arXiv subjects

Yuan Fang

Publications and source records attributed to Yuan Fang.

At least 181 records · Page 10Linked to original sources

arXiv2022

Fundamental CRB-Rate Tradeoff in Multi-antenna Multicast Channel with ISAC

This paper studies the multi-antenna multicast channel with integrated sensing and communication (ISAC), in which a multi-antenna base station (BS) sends common messages to a set of single-antenna communication users (CUs) and simultaneously estimates the parameters of an extended target via radar sensing. We investigate the fundamental performance limits of this ISAC system, in terms of the achievable rate for communication and the estimation Cramér-Rao bound (CRB) for sensing. First, we derive the optimal transmit covariance in semi-closed form to balance the CRB-rate (C-R) tradeoff, and accordingly characterize the outer bound of a so-called C-R region. It is shown that the optimal transmit covariance should be of full rank, consisting of both information-carrying and dedicated sensing signals in general. Next, we consider a practical joint information and sensing beamforming design, and propose an efficient approach to optimize the joint beamforming for balancing the C-R tradeoff. Numerical results are presented to show the C-R region achieved by the optimal transmit covariance and the joint beamforming, as compared to other benchmark schemes.

cs.IT↗

arXiv2022

Energy-Efficient Transmit Beamforming and Antenna Selection with Non-Linear PA Efficiency

This letter studies the energy-efficient design in a downlink multi-antenna multi-user system consisting of a multi-antenna base station (BS) and multiple single-antenna users, by considering the practical non-linear power amplifier (PA) efficiency and the on-off power consumption of radio frequency (RF) chain at each transmit antenna. Under this setup, we jointly optimize the transmit beamforming and antenna on/off selection at the BS to minimize its total power consumption while ensuring the individual signal-to-interference-plus-noise ratio (SINR) constraints at the users. However, due to the non-linear PA efficiency and the on-off RF chain power consumption, the formulated SINR-constrained power minimization problem is highly non-convex and difficult to solve. To tackle this issue, we propose an efficient algorithm to obtain a high-quality solution based on the technique of sequential convex approximation (SCA). We provide numerical results to validate the performance of our proposed design. It is shown that at the optimized solution, the BS tends to activate fewer antennas and use higher power transmission at each antenna to exploit the non-linear PA efficiency.

cs.IT↗

arXiv2022

Recipe for higher-order topology on the triangular lattice

We present a recipe for an electronic 2D higher order topological insulator (HOTI) on the triangular lattice that can be realized in a large family of materials. The essential ingredient is mirror symmetry breaking, which allows for a finite quadrupole moment and trivial $\mathbb{Z}_2$ index. The competition between spin-orbit coupling and the symmetry breaking terms gives rise to four topologically distinct phases; the HOTI phase appears when symmetry breaking dominates, including in the absence of spin-orbit coupling. We identify triangular monolayer adsorbate systems on the (111) surface of zincblende/diamond type substrates as ideal material platforms and predict the HOTI phase for $X=$(Al,B,Ga) on SiC.

cond-mat.mes-hall↗

arXiv2022

Clustering microbiome data using mixtures of logistic normal multinomial models

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted on a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings in flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

stat.ME↗

arXiv2022

Meta-Inductive Node Classification across Graphs

Semi-supervised node classification on graphs is an important research problem, with many real-world applications in information retrieval such as content classification on a social network and query intent classification on an e-commerce query graph. While traditional approaches are largely transductive, recent graph neural networks (GNNs) integrate node features with network structures, thus enabling inductive node classification models that can be applied to new nodes or even new graphs in the same feature space. However, inter-graph differences still exist across graphs within the same domain. Thus, training just one global model (e.g., a state-of-the-art GNN) to handle all new graphs, whilst ignoring the inter-graph differences, can lead to suboptimal performance. In this paper, we study the problem of inductive node classification across graphs. Unlike existing one-model-fits-all approaches, we propose a novel meta-inductive framework called MI-GNN to customize the inductive model to each graph under a meta-learning paradigm. That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs. To cope with the differences across graphs, MI-GNN employs a dual adaptation mechanism at both the graph and task levels. More specifically, we learn a graph prior to adapt for the graph-level differences, and a task prior to adapt for the task-level differences conditioned on a graph. Extensive experiments on five real-world graph collections demonstrate the effectiveness of our proposed model.

