arXiv subjects

Yu Han

Publications and source records attributed to Yu Han.

At least 109 records · Page 6Linked to original sources

arXiv2023

When Physical Layer Key Generation Meets RIS: Opportunities, Challenges, and Road Ahead

Physical layer key generation (PLKG) is a promising technology to obtain symmetric keys between a pair of wireless communication users in a plug-and-play manner. The shared entropy source almost entirely comes from the intrinsic randomness of the radio channel, which is highly dependent on the wireless environments. However, in some static/block fading wireless environments, the intrinsic randomness of the wireless channel is hard to be guaranteed. Very recently, thanks to reconfigurable intelligent surfaces (RISs) with their excellent ability on electromagnetic wave control, the wireless channel environment can be customized. In this article, we overview the RISaided PLKG in static indoor environments, including its channel model and hardware architectures. Then, we propose potential application scenarios and analyze the design challenges of RIS aided PLKG, including channel reciprocity, RIS reconfiguration speed and RIS deployment via proof-of-concept experiments on a RIS-aided PLKG prototype system. In particular, our experimental results show that the key generation rate is 15-fold higher than that without RIS in a static indoor environment. Next, we design a RIS jamming attack via a prototype experiment and discuss its possible attack-defense countermeasures. Finally, several conclusions and future directions are identified.

cs.CR↗

arXiv2023

Three-dimensional structural and compositional inhomogeneity in zeolites unraveled by low-dose electron ptychography

Structural and compositional inhomogeneity is common in zeolites and considerably affects their properties. Conventional transmission electron microscopy (TEM) cannot provide sufficient information on local structures in zeolites due to the thickness-limited lateral resolution, lack of depth resolution, and electron dose-constrained focusing. We demonstrate that a multislice ptychography method based on four-dimensional scanning TEM (4D-STEM) data can overcome these limitations. The 4D-STEM ptychography image obtained from a ~40-nm thick MFI zeolite exhibits an ultrahigh lateral resolution of ~0.85 Å, enabling the unambiguous identification of individual framework oxygen (O) atoms and the precise determination of the orientations of adsorbed molecules. Furthermore, a depth resolution of ~6.6 nm is achieved, allowing the three-dimensional probing of O vacancies and phase boundaries in zeolites. The results of the 4D-STEM ptychography provide an unprecedented complete understanding of the intergrowth of MFI and MEL zeolites in three dimensions. The 4D-STEM ptychography can be generally applied to various zeolites and other materials with similar electron beam sensitivity.

cond-mat.mtrl-sci↗

arXiv2023

Electron-irradiation-facilitated production of chemically homogenized nanotwins in nanolaminated carbides

Twin boundaries have been exploited to stabilize ultrafine grains and improve the mechanical properties of nanomaterials. The production of twin boundaries and nanotwins is however prohibitively challenging in carbide ceramics. Using scanning transmission electron microscopes as a unique platform for atomic-scale structure engineering, we demonstrate that twin platelets could be produced in carbides by engineering antisite defects. Antisite defects at metal sites in various layered ternary carbides are collectively and controllably generated and the metal elements are homogenized by electron irradiation, which transforms the twin-like lamellae into nanotwin platelets. Accompanying the chemical homogenization, α-Ti3AlC2 transforms to unconventional \b{eta}-Ti3AlC2. The chemical homogeneity and the width of the twin platelets can be tuned by the dose and energy of bombarding electrons. Chemically homogenized nanotwins can boost the hardness by ~45%. Our results provide a new way to produce ultrathin (<5 nm) nanotwin platelets in scientifically and technologically important carbide materials and showcase the feasibility of defect engineering by an angstrom-sized electron probe.

cond-mat.mtrl-sci↗

arXiv2022

Factoring integers with sublinear resources on a superconducting quantum processor

Shor's algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglog N), which is sublinear in the bit length of the integer $N$, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.

quant-ph↗

arXiv2022

Prediction of surface reconstructions using MAGUS

In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher (MAGUS). In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111) and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground states as well as a new SiC surface model in an extremely Si-rich environment.

cond-mat.mtrl-sci↗

arXiv2022

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

cs.CV↗

arXiv2022

Quantisation ambiguities and the effective dynamics of scalar-tensor theories in loop quantum cosmology

