arXiv subjects

Yufei Zhang

Publications and source records attributed to Yufei Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Gain-Bandwidth-Product-Induced Technical Bound in Time Transfer System without Inline Amplifiers

Time transfer plays a dispensable role in many fields including navigation and positioning, geodesy, and fundamental tests. However, in certain scenarios where effective relay node deployment is often not feasible, such as harsh environments with extremely poor infrastructure and emergency conditions, effective time transfer without inline amplifiers becomes crucial. In this situation, the maximum transmission distance is limited by the receiver's measurement capability, particularly its ability to amplify the signal. Here we propose a theoretical model, giving a technical lower bound of the detected signal stability at different transmission distances, induced by limited gain-bandwidth products. The results under common gain-bandwidth products show that while for shorter transmission distances, stability is mainly limited by the background noise of the time interval counter, for longer distances reaching the scale of 300 kilometers, the technical lower bound is below the level of 10 nanoseconds without any inline amplification devices. Therefore, the given technical bound offers guidance on managing the balance between distance and stability, together with the optimization of the receiver in long-distance time transfer without inline amplification.

physics.ins-det↗

arXiv2024

The domination polynomial of powers of paths and cycles

A dominating set in a graph is a set of vertices with the property that every vertex in the graph is either in the set or adjacent to something in the set. The domination sequence of the graph is the sequence whose $k$th term is the number of dominating sets of size $k$. Alikhani and Peng have conjectured that the domination sequence of every graph is unimodal. Beaton and Brown verified this conjecture for paths and cycles. Here we extend this to arbitrary powers of paths and cycles.

math.CO↗

arXiv2024

Continuous-Time Dynamic Decision Making with Costly Information

We consider a continuous-time linear-quadratic Gaussian control problem with partial observations and costly information acquisition. More precisely, we assume the drift of the state process to be governed by an unobservable Ornstein--Uhlenbeck process. The decision maker can additionally acquire information on the hidden state by conducting costly tests, thereby augmenting the available information. Combining the Kalman--Bucy filter with a dynamic programming approach, we show that the problem can be reduced to a deterministic control problem for the conditional variance of the unobservable state. Optimal controls and value functions are derived in a semi-explicit form, and we present an extensive study of the qualitative properties of the model. We demonstrate that both the optimal cost and the marginal cost increase with model uncertainty. We identify a critical threshold: below this level, it is optimal not to acquire additional information, whereas above this threshold, continuous information acquisition is optimal, with the rate increasing as uncertainty grows. For quadratic information costs, we derive the precise asymptotic behavior of the acquisition rate as uncertainty approaches zero and infinity.

math.OC↗

arXiv2024

Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidance

Fully-supervised monocular 3D hand reconstruction is often difficult because capturing the requisite 3D data entails deploying specialized equipment in a controlled environment. We introduce a weakly-supervised method that avoids such requirements by leveraging fundamental principles well-established in the understanding of the human hand's unique structure and functionality. Specifically, we systematically study hand knowledge from different sources, including biomechanics, functional anatomy, and physics. We effectively incorporate these valuable foundational insights into 3D hand reconstruction models through an appropriate set of differentiable training losses. This enables training solely with readily-obtainable 2D hand landmark annotations and eliminates the need for expensive 3D supervision. Moreover, we explicitly model the uncertainty that is inherent in image observations. We enhance the training process by exploiting a simple yet effective Negative Log Likelihood (NLL) loss that incorporates uncertainty into the loss function. Through extensive experiments, we demonstrate that our method significantly outperforms state-of-the-art weakly-supervised methods. For example, our method achieves nearly a 21\% performance improvement on the widely adopted FreiHAND dataset.