cs.LG↗

arXiv2022

TREND: TempoRal Event and Node Dynamics for Graph Representation Learning

Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is inductive due to its GNN architecture; (2) it captures the exciting effects between events by the adoption of the Hawkes process; (3) as our main novelty, it captures the individual and collective characteristics of events by integrating both event and node dynamics, driving a more precise modeling of the temporal process. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed model.

cs.SI↗

arXiv2022

Coordinated Power Control for Network Integrated Sensing and Communication

This correspondence paper studies a network integrated sensing and communication (ISAC) system that unifies the interference channel for communication and distributed radar sensing. In this system, a set of distributed ISAC transmitters send individual messages to their respective communication users (CUs), and at the same time cooperate with multiple sensing receivers to estimate the location of one target. We exploit the coordinated power control among ISAC transmitters to minimize their total transmit power while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs and the maximum Cramér-Rao lower bound (CRLB) requirement for target location estimation. Although the formulated coordinated power control problem is non-convex and difficult to solve in general, we propose two efficient algorithms to obtain high-quality solutions based on the semi-definite relaxation (SDR) and CRLB approximation, respectively. Numerical results show that the proposed designs achieve substantial performance gains in terms of power reduction, as compared to the benchmark with a heuristic separate communication-sensing design.

cs.IT↗

arXiv2022

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111). At low coverages, preferred islands with 2, 5 and 6 monolayers height develop, which agrees well with the surface energy calculation. We observe clear quantum well states as a result of electronic confinement and their dispersion agrees well with density functional theory calculations, indicating weak correlation effect despite strong contributions from Co 3d electrons. Ex-situ transport measurements show that superconductivity persists down to at least 10 monolayers, with reduced Tc but largely enhanced upper critical field. Our study opens up the opportunity to study the interplay between quantum confinement, interfacial symmetry breaking and superconductivity in an epitaxial silicide film, which is technologically relevant in microelectronics.

cond-mat.mes-hall↗

arXiv2022

Classification of Dirac points with higher-order Fermi arcs

Dirac semimetals lack a simple bulk-boundary correspondence. Recently, Dirac materials with four-fold rotation symmetry have been shown to exhibit a higher order bulk-hinge correspondence: they display "higher order Fermi arcs," which are localized on hinges where two surfaces meet and connect the projections of the bulk Dirac points. In this paper, we classify higher order Fermi arcs for Dirac semimetals protected by a rotation symmetry and the product of time-reversal and inversion. Such Dirac points can be either linear in all directions or linear along the rotation axis and quadratic in other directions. By computing the filling anomaly for momentum-space planes on either side of the Dirac point, we find that all linear Dirac points exhibit higher order Fermi arcs terminating at the projection of the Dirac point, while the Dirac points that are quadratic in two directions lack such higher order Fermi arcs. When higher order Fermi arcs do exist, they obey either a $\mathbb{Z}_2$ (four-fold rotation axis) or $\mathbb{Z}_3$ (three- or six-fold rotation axis) group structure. Finally, we build two models with six-fold symmetry to illustrate the cases with and without higher order Fermi arcs. We predict higher order Fermi arcs in Na$_3$Bi.

cond-mat.str-el↗

arXiv2022

Identifying the fingerprints of topological states by tuning magnetoresistance in a semimetal: the case of topological half-Heusler Pt1-xAuxLuSb

Topological materials often exhibit remarkably linear, non-saturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically non-trivial states in the emergence of such a behaviour has eluded clear demonstration in experiments. Here, by reducing the coupling between the topological surface states (TSS) and the bulk carriers we controllably tune the LMR behavior in Pt1-xAuxLuSb into distinct plateaus in Hall resistance, which we show arise from a quantum Hall phase. This allowed us to reveal how smearing of the Landau levels, which otherwise give rise to a quantum Hall phase, results in an LMR behavior due to strong interaction between the TSS with a positive g-factor and the bulk carriers. We establish that controlling the coupling strength between the surface and the bulk carriers in topological materials can bring about dramatic changes in their magnetotransport behavior. In addition, our work outlines a strategy to reveal macroscopic physical observables of TSS in compounds with a semi-metallic bulk band structure, as is the case in multi-functional Heusler compounds, thereby opening up opportunities for their utilization in hybrid quantum structures.

