arXiv subjects

Qing Gao

Publications and source records attributed to Qing Gao.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects' feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It's subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand's position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient $R^{2}$ of grasping process of 0.911, a Root Mean Squared Error ($RMSE$) of 2.47\degree, a success rate of grasping of 95.43$\%$, and an average duration of grasping process of 3.07$\pm$0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35$\%$. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand's functionality and provides valuable insights for future research.

cs.RO↗

arXiv2024

Quantum Discrete Adiabatic Linear Solver based on Block Encoding and Eigenvalue Separator

Linear system solvers are widely used in scientific computing, with the primary goal of solving linear system problems. Classical iterative algorithms typically rely on the conjugate gradient method. The rise of quantum computing has spurred interest in quantum linear system problems (QLSP), particularly following the introduction of the HHL algorithm by Harrow et al. in 2009, which demonstrated the potential for exponential speedup compared to classical algorithms. However, the performance of the HHL algorithm is constrained by its dependence on the square of the condition number. To address this limitation, alternative approaches based on adiabatic quantum computing (AQC) have been proposed, which exhibit complexity scaling linearly with the condition number. AQC solves QLSP by smoothly varying the parameters of the Hamiltonian. However, this method suffers from high Hamiltonian simulation complexity. In response, this work designs new Hamiltonians and proposes a quantum discrete adiabatic linear solver based on block encoding and eigenvalue separation techniques (BEES-QDALS). This approach bypasses Hamiltonian simulation through a first-order approximation and leverages block encoding to achieve equivalent non-unitary operations on qubits. By comparing the fidelity of the original algorithm and BEES-QDALS when solving QLSP with a fixed number of steps, and the number of steps required to reach a target fidelity, it is found that BEES-QDALS significantly outperforms the original algorithm. Specifically, BEES-QDALS achieves higher fidelity with the same number of steps and requires fewer steps to reach the same target fidelity.

quant-ph↗

arXiv2024

Probing the equation of state of neutron stars with captured primordial black holes

Gravitational waves (GWs) from primordial black holes (PBHs) inspiraling within neutron stars (NSs) -- should they exist -- are detectable by ground-based detectors and offer a unique insight into the internal structure of NSs. To provide accurate templates for GW searches, we solve Einstein's equations within NSs and calculate the orbital motion of the captured PBH by considering dynamical friction, accretion, and gravitational radiation. Equipped with precise GW waveforms for PBHs inspiraling inside NSs, we find that the Einstein Telescope can differentiate between various equations of state for NSs. As PBHs inspiral deeper into NSs, the GW frequency rises near the surface, then decreases to a constant value deeper within NSs. The distinctive characteristics of GW frequency serve as the smoking gun for GW signals emitted by PBHs inspiraling inside NSs and can be used to probe the nuclear matter in the crust and core of NSs.

gr-qc↗

arXiv2024

On the evidence of dynamical dark energy

To elucidate the robustness of the baryon acoustic oscillation (BAO) data measured by the Dark Energy Spectroscopic Instrument (DESI) in capturing the dynamical behavior of dark energy, we assess the model dependence of the evidence for dynamical dark energy inferred from the DESI BAO data. While the DESI BAO data slightly tightens the constraints on model parameters and increases the tension between the Chevallier-Polarski-Linder (CPL) model and the $Λ$CDM model, we find that the influence of DESI BAO data on the constraint of $w_0$ is small in the SSLCPL model. In comparison to the CPL model, the tension with the $Λ$CDM model is reduced for the SSLCPL model, suggesting that the evidence for dynamical dark energy from DESI BAO data is dependent on cosmological models. The inclusion of spatial curvature has little impact on the results in the SSLCPL model.

astro-ph.CO↗

arXiv2024

ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation

Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for targeted detection grows, directed grey-box fuzzing (DGF) has become essential, focusing on specific vulnerabilities. The initial seed corpus, which consists of carefully selected input samples that the fuzzer uses as a starting point, is fundamental in determining the paths that the fuzzer explores. A well-designed seed corpus can guide the fuzzer more effectively towards critical areas of the code, improving the efficiency and success of the fuzzing process. Even with its importance, many works concentrate on refining guidance mechanisms while paying less attention to optimizing the initial seed corpus. In this paper, we introduce ISC4DGF, a novel approach to generating optimized initial seed corpus for DGF using Large Language Models (LLMs). By leveraging LLMs' deep software understanding and refined user inputs, ISC4DGF creates precise seed corpus that efficiently trigger specific vulnerabilities. Implemented on AFL and tested against state-of-the-art fuzzers like AFLGo, FairFuzz, and Entropic using the Magma benchmark, ISC4DGF achieved a 35.63x speedup and 616.10x fewer target reaches. Moreover, ISC4DGF focused on more effectively detecting target vulnerabilities, enhancing efficiency while operating with reduced code coverage.