The Hamiltonian constraint of scalar-tensor theories in the Jordan frame is quantised using three quantisation prescriptions in loop quantum cosmology, from which we obtain three different effective Hamiltonian constraints. The corresponding effective equations of motion derived from these effective Hamiltonian constraints turn out to be drastically different. The implications of each set of effective equations of motion are discussed in detail. In the latter half of this paper, as a concrete example, we study the effective dynamics of a specific model with the non-minimal coupling function $F(ϕ)=1+ξκϕ^2$ and self-interacting quartic potential. Using numerical results, we find different features for different effective dynamics. Moreover, it is also found that the spacetime singularity is absent and the cosmological bounce exists in each effective dynamics of this model.

gr-qc↗

arXiv2022

Non-neglectable entropy effect on sintering of supported nanoparticles

Sintering refers to particle coalescence by heat, which has been known as a thermal phenomenon involving all aspects of natural science for centuries. It is particularly important in heterogeneous catalysis because normally sintering results in deactivation of the catalysts. In previous studies, the enthalpy contribution was considered to be dominant in sintering and the entropy effect is generally considered neglectable. However, we unambiguously demonstrate in this work that entropy could prevail over the enthalpy contribution to dominate the sintering behavior of supported nanoparticles (NPs) by designed experiments and improved theoretical framework. Using in situ Cs-corrected environmental scanning transmission electron microscopy and synchrotron-based ambient pressure X-ray photoelectron spectroscopy, we observe the unprecedent entropy-driven phenomenon that supported NPs reversibly redisperse upon heating and sinter upon cooling in three systems (Pd-CeO2, Cu-TiO2, Ag-TiO2). We quantitatively show that the configurational entropy of highly dispersed ad-atoms is large enough to reverse their sintering tendency at the elevated temperature. This work reshapes the basic understanding of sintering at the nanoscale and opens the door for various de-novo designs of thermodynamically stable nanocatalysts.

cond-mat.mtrl-sci↗

arXiv2021

Nonclassical cosmological dynamics in the low-energy limit of loop quantum scalar-tensor theory

In previous work, we showed that in loop quantum cosmology of scalar-tensor theory (STT) with the holonomy correction the background equations of motion in the Jordan frame have two branches, i.e., the $b_{+}$ branch and the $b_{-}$ branch. In the low-energy limit, the $b_{+}$ branch of the equations of motion reproduce the equations of motion of classical STT while the $b_{-}$ branch of equations of motion do not reproduce the classical equations. In this paper, we investigate cosmological dynamics in an expanding universe whose background is described by the the $b_{-}$ branch of equations of motion of STT, and we especially focus on the dynamics of the perturbations in the low-energy limit because it is most relevant to the current observational range. First, we find a low-energy effective Hamiltonian which can yield the low-energy limit of the $b_{-}$ branch of equations, this Hamiltonian consists of constraints whose constraint algebra is different from the classical case but also closed up to arbitrary order of perturbations. Remarkably, we find that this Hamiltonian can be transformed into the Hamiltonian of the Einstein frame by field redefinitions. Moreover, we also develop the linear cosmological perturbation theory and apply it to study the slow-roll inflation. Finally, we study a specific model of STT. In this model, a contracting universe described by classical STT in the remote past can pass through the bounce and evolve into an expanding universe whose background dynamics is described by the $b_{-}$ branch of equations of motion. It is shown that the slow-roll inflation can take place in this case, and the spectral indices of the slow-roll inflation agree well with the observations. The results in this paper indicate that there exists an alternative consistent theory which is different from the classical theory in the low-energy limit of loop quantum STT.