cond-mat.mtrl-sci↗

arXiv2021

Node-wise Localization of Graph Neural Networks

Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local contexts, making their distributions vary across the graph. Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes. To utilize node locality without overfitting, we propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. Globally, all nodes on the graph depend on an underlying global GNN to encode the general patterns across the graph; locally, each node is localized into a unique model as a function of the global model and its local context. Finally, we conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.

cs.LG↗

arXiv2021

Marginal regeneration-induced drainage of surface bubbles

The prediction of the lifetime of surface bubbles necessitates a better understanding of the thinning dynamics of the bubble cap. In 1959, Mysel \textit{et al.} \cite{mysels1959soap}, proposed that \textit{marginal regeneration} i.e. the rise of patches, thinner than the film should be taken into account to describe the film drainage. Nevertheless, an accurate description of these buoyant patches and of their dynamics as well as a quantification of their contribution to the thinning dynamics is still lacking. In this paper, we visualize the patches, and show that their rising velocities and sizes are in good agreement with models respectively based on the balance of gravitational and surface viscous forces and on a Rayleigh-Taylor like instability \cite{Seiwert2017,Shabalina2019}. Our results suggest that, in an environment saturated in humidity, the drainage induced by their dynamics correctly describes the film drainage at the apex of the bubble within the experimental error bars. We conclude that the film thinning of soap bubbles is indeed controlled, to a large extent, by \textit{marginal regeneration} in the absence of evaporation.

physics.flu-dyn↗

arXiv2021

Revealing the Heavy Quasiparticles in the Heavy-Fermion Superconductor CeCu2Si2

The superconducting order parameter of the first heavy-fermion superconductor CeCu2Si2 is currently under debate. A key ingredient to understand its superconductivity and physical properties is the quasiparticle dispersion and Fermi surface, which remains elusive experimentally. Here we present measurements from angle-resolved photoemission spectroscopy. Our results emphasize the key role played by the Ce 4f electrons for the low-temperature Fermi surface, highlighting a band-dependent conduction-f electron hybridization. In particular, we find a very heavy quasi-two-dimensional electron band near the bulk X point and moderately heavy three-dimensional hole pockets near the Z point. Comparison with theoretical calculations reveals the strong local correlation in this compound, calling for further theoretical studies. Our results provide the electronic basis to understand the heavy fermion behavior and superconductivity; implications for the enigmatic superconductivity of this compound are also discussed.

cond-mat.str-el↗

arXiv2021

Origin Pilot: a Quantum Operating System for Effecient Usage of Quantum Resources

The operating system is designed to manage the hardware and software resources of a computer. With the development of quantum computing, the management of quantum resources and cooperation between quantum systems and other computing resources (e.g. CPU, GPU and FPGA etc.) become the key challenge for the application of quantum computing to solve real world problems. In this paper we propose a quantum operating system, Origin Pilot. Origin Pilot includes the module of quantum task scheduling, quantum resource management, quantum program compilation and qubits' automatic calibration. With these modules, Origin Pilot can manage the quantum computing resources and solve the multi-quantum processor scheduling problem. It can also allow the parallel execution of multiple quantum programs and calibrate the quantum resource effectively. Thus, the performance of resources is guaranteed and the resource utilization is improved. By comparing the results with and without Origin Pilot, we evaluate the impact on a quantum circuit's fidelity of qubits mapping algorithm. We also evaluate the effectiveness of automatic calibration and parallel execution of multi-quantum processors. Finally, Origin Pilot can be easily customized for hybrid computing resources.

quant-ph↗

arXiv2021

Anisotropic c-f hybridization in the ferromagnetic quantum critical metal CeRh$_6$Ge$_4$

