface plasmons (LSPs) supported on metallic structures corrugated by very long and curved grooves have been recently proposed and demonstrated on an extremely thin metallic spiral structure (MSS) in the microwave regime. However, the mode profile for the magnetic LSPs was demonstrated by measuring only the electric field, not the magnetic field.

preprint2015arXiv

Constructing locally indistinguishable orthogonal product bases in an $m \otimes n$ system

Recently, Zhang et al [Phys. Rev. A 92, 012332 (2015)] presented $4d-4$ orthogonal product states that are locally indistinguishable and completable in a $d\otimes d$ quantum system. Later, Zhang et al. [arXiv: 1509.01814v2 (2015)] constructed $2n-1$ orthogonal product states that are locally indistinguishable in $m\otimes n$ ($3\leq m \leq n$). In this paper, we construct a locally indistinguishable and completable orthogonal product basis with $4p-4$ members in a general $m\otimes n$ ($3\leq m \leq n$) quantum system, where $p$ is an arbitrary integer from $3$ to $m$, and give a very simple but quite effective proof for its local indistinguishability. Specially, we get a completable orthogonal product basis with $8$ members that cannot be locally distinguished in $m\otimes n$ ($3\leq m \leq n$) when $p=3$. It is so far the smallest completable orthogonal product basis that cannot be locally distinguished in a $m\otimes n$ quantum system. On the other hand, we construct a small locally indistinguishable orthogonal product basis with $2p-1$ members, which is maybe uncompletable, in $m\otimes n$ ($3\leq m \leq n$ and $p$ is an arbitrary integer from $3$ to $m$). We also prove its local indistinguishability. As a corollary, we give an uncompletable orthogonal product basis with $5$ members that are locally indistinguishable in $m\otimes n$ ($3\leq m \leq n$). All the results can lead us to a better understanding of the structure of a locally indistinguishable product basis in $m \otimes n$.

preprint2015arXiv

Deep-subwavelength magnetic-coupling-dominant stereometamaterials

Here, we experimentally demonstrate that a stack of metallic spiral structures (MSSs) can have dominant magnetic coupling in both of its two configurations. This allows magnetic-coupling-dominant energy transport along a one-dimensional stack of MSSs at a deep-subwavlength scale, as demonstrated with near-field transmission measurement.

preprint2015arXiv

Determination of stabilizer states

The determination of many special types of quantum states has been studied thoroughly, such as the generalized |GHZ> states, |W> states equivalent under stochastic local operations and classical communication and Dicke states. In this paper, we are going to study another special entanglement states which is stabilizer states. The stabilizer states and their subset graph states play an important role in quantum error correcting codes, multipartite purification and so on. We show that all n- qubit stabilizer states are uniquely determined (among arbitrary states, pure or mixed) by their reduced density matrices for systems which are the supports of n independent generators of the corresponding stabilizer formalisms.

preprint2015arXiv

Local indistinguishability of orthogonal product states

In the general bipartite quantum system $m \otimes n$, Wang \emph{et al.} [Y.-L Wang \emph{et al.}, Phys. Rev. A \textbf{92}, 032313 (2015)] presented $3(m+n)-9$ orthogonal product states which cannot be distinguished by local operations and classical communication (LOCC). In this paper, we aim to construct less locally indistinguishable orthogonal product states in $m\otimes n $. First, in $3\otimes n (3< n)$ quantum system, we construct $3n-2$ locally indistinguishable orthogonal product states which are not unextendible product bases. Then, for $m\otimes n (4\leq m\leq n)$, we present $3n+m-4$ orthogonal product states which cannot be perfectly distinguished by LOCC. Finally, in the general bipartite quantum system $m\otimes n(3\leq m\leq n)$, we show a smaller set with $2n-1$ orthogonal product states and prove that these states are LOCC indistinguishable using a very simple but quite effective method. All of the above results demonstrate the phenomenon of nonlocality without entanglement.

preprint2015arXiv

Monte Carlo Study of a 137Cs calibration field of the China institute of atomic energy

The MCNP code was used to study the characteristics of gamma radiation field with collimated beam geometry. A close-to-reality simulation model of the facility was used for calculation air-kerma along the whole range of source-detector-distance (SDD) along the central beam and air-kerma off-axis beam profiles at two different source-detector-distance (SDD). The simulation results were tested by the measured results which were acquired in the Radiation Metrology Center of CIAE. Other characteristics such as the individual contributions of photons scattered in collimator, floor, walls, mobile platform and other parts of the irradiation halls to the total air kerma rate on the beam axis were calculated for the purpose of future improvement of metrological parameters in CIAE. Finally, factors which influence the simulation results were investigated, including e.g., detector volume effects or source density effects.