cs.SE↗

arXiv2024

On the duality in constant-roll inflation

There is a duality in the observables $n_s$, $r$ and the inflaton potential between large and small $η_H$ for the constant-roll inflation if the slow-roll parameter $ε_H$ is negligible. In general, the duality between $η_H$ and $\barη_H$ does not hold for the background evolution of the inflation. For some particular solutions for the constant-roll inflation with $η_H$ being a constant, we find that in the small field approximation, the potential takes the quadratic form and it remains the same when the parameter $η_H$ changes to $\barη_H=3-η_H$. If the scalar field is small and the contribution of $ε_H$ is negligible, we find that there exists the logarithmic duality and the duality between large and small $η_H$ for the primordial curvature perturbation in inflationary models with the quadratic potential.

gr-qc↗

arXiv2024

D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement

Three-dimensional (3D) human pose estimation using a monocular camera has gained increasing attention due to its ease of implementation and the abundance of data available from daily life. However, owing to the inherent depth ambiguity in images, the accuracy of existing monocular camera-based 3D pose estimation methods remains unsatisfactory, and the estimated 3D poses usually include much noise. By observing the histogram of this noise, we find each dimension of the noise follows a certain distribution, which indicates the possibility for a neural network to learn the mapping between noisy poses and ground truth poses. In this work, in order to obtain more accurate 3D poses, a Diffusion-based 3D Pose Refiner (D3PRefiner) is proposed to refine the output of any existing 3D pose estimator. We first introduce a conditional multivariate Gaussian distribution to model the distribution of noisy 3D poses, using paired 2D poses and noisy 3D poses as conditions to achieve greater accuracy. Additionally, we leverage the architecture of current diffusion models to convert the distribution of noisy 3D poses into ground truth 3D poses. To evaluate the effectiveness of the proposed method, two state-of-the-art sequence-to-sequence 3D pose estimators are used as basic 3D pose estimation models, and the proposed method is evaluated on different types of 2D poses and different lengths of the input sequence. Experimental results demonstrate the proposed architecture can significantly improve the performance of current sequence-to-sequence 3D pose estimators, with a reduction of at least 10.3% in the mean per joint position error (MPJPE) and at least 11.0% in the Procrustes MPJPE (P-MPJPE).

cs.CV↗

arXiv2023

Scalar induced gravitational waves in light of Pulsar Timing Array data

The power-law parametrization for the energy density spectrum of gravitational wave (GW) background is a useful tool to study its physics and origin. While scalar induced secondary gravitational waves (SIGWs) from some particular models fit the signal detected by NANOGrav, Parkers Pulsar Timing Array, European Pulsar Timing Array, and Chinese Pulsar Timing Array collaborations better than GWs from supermassive black hole binaries (SMBHBs), we test the consistency of the data with the infrared part of SIGWs which is somewhat independent of models. Through Bayesian analysis, we show that the infrared parts of SIGWs fit the data better than GW background from SMBHBs. The results give tentative evidence for SIGWs.

gr-qc↗

arXiv2023

Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% to 15%.

cs.CV↗

arXiv2023

Testing alternative theories of gravity with space-based gravitational wave detectors

We use gravitational waves (GWs) from binary black holes (BBHs) and neutron stars inspiraling into intermediate-mass black holes to evaluate how accurately the future space-based GW detectors such as LISA, Taiji and TianQin and their combined networks can determine source parameters and constrain alternative theories of gravity. We find that, compared with single detector, the detector network can greatly improve the estimation errors of source parameters, especially the sky localization, but the improvement of the constraint on the graviton mass $m_g$ and the Brans-Dicke coupling constant $ω_{BD}$ is small. We also consider possible scalar modes existed in alternative theories of gravity and we find the inclusion of the scalar mode has little effect on the constraints on source parameters, $m_g$, and $ω_{BD}$ and the parametrized amplitude $A_B$ of scalar modes are small. For the constraint on the graviton mass, we consider both the effects in the GW phase and the transfer function due to the mass of graviton. With the network of LISA, Taiji and TianQin, we get the lower bound on the graviton Compton wavelength $λ_g\gtrsim 1.24 \times 10^{20}$ m for BBHs with masses $(10^6+10^7)M_\odot$, and $A_B< 5.7\times 10^{-4}$ for BBHs with masses $(1+2)\times 10^5M_\odot$; $ω_{BD}>6.11\times10^{6}$ for neutron star-black hole binary with masses $(1.4+400)M_{\odot}$.

