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Jiang Xiao

Publications and source records attributed to Jiang Xiao.

At least 19 recordsLinked to original sources

Security Limits of Mining Before Validation in Nakamoto Consensus

Mining before validation allows miners to extend a newly received block before completing its validity checks, giving them a head start in the race for the next block reward. This head start, however, comes with a security risk: rejecting one invalid block also discards the honest work built on it, an effect missed when validation is treated as instantaneous. We quantify this risk in a model of Nakamoto consensus with bounded network delay and a validation-time bound independent of processing load. We establish an explicit threshold on adversarial mining power below which honest miners' fully validated chains continue to grow and agree on a stable history with high probability over any fixed observation period. We also construct an attack that repeatedly draws honest mining onto invalid branches. When the adversary produces more than one block on average during the allowed validation time, the attack can eventually remove a target block at any fixed initial confirmation depth. Combined with ordinary private mining, this attack yields a matching asymptotic bound in the fully decentralized regime, where each honest miner has negligible mining power. The results show how validation latency limits the security of mining before validation, beyond the constraint imposed by network delay.

cs.CR↗

Decoupled domain-texture switching from magnetic easy axis in kagome ferromagnet EuTi3Bi4

Magnetic anisotropy defines the easy axis of a magnetic material and governs the spatial arrangement of its domains. To date, anisotropy engineering has focused on reorienting the easy axis or tuning the anisotropy energy, both of which demand substantial energy input. Here, we demonstrate that magnetic domain textures can be switched without reorienting the easy axis, as observed in a kagome ferromagnet EuTi3Bi4 crystal. Using low-temperature magnetic force microscopy, we observe that the preferred orientation of magnetic domains switches from the a-axis to the b-axis upon temperature variation, and that this switching can also be triggered by an out-of-plane magnetic-field reset. Magnetization measurements and density functional theory calculations confirm a robust c-axis easy magnetization, ruling out a conventional spin-reorientation transition. Instead, the texture switching is governed by the temperature dependence of the in-plane variation of the Magnetic anisotropy energy landscape, which arises from two competing interactions with different decay rates: single-ion anisotropy favors a-oriented spin components, while nearest-neighbor anisotropic exchange favors b-oriented ones. Furthermore, the critical switching temperature is substantially elevated in a mechanically exfoliated EuTi3Bi4 flake. Our findings establish that macroscopic magnetic textures can be effectively manipulated by tuning the competition between in-plane anisotropic interactions, without the energy cost of reorienting the easy axis.

cond-mat.mtrl-sci↗

Two local zero-sum problems

In the present paper, we investigate two local zero-sum problems. Let $n,k\ge 2$. We denote by $\mathsf{D}^*(n,nk)$ (resp. $η^{*}(n,nk)$) the smallest positive integer $\ell$ (if exists) such that, from any given $\ell$ integers not divisible by $n$, one can select some (resp. at most $n$) of them whose sum is divisible by $n$ but not by $nk$. We prove that both $\mathsf{D}^*(n,nk)$ and $η^{*}(n,nk)$ are equal to $2n-1$ if $\mathrm{rad}(n) \mid \mathrm{rad}(k)$ and infinite otherwise. The corresponding inverse problem is also determined. We denote by $\mathsf{D}_n^{\times}$ (resp. $η_n^{\times}$) the smallest positive integer $\ell$ such that, from any given $\ell$ integers coprime to $n$, one can select some (resp. at most $n$) of them whose sum $σ$ satisfies $\gcd(σ, n^2)=n$. We prove that $\mathsf{D}_n^{\times}=η_n^{\times}=2n-1$ if $n$ is a prime power, and determine its inverse problem.

math.NT↗

Controllable highly oriented skyrmion track array in Fe3GaTe2

Magnetic skyrmions are emerging as promising candidates for next-generation information technologies, while the realization of scalable skyrmion lattices with tailored configurations is essential for advancing fundamental skyrmion physics and developing future applications. Here we achieved the controllable generation and regulation of a large-area, highly oriented skyrmion track array (STA) in ferromagnetic Fe3GaTe2 using a vector magnetic field manipulation technique. The orientation and ordering of STA, along with the types and density of skyrmions, are precisely controlled by modulating parameters during the manipulation. The critical roles of in-plane magnetic fields and Dzyaloshinskii-Moriya interaction in STA generation is further confirmed by micromagnetic simulation. Our findings develop a strategy for engineering large-area and highly-oriented skyrmion configurations, offering a new pathway for the future application of next-generation spintronic and information technologies.