preprint2015arXiv

Multi-user quantum key distribution with collective eavesdropping detection over collective-noise channels

A multi-user quantum key distribution protocol is proposed with single particles and the collective eavesdropping detection strategy on a star network. By utilizing this protocol, any two users of the network can accomplish quantum key distribution with the help of a serving center. Due to the utilization of collective eavesdropping detection strategy, the users of the protocol just need have the ability of performing certain unitary operations. Furthermore, we present three fault-tolerant versions of the proposed protocol, which can combat with the errors over different collective-noise channels. The security of all the proposed protocols is guaranteed by the theorems on quantum operation discrimination.

preprint2015arXiv

QKD-based quantum private query without a failure probability

In this paper, we present a quantum-key-distribution (QKD)-based quantum private query (QPQ) protocol utilizing single-photon signal of multiple optical pulses. It maintains the advantages of the QKD-based QPQ, i.e., easy to implement and loss tolerant. In addition, different from the situations in the previous QKD-based QPQ protocols, in our protocol, the number of the items an honest user will obtain is always one and the failure probability is always zero. This characteristic not only improves the stability (in the sense that, ignoring the noise and the attack, the protocol would always succeed), but also benefits the privacy of the database (since the database will no more reveal additional secrets to the honest users). Furthermore, for the user's privacy, the proposed protocol is cheat sensitive, and for security of the database, we obtain an upper bound for the leaked information of the database in theory.

preprint2015arXiv

Scintillation and Ionization Responses of Liquid Xenon to Low Energy Electronic and Nuclear Recoils at Drift Fields from 236 V/cm to 3.93 kV/cm

We present new measurements of the scintillation and ionization yields in liquid xenon for low energy electronic (about 3--7 keV$_{ee}$) and nuclear recoils (about 8--20 keV$_{nr}$) at different drift fields from 236 V/cm to 3.93 kV/cm, using a three-dimensional sensitive liquid xenon time projection chamber with high energy and position resolutions. Our measurement of signal responses to nuclear recoils agrees with predictions from the NEST model. However, our measured ionization (scintillation) yields for electronic recoils are consistently higher (lower) than those from the NEST model by about 5 e$^-$/keV$_{ee}$ (ph/keV$_{ee}$) at all scanned drift fields. New recombination parameters based on the Thomas-Imel box model are derived from our data. Given the lack of precise measurement of scintillation and ionization yields for low energy electronic recoils in liquid xenon previously, our new measurement provides so far the best available data covering low energy region at different drift fields for liquid xenon detectors relevant to dark matter searches.

preprint2015arXiv

Semi-device-independent randomness expansion with partially free random sources

By proposing device-independent protocols, S. Pironio et al. [Nature 464, 1021-1024 (2010)] and R. Colbeck et al. [Nature Physics 8, 450-453 (2012)] proved that new randomness can be generated by using perfectly free random sources or partially free ones as seed. Subsequently, Li et al. [Phys. Rev. A 84, 034301 (2011)] studied this topic in the framework of semi-device-independent and proved that new randomness can be obtained from perfectly free random sources. Here we discuss whether and how partially free random sources bring us new randomness in semi-device-independent scenario. We propose a semi-device-independent randomness expansion protocol with partially free random sources, and obtain the condition that the partially free random sources should satisfy to generate new randomness. In the process of analysis, we acquire a new 2-dimensional quantum witness. Furthermore, we get the analytic relationship between the generated randomness and the 2-dimensional quantum witness violation.

preprint2015arXiv

Topological Acoustics

The manipulation of acoustic wave propagation in fluids has numerous applications, including some in everyday life. Acoustic technologies frequently develop in tandem with optics, using shared concepts such as waveguiding and metamedia. It is thus noteworthy that an entirely novel class of electromagnetic waves, known as topological edge states, has recently been demonstrated. These are inspired by the electronic edge states occurring in topological insulators, and possess a striking and technologically promising property: the ability to travel in a single direction along a surface without backscattering, regardless of the existence of defects or disorder. Here, we develop an analogous theory of topological fluid acoustics, and propose a scheme for realizing topological edge states in an acoustic structure containing circulating fluids. The phenomenon of disorder-free one-way sound propagation, which does not occur in ordinary acoustic devices, may have novel applications for acoustic isolators, modulators, and transducers.

