arXiv subjects

Xuan Zhao

Publications and source records attributed to Xuan Zhao.

At least 73 records · Page 4Linked to original sources

arXiv2023

Error estimate of the nonuniform $L1$ type formula for the time fractional diffusion-wave equation

In this paper, a temporal nonuniform $L1$ type difference scheme is built up for the time fractional diffusion-wave equation with the help of the order reduction technique. The unconditional convergence of the nonuniform difference scheme is proved rigorously in $L^2$ norm. Our main tool is the discrete complementary convolution kernels with respect to the coefficient kernels of the L1 type formula. The positive definiteness of the complementary convolution kernels is shown to be vital to the stability and convergence. To the best of our knowledge, this property is proved at the first time on the nonuniform time meshes. Two numerical experiments are presented to verify the accuracy and the efficiency of the proposed numerical methods.

math.NA↗

arXiv2023

On some analytic properties of nabla tempered fractional calculus

Despite many applications regarding fractional calculus have been reported in literature, it is still unknown how to model some practical process. One major challenge in solving such a problem is that, the nonlocal property is needed while the infinite memory is undesired. Under this context, a new kind nabla fractional calculus accompanied by a tempered function is formulated. However, many properties of such fractional calculus needed to be discovered. From this, this paper gives particular emphasis to the topic. Some remarkable properties like the equivalence relation, the nabla Taylor formula, and the nabla Laplace transform for such nabla fractional calculus are developed and analyzed. It is believed that this work greatly enriches the mathematical theory of nabla tempered fractional calculus and provides high value and huge potential for further applications.

math.GM↗

arXiv2023

Error analysis of the implicit variable-step BDF2 method for the molecular beam epitaxial model with slope selection

We derive unconditionally stable and convergent variable-step BDF2 scheme for solving the MBE model with slope selection. The discrete orthogonal convolution kernels of the variable-step BDF2 method is commonly utilized recently for solving the phase field models. In this paper, we further prove some new inequalities, concerning the vector forms, for the kernels especially dealing with the nonlinear terms in the slope selection model. The convergence rate of the fully discrete scheme is proved to be two both in time and space in $L^2$ norm under the setting of the variable time steps. Energy dissipation law is proved rigorously with a modified energy by adding a small term to the discrete version of the original free energy functional. Two numerical examples including an adaptive time-stepping strategy are given to verify the convergence rate and the energy dissipation law.

math.NA↗

arXiv2022

Asymptotically compatible energy of variable-step fractional BDF2 formula for time-fractional Cahn-Hilliard model

A new discrete energy dissipation law of the variable-step fractional BDF2 (second-order backward differentiation formula) scheme is established for time-fractional Cahn-Hilliard model with the Caputo's fractional derivative of order $α\in(0,1)$, under a weak step-ratio constraint $0.4753\le τ_k/τ_{k-1}<r^*(α)$, where $τ_k$ is the $k$-th time-step size and $r^*(α)\ge4.660$ for $α\in(0,1)$.We propose a novel discrete gradient structure by a local-nonlocal splitting technique, that is, the fractional BDF2 formula is split into a local part analogue to the two-step backward differentiation formula of the first derivative and a nonlocal part analogue to the L1-type formula of the Caputo's derivative. More interestingly, in the sense of the limit $α\rightarrow1^-$, the discrete energy and the corresponding energy dissipation law are asymptotically compatible with the associated discrete energy and the energy dissipation law of the variable-step BDF2 method for the classical Cahn-Hilliard equation, respectively. Numerical examples with an adaptive stepping procedure are provided to demonstrate the accuracy and the effectiveness of our proposed method.

math.NA↗

arXiv2022

High Order Compact Finite Difference Methods for Non-Fickian Flows in Porous Media

In this work, fourth-order compact block-centered finite difference (CBCFD) schemes combined with the Crank-Nicolson discretization are constructed and analyzed for solving parabolic integro-differential type non-Fickian flows in one-dimensional and two-dimensional cases. Stability analyses of the constructed schemes are derived rigorously. We also obtain the optimal second-order convergence in temporal increment and the fourth-order convergence in spatial direction for both velocity and pressure. To verify the validity of the CBCFD schemes, we present some experiments to show that the numerical results are in agreement with our theoretical analysis.