cs.CV↗

arXiv2024

Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence Models

Flow topology optimization (ToOpt) based on Darcy's source term is widely used in the field of ToOpt. It has a high degree of freedom and requires no initial configuration, making it suitable for conceptual aerodynamic design. Two problems of ToOpt are addressed in this paper to apply the ToOpt method to high-Reynolds-number turbulent flow. First, the relationship between the minimum magnitude of Darcy's source term needed to model the solid and the characteristic variables of the flow (freestream velocity, length scale and fluid viscosity) at high Reynolds numbers is quantitively studied. A strategy for setting Darcy's source term is then proposed. Second, the modified Launder - Sharma k-e (LSKE) model with modification terms related to Darcy's source is developed. The ToOpt of a low-drag profile in turbulent flow is studied using the new model. The model is validated to reflect the influence of solids on turbulence even at Reynolds numbers as high as one million. Finally, a wall-distance computation method that can recognize the solid modeled by Darcy's source term is developed and integrated into the modified shear stress transport (SST) turbulence model. The ToOpt of a rotor-like geometry, which is of great importance in aerodynamic design, is conducted using the modified SST model, proving the model's ability to handle the ToOpt of turbomachinery.

physics.flu-dyn↗

arXiv2024

Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task

Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention has been paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAIT, paving the way towards more effective and insightful KG analysis techniques.

cs.LG↗

arXiv2024

$ε$-Policy Gradient for Online Pricing

Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an $ε$-policy gradient algorithm for the online pricing learning task. The algorithm extends $ε$-greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability $ε$ and the exploitation cost in terms of the gradient descent optimization and gradient estimation errors. The algorithm achieves an expected regret of order $\mathcal{O}(\sqrt{T})$ (up to a logarithmic factor) over $T$ trials.

cs.LG↗

arXiv2024

A fast iterative PDE-based algorithm for feedback controls of nonsmooth mean-field control problems

We propose a PDE-based accelerated gradient algorithm for optimal feedback controls of McKean-Vlasov dynamics that involve mean-field interactions both in the state and action. The method exploits a forward-backward splitting approach and iteratively refines the approximate controls based on the gradients of smooth costs, the proximal maps of nonsmooth costs, and dynamically updated momentum parameters. At each step, the state dynamics is approximated via a particle system, and the required gradient is evaluated through a coupled system of nonlocal linear PDEs. The latter is solved by finite difference approximation or neural network-based residual approximation, depending on the state dimension. We present exhaustive numerical experiments for low and high-dimensional mean-field control problems, including sparse stabilization of stochastic Cucker-Smale models, which reveal that our algorithm captures important structures of the optimal feedback control and achieves a robust performance with respect to parameter perturbation.

math.OC↗

arXiv2024

PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos

While current methods have shown promising progress on estimating 3D human motion from monocular videos, their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper, we introduce Physics-aware Pretrained Transformer (PhysPT), which improves kinematics-based motion estimates and infers motion forces. PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner. Moreover, it incorporates physics principles governing human motion. Specifically, we build a physics-based body representation and contact force model. We leverage them to impose novel physics-inspired training losses (i.e., force loss, contact loss, and Euler-Lagrange loss), enabling PhysPT to capture physical properties of the human body and the forces it experiences. Experiments demonstrate that, once trained, PhysPT can be directly applied to kinematics-based estimates to significantly enhance their physical plausibility and generate favourable motion forces. Furthermore, we show that these physically meaningful quantities translate into improved accuracy of an important downstream task: human action recognition.

