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Feng Ye

Publications and source records attributed to Feng Ye.

At least 37 records · Page 2Linked to original sources
arXiv2026

Magnetism and magnetoelastic effect in 2D van der Waals multiferroic CuCrP2S6

We report a magnetic and neutron diffraction study on the ground state magnetism and field evolution of single crystal van der Waals multiferroic CuCrP2S6. The ordered moments align along the b axis in the A-type antiferromagnetic configuration with a spin-flop transition along the same direction. Field application along a introduces a smooth transition to a fully-polarized ferromagnetic state via in-plane spin rotation. These findings resolve the ambiguity of the ground state magnetization direction in CuCrP2S6 and uncover its field responses, providing a firm basis for future magnetoelectric study. A magnetoelastic coupling effect connecting the interlayer spacing and the magnetic order was further revealed, highlighting the out-of-plane strain as an effective control knob for tuning magnetism both in this system and in related van der Waals magnets.

cond-mat.str-el↗
arXiv2026

Evidence for itinerant electron-local moment interaction in Li-doped $α$-MnTe

We use inelastic neutron scattering (INS) and angle-resolved photoemission spectroscopy (ARPES) to study the impact of Li doping on the semiconducting altermagnet $α$-MnTe. Introducing Li results in a spin reorientation from in-plane to out-of-plane direction and increases the density of itinerant carriers. While our ARPES measurements do not indicate any notable doping-induced changes in the electronic band structure or the magnitude of the altermagnetic band splitting, our INS measurements reveal an abrupt carrier-induced decrease in the spin wave lifetime near the zone boundary at high energies. This finding is consistent with a new magnon decay channel driven by doping-induced subtle changes in the band structure and enhanced interactions between Mn$^{2+}$ local moments and itinerant electrons. By extracting the local dynamic susceptibility from INS spectra and applying the total moment sum rule, we find that both undoped and Li-doped $α$-MnTe exhibit the full expected Mn$^{2+}$ local moment of $\approx5.9~μ_\mathrm{B}$ with $S=5/2$. These findings suggest that $α$-MnTe hosts robust local-moment altermagnetism which shows a breakdown at high energies upon addition of itinerant carriers, highlighting the importance of carrier-spin coupling in magneto-transport and spin dynamic properties of altermagnets even in the dilute-carrier limit.

cond-mat.str-el↗
arXiv2026

Fine-Grained Network Traffic Classification with Contextual QoS Profiling

Accurate network traffic classification is vital for managing modern applications with strict Quality of Service (QoS) demands, such as edge computing, real-time XR, and autonomous systems. While recent advances in application-level classification show high accuracy, they often miss fine-grained in-app QoS variations critical for service differentiation. This paper proposes a hierarchical graph neural network (GNN) framework that combines a three-level graph representation with an automated QoS-aware assignment algorithm. The model captures multi-scale temporal patterns via packet aggregation, time-window clustering, and session-level behavior modeling. QoS priorities are derived using five key metrics (bandwidth, jitter, packet stability, burst frequency, and burst stability), processed through logarithmic transformation and weighted ranking. Evaluations across 14 usage scenarios from YouTube, Prime Video, TikTok, and Zoom show that the proposed GNN significantly outperforms state-of-the-art methods in service-level classification. The QoS-aware assignment further refines classification to enhance user experience. This work advances QoS-aware traffic classification by enabling precise in-app usage differentiation and adaptive service prioritization in dynamic network environments.

cs.NI↗
arXiv2026

Radio Radiance Field: The New Frontier of Spatial Wireless Channel Representation