preprint2015arXiv

Vertical Transport of Subwavelength Localized Surface Electromagnetic Modes

Transport of subwavelength electromagnetic (EM) energy has been achieved through near-field coupling of highly confined surface EM modes supported by plasmonic nanoparticles, in a configuration usually staying on a two-dimensional (2D) substrate. Vertical transport of similar modes along the third dimension, on the other hand, can bring more flexibility in designs of functional photonic devices, but this phenomenon has not been observed in reality. In this paper, designer (or spoof) surface plasmon resonators (plasmonic meta-atoms) are stacked in the direction vertical to their individual planes in demonstrating vertical transport of subwavelength localized surface EM modes. Dispersion relation of this vertical transport is determined from coupled mode theory and is verified with near-field transmission spectrum and field mapping with a microwave near-field scanning stage. This work extends the near-field coupled resonator optical waveguide (CROW) theory into the vertical direction, and may find applications in novel three-dimensional slow light structures, filters, and photonic circuits.

preprint2015arXiv

Waveform Selectivity at the Same Frequency

Electromagnetic properties depend on the composition of materials, i.e. either angstrom scales of molecules or, for metamaterials, subwavelength periodic structures. Each material behaves differently in accordance with the frequency of an incoming electromagnetic wave due to the frequency dispersion or the resonance of the periodic structures. This indicates that if the frequency is fixed, the material always responds in the same manner unless it has nonlinearity. However, such nonlinearity is controlled by the magnitude of the incoming wave or other bias. Therefore, it is difficult to distinguish different incoming waves at the same frequency. Here we present a new concept of circuit-based metasurfaces to selectively absorb or transmit specific types of waveforms even at the same frequency. The metasurfaces, integrated with schottky diodes as well as either capacitors or inductors, selectively absorb short or long pulses, respectively. The two types of the circuit elements are then combined to absorb or transmit specific waveforms in between. This waveform selectivity gives us another freedom to control electromagnetic waves in various fields including wireless communications, as our simulation reveals that the metasurfaces are capable of varying bit error rates in response to waveforms.

preprint2014arXiv

Decompositions of Edge-Colored Digraphs: A New Technique in the Construction of Constant-Weight Codes and Related Families

We demonstrate that certain Johnson-type bounds are asymptotically exact for a variety of classes of codes, namely, constant-composition codes, nonbinary constant-weight codes and multiply constant-weight codes. This was achieved via an interesting application of the theory of decomposition of edge-colored digraphs.

preprint2014arXiv

General bounds for quantum discord and discord distance

For any bipartite state, how strongly can one subsystem be quantum correlated with another? Using the Koashi-Winter relation, we study the upper bound of purified quantum discord, which is given by the sum of the von Neumann entropy of the unmeasured subsystem and the entanglement of formation shared between the unmeasured subsystem with the environment. In particular, we find that the Luo et al.'s conjecture on the quantum correlations and the Lindblad conjecture are all ture, when the entanglement of formation vanishes. Let the difference between the left discord and the right discord be captured by the discord distance. If the Lindblad conjecture is true, we show that the joint entropy is a tight upper bound for the discord distance. Further, we obtain a necessary and sufficient condition for saturating upper bounds of purified quantum discord and discord distance separately with the equality conditions for the Araki-Lieb inequality and the Lindblad conjecture. Furthermore, we show that the subadditive relation holds for any bipartite quantum discord.

preprint2014arXiv

LightLDA: Big Topic Models on Modest Compute Clusters

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We consider this challenge in the context of topic modeling on web-scale corpora, and show that with a modest cluster of as few as 8 machines, we can train a topic model with 1 million topics and a 1-million-word vocabulary (for a total of 1 trillion parameters), on a document collection with 200 billion tokens -- a scale not yet reported even with thousands of machines. Our major contributions include: 1) a new, highly efficient O(1) Metropolis-Hastings sampling algorithm, whose running cost is (surprisingly) agnostic of model size, and empirically converges nearly an order of magnitude faster than current state-of-the-art Gibbs samplers; 2) a structure-aware model-parallel scheme, which leverages dependencies within the topic model, yielding a sampling strategy that is frugal on machine memory and network communication; 3) a differential data-structure for model storage, which uses separate data structures for high- and low-frequency words to allow extremely large models to fit in memory, while maintaining high inference speed; and 4) a bounded asynchronous data-parallel scheme, which allows efficient distributed processing of massive data via a parameter server. Our distribution strategy is an instance of the model-and-data-parallel programming model underlying the Petuum framework for general distributed ML, and was implemented on top of the Petuum open-source system. We provide experimental evidence showing how this development puts massive models within reach on a small cluster while still enjoying proportional time cost reductions with increasing cluster size, in comparison with alternative options.

