BZPEERReading, trust and collaboration for researchDiscover
Workspaces
Network
Opportunities
Account
Researcher profile
Rui Chen contributes to research discovery and scholarly infrastructure.
Trust snapshot
Actions
Identity and collaboration
Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.
Log in to claimDirect collaboration
Claim this author entity first to unlock direct invitations.
Research graph
Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.
BZPEER is loading the nearby papers, people, topics and institutions for this page.
Published work
In this paper, we generalize the method of Gan-Ichino and Atobe in [GI16][A18] to the field of real numbers and prove the basic tempered case of the local Gan-Gross-Prasad conjecture for Fourier-Jacobi models of symplectic-metaplectic groups, based on the tempered case of the conjecture for Bessel models proved in [CL22] by Chen-Luo.
Motivated by the recent experimental realization of the half-quantized Hall effect phase in a three-dimensional (3D) semi-magnetic topological insulator [M. Mogi et al., Nature Physics 18, 390 (2022)], we propose a scheme for realizing the half-quantized Hall effect and axion insulator in experimentally mature 3D topological insulator heterostructures. Our approach involves optically pumping and/or magnetically doping the topological insulator surface, such as to break time reversal and gap out the Dirac cones. By toggling between left and right circularly polarized optical pumping, the sign of the half-integer Hall conductance from each of the surface Dirac cones can be controlled, such as to yield half-quantized ($0+1/2$), axion ($-1/2+1/2=0$), and Chern ($1/2+1/2=1$) insulator phases. We substantiate our results based on detailed band structure and Berry curvature numerics on the Floquet Hamiltonian in the high-frequency limit. Our paper showcases how topological phases can be obtained through mature experimental approaches such as magnetic layer doping and circularly polarized laser pumping and opens up potential device applications such as a polarization chirality-controlled topological transistor.
In this work, we investigate the interactions between an excited anti-charm meson doublet $(\bar{D}_1, \bar{D}_2^*)$ and ground-state octet baryons $(N, Λ, Σ, Ξ)$ with the aim of identifying possible molecular pentaquark states. A systematic analysis is performed within the one-boson-exchange model, which incorporates both $S$-wave and $P$-wave interactions, $S$-$D$ wave mixing, and coupled-channel effects. By solving the Schrödinger equations, we can predict a rich spectrum of loosely bound anti-charm molecular pentaquarks with strangeness $|S| = 0, 1, 2$. Our results provide specific quantum number assignments and mass range predictions to guide future experimental searches at facilities such as LHCb and Belle II. The discovery of such states would significantly enrich the hadron spectrum and serve as a critical test of theoretical models for hadronic interactions.
Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies on closed-source models~(Veo~3, Nano Banana Pro) or requires task-specific instruction tuning, leaving open whether publicly available image-editing models possess zero-shot vision abilities out of the box. We conduct a systematic evaluation of three open-source image-editing models -- Qwen-Image-Edit, FireRed-Image-Edit, and LongCat-Image-Edit -- on dense visual prediction tasks \emph{without any fine-tuning}. We benchmark monocular depth estimation on NYUv2 and DIODE, surface normal estimation on NYUv2, and semantic segmentation on Cityscapes, covering both geometric and semantic scene understanding. Results show that open-source image-editing models exhibit non-trivial zero-shot visual understanding. On NYUv2 surface normals, FireRed-Image-Edit achieves a mean angular error of $17.69^\circ$, surpassing the fine-tuned Marigold ($20.86^\circ$) and matching the instruction-tuned Vision Banana ($17.78^\circ$) without any task-specific training. On NYUv2 depth estimation, LongCat-Image-Edit obtains $δ_1{=}0.822$ with affine alignment, and Qwen-Image-Edit leads on DIODE Indoor ($δ_1{=}0.868$). On Cityscapes semantic segmentation, Qwen-Image-Edit reaches 25.7 mIoU at the 19-class level and 49.5 mIoU at a coarser 7-category level. By comparing three independently trained editors, we test whether zero-shot vision ability is an emergent property of image-editing pretraining rather than a model-specific artifact. Code, evaluation scripts, and all results are publicly released to serve as a reproducible baseline for future work.
This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robots pose significant safety risks due to their physical capabilities of interacting with complex environments. The physical structures of humanoid robots further add complexity to the design of general safety solutions. To facilitate safe deployment of complex robot systems, SPARK can be used as a toolbox that comes with state-of-the-art safe control algorithms in a modular and composable robot control framework. Users can easily configure safety criteria and sensitivity levels to optimize the balance between safety and performance. To accelerate humanoid safety research and development, SPARK provides simulation benchmarks that compare safety approaches in a variety of environments, tasks, and robot models. Furthermore, SPARK allows quick deployment of synthesized safe controllers on real robots. For hardware deployment, SPARK supports Apple Vision Pro (AVP) or a Motion Capture System as external sensors, while offering interfaces for seamless integration with alternative hardware setups at the same time. This paper demonstrates SPARK's capability with both simulation experiments and case studies with a Unitree G1 humanoid robot. Leveraging these advantages of SPARK, users and researchers can significantly improve the safety of their humanoid systems as well as accelerate relevant research. The open source code is available at: https://github.com/intelligent-control-lab/spark.