cond-mat.mtrl-sci↗

Tunable bifurcation of magnetic anisotropy and bi-oriented antiferromagnetic order in kagome metal GdTi3Bi4

The novel kagome family RTi3Bi4 (R: rare-earth) offers a unique platform for exploring distinctive physical phenomena such as anisotropy, spin density wave, and anomalous Hall effect. In particular, the magnetic frustration and behavior of magnetic anisotropy in antiferromagnetic (AFM) kagome materials are of great interest for the fundamental studies and hold promise for next-generation device applications. Here, we report a tunable bifurcation of magnetic anisotropic and bi-oriented AFM order observed in the quasi-1D kagome antiferromagnet GdTi3Bi4. The magnetic domain evolutions during two plateau transition processes are directly visualized, unveiling a pronounced in-plane anisotropy along the a-axis. Temperature-dependent characterization reveals a bifurcation transition of anisotropy at approximately 2 K, where the a-axis anisotropy splits into two special orientations, revealing a hidden bi-oriented in-plane AFM order deviating from the high-symmetry direction by 7 degree. More intriguingly, the characteristics of the bifurcated anisotropy are clearly illustrated through vector magnetic field modulation, revealing three distinct in-plane domain phases in the transverse magnetic field phase diagram. Our results not only provide valuable insights into the tunable bifurcation of magnetic anisotropic in GdTi3Bi4, but also pave a novel pathway for AFM spintronics development.

cond-mat.str-el↗

Coherent perfect absorption of anti-modes in an indirect coupled magnon-polariton system

In this work, we report coherent perfect absorption (CPA) of anti-modes in an indirectly coupled magnon--polariton system. By examining both single and indirectly coupled cases, we experimentally distinguish the modal decay rate $γ$ from the effective decay rate $γ_{\rm{eff}}$. At CPA, $γ_{\rm{eff}} = 0$, leading to a vanishing output and a visually narrow spectrum in the dB-scale, while the intrinsic linewidth set by $2γ$ remains unchanged, demonstrating that the effective decay rate dictates the spectral amplitude rather than the physical loss. Furthermore, in the indirectly coupled system, CPA persists over a broad, magnetically tunable detuning range, in contrast to the single-detuning CPA observed in the directly coupled case, thereby enabling magnetically reconfigurable and frequency-selective microwave absorbers.

cond-mat.mes-hall↗

Long-distance spin transport in frustrated hyperkagome magnet Gd3Ga5O12

Transport of spin angular momentum over large distance has been a long sought-after goal in the field of spintronics. While the majority of the research effort has been devoted to the spin transport properties of magnetically ordered materials, spin transport in magnetically frustrated materials has received little attention. Here, we report an anomalous state in frustrated hyperkagome magnetic insulator Gd3Ga5O12, where spin angular momenta can be transported over a long distance of 480 μm, far exceeding the transport distance of any diffusive spin current in magnetically ordered materials, to the best of our knowledge. Monte Carlo simulations reveal significant spin fluctuations, spin-spin correlations and an absence of conventional magnons in such anomalous state; while the response of the anomalous state to perturbation is found to be akin to an overdamped forced oscillator. We find close relation of such state to the correlated ``director'' state in the material. Our result provides an effective electrical technique to characterize spin-spin correlations and frustrations; it also unveils the potential of frustrated magnets as powerful channel materials for spin transport.

cond-mat.mtrl-sci↗

Quantum Entanglement and Teleportation of Magnons in Coupled Spin Chains

This study explores how entanglement and quantum teleportation of magnons can be achieved in coupled spin chain systems. By utilizing different magnetic configurations, we show that parallel spin chains function like magnonic beam splitters, whereas anti-parallel chains produce two-magnon squeezing and strong entanglement. Combining these components, we design magnonic circuits capable of continuous-variable quantum entanglement and teleportation, supported by quantum Langevin simulations.