preprint2014arXiv

Multi-timescale microscopic theory for radiation degradation of electronic and optoelectronic devices

A multi-timescale hybrid model is proposed to study microscopically the degraded performance of electronic devices, covering three individual stages of radiation effects studies, including ultrafast displacement cascade, intermediate defect stabilization and cluster formation, as well as slow defect reaction and migration. Realistic interatomic potentials are employed in molecular-dynamics calculations for the first two stages up to 100\,ns as well as for the system composed of layers with thickness of hundreds times of lattice constant. These quasi-steady-state results for individual layers are input into a rate-diffusion theory as initial conditions to calculate the steady-state distribution of point defects in a mesoscopic-scale layered-structure system, including planar biased dislocation loops and spherical neutral voids, on a much longer time scale. Assisted by the density-functional theory for specifying electronic properties of point defects, the resulting spatial distributions of these defects and clusters are taken into account in studying the degradation of electronic and optoelectronic devices, e.g., carrier momentum-relaxation time, defect-mediated non-radiative recombination, defected-assisted tunneling of electrons and defect or charged-defect Raman scattering as well. Such theoretical studies are expected to be crucial in fully understanding the physical mechanism for identifying defect species, performance degradations in field-effect transistors, photodetectors, light-emitting diodes and solar cells, and in the development of effective mitigation methods during their microscopic structure design stages.

preprint2014arXiv

Necessary and sufficient conditions for positive semidefinite quantum mutual information matrices

For any $n$-partite state $ρ_{A_{1}A_{2}\cdot\cdot\cdot A_{n}}$, we define its quantum mutual information matrix as an $n$ by $n$ matrix whose $(i,j)$-entry is given by quantum mutual information $I(ρ_{A_{i}A_{j}})$. Although each entry of quantum mutual information matrix, like its classical counterpart, is also used to measure bipartite correlations, the similarity ends here: quantum mutual information matrices are not always positive semidefinite even for collections of up to 3-partite states. In this work, we obtain necessary and sufficient conditions for the positive semidefinite quantum mutual information matrix. We further define the \emph{genuine} $n$-partite mutual information which can be easily calculated. This definition is symmetric, nonnegative, bounded and more accurate for measuring multipartite states.

preprint2014arXiv

Parton distribution amplitudes of light vector mesons

A rainbow-ladder truncation of QCD's Dyson-Schwinger equations is used to calculate rho- and phi-meson valence-quark (twist-two parton) distribution amplitudes (PDAs) via a light-front projection of their Bethe-Salpeter wave functions, which possess S- and D-wave components of comparable size in the meson rest frame. All computed PDAs are broad concave functions, whose dilation with respect to the asymptotic distribution is an expression of dynamical chiral symmetry breaking. The PDAs can be used to define an ordering of valence-quark light-front spatial-extent within mesons: this size is smallest within the pion and increases through the perp-polarisation to the parallel-polarisation of the vector mesons; effects associated with the breaking of SU(3)-flavour symmetry are significantly smaller than those associated with altering the polarisation of vector mesons. Notably, the predicted pointwise behaviour of the rho-meson PDAs is in quantitative agreement with that inferred recently via an analysis of diffractive vector-meson photoproduction experiments.

preprint2014arXiv

Photoacoustic elastic oscillation and characterization

Photoacoustic imaging and sensing have been studied extensively to probe the optical absorption of biological tissue in multiple scales ranging from large organs to small molecules. However, its elastic oscillation characterization is rarely studied and has been an untapped area to be explored. In literature, photoacoustic signal induced by pulsed laser is commonly modelled as a bipolar "N-shape" pulse from an optical absorber. In this paper, the photoacoustic damped oscillation is predicted and modelled by an equivalent mass-spring system by treating the optical absorber as an elastic oscillator. The photoacoustic simulation incorporating the proposed oscillation model shows better agreement with the measured signal from an elastic phantom, than conventional photoacoustic simulation model. More interestingly, the photoacoustic damping oscillation effect could potentially be a useful characterization approach to evaluate biological tissue's mechanical properties in terms of relaxation time, peak number and ratio beyond optical absorption only, which is experimentally demonstrated in this paper.