Massive MIMO, among other ground-breaking technologies, is being developed for the next-generation wireless systems to support requirements in terms of data rates, reliability, latency, intelligence, security and energy efficiency. Accurate channel estimation remains a key challenge in fully exploiting massive MIMO. While recent research has explored aspects such as near-field effects, spatial non-stationarity, and channel sparsity, many practical estimation and modeling techniques still provide limited CSI, often dominated by aggregate channel gain and delay, without full spatial characteristics. Although wideband models and phased-array techniques can capture delay and angular information, many practical estimation methods still lack comprehensive spatial resolution, including polarization, which limits their effectiveness for advanced massive MIMO techniques. This article introduces the concept of radio radiance field (RRF), which captures the spatial distribution and directionality of radio propagation. From RRF, a comprehensive spatial representation of the wireless channel, referred to as Spatial-CSI, can be derived. Owing to the comprehensive geometric and radio information, RRF can be implemented directly for beamforming, delay-alignment modulation, and many other techniques in massive MIMO and reflective intelligent surface implementations. An RRF can also serve as a digital radio twin, which is a virtual representation of the radio environment that includes both geometric structure and radio propagation characteristics, enabling real-time simulation and optimization of wireless systems. It paves the way for various applications from communications to sensing in the next-generation wireless communication systems.

cs.NI↗
arXiv2026

Long-range magnetic order with disordered spin orientations in a high-entropy antiferromagnet

Disorder in magnetic systems typically suppresses long-range order, promoting short-range states such as spin glasses and magnetic clusters. This is particularly prominent in high-entropy materials, characterized by the random distributions of local magnetic entities and exchange interactions. However, in rare exceptions, long-range magnetic order can persist in high-entropy systems, while the microscopic characters and underlying mechanisms remain elusive, especially the magnetic behaviors of individual elements. Here, combining neutron diffraction and resonant soft x-ray scattering, we have conducted an element-specific investigation into the magnetic order of a high-entropy honeycomb-lattice van der Waals material (Mn1/4Fe1/4Co1/4Ni1/4)PS3. Despite significant atomic disorder, long-range zigzag antiferromagnetic order is observed below 72 K, with all four transition-metal elements participating in a unified phase transition. However, the spin orientations of various elements are distinct, attributed to the competition between single-ion anisotropies and exchange interactions. Our findings showcase a novel form of long-range magnetic order with disordered spin orientations, which is synergically stabilized by distinct magnetic elements in a high entropy magnet, offering a new paradigm for understanding complex magnetic systems.

cond-mat.str-el↗
arXiv2026

Coexisting Paramagnetic Spins and Long-Range Magnetic Order in Ba$_4$(Ru$_{0.92}$Ir$_{0.08}$)$_3$O$_{10}$

We investigate the effect of dilute Ir substitution on the magnetism of the trimer-based ruthenate Ba$_4$Ru$_3$O$_{10}$ using neutron diffraction, magnetic susceptibility measurements, atomistic simulations, and first-principles calculations. Neutron diffraction shows that Ir doping preserves the zigzag antiferromagnetic structure and the ordered-moment magnitude of the parent compound, in which the moments reside exclusively on the two outer Ru(2) sites of each $\rm Ru_3O_{12}$ trimer, while the central Ru(1) site remains nonmagnetic. The Néel temperature is reduced from $\approx\!105$ K to 84.0(1) K upon 8% Ir substitution, while magnetic susceptibility reveals a pronounced low-temperature Curie-like upturn, indicating the coexistence of paramagnetic spins with long-range antiferromagnetic order. Density-functional calculations shows that Ir preferentially occupies the central Ru(1) site, where its extended $5d$ orbitals disrupt the Ru-Ru molecular-orbital network and intra/inter-trimer exchange pathways. Atomistic simulations incorporating this paramagnetic dilution reproduce the suppressed ordering temperature and the coexistence of ordered and paramagnetic components.