math.AP↗

arXiv2022

Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation

With increasing digitalization, Artificial Intelligence (AI) is becoming ubiquitous. AI-based systems to identify, optimize, automate, and scale solutions to complex economic and societal problems are being proposed and implemented. This has motivated regulation efforts, including the Proposal of an EU AI Act. This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them, focusing on (but not limited to) the Proposal. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. Then, we map these perspectives along three axes of interests: (i) Standardization vs. Localization, (ii) Utilitarianism vs. Egalitarianism, and (iii) Consequential vs. Deontological ethics which leads us to identify a pattern of common arguments and tensions between these axes. Positioning the discussion within the axes of interest and with a focus on reconciling the key tensions, we identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.

cs.CY↗

arXiv2022

Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation

While achieving remarkable success for medical image segmentation, deep convolution neural networks (DCNNs) often fail to maintain their robustness when confronting test data with the novel distribution. To address such a drawback, the inductive bias of DCNNs is recently well-recognized. Specifically, DCNNs exhibit an inductive bias towards image style (e.g., superficial texture) rather than invariant content (e.g., object shapes). In this paper, we propose a method, named Invariant Content Synergistic Learning (ICSL), to improve the generalization ability of DCNNs on unseen datasets by controlling the inductive bias. First, ICSL mixes the style of training instances to perturb the training distribution. That is to say, more diverse domains or styles would be made available for training DCNNs. Based on the perturbed distribution, we carefully design a dual-branches invariant content synergistic learning strategy to prevent style-biased predictions and focus more on the invariant content. Extensive experimental results on two typical medical image segmentation tasks show that our approach performs better than state-of-the-art domain generalization methods.

eess.IV↗

arXiv2022

Pomeranchuk Effect and Tunable Quantum Phase Transitions in 3L-MoTe2/WSe2

Many sought-after exotic states of matter are known to emerge close to quantum phase transitions, such as quantum spin liquids (QSL) and unconventional superconductivity. It is thus desirable to experimentally explore systems that can be continuously tuned across these transitions. Here, we demonstrate such tunability and the electronic correlation effects in triangular moiré superlattices formed between trilayer MoTe$_2$ and monolayer WSe$_2$ (3L-MoTe$_2$/WSe$_2$). Through electric transport measurements, we firmly establish the Pomeranchuk effect observed at half filling of the first moiré subband, where increasing temperature paradoxically enhances charge localization. The system simultaneously exhibits the characteristic of a Fermi liquid with strongly renormalized effective mass, suggesting a correlated metal state. The state is highly susceptible to out-of-plane electric and magnetic fields, which induce a Lifshitz transition and a metal-insulator transition (MIT), respectively. It enables identification of a tricritical point in the quantum phase diagram at the base temperature. We explain the Lifshitz transition in terms of interlayer charge transfer by applying the vertical electric field, which leads to the emergence of a new Fermi surface and immediate suppression of the Pomeranchuk effect. The existence of quantum criticality in the magnetic filed induced MIT is supported by scaling behaviors of the resistance. Our work shows the 3L-MoTe$_2$/WSe$_2$ lies in the vicinity to the MIT point of the triangular lattice Hubbard model, rendering it a unique system to manifest the rich correlation effects at an intermediate interaction strength.

cond-mat.str-el↗

arXiv2022

"I Don't Want People to Look At Me Differently": Designing User-Defined Above-the-Neck Gestures for People with Upper Body Motor Impairments

Recent research proposed eyelid gestures for people with upper-body motor impairments (UMI) to interact with smartphones without finger touch. However, such eyelid gestures were designed by researchers. It remains unknown what eyelid gestures people with UMI would want and be able to perform. Moreover, other above-the-neck body parts (e.g., mouth, head) could be used to form more gestures. We conducted a user study in which 17 people with UMI designed above-the-neck gestures for 26 common commands on smartphones. We collected a total of 442 user-defined gestures involving the eyes, the mouth, and the head. Participants were more likely to make gestures with their eyes and preferred gestures that were simple, easy-to-remember, and less likely to draw attention from others. We further conducted a survey (N=24) to validate the usability and acceptance of these user-defined gestures. Results show that user-defined gestures were acceptable to both people with and without motor impairments.