In this paper, we focus on the simulation of active stereovision depth sensors, which are popular in both academic and industry communities. Inspired by the underlying mechanism of the sensors, we designed a fully physics-grounded simulation pipeline that includes material acquisition, ray-tracing-based infrared (IR) image rendering, IR noise simulation, and depth estimation. The pipeline is able to generate depth maps with material-dependent error patterns similar to a real depth sensor in real time. We conduct real experiments to show that perception algorithms and reinforcement learning policies trained in our simulation platform could transfer well to the real-world test cases without any fine-tuning. Furthermore, due to the high degree of realism of this simulation, our depth sensor simulator can be used as a convenient testbed to evaluate the algorithm performance in the real world, which will largely reduce the human effort in developing robotic algorithms. The entire pipeline has been integrated into the SAPIEN simulator and is open-sourced to promote the research of vision and robotics communities.
In recent years, Floquet engineering has attracted considerable attention as a promising approach for tuning topological phase transitions. In this work, we investigate the effects of high-frequency time-periodic driving in a four-dimensional (4D) topological insulator, focusing on topological phase transitions at the off-resonant quasienergy gap. The 4D topological insulator hosts gapless three-dimensional boundary states characterized by the second Chern number $C_{2}$. We demonstrate that the second Chern number of 4D topological insulators can be modulated by tuning the amplitude of time-periodic driving. This includes transitions from a topological phase with $C_{2}=\pm3$ to another topological phase with $C_{2}=\pm1$, or to a topological phase with an even second Chern number $C_{2}=\pm2$ which is absent in the 4D static system. Finally, the approximation theory in the high-frequency limit further confirms the numerical conclusions.
Delicate industrial insertion tasks (e.g., PC board assembly) remain challenging for industrial robots. The challenges include low error tolerance, delicacy of the components, and large task variations with respect to the components to be inserted. To deliver a feasible robotic solution for these insertion tasks, we also need to account for hardware limits of existing robotic systems and minimize the integration effort. This paper proposes a composable framework for efficient integration of a safe insertion policy on existing robotic platforms to accomplish these insertion tasks. The policy has an interpretable modularized design and can be learned efficiently on hardware and transferred to new tasks easily. In particular, the policy includes a safe insertion agent as a baseline policy for insertion, an optimal configurable Cartesian tracker as an interface to robot hardware, a probabilistic inference module to handle component variety and insertion errors, and a safe learning module to optimize the parameters in the aforementioned modules to achieve the best performance on designated hardware. The experiment results on a UR10 robot show that the proposed framework achieves safety (for the delicacy of components), accuracy (for low tolerance), robustness (against perception error and component defection), adaptability and transferability (for task variations), as well as task efficiency during execution plus data and time efficiency during learning.
Programmable photonic integrated circuits (PICs) have recently gained significant interest due to their potential in creating next-generation technologies ranging from artificial neural networks and microwave photonics to quantum information processing. The fundamental building block of such programmable PICs is a tunable 2 x 2 switch, traditionally controlled by the thermo-optic or free-carrier dispersion. Yet, these implementations are power-hungry, volatile, and have a large footprint (typically > 100 um). Therefore, a truly 'set-and-forget' type 2 x 2 switch with zero static power consumption is highly desirable for large-scale PICs. Here, we report a broadband nonvolatile electrically programmable 2 x 2 silicon photonic switch based on the phase-change material Ge2Sb2Te5. The directional coupler switch exhibits a compact coupling length (64 um), small insertion loss (<2 dB), and minimal crosstalk (<-8 dB) across the entire telecommunication C-band while maintaining a record-high endurance of over 2,800 switching cycles. This demonstrated switch constitutes a critical component for realizing future generic programmable silicon photonic systems.
Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.
In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from a representative target sample. We develop a weighting approach based on summary-level information from the target sample to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within a source sample are calibrated by the summary-level information of the target sample. Our approach also seeks additional covariate balance between the treated and control groups in the source sample. We study the asymptotic behavior of the corresponding weighted estimator for the target population ATE under a wide range of conditions. The theoretical implications are confirmed in simulation studies and a real data application.