cond-mat.mes-hall↗

Wave Computing based on Dynamical Networks: Applications in Optimization Problems

We develop a computing framework that leverages wave propagation within an interconnected network, where nodes and edges possess wave manipulation capabilities, such as frequency mixing or time delay. This computing paradigm can not only achieve intrinsic parallelism like existing works by the exploration of an exponential number of possibilities simultaneously with very small number of hardware units, but also extend this unique characteristic to a multidimensional space including spatial, temporal and frequency domains, making it particularly effective for addressing NP-hard problems. The proposed architecture has been validated through SPICE simulations, demonstrating its potential capability in solving several NP-hard problems, such as the Number Partitioning Problem, the 0/1 Knapsack Problem, and the Traveling Salesman Problem.

cs.ET↗

Anatomy of Spin Wave Polarization in Ferromagnets

Spin waves in ferromagnetic materials are predominantly characterized by right-handed circular polarization due to symmetry breaking induced by net magnetization. However, magnetic interactions, including the external magnetic field, Heisenberg exchange, Dzyaloshinskii-Moriya interaction, and dipole-dipole interaction, can modify this behavior, leading to elliptical polarization. This study provides a systematic analysis of these interactions and their influence on spin wave polarization, establishing principles to predict traits such as polarization degree and orientation based on equilibrium magnetization textures. The framework is applied to diverse magnetic configurations, including spin spirals, domain walls, and Skyrmions, offering a comprehensive yet simple approach to understanding polarization dynamics in ferromagnetic systems.

cond-mat.mes-hall↗

Alternating Spintronics: Capacitive Behavior of Spin Valves and Resonator Applications

This study explores the time-dependent spin transport phenomena in magnetic heterostructures under alternating currents (AC), advancing the relatively underdeveloped field of alternating spintronics. Employing a time-dependent spin diffusion model, we show that the interplay of AC frequencies and spin relaxation times reveals significant differences in spin accumulation patterns compared to conventional direct current (DC) scenarios. Of particular interest is the emergence of capacitive-like impedance in a spin valve under AC conditions, which is especially pronounced in antiparallel spin configurations. These findings open up possibilities for developing high-frequency spintronic devices, including the proposed "spin resonator", which functions like a standard LC resonator but without a traditional capacitor.

cond-mat.mes-hall↗

Micromagnetic Study of the Dipolar-Exchange Spin Waves in Antiferromagnetic Thin Films

In antiferromagnets, dipolar coupling is often disregarded due to the cancellation of magnetic moments between the two sublattices, leaving spin-wave dispersion predominantly determined by exchange interactions. However, antiferromagnetic spin waves typically involve a slight misalignment of the magnetic moments on the sublattices, giving rise to a small net magnetization that enables long-range dipolar coupling. In this paper, we investigate the role of this dipolar coupling in spin-wave excitations and its influence on the spin-wave dispersion. Our findings show that: (i) when the Néel vector is perpendicular to the film plane or lies within the film plane and parallel to the wave vector, the dispersion branches can be divided into two groups -- those unaffected by the dipolar field and those influenced by it. In these cases, the total magnetic moment remains linearly polarized, but the polarization directions differ between the two types of branches; (ii) when the Néel vector lies in the film plane and is perpendicular to the wave vector, the dipolar interactions affect both types of dispersion branches, leading to their hybridization. This hybridization alters the polarization of the magnetic moment, resulting in elliptical polarization.

cond-mat.mes-hall↗

Falcon: Advancing Asynchronous BFT Consensus for Lower Latency and Enhanced Throughput

Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous Common Sub-seQuence (ACSQ). The ACSQ protocol consists of two primary components: the Asynchronous Common Subset (ACS) protocol and a block sorting mechanism, with the ACS protocol comprising two stages: broadcast and agreement. However, current protocols encounter three critical issues: high latency arising from the execution of the agreement stage, latency instability due to the integral-sorting mechanism, and reduced throughput caused by block discarding. To address these issues,we propose Falcon, an asynchronous BFT protocol that achieves low latency and enhanced throughput. Falcon introduces a novel broadcast protocol, Graded Broadcast (GBC), which enables a block to be included in the ACS set directly, bypassing the agreement stage and thereby reducing latency. To ensure safety, Falcon incorporates a new binary agreement protocol called Asymmetrical Asynchronous Binary Agreement (AABA), designed to complement GBC. Additionally, Falcon employs a partial-sorting mechanism, allowing continuous rather than simultaneous block committing, enhancing latency stability. Finally, we incorporate an agreement trigger that, before its activation, enables nodes to wait for more blocks to be delivered and committed, thereby boosting throughput. We conduct a series of experiments to evaluate Falcon, demonstrating its superior performance.