cond-mat.str-el↗
arXiv2026

LLM-supported 3D Modeling Tool for Radio Radiance Field Reconstruction

Accurate channel estimation is essential for massive multiple-input multiple-output (MIMO) technologies in next-generation wireless communications. Recently, the radio radiance field (RRF) has emerged as a promising approach for wireless channel modeling, offering a comprehensive spatial representation of channels based on environmental geometry. State-of-the-art RRF reconstruction methods, such as RF-3DGS, can render channel parameters, including gain, angle of arrival, angle of departure, and delay, within milliseconds. However, creating the required 3D environment typically demands precise measurements and advanced computer vision techniques, limiting accessibility. This paper introduces a locally deployable tool that simplifies 3D environment creation for RRF reconstruction. The system combines finetuned language models, generative 3D modeling frameworks, and Blender integration to enable intuitive, chat-based scene design. Specifically, T5-mini is finetuned for parsing user commands, while all-MiniLM-L6-v2 supports semantic retrieval from a local object library. For model generation, LLaMA-Mesh provides fast mesh creation, and Shap-E delivers high-quality outputs. A custom Blender export plugin ensures compatibility with the RF-3DGS pipeline. We demonstrate the tool by constructing 3D models of the NIST lobby and the UW-Madison wireless lab, followed by corresponding RRF reconstructions. This approach significantly reduces modeling complexity, enhancing the usability of RRF for wireless research and spectrum planning.

cs.NI↗
arXiv2026

Strain-tunable anomalous Hall effect in hexagonal MnTe

The ability to control and manipulate time-reversal ($T$) symmetry-breaking phases with near-zero net magnetization is a sought-after goal in spintronic devices. The recently discovered hexagonal altermagnet manganese telluride ($α$-MnTe) is a prime example. It has a compensated altermagnetic ground state where the magnetic moments are aligned in each layer and stacked antiparallel along the $c$ axis, yet it exhibits a spontaneous anomalous Hall effect (AHE) that breaks the $T$-symmetry with a vanishingly small $c$-axis ferromagnetic (FM) moment. However, the presence of three 120$^\circ$ separated in-plane magnetic domains presents a challenge in understanding the origin of the AHE and the effective control of the altermagnetic state. Here we use neutron scattering to show that symmetry breaking anisotropic strain, induced by compressive uniaxial pressure along the nearest-neighbor (NN) Mn-Mn bond directions, detwins $α$-MnTe into a single in-plane magnetic domain. This control over in-plane domains allows us to unambiguously establish that the in-plane moments are aligned along the NNN Mn-Mn bond direction, irrespective of the applied strain directions. Mounting the sample on a piezoelectric strain cell along both NN and NNN directions can drive the sample into a single-domain state that significantly sharpens the AHE hysteresis loop and extends the AHE to lower temperatures. Furthermore, tuning the uniaxial strain reverses the sign of the AHE near room temperature. Remarkably, this is achieved without altering the altermagnetic phase-transition temperature or substantially changing the small $c$-axis FM moment. Combined with our phenomenological model, we argue that these effects result from the modification of the electronic Berry curvature by a combination of both spin-orbit coupling and strain. (See the full abstract in the PDF.)

cond-mat.str-el↗
arXiv2026

Kondo driven suppression of charge density wave in Van der Waals material UTe$_3$

Competing electronic instabilities lie at the heart of emergent phenomena in quantum materials. In low-dimensional metals, Fermi-surface nesting can drive charge density wave (CDW) formation through a Peierls-like mechanism, while in strongly correlated systems, Kondo hybridization reconstructs the electronic structure by entangling localized moments with itinerant electrons. How these two fundamentally different instabilities interact$-$whether they coexist, compete, or mutually exclude each other$-$remains an open question. Here, we present suppression of charge density wave via the Kondo interaction in van der Waals material UTe$_3$. The angle-resolved photoemission spectroscopy (ARPES) data reveals Fermi surface nesting under similar conditions as seen in RETe$_3$ compounds. Despite that, no CDW is found in UTe$_3$ after an extensive search. We demonstrate that strong hybridization between U 5$f$ electrons and Te $p$ states reconstructs the low-energy electronic structure, removes the instability, and preempts CDW formation. Our results reveal a rare example where Kondo hybridization preempts density wave formation, offering a new route to controlling ordering phenomena in correlated 2D materials.