In this work, we adopt the one-boson-exchange model to study the $D^{(*)}K^{(*)}$ interactions. With the same parameters, we can simultaneously reproduce the masses of the $D_{s0}(2317)$, $D_{s1}(2460)$, and $T_{c\bar{s}}^{a0(++)}(2900)$ recently observed by the LHCb collaboration in the hadronic molecular picture, where the $D_{s0}(2317)$, $D_{s1}(2460)$, and $T_{c\bar{s}}^{a0(++)}(2900)$ are regarded as the $DK[I(J^P)=0(0^+)]$, $D^*K[0(1^+)]$, and $D^*K^*[1(0^+)]$ charmed-strange molecular states, respectively. In addition, we extend to study the $Λ_cK^{(*)}$ and $Σ_cK^{(*)}$ interactions and predict two possible charmed-strange molecular pentaquarks, the single $Σ_cK^*$ state with $I(J^P)=1/2(1/2^-)$ and $3/2(3/2^-)$. After considering the coupled channel effects, our results show that the coupled $Λ_cK^*/Σ_cK^*$ molecular state with $I(J^P)=1/2(1/2^-)$ and the coupled $Σ_cK/Λ_cK^*$ molecular state with $I(J^P)=1/2(1/2^-)$ can be good hadronic molecular candidates, the $Λ_cK^*({}^2S_{1/2})$ and $Σ_cK({}^2S_{1/2})$ are the dominant channels, respectively.
Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity can not be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.
Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets. Then, we derive the posterior Cramer-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze the transmission rate of the integrated communication system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the transmission rate of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.
The layer Hall effect describes electrons spontaneously deflected to opposite sides at different layers, which has been experimentally reported in the MnBi$_2$Te$_4$ thinfilms under perpendicular electric fields [Gao et al., Nature 595, 521 (2021)]. Here, we reveal a universal origin of the layer Hall effect in terms of the so-called hidden Berry curvature, as well as material design principles. Hence, it gives rise to zero Berry curvature in momentum space but nonzero layer-locked hidden Berry curvature in real space. We show that compared to that of a trivial insulator, the layer Hall effect is significantly enhanced in antiferromagnetic topological insulators. Our universal picture provides a paradigm for revealing the hidden physics as a result of the interplay between the global and local symmetries, and can be generalized in various scenarios.
Stimulated by the meson-baryon molecular interpretations of the $P_c$ states ($P_c(4312)/P_c(4440)/P_c(4457)$), we systematically study the interactions between an $S-$wave charm-strange baryon $Ξ_c^{(\prime,*)}$ and an anti-charmed meson $\bar{D}^{(*)}$ in a coupled channel analysis. Effective potentials for the $Ξ_c^{(\prime,*)}\bar{D}^{(*)}$ interactions in a one-boson-exchange model can be related to those in the $Σ_c^{(*)}\bar{D}^{(*)}$ systems by using the $SU(3)$ flavor symmetry and heavy quark symmetry. Our results can predict several promising hidden-charm molecular pentaquarks with strangeness $|S|=1$, which include the $Ξ_c^{\prime}\bar{D}$ states with $I(J^P)=0,1(1/2^-)$, the $Ξ_c^*\bar{D}$ states with $0,1(3/2^-)$, the $Ξ_c^{\prime}\bar{D}^*$ states with $0(1/2^-)$ and $0,1(3/2^-)$, and the $Ξ_c^*\bar{D}^*$ states with $0(1/2^-,3/2^-,5/2^-)$.
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mask a large proportion of edges and try to reconstruct these missing edges during training. MGAE has two core designs. First, we find that masking a high ratio of the input graph structure, e.g., $70\%$, yields a nontrivial and meaningful self-supervisory task that benefits downstream applications. Second, we employ a graph neural network (GNN) as an encoder to perform message propagation on the partially-masked graph. To reconstruct the large number of masked edges, a tailored cross-correlation decoder is proposed. It could capture the cross-correlation between the head and tail nodes of anchor edge in multi-granularity. Coupling these two designs enables MGAE to be trained efficiently and effectively. Extensive experiments on multiple open datasets (Planetoid and OGB benchmarks) demonstrate that MGAE generally performs better than state-of-the-art unsupervised learning competitors on link prediction and node classification.
Binary polynomial optimization is equivalent to the problem of minimizing a linear function over the intersection of the multilinear set with a polyhedron. Many families of valid inequalities for the multilinear set are available in the literature, though giving a polyhedral characterization of the convex hull is not tractable in general as binary polynomial optimization is NP-hard. In this paper, we study the cardinality constrained multilinear set in the special case when the number of monomials is exactly two. We give an extended formulation, with two more auxiliary variables and exponentially many inequalities, of the convex hull of solutions of the standard linearization of this problem. We also show that the separation problem can be solved efficiently.
We investigate new methods for generating Lagrangian cuts to solve two-stage stochastic integer programs. Lagrangian cuts can be added to a Benders reformulation, and are derived from solving single scenario integer programming subproblems identical to those used in the nonanticipative Lagrangian dual of a stochastic integer program. While Lagrangian cuts have the potential to significantly strengthen the Benders relaxation, generating Lagrangian cuts can be computationally demanding. We investigate new techniques for generating Lagrangian cuts with the goal of obtaining methods that provide significant improvements to the Benders relaxation quickly. Computational results demonstrate that our proposed method improves the Benders relaxation significantly faster than previous methods for generating Lagrangian cuts and, when used within a branch-and-cut algorithm, significantly reduces the size of the search tree for three classes of test problems.