gr-qc↗

arXiv2023

Detecting new fundamental fields with Pulsar Timing Arrays

Strong evidence of the existence of the Stochastic Gravitational-Wave Background (SGWB) has been reported by the NANOGrav, PPTA, EPTA and CPTA collaborations. The Bayesian posteriors of the Gravitational-Wave Background (GWB) amplitude and spectrum are compatible with current astrophysical predictions for the GWB from the population of supermassive black hole binaries (SMBHBs). In this paper, we discuss the corrections arising from the extra scalar or vector radiation to the characteristic dimensionless strain in PTA experiments and explore the possibility to detect charges surrounding massive black holes, which could give rise to SGWB with vector or scalar polarizations. The parametrized frequency-dependent characteristic dimensionless strain is used to take a Bayesian analysis and the Bayes factor is also computed for charged and neutral SMBHBs. The Bayesian posterior of GWB tensor amplitude is $\log_{10} A_T=-14.85^{+0.26}_{-0.38}$ and spectral exponent $α=-0.60^{+0.32}_{-0.36}$. The Bayesian posterior for vector or scalar amplitude $A_{V, S}$ is nearly flat and there is nearly no constraint from the current observation data. The Bayesian factor is $0.71$ far less than 100, so the current observation can not support the existence of the charged SMBHB.

gr-qc↗

arXiv2023

An integral sliding-mode parallel control approach for general nonlinear systems via piecewise affine linear models

The fundamental problem of stabilizing a general nonaffine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this article. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a nonaffine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semiglobally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: (i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches; (ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system; and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach.

eess.SY↗

arXiv2022

Constraint on the mass of graviton with gravitational waves

We consider the effects of the mass of graviton on both the waveform of gravitational waves and the antenna response to gravitational waves. We find the effect on the response function is negligible for small mass. By using the Fisher matrix method, we make parameter estimations with space-based gravitational wave detectors for massive black hole binaries in massive gravity theory. The wavelength of massive graviton is constrained to be $λ_g\gtrsim 10^{17}$ m and the mass is constrained to be $m_g\lesssim 10^{-60}$ kg by one year's observation of massive black hole binaries with space-based gravitational wave detectors.

gr-qc↗

arXiv2022

Deep Depth Completion from Extremely Sparse Data: A Survey

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on three widely used benchmarks, including indoor and outdoor datasets. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions.

cs.CV↗

arXiv2022

Robust optimization for quantum reinforcement learning control using partial observations

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing.

quant-ph↗

arXiv2022

Primordial black holes and secondary gravitational waves from natural inflation

The production of primordial black hole (PBH) dark matter (DM) and the generation of scalar induced secondary gravitational waves by using the enhancement mechanism with a peak function in the non-canonical kinetic term in natural inflation is discussed. We show explicitly that the power spectrum for the primordial curvature perturbation can be enhanced at $10^{12}$ Mpc$^{-1}$, $10^{8}$ Mpc$^{-1}$ and $10^{5}$ Mpc$^{-1}$ by adjusting the model parameters. With the enhanced primordial curvature perturbations, we show the production of PBH DM with peak masses around $10^{-13}\ M_{\odot}$, the Earth's mass and the stellar mass, and the generation of scalar induced gravitational waves (SIGWs) with peak frequencies around mHz, $10^{-6}$ Hz and nHz, respectively. The PBHs with the mass scale $10^{-13}\ M_{\odot}$ can make up almost all the DM and the associated SIGWs is testable by spaced based gravitational wave observatory.

gr-qc↗

arXiv2021

On the linear transformation between inertial frames

In the derivation of Lorentz transformation, linear transformation between inertial frames is one of the most important steps. In teaching special relativity, we usually use the homogeneity and isotropy of spacetime to argue that the transformation must be linear transformation without providing any rigorous detail. Here in the first time we provide a solid mathematical proof of the argument that the transformation between two inertial frames must be linear because of the homogeneity and isotropy of spacetime.

physics.gen-ph↗

arXiv2021

A Quantum Convolutional Neural Network for Image Classification

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.

quant-ph↗