cond-mat.str-el↗
arXiv2026

Uniaxial strain tuned magnetism of the altermagnet candidate h-FeS

Altermagnets are collinear magnetic materials with 'alter'nating local crystalline environments, characterized by joint spin and crystalline symmetries that enable ferromagnetic-like transport properties but with vanishing net magnetization. Hexagonal FeS (h-FeS) is a recently identified altermagnet candidate that shows a spontaneous anomalous Hall effect (AHE) accompanied by a tiny net magnetization. Here, we show that both the spontaneous AHE and magnetization can be effectively suppressed by an in-plane compressive strain. Since neutron diffraction measurements show that the applied uniaxial strain only modifies the in-plane domain population but does not affect the in-plane magnetic structure, the major effect of the applied strain is to tune the small $c$-axis ferromagnetic moment. Our results demonstrate a strong correlation between the tiny net magnetization and the spontaneous AHE in h-FeS, and show that uniaxial strain provides an effective knob to tune both properties in this altermagnet candidate for spintronic applications.

cond-mat.mtrl-sci↗
arXiv2026

Accessibility and Serviceability Assessment to Inform Offshore Wind Energy Development and Operations off the U.S. East Coast

The economic success of offshore wind energy projects relies on accurate projections of the construction, and operations and maintenance (O&M) costs. These projections must consider the logistical complexities introduced by adverse met-ocean conditions that can prohibit access to the offshore assets for sustained periods of time. In response, the goal of this study is two-fold: (1) to provide high-resolution estimates of the accessibility of key offshore wind energy areas in the United States (U.S.) East Coast--a region with significant offshore wind energy potential; and (2) to introduce a new operational metric, called serviceability, as motivated by the need to assess the accessibility of an offshore asset along a vessel travel path, rather than at a specific site, as commonly carried out in the literature. We hypothesize that serviceability is more relevant to offshore operations than accessibility, since it more realistically reflects the success and safety of a vessel operation along its journey from port to site and back. Our analysis reveals high temporal and spatial variations in accessibility and serviceability, even for proximate offshore locations. We also find that solely relying on numerical met-ocean data can introduce considerable bias in estimating accessibility and serviceability, raising the need for a statistical treatment that combines both numerical and observational data sources, such as the one proposed herein. Collectively, our analysis sheds light on the value of high-resolution met-ocean information and models in supporting offshore operations, including but not limited to future offshore wind energy developments.

stat.AP↗
arXiv2026

Economic and Reliability Value of Improved Offshore Wind Forecasting in Bulk Power Grid Operation: A Case Study of The New York Power Grid

This study investigates the economic and reliability benefits of improved offshore wind forecasting for grid operations along the U.S. East Coast. We introduce and evaluate a state-of-the-art, machine-learning-based offshore wind forecasting model tailored for this region by integrating its improved forecasts into a dynamic reserve procurement framework aligned with New York Independent System Operator (NYISO) practices to evaluate their economic value. To determine system-wide reserve needs, plant-specific reserves are aggregated. However, conventional methods overlook spatial correlation across sites, often leading to over procurement. To address this, we propose a risk-based reserve aggregation technique that leverages spatial diversification. Additionally, we evaluate the reliability improvements enabled by the enhanced offshore wind forecast. To evaluate the operational impact, we propose an operational resource adequacy framework that captures uncertainty from forecast errors and grid conditions. Using this framework, we quantify key reliability metrics under different offshore wind forecast scenarios. Using New York State as a case study, we find that the improved forecast enables more accurate reserve estimation, reducing procurement costs by 5.53% in 2035 scenario compared to a well-validated numerical weather prediction model. Applying the risk-based aggregation further reduces total production costs by 7.21%. From a reliability perspective, the improved forecasts lower the system Loss of Load Probability (LOLP) by approximately 19% in the 2035 scenario, highlighting its potential to enhance system reliability during real-time grid operations.

eess.SY↗
arXiv2026

Codimension-Two Spiral Spin-Liquid in the Effective Honeycomb-Lattice Compound Cs$_3$Fe$_2$Cl$_9$