cs.DC↗

Boosting End-to-End Database Isolation Checking via Mini-Transactions (Extended Version)

Transactional isolation guarantees are crucial for database correctness. However, recent studies have uncovered numerous isolation bugs in production databases. The common black-box approach to isolation checking stresses databases with large, concurrent, randomized transaction workloads and verifies whether the resulting execution histories satisfy specified isolation levels. For strong isolation levels such as strict serializability, serializability, and snapshot isolation, this approach often incurs significant end-to-end checking overhead during both history generation and verification. We address these inefficiencies through the novel design of Mini-Transactions (MTs). MTs are compact, short transactions that execute much faster than general workloads, reducing overhead during history generation by minimizing database blocking and transaction retries. By leveraging MTs' read-modify-write pattern, we develop highly efficient algorithms to verify strong isolation levels in linear or quadratic time. Despite their simplicity, MTs are semantically rich and effectively capture common isolation anomalies described in the literature. We implement our verification algorithms and an MT workload generator in a tool called MTC. Experimental results show that MTC outperforms state-of-the-art tools in both history generation and verification. Moreover, MTC can detect bugs across various isolation levels in production databases while maintaining the effectiveness of randomized testing with general workloads, making it a cost-effective solution for black-box isolation checking.

cs.DB↗

The Connection between Spin Wave Polarization and Dissipation

This study establishes a fundamental connection between the dissipation and polarization of spin waves, which are often treated as independent phenomena. Through theoretical analysis and numerical validation, we demonstrate that within the linearized spin wave regime, a spin wave mode's dissipation rate, defined as the ratio of linewidth to the resonance frequency, exceeds Gilbert damping by a factor given by its spatially averaged polarization. This average is governed by a non-positive definite weight, whose magnitude depends on the magnon density of the local excitation, while its sign is dictated by the local polarization handedness. Remarkably, this universal connection applies across diverse magnetic interactions and textures, offering crucial insights into spin wave dynamics and dissipation.

cond-mat.mes-hall↗

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

Physical neural networks using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training physical neural networks are difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e. training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via autonomous physical process, eliminating the requirements on external computation resources.Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN) and unsupervised learning is intrinsically performed via the evolution of physical process. Using magnetic texture defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform towards efficient autonomous training directly in materials.

cond-mat.dis-nn↗

Dynamic Control of Coupling Regimes in Oscillator Systems via Tunable Open Channels

We explore a system comprising two oscillators that are coupled to an open channel at distinct locations. The coupling nature can be adjusted to be coherent, dissipative, or a combination of both, controlled by a tunable phase resulting from wave propagation between the oscillators. This setup allows us to observe characteristic energy level behaviors: level repulsion occurs with coherent coupling, while level attraction is observed with dissipative coupling. In the regime of dissipative coupling, one of the eigenmodes becomes a dark mode, rendering it immune to external perturbations. By leveraging the tunable coupling through the open channel, we introduce a novel method for manipulating this dark mode, achieving both efficient excitation and an extended lifetime. Our results have broad applicability across various physical systems, including optical, acoustic, and magnetic configurations, underscoring the potential for innovative applications in mode storage and signal processing.

cond-mat.mes-hall↗

Physical Neural Networks with Self-Learning Capabilities

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials. These networks harness the distinctive characteristics of physical systems to carry out computations effectively, potentially surpassing the constraints of conventional digital neural networks. A recent advancement known as ``physical self-learning'' aims to achieve learning through intrinsic physical processes rather than relying on external computations. This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems. Prevailing learning strategies are discussed that contribute to the realization of physical self-learning. Despite challenges in understanding fundamental mechanism of learning, this work highlights the progress towards constructing intelligent hardware from the ground up, incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

physics.app-ph↗