The existence of long range magnetic order in low dimensional magnetic systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets, has attracted intensive studies of new physical phenomena. The vdW Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast tunability of magnetic properties. Particularly, a ferromagnetic ordering temperature ($T_{\text{C}}$) above room temperature at $N$ = 5 (F5GT) is observed. Here, our study shows that, by nickel (Ni) substitution of iron (Fe) in F5GT, a record high $T_{\text{C}}$ = 478(6) K is achieved. Importantly, pervasive, beyond-room-temperature ferromagnetism exists in almost the entire doping range of the phase diagram of Ni-F5GT. We argue that this striking observation in Ni-F5GT can be possibly due to several contributing factors, in which the structural alteration enhanced 3D magnetic couplings might be critical for enhancing the ferromagnetic order.
Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate {the} use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms. The dataset is available at https://github.com/S2Looking/.
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric) and that highway throughput is improved by optimally selecting the cooperating vehicles. We show that analytical solutions for the optimal trajectories can be derived and are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver. When feasible solutions do not exist, we include a time relaxation method trading off a longer maneuver time with reduced disruption. Our analysis is also extended to multiple sequential maneuvers. Simulation results where the controllers are implemented show their effectiveness in terms of safety guarantees and up to 35% throughput improvement compared to maneuvers with no vehicle cooperation.
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.
We propose a new method for separating valid inequalities for the epigraph of a function of binary variables. The proposed inequalities are disjunctive cuts defined by disjunctive terms obtained by enumerating a subset $I$ of the binary variables. We show that by restricting the support of the cut to the same set of variables $I$, a cut can be obtained by solving a linear program with $2^{|I|}$ constraints. While this limits the size of the set $I$ used to define the multi-term disjunction, the procedure enables generation of multi-term disjunctive cuts using far more terms than existing approaches. We present two approaches for choosing the subset of variables. Experience on three MILP problems with block diagonal structure using $|I|$ up to size 10 indicates the sparse cuts can often close nearly as much gap as the multi-term disjunctive cuts without this restriction and in a fraction of the time. We also find that including these cuts within a cut-and-branch solution method for these MILP problems leads to significant reductions in solution time or ending optimality gap for instances that were not solved within the time limit. Finally, we describe how the proposed approach can be adapted to optimally "tilt" a given valid inequality by modifying the coefficients of a sparse subset of the variables.
In this paper, we investigate the twisted GGP conjecture for certain tempered representations using the theta correspondence and establish some special cases, namely when the L-parameter of the unitary group is the sum of conjugate-dual characters of the appropriate sign.
As one of the most fascinating confined water/ice phenomena, two-dimensional square ice has been extensively studied and experimentally confirmed in recent years. Apart from the unidirectional homogeneous square icing patterns considered in previous studies, the multidirectional partitioned square icing patterns are discovered in this study and characterized by molecular dynamics (MD) simulations. Square icing parameters are proposed to quantitatively distinguish the partitioned patterns from the homogeneous patterns and the liquid water. The number of graphene monolayers n is varied in this study, and the results show that it is more energetically favorable to form partitioned square icing patterns when the water molecules are confined between graphite sheets (n >= 2) compared to graphene (n = 1). This phenomenon is insensitive to n as long as n >= 2, because of the short-range nature of the interaction between water molecules and the carbon substrate. Moreover, it is energetically unfavorable to form partitioned square icing patterns for a single layer of water molecules even for n >= 2, verifying that the interaction between layers of water molecules is another dominant factor in the formation of partitioned structures. The conversion from partitioned structure to homogenous square patterns is investigated by changing the pressure and the temperature. Based on the comprehensive MD simulations, this study unveils the formation mechanism of the partitioned square icing patterns.
The main goal of this paper is to study the unitary Friedberg-Jacquet period through their connection with the unitary Shalika period. Locally we study the multiplicity of unitary Friedberg-Jacquet periods for discrete series. Globally we prove one direction of a conjecture of Xiao-Zhang, stating that the non-vanishing of a global unitary Friedberg-Jacquet period implies the non-vanishing of the central value of the (twisted) standard L-function.
Creating and manipulating topological states is a key goal of condensed matter physics. Periodic driving offers a powerful method to manipulate electronic states, and even to create topological states in solids. Here, we investigate the tunable Floquet states in a periodically driven higher-order nodal line semimetal with both spatial inversion and time-reversal symmetries. We found that the Floquet Weyl semimetal states, which support both one-dimensional hinge Fermi arc and two-dimensional surface Fermi arc states, can be induced in the higher-order nodal-line semimetal by shining circularly polarized light. Moreover, we show that the location of Weyl nodes and the curvature of surface Fermi arcs can be tuned by adjusting the propagation direction and incident angle of light.