Heavy fermion compounds exhibiting a ferromagnetic quantum critical point have attracted considerable interest. Common to two known cases, i.e., CeRh$_6$Ge$_4$ and YbNi$_4$P$_2$, is that the 4f moments reside along chains with a large inter-chain distance, exhibiting strong magnetic anisotropy that was proposed to be vital for the ferromagnetic quantum criticality. Here we report an angle-resolved photoemission study on CeRh6Ge4, where we observe sharp momentum-dependent 4f bands and clear bending of the conduction bands near the Fermi level, indicating considerable hybridization between conduction and 4f electrons. The extracted hybridization strength is anisotropic in momentum space and is obviously stronger along the Ce chain direction. The hybridized 4f bands persist up to high temperatures, and the evolution of their intensity shows clear band dependence. Our results provide spectroscopic evidence for anisotropic hybridization between conduction and 4f electrons in CeRh$_6$Ge$_4$, which could be important for understanding the electronic origin of the ferromagnetic quantum criticality.

cond-mat.str-el↗

arXiv2021

Bandwidth-control orbital-selective delocalization of 4f electrons in epitaxial Ce films

The 4f-electron delocalization plays a key role in the low-temperature properties of rare-earth metals and intermetallics, including heavy fermions and mix-valent compounds, and is normally realized by the many-body Kondo coupling between 4f and conduction electrons. Due to the large onsite Coulomb repulsion of 4f electrons, the bandwidth-control Mott-type delocalization, commonly observed in d-electron systems, is difficult in 4f-electron systems and remains elusive in spectroscopic experiments. Here we demonstrate that the bandwidth-control orbital-selective delocalization of 4f electrons can be realized in epitaxial Ce films by thermal annealing, which results in a metastable surface phase with a reduced layer spacing. The resulting quasiparticle bands exhibit large dispersion with exclusive 4f character near E_F and extend reasonably far below the Fermi energy, which can be explained from the Mott physics. The experimental quasiparticle dispersion agrees surprisingly well with density-functional theory calculation and also exhibits unusual temperature dependence, which could be a direct consequence of the delicate interplay between the bandwidth-control Mott physics and the coexisting Kondo hybridization. Our work therefore opens up the opportunity to study the interaction between two well-known localization-delocalization mechanisms in correlation physics, i.e., Kondo vs Mott, which can be important for a fundamental understanding of 4f-electron systems.

cond-mat.str-el↗

arXiv2021

Filling anomaly for general 2D and 3D $C_4$ symmetric lattices

In this manuscript, we derive symmetry indicator formulas for the filling anomaly on 2D square lattices with and without time reversal, inversion symmetry, or their product, in the presence of spin-orbit coupling. We go beyond previous work by considering lattices with atoms occupying multiple Wyckoff positions. We also provide an algorithm using the Smith normal form that systematizes the derivation. The formulas determine the corner charge in 2D atomic or fragile topological insulators, as well as in 3D insulators and semimetals by studying their 2D slices. We apply our results to a 3D tight-binding model on a body-centered tetragonal lattice, whose projection into the 2D plane has two atoms in the unit cell. Our symmetry indicators correctly describe the higher-order hinge states and Fermi arcs in cases where the existing indicators do not apply.

cond-mat.str-el↗

arXiv2020

Reflectivity and Spectrum of Relativistic Flying Plasma Mirrors

Flying plasma mirrors induced by intense lasers has been proposed as a promising way to generate few-cycle EUV or X-ray lasers. In addition, if such a relativistic plasma mirror can accelerate, then it would serve as an analog black hole to investigate the information loss paradox associated with the black hole Hawking evaporation. Among these applications, the reflectivity, which is usually frequency-dependent, would affect the outgoing photon spectrum and therefore impact on the analysis of the physics under investigation. In this paper, these two issues are investigated analytically and numerically with one-dimensional particle-in-cell (PIC) simulations. Based on our simulation results, we propose a new model that provides a better estimate of the reflectivity than those studied previously. Besides, we found that the peak frequency of the reflected spectrum of a gaussian incident wave deviates from the expected value, $4γ^2ω$, due to the dependence of reflectivity on the frequency of the incident wave.

physics.plasm-ph↗