cs.CV↗

arXiv2024

Minkowski Functionals of Large-Scale Structure as a Probe of Modified Gravity

In this study, we explore the potential of utilizing the four Minkowski functionals, which can fully describe the morphological properties of the large-scale structures, as a robust tool for investigating the modified gravity, particularly on non-linear and quasi-linear scales. With the assistance of the N-body simulation, we employ the Minkowski functionals to probe the Hu-Sawicki f(R) gravity model. The focus is on understanding the morphorlogical properties extracted by the Minkowski functionals and their sensitivity to modified gravity. Our analysis involves a comprehensive examination of the cosmic variance arising from finite simulation volumes. By systematically varying smoothing scales and redshifts, we quantify the information encoded in the Minkowski functionals measured from the dark-matter density field. The goal is to assess the capacity of the Minkowksi functionals to constrain the model and explore potential improvements through their combination. Additionally, we investigate the impact of using biased tracers such as dark matter halos and the halo occupation distribution galaxies on the modified gravity signatures within the Minkowksi functionals of the LSS. Furthermore, we evaluate the influence of the redshift space distortion on the observed results. In summary, our study suggests that the Minkowski functionals of the large-scale structures hold promise as a stringent tool for constraining modified gravity and offer valuable insights into the morphological features of the cosmic web.

astro-ph.CO↗

arXiv2024

Convergence of policy gradient methods for finite-horizon exploratory linear-quadratic control problems

We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the state variable and whose covariance is state-independent. Contrary to discrete-time problems, the cost is noncoercive in the policy and not all descent directions lead to bounded iterates. We propose geometry-aware gradient descents for the mean and covariance of the policy using the Fisher geometry and the Bures-Wasserstein geometry, respectively. The policy iterates are shown to satisfy an a-priori bound, and converge globally to the optimal policy with a linear rate. We further propose a novel PG method with discrete-time policies. The algorithm leverages the continuous-time analysis, and achieves a robust linear convergence across different action frequencies. A numerical experiment confirms the convergence and robustness of the proposed algorithm.

math.OC↗

arXiv2024

Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design

Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features.

physics.flu-dyn↗

arXiv2024

Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model

The transonic buffet is a detrimental phenomenon occurs on supercritical airfoils and limits aircraft's operating envelope. Traditional methods for predicting buffet onset rely on multiple computational fluid dynamics simulations to assess a series of airfoil flowfields and then apply criteria to them, which is slow and hinders optimization efforts. This article introduces an innovative approach for rapid buffet onset prediction. A machine-learning flowfield prediction model is pre-trained on a large database and then deployed offline to replace simulations in the buffet prediction process for new airfoil designs. Unlike using a model to directly predict buffet onset, the proposed technique offers better visualization capabilities by providing users with intuitive flowfield outputs. It also demonstrates superior generalization ability, evidenced by a 32.5% reduction in average buffet onset prediction error on the testing dataset. The method is utilized to optimize the buffet performance of 11 distinct airfoils within and outside the training dataset. The optimization results are verified with simulations and proved to yield improved samples across all cases. It is affirmed the pre-trained flowfield prediction model can be applied to accelerate aerodynamic shape optimization, while further work still needs to raise its reliability for this safety-critical task.

physics.flu-dyn↗

arXiv2024

Aerodynamic Prediction of a CRM High-lift Configuration using a modified three equation turbulence mode

Aerodynamic simulations were carried out in the study presented in this paper focusing on the stall performance of the High-Lift Common Research Model obtained from the fourth AIAA High-Lift Prediction Workshop. Various turbulence models of Reynolds-average Navier-Stokes simulations are analyzed. A modified version of the transitional k-(v^2 )-ω model developed to enhance stall prediction accuracy for high-lift configurations with a nacelle chine. The vortex generator, three-element airfoil, and high-lift model are numerically simulated. The results reveal that implementing a k-(v^2 )-ω model with separation shear layer fixed notably enhances the stall prediction behavior for both the three-element airfoil and high-lift configuration without affecting the prediction of the vortex strength of a vortex generator. Moreover, incorporating rotation correction into the SPF k-(v^2 )-ω model improves the prediction of vortex strength and further enhances stall prediction for the high-lift configuration. The relative error in predicting the maximum lift coefficient is less than 5% of the experimental data. The study also investigated the impact of the nacelle chine on the stall behavior of the high-lift configuration. The results demonstrate that the chine vortex can mitigate the adverse effects of the nacelle/pylon vortex system and increase the maximum lift coefficient.