We investigate sample average approximation (SAA) for two-stage stochastic programs without relatively complete recourse, i.e., for problems in which there are first-stage feasible solutions that are not guaranteed to have a feasible recourse action. As a feasibility measure of the SAA solution, we consider the "recourse likelihood", which is the probability that the solution has a feasible recourse action. For $ε\in (0,1)$, we demonstrate that the probability that a SAA solution has recourse likelihood below $1-ε$ converges to zero exponentially fast with the sample size. Next, we analyze the rate of convergence of optimal solutions of the SAA to optimal solutions of the true problem for problems with a finite feasible region, such as bounded integer programming problems. For problems with non-finite feasible region, we propose modified "padded" SAA problems and demonstrate in two cases that such problems can yield, with high confidence, solutions that are certain to have a feasible recourse decision. Finally, we conduct a numerical study on a two-stage resource planning problem that illustrates the results, and also suggests there may be room for improvement in some of the theoretical analysis.
The isospin breaking effect plays an essential role in generating hadronic molecular states with a very tiny binding energy. Very recently, the LHCb Collaboration observed a very narrow doubly charmed tetraquark $T_{cc}^+$ in the $D^0D^0π$ mass spectrum, which lies just below the $D^0D^{*+}$ threshold around 273 keV. In this work, we study the $D^0D^{*+}/D^+D^{*0}$ interactions with the one-boson-exchange effective potentials and consider the isospin breaking effect carefully. We not only reproduce the mass of the newly observed $T_{cc}^+$ very well in the doubly charmed molecular tetraquark scenario, but also predict the other doubly charmed partner resonance $T_{cc}^{\prime+}$ with $m=3876~\text{MeV}$, and $Γ= 412~\text{keV}$. The prime decay modes of the $T_{cc}^{\prime+}$ are $D^0D^+γ$ and $D^+D^0π^0$. The peculiar characteristic mass spectrum of the $D^0D^{*+}/D^+D^{*0}$ molecular systems can be applied to identify the doubly charmed molecular states.
Inspired by the recent evidence of $P_{cs}(4459)$ reported by LHCb, we continue to perform the investigation of hidden-charm molecular pentaquarks with double strangeness, which are composed of an $S$-wave charmed baryon $Ξ_c^{(\prime,*)}$ and an $S$-wave anti-charmed-strange meson $\bar{D}_s^{(*)}$. Both the $S$-$D$ wave mixing effect and the coupled channel effect are taken into account in realistic calculation. A dynamics calculation shows that there may exist two types of hidden-charm molecular pentaquark with double strangeness, i.e., the $Ξ_{c}^{*}\bar D_s^*$ molecular state with $J^P={5}/{2}^{-}$ and the $Ξ_{c}^{\prime}\bar D_s^*$ molecular state with $J^P={3}/{2}^{-}$. According to this result, we strongly suggest the experimental exploration of hidden-charm molecular pentaquarks with double strangeness. Facing such opportunity, obviously the LHCb will have great potential to hunt for them, with the data accumulation at Run III and after High-Luminosity-LHC upgrade.
In our former work [arXiv:2011.07214], the $P_{cs}(4459)$ observed by the LHCb Collaboration can be explained as a coupled strange hidden-charm $Ξ_c\bar{D}^*/Ξ_c^*\bar{D}/Ξ_c'\bar{D}^*/Ξ_c^*\bar{D}^*$ molecule with $I(J^P)=0(3/2^-)$. Here, we further discuss the two-body strong decay behaviors of the $P_{cs}(4459)$ in the meson-baryon molecular scenario by input the former obtained bound solutions. Our results support the $P_{cs}(4459)$ as the strange hidden-charm $Ξ_c\bar{D}^*$ molecule with $I(J^P)=0(3/2^-)$. The relative decay ratio between $Λ_cD_s^*$ and $J/ψΛ$ is around 10, where the partial decay width for the $Λ_cD_s^*$ channel is around 0.6 to 2.0 MeV.
With a quark level interaction, we give a unified description of the loosely bound molecular systems composed of the heavy flavor hadrons $(\bar{D},\bar{D}^*)$, $(Λ_c, Σ_c, Σ_c^*)$, and $(Ξ_c, Ξ_c^\prime,Ξ_c^*)$. Using the $P_c$ states as inputs to fix the interaction strength of light quark-quark pairs, we reproduce the observed $P_{cs}$ and $T_{cc}^+$ states and predict another narrow $T_{cc}^{\prime+}$ state with quantum numbers $[D^*D^*]_{J=1}^{I=0}$. If we require a satisfactory description of the $T_{cc}^+$ and $P_c$ states simultaneously, our framework prefers the assignments of the $P_{c}(4440)$ and $P_{c}(4457)$ as the $[Σ_c\bar{D}^*]_{J=1/2}^{I=1/2}$ and $[Σ_c\bar{D}^*]_{J=3/2}^{I=1/2}$ states, respectively. We propose the isospin criterion to explain naturally why the experimentally observed $T_{cc}$, $P_c$, and $P_{cs}$ molecular candidates prefer the lowest isospin numbers. We also predict the loosely bound states for the bottom di-hadrons.
