arXiv subjects

Zhiyuan Xie

Publications and source records attributed to Zhiyuan Xie.

10 recordsLinked to original sources

arXiv2026

Designing electronic magnetoelectric matter with organic quantum spin trimers

Magnetoelectric (ME) phenomena are commonly driven by spin-lattice coupling. Here we demonstrate a different route based on frustrated quantum spin trimers that intrinsically intertwine magnetic moments and electric dipoles. Using molecular design principles, we realize a weakly coupled lattice of equilateral $S=1/2$ spin trimers in the organic radical crystal TNN$\cdot$CH$_3$CN. In this material, correlated electronic fluctuations within each trimer generate electric dipoles, while geometrically frustrated intertrimer interactions organize them into collective ME states. Magnetization, thermodynamic, and dielectric measurements reveal multiple magnetic-field-induced phases, including the $1/3$-magnetization plateau marked by pronounced dielectric anomalies. Effective low-energy theories and numerical simulations show that these phenomena are driven by electronically generated trimer dipoles whose collective order is stabilized by frustration relief of the intertrimer interactions, establishing a direct connection between geometric frustration and emergent magnetoelectricity. Our results identify quantum spin trimers as multifunctional building blocks, providing a bottom-up route for designing correlated ME materials from electronically active quantum spin clusters.

cond-mat.str-el↗

arXiv2026

Continuous-wave, high-resolution, ultra-broadband mid-infrared nonlinear spectroscopy with tunable plasmonic nanocavities

Vibrational sum- and difference-frequency generation (SFG and DFG) spectroscopy probes the nonlinear response of interfaces at mid-infrared (MIR) wavelengths while detecting upconverted signals in the visible. Recent work has moved from large-area films and colloids to nanoscale structures using dual-resonant plasmonic nanocavities that co-confine light and matter in deep-subwavelength volumes. Here we implement high-resolution ($<1$~cm$^{-1}$), continuous-wave ultrabroadband vSFG, vDFG, and four-wave mixing (FWM) coherent spectroscopy from 860 to 1670~cm$^{-1}$ on dual-resonant antennas under ambient conditions. Using a commercial, broadly tunable quantum-cascade laser and eliminating geometric phase matching simplify acquisition and expand spectral reach. The resulting spectra exhibit coherent interference between resonant (vibrational) and nonresonant (electronic) contributions to the effective $χ^{(2)}$, previously accessible only under fs/ps excitation. Simultaneous measurement of SFG and DFG enables a {ratiometric} analysis that suppresses common-mode drifts and helps reveal vibrational resonances. We demonstrate versatility and reproducibility across several analytes that span distinct relative strengths of vibrational vs. electronic nonlinearities. Together, these capabilities position our approach as a scalable route to multiplexed, high-resolution MIR sensing and a practical basis for chip-level, label-free coherent spectroscopy. It opens a feasible path toward single- and few-molecule optomechanical studies using nanoscale trapping strategies.

physics.optics↗

arXiv2026

Study of Class-Incremental Radio Frequency Fingerprint Recognition Without Storing Exemplars

The rapid proliferation of wireless devices makes robust identity authentication essential. Radio Frequency Fingerprinting (RFF) exploits device-specific, hard-to-forge physical-layer impairments for identification, and is promising for IoT and unmanned systems. In practice, however, new devices continuously join deployed systems while per-class training data are limited. Conventional static training and naive replay of stored exemplars are impractical due to growing class cardinality, storage cost, and privacy concerns. We propose an exemplar-free class-incremental learning framework tailored to RFF recognition. Starting from a pretrained feature extractor, we freeze the backbone during incremental stages and train only a classifier together with lightweight Adapter modules that perform small task-specific feature adjustments. For each class we fit a diagonal Gaussian Mixture Model (GMM) to the backbone features and sample pseudo-features from these fitted distributions to rehearse past classes without storing raw signals. To improve robustness under few-shot conditions we introduce a time-domain random-masking augmentation and adopt a multi-teacher distillation scheme to compress stage-wise Adapters into a single inference Adapter, trading off accuracy and runtime efficiency. We evaluate the method on large, self-collected ADS-B datasets: the backbone is pretrained on 2,175 classes and incremental experiments are run on a disjoint set of 669 classes with multiple rounds and step sizes. Against several representative baselines, our approach consistently yields higher average accuracy and lower forgetting, while using substantially less storage and avoiding raw-data retention. The proposed pipeline is reproducible and provides a practical, low-storage solution for RFF deployment in resource- and privacy-constrained environments.