physics.flu-dyn↗

arXiv2024

Dynamics of Superconformal Axion: Quality and Scalegenesis

We explore a dynamical mechanism to realize the emergence of a global $U(1)_{\rm PQ}$ symmetry and its spontaneous breaking at an intermediate scale for an axion solution to the strong CP problem. Such a dynamics is provided by a new supersymmetric QCD near the middle of conformal window that couples to fields spontaneously breaking the $U(1)_{\rm PQ}$ symmetry. A large anomalous dimension of the $U(1)_{\rm PQ}$ breaking fields leads to the suppression of explicit $U(1)_{\rm PQ}$-violating higher dimensional operators. The $U(1)_{\rm PQ}$ breaking vacuum is generated at a scale hierarchically smaller than the Planck scale by a non-perturbative effect. The $U(1)_{\rm PQ}$ breaking drives the conformal breaking, and all the new quarks become massive. The axion potential is generated by the ordinary color $SU(3)_C$ effect as the $U(1)_{\rm PQ}$ symmetry is only anomalous under the $SU(3)_C$. The saxion direction is stabilized by supersymmetry breaking and cosmologically harmless.

hep-ph↗

arXiv2023

UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts

Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application. However, most Med-VLP models learn task-specific representations independently from scratch, thereby leading to great inflexibility when they work across multiple fine-tuning tasks. In this work, we propose UniDCP, a Unified medical vision-language model with Dynamic Cross-modal learnable Prompts, which can be plastically applied to multiple medical vision-language tasks. Specifically, we explicitly construct a unified framework to harmonize diverse inputs from multiple pretraining tasks by leveraging cross-modal prompts for unification, which accordingly can accommodate heterogeneous medical fine-tuning tasks. Furthermore, we conceive a dynamic cross-modal prompt optimizing strategy that optimizes the prompts within the shareable space for implicitly processing the shareable clinic knowledge. UniDCP is the first Med-VLP model capable of performing all 8 medical uni-modal and cross-modal tasks over 14 corresponding datasets, consistently yielding superior results over diverse state-of-the-art methods.

cs.CV↗

arXiv2023

Hundred-Femtosecond-Level Concise Optical Time Delay Measurement System Based on Linear Optical Sampling

Fiber-delay measurement is one of the key fundamental technologies in numerous fields. Here we propose and experimentally demonstrate a high-precision and concise optical time delay measurement system based on the technique of linear optical sampling, reaching the precision better than 100 fs under averaging. The use of only two optical frequency combs without locking the carrier-envelope-offset frequency greatly simplifies the structure of the time-delay measurement system. We also experimentally investigate the current limitations on the precision of the system. The timing jitter noises of two sources are mainly non-common mode, and are both restricted to the frequency sources. Our results indicate that the proposed device can measure fiber length fluctuations below 10 $μ{\rm{m}}$, paving the way for further analyses of the external disturbances on the fiber link.

physics.optics↗

arXiv2023

Time-interval Measurement with Linear Optical Sampling at the Femtosecond Level

High-precision time-interval measurement is a fundamental technique in many advanced applications, including time and distance metrology, particle physics, and ultra-precision machining. However, many of these applications are confined by the imprecise time-interval measurement of electrical signals, restricting the performance of the ultimate system to a few picoseconds, which limits ultra-high-precision applications. Here, we demonstrate an optical means of the time-interval measurement of electrical signals that can successfully achieve femtosecond (fs)-level precision. The setup is established using the optical-frequency-comb (OFC)-based linear optical sampling technique to realize timescale-stretched measurement. We achieve the measurement precision of 82 fs for a single LOS scan measurement and 3.05 fs for the 100-times average with post-processing, which is three orders of magnitude higher than the results of older electrical methods. The high-precision time interval measurement of electrical signals can substantially improve precision measurement technologies.

physics.optics↗