The quest for the topological phases of matter in an aperiodic system has been greatly developed recently. Here we investigate the effects of disorder on topological phases of a two-dimensional Ammann-Beenker tiling quasicrystalline lattice. For comparison purposes, we also consider the case of a periodic snub-square crystalline lattice, which has the same primitive tiles as the Ammann-Beenker tiling quasicrystalline lattice. By calculating the spin Bott index and the two-terminal conductance, we confirm that the topological phases with disorder share the similar properties in the two systems which possess different symmetry and periodicity. It is shown that the quantum spin Hall states are robust against weak disorder in both the quasicrystalline lattice and the crystalline lattice. More interesting is that topological Anderson insulator phases induced by disorder appear in the two systems. Furthermore, the quantized conductance plateau contributed by the topological Anderson insulator phase is verified by the distribution of local currents.
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this paper, we investigate how to develop energy efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for "working" (i.e., local computing) and that for "talking" (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for graphics processing unit (GPU) computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energy-efficient learning techniques (e.g., gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.
Inspired by the newly $Z_{cs}(3985)^-$ reported by the BESIII Collaboration in the $K^+$ recoil-mass spectrum of the of $e^+e^-\to (D^{*0}D_s^-/D^0D_s^{*-})K^+$ processes, we perform a dynamical study on the $D^{(*)0}D_s^{*-}$ interactions by adopting a one-boson-exchange model and considering the coupled channel effect. After producing the phase shifts for all the discussed channels, our results exclude the newly $Z_{cs}(3985)^-$ as a $D^{*0}D_s^{-}/D^{0}D_s^{*-}/D^{*0}D_s^{*-}$ resonance with $I(J^P)=1/2(1^+, 0^-, 1^-, 2^-)$.
In 2000, based on his procedure for constructing explicit towers of modular curves, Elkies deduced explicit equations of rank-2 Drinfeld modular curves which coincide with the asymptotically optimal towers of curves constructed by Garcia and Stichtenoth. In 2015, Bassa, Beelen, Garcia, and Stichtenoth constructed a celebrated (recursive and good) tower (BBGS-tower for short) of curves and outlined a modular interpretation of the defining equations. Soon after that, Gekeler studied in depth the modular curves coming from sparse Drinfeld modules. In this paper, to establish a link between these existing results, we propose and prove a generalized Elkies' Theorem which tells in detail how to directly describe a modular interpretation of the equations of rank-m Drinfeld modular curves with m>=2.
Recently, the intrinsic magnetic topological insulator MnBi$_2$Te$_4$ has attracted great attention. It has an out-of-plane antiferromagnetic order, which is believed to open a sizable energy gap in the surface states. This gap, however, was not always observable in the latest angle-resolved photoemission spectroscopy (ARPES) experiments. To address this issue, we analytically derive an effective model for the two-dimensional (2D) surface states by starting from a three-dimensional (3D) Hamiltonian for bulk MnBi$_2$Te$_4$ and taking into account the spatial profile of the bulk magnetization. Our calculations suggest that the diminished surface gap may be caused by a much smaller and more localized intralayer ferromagnetic order. In addition, we calculate the spatial distribution and penetration depth of the surface states, which indicates that the surface states are mainly embedded in the first two septuple layers from the terminating surface. From our analytical results, the influence of the bulk parameters on the surface states can be found explicitly. Furthermore, we derive a $\bf{k}\cdot \bf{p}$ model for MnBi$_2$Te$_4$ thin films and show the oscillation of the Chern number between odd and even septuple layers. Our results will be helpful for the ongoing explorations of the MnBi$_x$Te$_y$ family.
Stimulated by the $P_{cs}(4459)$ reported by the LHCb Collaboration, we perform a single $Ξ_c\bar{D}^*$ channel and a coupled $Ξ_c\bar{D}^*/Ξ_c^*\bar{D}/Ξ_c^{\prime}\bar{D}^*/Ξ_c^*\bar{D}^*$ channel analysis by using a one-boson-exchange model. Our results indicate that the newly $P_{cs}(4459)$ cannot be a pure $Ξ_c\bar{D}^*$ molecular state, but a coupled $Ξ_c\bar{D}^*/Ξ_c^*\bar{D}/Ξ_c^{\prime}\bar{D}^*/Ξ_c^*\bar{D}^*$ bound state with $I(J^P)=0(3/2^-)$, where the $Ξ_c\bar{D}^*$ and $Ξ_c^*\bar{D}$ components are dominant. Meanwhile, we find the interactions from the $Ξ_c^{\prime}\bar{D}^*$ system with $0(1/2^-)$, the $Ξ_c^{*}\bar{D}$ system with $1(3/2^-)$, and the $Ξ_c^{*}\bar{D}^*$ system with $1(1/2^-)$ are strongly attractive, where one can expect possible strange hidden-charm molecular or resonant structures near the these thresholds with the assigned quantum numbers.