cs.HC↗

arXiv2022

Track Recognition for the $ΔE-E$ Telescopes with Silicon Strip Detectors

For the high granularity and high energy resolution, Silicon Strip Detector (SSD) is widely applied in assembling telescopes to measure the charged particles in heavy ion reactions. In this paper, we present a novel method to achieve track recognition in the SSD telescopes of the Compact Spectrometer for Heavy Ion Experiment (CSHINE). Each telescope consists of a single-sided silicon strip detector (SSSSD) and a double-sided silicon strip detector (DSSSD) backed by $3 \times 3$ CsI(Tl) crystals. Detector calibration and track reconstruction are implemented. Special decoding algorithm is developed for the multi-track recognition procedure to deal with the multi-hit effect convoluted by charge sharing and the missing signals with certain probability. It is demonstrated that the track recognition efficiency of the method is approximately 90\% and 80\% for the DSSSD-CsI and SSSSD-DSSSD events, respectively.

physics.ins-det↗

arXiv2022

Reconstruction of Fission Events in Heavy Ion Reactions with CSHINE

We report the reconstruction method of the fast fission events in 25 MeV/u $^{86}$Kr +$^{208}$Pb reactions at the Compact Spectrometer for Heavy IoN Experiment (CSHINE). The fission fragments are measured by three large-area parallel plate avalanche counters, which can deliver the position and the arrival timing information of the fragments. The start timing information is given by the radio frequency of the cyclotron. Using the velocities of the two fission fragments, the fission events are reconstructed. The broadening of both the velocity distribution and the azimuthal difference of the fission fragments decrease with the folding angle, in accordance with the picture that fast fission occurs. The anisotropic angular distribution of the fission axis also reveals consistently the dynamic feature the fission events.

nucl-ex↗

arXiv2021

Homogeneous Low-Resolution Face Recognition Method based Correlation Features

Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images makes it difficult for high-resolution face recognition algorithms to extract effective feature information, Algorithms applied to high-resolution face recognition are difficult to migrate directly to low-resolution situations. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low-resolution surveillance cameras. This paper study on the Correlation Features-based Face Recognition (CoFFaR) method which using for homogeneous low-resolution surveillance videos, the theory, experimental details, and experimental results are elaborated in detail. The experimental results validate the effectiveness of the correlation features method that improves the accuracy of homogeneous face recognition in surveillance security scenarios.

cs.CV↗

arXiv2021

Properties of the fast fission and the coincident emissions of light charged particles in $^{40}$Ar + $^{197}$Au reactions at 30 MeV/u

The experiment of Ar+Au reactions at 30 MeV/u have been performed using the Compact Spectrometer for Heavy IoN Experiments (CSHINE) in phase I. The light-charged particles are measured by the silicon stripe telescopes in coincidence with the fission fragments recorded by the parallel plate avalanche counters. The distribution properties of the azimuth difference $Δϕ$ and the time-of-flight difference $ΔTOF$ of the fission fragments are presented varying the folding angles which represents the linear momentum transfer from the projectile to the reaction system. The relative abundance of the light charged particles in the fission events to the inclusive events is compared as a function of the laboratory angle $θ_{\rm lab}$ ranging from $18^\circ$ to $60^\circ$ in various folding angle windows. The angular evolution of the yield ratios of p/d and t/d in coincidence with fission fragments is investigated. In a relative comparison, tritons are more abundantly emitted at small angles, while protons are more abundant at large angles. The angular evolution of the neutron richness of the light-charged particles is consistent with the results obtained in previous inclusive experiments.