gr-qc↗

arXiv2021

Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna's directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

eess.SP↗

arXiv2021

High-speed and single-mode FP laser based on parity-time symmetry

The ability to manipulate cavity resonant modes is of critical importance in laser physics and applications. By exploiting the parity time (PT) symmetry, we propose and experimentally realize a single-mode FP laser with improved output power and high-speed modulation have been demonstrated. The proposed PT symmetric laser consists of two coupled structurally identical FP resonators. The gain and loss in two FP resonators can be manipulated independently by changing the injection currents. In the PT symmetric FP laser, single-mode operation is accomplished by selectively breaking of PT symmetry depending solely on the relation between gain-loss and coupling. Single-mode lasing with output power of 1.7 dBm and a sidemode suppression ratio (SMSR) exceeding 24 dB is demonstrated. The 3 dB bandwidth of 7.9 GHz is achieved and clear eye-openings were obtained for 2.5 Gbps and 10Gbps NRZ operation over 10 km single-mode fibers. Furthermore, the PT symmetry breaking is experimentally confirmed with measured loss and coupling coefficient of two FP resonators. The influence of cavity length, facet reflectivity, and electrical isolation between two P-side electrodes on the side mode suppression ratio and output optical power is also been demonstrated, paving the way for further improvement of the PT symmetric FP laser.

physics.optics↗

arXiv2021

The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo

Particle filtering methods are widely applied in sequential state estimation within nonlinear non-Gaussian state space model. However, the traditional particle filtering methods suffer the weight degeneracy in the high-dimensional state space model. Currently, there are many methods to improve the performance of particle filtering in high-dimensional state space model. Among these, the more advanced method is to construct the Sequential Makov chian Monte Carlo (SMCMC) framework by implementing the Composite Metropolis-Hasting (MH) Kernel. In this paper, we proposed to discrete the Zig-Zag Sampler and apply the Zig-Zag Sampler in the refinement stage of the Composite MH Kernel within the SMCMC framework which is implemented the invertible particle flow in the joint draw stage. We evaluate the performance of proposed method through numerical experiments of the challenging complex high-dimensional filtering examples. Nemurical experiments show that in high-dimensional state estimation examples, the proposed method improves estimation accuracy and increases the acceptance ratio compared with state-of-the-art filtering methods.

stat.CO↗

arXiv2021

Device-Independent-Quantum-Randomness-Enhanced Zero-Knowledge Proof

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, non-interactive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain and social networks. However, the heuristic is typically built upon the random oracle model making ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol. Here, we present a quantum resolution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test and delivers them with postquantum cryptography (PQC) authentication. Employing this service, we conceive and implement a NIZKP of the three-colouring problem. By bridging together three prominent research themes, quantum non-locality, PQC and ZKP, we anticipate this work to open a new paradigm of quantum information science.

quant-ph↗

arXiv2021

Graph Random Neural Network for Semi-Supervised Learning on Graphs

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS (GRAND) -- to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https://github.com/Grand20/grand.

cs.LG↗

arXiv2021

On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network

Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback. Recently, reconfigurable intelligent surfaces (RISs) emerge as a promising technology to enhance the performance of future wireless networks. However, since the artificial electromagnetic characteristics of RISs do not strictly follow the normal laws of nature, it brings up a question: does the channel reciprocity hold in RIS-assisted TDD wireless networks? After briefly reviewing the reciprocity theorem, in this article, we show that there still exists channel reciprocity for RIS-assisted wireless networks satisfying certain conditions. We also experimentally demonstrate the reciprocity at the sub-6 GHz and the millimeter-wave frequency bands by using two fabricated RISs. Furthermore, we introduce several RIS-assisted approaches to realizing nonreciprocal channels. Finally, potential opportunities brought by reciprocal/nonreciprocal RISs and future research directions are outlined.

cs.IT↗

arXiv2021

Enhancing Crystal Structure Prediction by decomposition methods based on graph theory

Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows exponentially with system size. In this work, we proposed two crossover-mutation schemes based on graph theory to accelerate the evolutionary structure searching. These schemes can detect molecules or clusters inside periodic networks using quotient graphs for crystals and the decomposition can dramatically reduce the searching space. Sufficient examples for the test, including the high pressure phases of methane, ammonia, MgAl2O4, and boron, show that these new evolution schemes can obviously improve the success rate and searching efficiency compared with the standard method in both isolated and extended systems.

cond-mat.mtrl-sci↗

arXiv2021

A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. In this article, we propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method. The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.

cs.LG↗

arXiv2021

Interplay between RIS and AI in Wireless Communications: Fundamentals, Architectures, Applications, and Open Research Problems

Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI; specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.

cs.IT↗