preprint2014arXiv

Photoacoustics meets ultrasound: micro-Doppler photoacoustic effect and detection by ultrasound

In recent years, photoacoustics has attracted intensive research for both anatomical and functional biomedical imaging. However, the physical interaction between photoacoustic generated endogenous waves and an exogenous ultrasound wave is a largely unexplored area. Here, we report the initial results about the interaction of photoacoustic and external ultrasound waves leading to a micro-Doppler photoacoustic (mDPA) effect, which is experimentally observed and consistently modelled. It is based on a simultaneous excitation on the target with a pulsed laser and continuous wave (CW) ultrasound. The thermoelastically induced expansion will modulate the CW ultrasound and leads to transient Doppler frequency shift. The reported mDPA effect can be described as frequency modulation of the intense CW ultrasound carrier through photoacoustic vibrations. This technique may open the possibility to sensitively detect the photoacoustic vibration in deep optically and acoustically scattering medium, avoiding acoustic distortion that exists in state-of-the-art pulsed photoacoustic imaging systems.

preprint2014arXiv

Post-processing of the oblivious key in quantum private queries

Quantum private query (QPQ) is a kind of quantum protocols to protect both users' privacy in their communication. There is an interesting example, that is, Alice wants to buy one item from Bob's database, which is composed of a quantity of valuable messages. QPQ protocol is the communication procedure ensuring that Alice can get only one item from Bob, and at the same time, Bob cannot know which one was taken by Alice. Owing to its practicability, quantum-key-distribution-based QPQ has draw much attention in recent years. However, the post-processing of the key in such protocols, called oblivious key, remains far from being satisfactorily known. Especially, the error correction method for such special key is still missing. Here we focus on the post-processing of the oblivious key, including both dilution and error correction. On the one hand, we demonstrate that the previous dilution method, which greatly reduces the communication complexity, will bring Alice the chance to illegally obtain much additional information about Bob's database. Simulations show that by very limited queries Alice can obtain the whole database. On the other hand, we present an effective error-correction method for the oblivious key, which completes its post-processing and makes such QPQ more practical.

preprint2014arXiv

Zero mode in a strongly coupled quark gluon plasma

In connection with massless two-flavour QCD, we analyse the chiral symmetry restoring phase transition using three distinct gluon-quark vertices and two different assumptions about the long-range part of the quark-quark interaction. In each case, we solve the gap equation, locate the transition temperature T_c, and use the maximum entropy method to extract the dressed-quark spectral function at T>T_c. Our best estimate for the chiral transition temperature is T_c=(147 +/- 8)MeV; and the deconfinement transition is coincident. For temperatures markedly above T_c, we find a spectral density that is consistent with those produced using a hard thermal loop expansion, exhibiting both a normal and plasmino mode. On a domain T\in[T_c,T_s], with T_s approximately 1.5T_c, however, with each of the six kernels we considered, the spectral function contains a significant additional feature. Namely, it displays a third peak, associated with a zero mode, which is essentially nonperturbative in origin and dominates the spectral function at T=T_c. We suggest that the existence of this mode is a signal for the formation of a strongly-coupled quark-gluon plasma and that this strongly-interacting state of matter is likely a distinctive feature of the QCD phase transition.

preprint2013arXiv

Cryptanalysis of a multi-party quantum key agreement protocol with single particles

Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x] presented an efficient multi-party quantum key agreement (QKA) protocol by employing single particles and unitary operations. The aim of this protocol is to fairly and securely negotiate a secret session key among $N$ parties with a high qubit efficiency. In addition, the authors claimed that no participant can learn anything more than his/her prescribed output in this protocol, i.e., the sub-secret keys of the participants can be kept secret during the protocol. However, here we points out that the sub-secret of a participant in Sun et al.'s protocol can be eavesdropped by the two participants next to him/her. In addition, a certain number of dishonest participants can fully determine the final shared key in this protocol. Finally, we discuss the factors that should be considered when designing a really fair and secure QKA protocol.