eess.SP↗

arXiv2025

Transmitter Identification via Volterra Series Based Radio Frequency Fingerprint

The growing number of wireless devices increases the need for secure network access. Radio Frequency Fingerprinting (RFF), a physical-layer authentication method, offers a promising solution as it requires no cryptography and resists spoofing. However, existing RFF approaches often lack a unified theory and effective feature extraction. Many methods use handcrafted signal features or direct neural network classification, leading to limited generalization and interpretability. In this work, we model the transmitter as a black box and analyze its impact on transmitted signals. By treating the deviation from an ideal signal as hardware-induced distortion, we represent the received signal using a Volterra series, using its kernels to capture linear and nonlinear hardware traits. To manage the high dimensionality of these kernels, we approximate them via wavelet decomposition and estimate coefficients through least-squares fitting. The resulting wavelet coefficients provide compact yet informative hardware representations, which are classified using a complex-valued neural network. Experiments on a public LoRa dataset show state-of-the-art performance, with over 98% accuracy in static channels and above 90% under multipath and Doppler effects. The proposed approach improves both interpretability and generalization across varying channel conditions.

cs.CR↗

arXiv2025

Two Plaquette-Singlet Phases and Emergent SO(5) Deconfined Quantum Criticality in SrCu2(BO3)2

The deconfined quantum critical point (DQCP) has become a central open concept in the physics of quantum matter, and its proposed presence in the Shastry-Sutherland model was followed by the experimental observation of at least a minimal DQC scenario induced by an applied magnetic field in SrCu$_2$(BO$_3$)$_2$. However, the nature of the plaquette-singlet phase in SrCu$_2$(BO$_3$)$_2$ remains unresolved, and with it the identification of the DQCP symmetry from among several theoretical scenarios. Here we perform detailed high-pressure $^{11}$B NMR studies to reveal the presence of both the full-plaquette (FP) and empty-plaquette (EP) phases in SrCu$_2$(BO$_3$)$_2$, phase-separated at a first-order, pressure-driven transition with a volume-fraction effect. The field-driven transition from the EP to the antiferromagnetic (AFM) phase complements our previous observations of the FP--AFM transition, with both showing deconfined quantum criticality, while the scaling of the spin-lattice relaxation rate near the EP--AFM transition, $1/T_1 \propto T^{0.6}$, suggests a DQCP governed by a different universality class. We discuss possible extensions to the Shastry-Sutherland model that account for these pressure and field effects. The expanded phase space we discover mandates an SO(5) DQCP symmetry, and hence our results take an important step towards a complete understanding of deconfined quantum criticality in SrCu$_2$(BO$_3$)$_2$.

cond-mat.str-el↗

arXiv2024

Light Emission and Conductance Fluctuations in Electrically Driven and Plasmonically Enhanced Molecular Junctions