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely physics-based. We see value in these principled models, and blend these physical models as priors into a neural network architecture. This proposed approach achieves results that exceed the previous state-of-the-art on a challenging dataset we introduce. This dataset consists of polarization images taken over a range of object textures, paints, and lighting conditions. We report that our proposed method achieves the lowest test error on each tested condition in our dataset, showing the value of blending data-driven and physics-driven approaches.
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally improve the performance of individual task due to knowledge sharing in multi-task learning. While a few existing works have studied long-term rewards in recommendations, they mainly focus on a single recommendation task. In this paper, we propose {\it PoDiRe}: a \underline{po}licy \underline{di}stilled \underline{re}commender that can address long-term rewards of recommendations and simultaneously handle multiple recommendation tasks. This novel recommendation solution is based on a marriage of deep reinforcement learning and knowledge distillation techniques, which is able to establish knowledge sharing among different tasks and reduce the size of a learning model. The resulting model is expected to attain better performance and lower response latency for real-time recommendation services. In collaboration with Samsung Game Launcher, one of the world's largest commercial mobile game platforms, we conduct a comprehensive experimental study on large-scale real data with hundreds of millions of events and show that our solution outperforms many state-of-the-art methods in terms of several standard evaluation metrics.
The dynamical and static Rashba effects in hybrid methylammonium (MA) lead halide perovskites have recently been theoretically predicted. However, only the static effect was experimentally confirmed so far. Here we report on the dynamical Rashba effect observed using snapshot transient absorption spectral imaging with 400 nm pumping for a fully encapsulated film of 20-nm-sized 3D MAPbBr3 nanocrystals. The effect causes a 240 meV splitting of the lowest-energy absorption bleaching band, initially appearing over sub-ps timescale and progressively stabilizing to 60 meV during 500 ps. The integrated intensities of the split subbands demonstrate a photon-helicity-dependent asymmetry, thus proving the Rashba-type splitting and providing direct experimental evidence for the Rashba spin-split edge states in lead halide perovskite materials. The ultrafast dynamics is governed by the relaxation of two-photon-excited electrons in the Rashba spin-split system caused by a built-in electric field originating from dynamical charge separation in the entire MAPbBr3 nanocrystal.
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.
Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with a limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve the depth quality. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy & smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD, and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance.
The $P_c(4312)$, $P_c(4440)$, and $P_c(4457)$ observed by the LHCb Collaboration are very likely to be $S$-wave $Σ_c\bar{D}^{(*)}$ molecular candidates due to their near-threshold character. In this work, we study the hidden-charm decay modes of these $P_c$ states, $P_c\to J/ψp(η_cp)$, using a quark interchange model. The decay mechanism for the $P_c\to J/ψp(η_cp)$ processes arises from the quark-quark interactions, where all parameters are determined by the mass spectra of mesons. We present our results in two scenarios. In scenario I, we perform the dynamical calculations and treat the $P_c$ states as pure $Σ_c \bar D^{(*)}$ molecules. In scenario II, after considering the coupled channel effect between different flavor configurations $Σ^{(*)}_c\bar D^{(*)}$, we calculate these partial decay widths again. The decay patterns in these two scenarios can help us to explore the molecular assignment and the inner flavor configurations for the $P_c$ states. In particular, the decay widths of $Γ(P_c(4312)\toη_cp)$ are comparable to the $J/ψp$ decay widths in both of these two scenarios. Future experiments like LHCb may confirm the existence of the $P_c(4312)$ in the $η_cp$ channel.
Inspired by the observations of three $P_c$ states, we systematically investigate interactions between an $S$-wave charmed baryon $\mathcal{B}_{c}^{(*)}=Λ_c/Σ_c/Σ_c^{*}$ and an anticharmed meson $\bar T=\bar D_1/\bar D_2^*$ with the one-pion-exchange potential model and the one-boson-exchange potential model, and search for possible new types of $P_c$ states with the structures of $\mathcal{B}_{c}^{(*)}\bar T$. Both $S$-$D$ wave mixing and coupled channel effects are considered. Our results suggest that in some $\mathcal{B}_{c}^{(*)}\bar T$ systems there are ideal candidates of new types of $P_c$ states, i.e., the $Σ_c\bar{D}_1$ state with $I(J^P)=1/2(1/2^+)$, the $Σ_c\bar{D}_2^*$ state with $I(J^P)=1/2(3/2^+)$, the $Σ_c^*\bar{D}_1$ state with $I(J^P)=1/2(1/2^+)$, and the $Σ_c^*\bar{D}_2^*$ states with $I(J^P)=1/2(1/2^+, 3/2^+)$, and we suggest that these predicted new types of $P_c$ states can be detected in the process $Λ_b^0 \to ψ(2S) p π^{-}$. Meanwhile, we also extend our study to the interactions between an $S$-wave charmed baryon and a charmed meson in a $T$ doublet, and we predict a series of double-charm molecular pentaquarks.