preprint2013arXiv

Enhanced No-Go Theorem for Quantum Position Verification

Based on the instantaneous nonlocal quantum computation (INQC), Buhrman et al. proposed an excellent attack strategy to quantum position verification (QPV) protocols in 2011, and showed that, if the colluding adversaries are allowed to previously share unlimited entangled states, it is impossible to design an unconditionally secure QPV protocol in the previous model. Here, trying to overcome this no-go theorem, we find some assumptions in the INQC attack, which are implicit but essential for the success of this attack, and present three different QPV protocols where these assumptions are not satisfied. We show that for the general adversaries, who execute the attack operations at every common time slot or the time when they detect the arrival of the challenge signals from the verifiers, secure QPV is achievable. This implies practically secure QPV can be obtained even if the adversaries is allowed to share unlimited entanglement previously. Here by "practically" we mean that in a successful attack the adversaries need launch a new round of attack on the coming qubits with extremely high frequency so that none of the possible qubits, which may be sent at random time, will be missed. On the other side, using such Superdense INQC (SINQC) attack, the adversaries can still attack the proposed protocols successfully in theory. The particular attack strategies to our protocols are presented respectively. On this basis, we demonstrate the impossibility of secure QPV with looser assumptions, i.e. the enhanced no-go theorem for QPV.

preprint2013arXiv

Experimental demonstration of an ultra-thin three-dimensional thermal cloak

We report the first experimental realization of a three-dimensional thermal cloak shielding an air bubble in a bulk metal without disturbing external thermal flux. The cloak is made of a thin layer of homogeneous and isotropic material with specially designed three-dimensional manufacturing. The thickness of cloak is 100 um while the cloaked air bubble has a diameter of 1 cm, achieving the ratio between dimensions of the cloak and the cloaked object two orders smaller than previous thermal cloaks which were mainly realized in a two-dimensional geometry. This work can find applications in novel thermal devices in the three-dimensional physical space.

preprint2013arXiv

Identification time-delayed fractional order chaos with functional extrema model via differential evolution

In this paper, a novel inversion mechanism of functional extrema model via the differential evolution algorithms(DE), is proposed to exactly identify time-delays fractional order chaos systems. With the functional extrema model, the unknown time-delays, systematic parameters and fractional-orders of the fractional chaos, are converted into independent variables of a non-negative multiple modal functions' minimization, as a particular case of the functional extrema model's minimization. And the objective of the model is to find their optimal combinations by DE in the predefined intervals, such that the objective functional is minimized. Simulations are done to identify two classical time-delayed fractional chaos, Logistic and Chen system, both in cases with noise and without. The experiments' results show that the proposed inversion mechanism for time-delay fractional-order chaotic systems is a successful methods, with the advantages of high precision and robustness.

preprint2012arXiv

Identification of unknown parameters and orders via cuckoo search oriented statistically by differential evolution for non-commensurate fractional order chaotic systems

Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for non-commensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically the differential evolution (CSODE). Firstly, a novel Gao's mathematical model is put and analysed in three sub-models, not only for the unknown orders and parameters' identification but also for systems' reconstruction of fractional chaos systems with time-delays or not. Then the problems of fractional-order chaos' identification are converted into a multiple modal non-negative functions' minimization through a proper translation, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find best combinations of fractional-orders and systematic parameters of fractional order chaotic systems as special independent variables such that the objective function is minimized. Simulations are done to estimate a series of non-commensurate and hyper fractional chaotic systems with the new approaches based on CSODE, the cuckoo search and differential evolution respectively. The experiments' results show that the proposed identification mechanism based on CSODE for fractional-orders and parameters is a successful methods for fractional-order chaotic systems, with the advantages of high precision and robustness.

preprint2012arXiv

Inversion mechanism of uncertain orders and parameters for the non-commensurate and hyper fractional order chaotic systems via differential evolution

In this paper, a novel uncertain fractional-orders and parameters' inversion mechanism via the differential evolution algorithms (DE) with a general mathematical model is proposed for non-commensurate and hyper fractional chaotic systems. The problems of fractional-order chaos' inversion estimation are converted into multiple modal non-negative objective functions' minimization, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find optimal combinations of fractional-orders and systematic parameters by DE in the predefined intervals for fractional order chaotic systems such that the objective function is minimized. Simulations are done to estimate a series of non-commensurate and hyper fractional chaotic systems. The experiments' results show that the proposed inversion mechanism for fractional-order chaotic systems is a successful methods with the advantages of high precision and robustness.

preprint2012arXiv

Reconstruction mechanism with self-growing equations for hyper, improper and proper fractional chaotic systems through a novel non-Lyapunov approach

Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. However there exist basic hypotheses in traditional estimation methods, that is, the parameters and fractional orders are partially known or the known data series coincide with definite forms of fractional chaotic differential equations except some uncertain parameters and fractional orders. What should I do when these hypotheses do not exist? In this paper, a non-Lyapunov novel approach with a novel united mathematical model is proposed to reconstruct fractional chaotic systems, through the fractional-order differential equations self-growing mechanism by some genetic operations ideas independent of these hypotheses. And the cases of identifying the unknown parameters and fractional orders of fractional chaotic systems can be thought as special cases of the proposed united mathematical reconstruction method in non-Lyapunov way. The problems of fractional-order chaos reconstruction are converted into a multiple modal non-negative special functions' minimization through a proper translation, which takes fractional-order differential equations as its particular independent variables instead of the unknown parameters and fractional orders. And the objective is to find best form of fractional-order differential equations such that the objective function is minimized. Simulations are done to reconstruct a series of hyper and normal fractional chaotic systems. The experiments' results show that the proposed self-growing mechanism of fractional-order differential equations with genetic operations is a successful methods for fractional-order chaotic systems' reconstruction, with the advantages of high precision and robustness.

preprint2012arXiv

Self-Charged Graphene Battery Harvests Electricity from Thermal Energy of the Environment

The energy of ionic thermal motion presents universally, which is as high as 4 kJ\bullet kg-1\bullet K-1 in aqueous solution, where thermal velocity of ions is in the order of hundreds of meters per second at room temperature1,2. Moreover, the thermal velocity of ions can be maintained by the external environment, which means it is unlimited. However, little study has been reported on converting the ionic thermal energy into electricity. Here we present a graphene device with asymmetric electrodes configuration to capture such ionic thermal energy and convert it into electricity. An output voltage around 0.35 V was generated when the device was dipped into saturated CuCl2 solution, in which this value lasted over twenty days. A positive correlation between the open-circuit voltage and the temperature, as well as the cation concentration, was observed. Furthermore, we demonstrated that this finding is of practical value by lighting a commercial light-emitting diode up with six of such graphene devices connected in series. This finding provides a new way to understand the behavior of graphene at molecular scale and represents a huge breakthrough for the research of self-powered technology. Moreover, the finding will benefit quite a few applications, such as artificial organs, clean renewable energy and portable electronics.

preprint2012arXiv

Self-growing differential equations for hyperchaotic systems reconstruction by modified genetic programming in a novel non-Lyapunov approach

This paper has been withdrawn by the author due to a crucial sign error in equation 1.

preprint2011arXiv

Cryptanalysis of the arbitrated quantum signature protocols

As a new model for signing quantum message, arbitrated quantum signature (AQS) has recently received a lot of attention. In this paper we study the cryptanalysis of previous AQS protocols from the aspects of forgery and disavowal. We show that in these protocols the receiver Bob can realize existential forgery of the sender's signature under known message attack. Bob can even achieve universal forgery when the protocols are used to sign a classical message. Furthermore, the sender Alice can successfully disavow any of her signatures by simple attack. The attack strategies are described in detail and some discussions about the potential improvements of the protocols are given. Finally we also present several interesting topics in future study on AQS protocols.

preprint2011arXiv

Dense-Coding Attack on Three-Party Quantum Key Distribution Protocols

Cryptanalysis is an important branch in the study of cryptography, including both the classical cryptography and the quantum one. In this paper we analyze the security of two three-party quantum key distribution protocols (QKDPs) proposed recently, and point out that they are susceptible to a simple and effective attack, i.e. the dense-coding attack. It is shown that the eavesdropper Eve can totally obtain the session key by sending entangled qubits as the fake signal to Alice and performing collective measurements after Alice's encoding. The attack process is just like a dense-coding communication between Eve and Alice, where a special measurement basis is employed. Furthermore, this attack does not introduce any errors to the transmitted information and consequently will not be discovered by Alice and Bob. The attack strategy is described in detail and a proof for its correctness is given. At last, the root of this insecurity and a possible way to improve these protocols are discussed.

preprint2011arXiv

Fitting CoGeNT Modulation with an Inelastic, Isospin-Violating $Z'$ Model

We reanalyze the annual modulation data observed by CoGeNT experiment and show that if the annually modulated anomaly detected by CoGeNT is induced by collision between dark matter particle and nucleus, it can be fitted by a $Z'$ model with inelastic dark matter and isospin-violating interaction, and the constraint from XENON100 can be avoided. This $Z'$ model is strongly constrained by collider physics that the upper bound of the mass of $Z'$ is around twice of the mass of dark matter.