Electrically connected and plasmonically enhanced molecular junctions combine the optical functionalities of high field confinement and enhancement (cavity function), and of high radiative efficiency (antenna function) with the electrical functionalities of molecular transport. Such combined optical and electrical probes have proven useful for the fundamental understanding of metal-molecule contacts and contribute to the development of nanoscale optoelectronic devices including ultrafast electronics and nanosensors. Here, we employ a self-assembled metal-molecule-metal junction with a nanoparticle bridge to investigate correlated fluctuations in conductance and tunneling-induced light emission at room temperature. Despite the presence of hundreds of molecules in the junction, the electrical conductance and light emission are both highly sensitive to atomic-scale fluctuations -- a phenomenology reminiscent of picocavities observed in Raman scattering and of luminescence blinking from photo-excited plasmonic junctions. Discrete steps in conductance associated with fluctuating emission intensities through the multiple plasmonic modes of the junction are consistent with a finite number of randomly localized, point-like sources dominating the optoelectronic response. Contrasting with these microscopic fluctuations, the overall plasmonic and electronic functionalities of our devices feature long-term survival at room temperature and under an electrical bias of a few volts, allowing for measurements over several months.

cond-mat.mes-hall↗

arXiv2024

Direct and Indirect Hydrogen Storage: Dynamics and Interactions in the Transition to a Renewable Energy Based System for Europe

To move towards a low-carbon society by 2050, understanding the intricate dynamics of energy systems is critical. Our study examines these interactions through the lens of hydrogen storage, dividing it into 'direct' and 'indirect' hydrogen storage. Direct hydrogen storage involves electrolysis-produced hydrogen being stored before use, while indirect storage first transforms hydrogen into gas via the Sabatier process for later energy distribution. Firstly, we utilize the PyPSA-Eur-Sec-30-path model to capture the interactions within the energy system. The model is an hour-level, one node per country system that encompasses a range of energy transformation technologies, outlining a pathway for Europe to reduce carbon emissions by 95 percent by 2050 compared to 1990, with updates every 5 years. Subsequently, we employ both quantitative and qualitative approaches to thoroughly analyze these complex relationships. Our research indicates that during the European green transition, cross-country flow of electricity will play an important role in Europe's rapid decarbonization stage before the large-scale introduction of energy storage. Under the paper cost assumptions, fuel cells are not considered a viable option. This research further identifies the significant impact of natural resource variability on the local energy mix, highlighting indirect hydrogen storage as a common solution due to the better economic performance and actively fluctuation pattern. Specifically, indirect hydrogen storage will contribute at least 60 percent of hydrogen storage benefits, reaching 100 percent in Italy. Moreover, its fluctuation pattern will change with the local energy structure, which is a distinct difference with the unchanged pattern of direct hydrogen storage and battery storage.

eess.SY↗

arXiv2022

GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture thatincorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score. The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis.

eess.IV↗

arXiv2015

Approaching conformality with the Tensor Renormalization Group method

We discuss the Tensor Renormalization Group (TRG) method for the O(2) model with a chemical potential in 1+1 dimensions with emphasis on near gapless/conformal situations. We emphasize the role played by the late Leo Kadanoff in the development of this theoretical framework. We describe the entanglement entropy in the superfluid phase (see arXiv:1507.01471 for details). We present recent progress on optimized truncation methods.

hep-lat↗

arXiv2013

Comparing Tensor Renormalization Group and Monte Carlo calculations for spin and gauge models

We show that the Tensor Renormalization Group (TRG) method can be applied to O(N) spin models, principal chiral models and pure gauge theories (Z2, U(1) and SU(2)) on (hyper) cubic lattices. We explain that contrarily to some common belief, it is very difficult to write compact formulas expressing the blockspinning of lattice models. We show that in contrast to other approaches, the TRG formulation allows us to write exact blocking formulas with numerically controllable truncations. The basic reason is that the TRG blocking separates neatly the degrees of freedom inside the block and which are integrated over, from those kept to communicate with the neighboring blocks. We argue that the TRG is a method that can handle large volumes, which is crucial to approach quasi-conformal systems. The method can also get rid of some sign problems. We discuss recent results regarding the critical properties of the 2D O(2) nonlinear sigma model with complex beta and chemical potential. As some of these results appeared in a recently published paper (PRD 88, 056005) and two recent preprints (arXiv:1309.4963 and arXiv:1309.6623), these proceedings rather emphasize the conceptual aspects of our ongoing effort.

hep-lat↗