A codimension-two spiral spin-liquid is a correlated paramagnetic state with one-dimensional ground state degeneracy hosted within a three-dimensional lattice. Here, via neutron scattering experiments and numerical simulations, we establish the existence of a codimension-two spiral spin-liquid in the effective honeycomb-lattice compound Cs$_3$Fe$_2$Cl$_9$, which demonstrates a novel path to spiral spin-liquids by overcoming the long-standing impediment of weak further-neighbor interactions. In the long-range ordered regime, competing spiral and spin density wave orders emerge as a function of applied magnetic field, among which a possible order-by-disorder transition is identified.

cond-mat.str-el↗
arXiv2025

Intracavity-birefringence-enabled soliton states and wavelength control in the (C + L)-band fiber lasers

We demonstrate a compact all-fiber nonlinear-polarization-evolution (NPE) fiber laser capable of outputting wavelength-manipulated multiple laser states in the C + L band. Leveraging the intracavity birefringence-induced filtering effect, without external spectral filters, the laser achieves wavelength-tunable conventional solitons (CSs) and soliton molecules (SMs) with a tuning range of 72.85 nm and 45.54 nm, respectively, by adjusting the polarization states and pump power. Furthermore, wavelength-switchable harmonic mode-locking (over 1 GHz) and dual-wavelength mode-locking can also be achieved in the C + L band. To the best of our knowledge, this is the first experimental realization of wavelength-manipulated multiple soliton states covering the C + L band in a single-fiber-laser oscillator. Two examples of digital multi-letter encoding and transmission are further demonstrated on this platform by means of pump-driven wavelength/soliton states switching. By enabling controllable multi-state solitons and broad spectral tunability within a compact architecture, our work provides a promising source for spectrally diverse and temporally stable pulsed light sources that hold significant promise for applications in microscopy, bioimaging, and LiDAR.

physics.optics↗
arXiv2025

Spin Excitations and Flat Electronic Bands in a Cr-based Kagome Superconductor

In the quest for topology- and correlation-driven quantum states, kagome lattice materials have garnered significant interest for their band structures, featuring flat bands (FBs) from the quantum destructive interference of the electronic wavefunction. Tuning an FB to the chemical potential could induce electronic instabilities and emergent orders. Despite extensive studies, direct evidence of FBs tuned to the chemical potential and their role in emergent orders in bulk materials remains lacking. Using angle-resolved photoemission spectroscopy, resonant inelastic X-ray scattering, and density functional theory, we show that the low-energy structure of the Cr-based kagome metal superconductor {\Cr} is dominated by FBs at the Fermi level. We also observe low-energy magnetic excitations evolving across the low-temperature transition, largely consistent with the FB shift. Our results suggest that the low-temperature order contains a magnetic origin and that the kagome FBs may play a role in the emergence of this order.

cond-mat.str-el↗
arXiv2025

DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting

To unlock access to stronger winds, the offshore wind industry is advancing towards significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.

cs.LG↗
arXiv2025

Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks

In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Extant contrastive learning networks, whose two encoders often implicitly interact with each other by parameter sharing or momentum updating, may not fully exploit the complementarity and similarity of the positive pairs to extract clustering features from input data. To explicitly fuse the learned features of positive pairs, we design a novel multiple fusing-augmenting ViT blocks (MFAVBs) based on the excellent feature learning ability of Vision Transformers (ViT). Firstly, two preprocessed augmentions as positive pairs are separately fed into two shared-weight ViTs, then their output features are fused to input into a larger ViT. Secondly, the learned features are split into a pair of new augmented positive samples and passed to the next FAVBs, enabling multiple fusion and augmention through MFAVBs operations. Finally, the learned features are projected into both instance-level and clustering-level spaces to calculate the cross-entropy loss, followed by parameter updates by backpropagation to finalize the training process. To further enhance ability of the model to distinguish between similar images, our input data for the network we propose is preprocessed augmentions with features extracted from the CLIP pretrained model. Our experiments on seven public datasets demonstrate that MFAVBs serving as the backbone for contrastive clustering outperforms the state-of-the-art techniques in terms of clustering performance.

cs.CV↗