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences. Methods: We performed a systematic review to collate data from observational studies examining associations of sex differences with clinical outcomes of COVID-19. PubMed, Web of Science and four preprint servers were searched for relevant studies. Data were extracted and analyzed using meta-analysis where possible, with summary data presented otherwise. Publicly available bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data were analyzed to explore the potential mechanisms underlying the observed association. Results: 39 studies met inclusion criteria, representing 77932 patients, of which 41510 (53.3%) were males. Men were at a markedly increased risk of developing severe cases compared with women. Furthermore, the pooled odds ratio (OR) of mortality for male group compared with the female group indicated significant higher mortality rate for male. Data from scRNA-seq suggest that men have a higher amount of ACE2-expressing pulmonary alveolar type II cells than women. Sex-based immunological differences exist. The expression of androgen receptor (AR) is positively correlated with ACE2, and there is evidence that AR may directly regulate the expression of ACE2. Conclusions: This meta-analysis detected an increased severity and mortality rate in the male populations with COVID-19, which might be attributable to the sex-based differences in cellular compositions and immunological microenvironments of the lung. The host cell receptor ACE2 is likely regulated by AR signaling pathway, which is identified as a potential target for prevention and treatment of SARS-Cov-2 infections in men.
Although the structural phase transitions in single-crystal hybrid methyl-ammonium (MA) lead halide perovskites (MAPbX3, X = Cl, Br, I) are common phenomena, they have never been observed in the corresponding nanocrystals. Here we demonstrate that two-photon-excited photoluminescence (PL) spectroscopy is capable of monitoring the structural phase transitions in MAPbX3 nanocrystals because nonlinear susceptibilities govern the light absorption rates. We provide experimental evidence that the orthorhombic-to-tetragonal structural phase transition in a single layer of 20-nm-sized 3D MAPbBr3 nanocrystals is spread out within the 70 - 140 K range. This structural phase instability range arises because, unlike in single-crystal MAPbX3, free rotations of MA ions in the corresponding nanocrystals are no longer restricted by a long-range MA dipole order. The resulting configurational entropy loss can be even enhanced by the interfacial electric field arising due to charge separation at the MAPbBr3/ZnO heterointerface, extending the orthorhombic-to-tetragonal structural phase instability range from 70 to 230 K. We conclude that the weak sensitivity of conventional one-photon-excited PL spectroscopy to the structural phase transitions in 3D MAPbX3 nanocrystals results from the structural phase instability providing negligible distortions of PbX6 octahedra. In contrast, the intensity of two-photon-excited PL and electric-field-induced one-photon-excited PL still remains sensitive enough to weak structural distortions due to the higher rank tensor nature of nonlinear susceptibilities involved. We also show that room-temperature PL originates from the radiative recombination of the optical-phonon vibrationally excited polaronic quasiparticles with energies might exceed the ground-state Frohlich polaron and Rashba energies due to optical-phonon bottleneck.
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-smoothing is one of the key issues which limit the performance of GNNs as the number of layers increases. It is because the stacked aggregators would make node representations converge to indistinguishable vectors. Several attempts have been made to tackle the issue by bringing linked node pairs close and unlinked pairs distinct. However, they often ignore the intrinsic community structures and would result in sub-optimal performance. The representations of nodes within the same community/class need be similar to facilitate the classification, while different classes are expected to be separated in embedding space. To bridge the gap, we introduce two over-smoothing metrics and a novel technique, i.e., differentiable group normalization (DGN). It normalizes nodes within the same group independently to increase their smoothness, and separates node distributions among different groups to significantly alleviate the over-smoothing issue. Experiments on real-world datasets demonstrate that DGN makes GNN models more robust to over-smoothing and achieves better performance with deeper GNNs.
Current understanding of higher-order topological insulators (HOTIs) is based primarily on crystalline materials. Here, we propose that HOTIs can be realized in quasicrystals. Specifically, we show that two distinct types of second-order topological insulators (SOTIs) can be constructed on the quasicrystalline lattices (QLs) with different tiling patterns. One is derived by using a Wilson mass term to gap out the edge states of the quantum spin Hall insulator on QLs. The other is the quasicrystalline quadrupole insulator (QI) with a quantized quadrupole moment. We reveal some unusual features of the corner states (CSs) in the quasicrystalline SOTIs. We also show that the quasicrystalline QI can be simulated by a designed electrical circuit, where the CSs can be identified by measuring the impedance resonance peak. Our findings not only extend the concept of HOTIs into quasicrystals but also provide a feasible way to detect the topological property of quasicrystals in experiments.