nucl-ex↗

arXiv2021

Reinforcement Learning Agent Training with Goals for Real World Tasks

Reinforcement Learning (RL) is a promising approach for solving various control, optimization, and sequential decision making tasks. However, designing reward functions for complex tasks (e.g., with multiple objectives and safety constraints) can be challenging for most users and usually requires multiple expensive trials (reward function hacking). In this paper we propose a specification language (Inkling Goal Specification) for complex control and optimization tasks, which is very close to natural language and allows a practitioner to focus on problem specification instead of reward function hacking. The core elements of our framework are: (i) mapping the high level language to a predicate temporal logic tailored to control and optimization tasks, (ii) a novel automaton-guided dense reward generation that can be used to drive RL algorithms, and (iii) a set of performance metrics to assess the behavior of the system. We include a set of experiments showing that the proposed method provides great ease of use to specify a wide range of real world tasks; and that the reward generated is able to drive the policy training to achieve the specified goal.

cs.AI↗

arXiv2021

Statistical Characteristics of Driver Acceleration Behavior and Its Probability Model

Naturalistic driving data were applied to study driver acceleration behaviour, and a probability model of the driver was proposed. First, the question of whether the database is large enough is resolved using kernel density estimation and Kullback-Liebler divergence. Next, the convergence database is utilised to achieve the bivariate acceleration distribution pattern. Subsequently, two probability models are proposed to explain the pattern. Finally, the statistical characteristics of the acceleration behaviours are studied to verify the probability models. The longitudinal and lateral acceleration behaviours always approximate a similar Pareto distribution. The braking, accelerating, and steering manoeuvres become more intense at first and then less intense as the velocity increases. These behaviours characteristics reveal the mechanism of the quadrangle bivariate acceleration distribution pattern. The bivariate acceleration behaviour of the driver will never reach a circle-shaped pattern. The bivariate Pareto distribution model can be applied to describe the bivariate acceleration behaviour of the driver.

cs.RO↗

arXiv2021

EvoK: Connecting loved ones through Heart Rate sharing

In this work, we present EvoK, a new way of sharing one's heart rate with feedback from their close contacts to alleviate social isolation and loneliness. EvoK consists of a pair of wearable prototype devices (i.e., sender and receiver). The sender is designed as a headband enabling continuous sensing of heart rate with aesthetic designs to maximize social acceptance. The receiver is designed as a wristwatch enabling unobtrusive receiving of the loved one's continuous heart rate with multi-modal notification systems.

cs.HC↗

arXiv2020

A Gradual Takeover Strategy of the Active Safety System

A gradual takeover strategy is proposed, in which the dynamic driving privilege assignment in real-time and the driving privilege gradual handover are realized. Firstly, the driving privilege assignment based on the risk level is achieved. The naturalistic driving data is applied to study the driver behavior during danger. TTC (time to collision) is defined as an obvious risk measure, whereas the time before the host vehicle has to brake assuming that the target vehicle is braking is defined as the potential risk measure, i.e. the time margin (TM). A risk assessment algorithm is proposed based on the obvious risk and potential risk. Secondly, the driving privilege gradual handover is realized. The non-cooperative MPC (model predictive control) is employed to resolve the conflicts between the driver and active safety system. The naturalistic driving data are applied to verify the effectiveness of the risk assessment algorithm, and the risk assessment algorithm performs better than TTC in the ROC (receiver operating characteristic). It is identified that the Nash equilibrium of the non-cooperative MPC can be achieved by using a non-iterative method. The driving privilege gradual handover is realized by using the confidence matrixes updating. The simulation verification shows that the gradual takeover strategy can achieve the driving privilege gradual handover between the driver and active safety system.

eess.SY↗

arXiv2020

Autonomous Social Distancing in Urban Environments using a Quadruped Robot

COVID-19 pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19. Against this backdrop, we propose that the surveillance robots can not only monitor but also promote social distancing. Robots can be flexibly deployed and they can take precautionary actions to remind people of practicing social distancing. In this paper, we introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd-aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to over-crowded pedestrians. We demonstrate and validate that our robot can be operated autonomously by conducting several experiments in various urban scenarios.

cs.RO↗