preprint2011arXiv

Flexible quantum private queries based on quantum key distribution

We present a flexible quantum-key-distribution-based protocol for quantum private queries. Similar to M. Jakobi et al's protocol [Phys. Rev. A 83, 022301 (2011)], it is loss tolerant, practical and robust against quantum memory attack. Furthermore, our protocol is more flexible and controllable. We show that, by adjusting the value of $θ$, the average number of the key bits Alice obtains can be located on any fixed value the users wanted for any database size. And the parameter $k$ is generally smaller (even $k=1$ can be achieved) when $θ<π/4$, which implies lower complexity of both quantum and classical communications. Furthermore, the users can choose a smaller $θ$ to get better database security, or a larger $θ$ to obtain a lower probability with which Bob can correctly guess the address of Alice's query.

preprint2010arXiv

Supporting Information for the Paper: Optimal Ternary Constant-Composition Codes of Weight Four and Distance Five, IEEE Trans. Inform. Theory, To Appear

Supporting Information for the Paper: Optimal Ternary Constant-Composition Codes of Weight Four and Distance Five, IEEE Trans. Inform. Theory, To Appear.

Fei Gao

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132 published item(s)

Pervasive Vulnerability Analysis and Defense for QKD-based Quantum Private Query

VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving

CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System

A Linear and Exact Algorithm for Whole-Body Collision Evaluation via Scale Optimization

Generalizing the intention-to-treat effect of an active control against placebo from historical placebo-controlled trials to an active-controlled trial: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study

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

ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

Automatic Parameter Adaptation for Quadrotor Trajectory Planning

Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles

Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors

Certifiably Optimal Mutual Localization with Anonymous Bearing Measurements

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

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

Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments

Dynamic Free-Space Roadmap for Safe Quadrotor Motion Planning

Elastic Tracker: A Spatio-temporal Trajectory Planner Flexible Aerial Tracking

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

Generating Large-Scale Trajectories Efficiently using Double Descriptions of Polynomials

Geometrically Constrained Trajectory Optimization for Multicopters

Global boundedness and Allee effect for a nonlocal time fractional p-Laplacian reaction-diffusion equation

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

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

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

Hand-held 3D Photoacoustic Imager with GPS

LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

Mode-selective Single-dipole Excitation and Controlled Routing of Guided Waves in a Multi-mode Topological Waveguide

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

NEDMP: Neural Enhanced Dynamic Message Passing

Parton distributions of light quarks and antiquarks in the proton

Passive Photoacoustic Effect

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

Photoacoustic Imaging Based on AlN MF-PMUT with Broadened Bandwidth

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

Real-Time Trajectory Planning for Aerial Perching

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

Solution of integrals with fractional Brownian motion for different Hurst indices

Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

Surface engineering for cellulose as a boosted Layer-by-Layer assembly: excellent flame retardancy and improved durability with introduction of bio-based "molecular glue"

The Visual-Inertial-Dynamical Multirotor Dataset

Topological metasurface: From passive toward active and beyond

Ultrafast coherent control of a hole spin qubit in a germanium quantum dot

Valence quark ratio in the proton

Bridging Unpaired Facial Photos And Sketches By Line-drawings

CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight

Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography

Defining and Estimating Subgroup Mediation Effects with Semi-Competing Risks Data

Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight

Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Phase structure of 2+1-flavour QCD and the magnetic equation of state

Quantum algorithm for Neighborhood Preserving Embedding

Quantum algorithms for anomaly detection using amplitude estimation

Radiation-tolerant high-entropy alloys via interstitial-solute-induced chemical heterogeneities

Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform

Statistical Considerations for Cross-Sectional HIV Incidence Estimation Based on Recency Test

Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

Chiral plasmons with twisted atomic bilayers

Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs

Detailed Proofs of Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

Dipole coupling of a tunable hole double quantum dot in germanium hut wire to a microwave resonator

Hole spin in tunable Ge hut wire double quantum dot

Making Robots Draw A Vivid Portrait In Two Minutes

Portable probe design for photoacoustic imaging in vivo

Quantum restricted Boltzmann machine universal for quantum computation

RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight

Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths

Self-controlled growth of highly uniform Ge/Si hut wires for scalable qubit devices

Thermal properties of $π$ and $ρ$ meson

Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

Pion and Kaon Structure at the Electron-Ion Collider

Acoustic higher-order topological insulator on a Kagome lattice

Coherence of Superpositions

Creating cat states in one-dimensional quantum walks using delocalized initial states

Distribution amplitudes of radially-excited pi- and K-mesons