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Feng Ye

Publications and source records attributed to Feng Ye.

At least 19 recordsLinked to original sources
arXiv2026

ECO-COMM: An Ultra Low-Latency Event Camera based Optical Communication System

Ultralow-latency communication is critical for emerging next-generation applications such as XR, real-time control, and distributed sensing. We present ECO-COMM, an event-camera-based optical communication system for ultra-low-latency device association and lightweight information exchange. By exploiting the asynchronous sensing and microsecond-level temporal resolution of event cameras, ECO-COMM captures high-frequency optical signals without frame-based acquisition delays. We identify and analyze key hardware-induced challenges in event-camera communication, including timestamp inconsistency, readout contention, trailing effects, and the inevitable refractory period, and develop hardware-aware mitigation techniques to address them. Focusing on a single transmitter-receiver optical link, ECO-COMM establishes the feasibility of practical ultra-low-latency event-camera communication using commercially available hardware.A prototype implementation using an eight-LED transmitter and an off-the-shelf event camera achieves device association within 15 microseconds, symbol latency as low as 100 microseconds, and end-to-end latency below 8 milliseconds for 32-byte payloads at 0.1% bit error rate. ECO-COMM establishes a practical and complementary communication paradigm for ultra-low-latency systems where responsiveness and temporal precision are paramount.

cs.NI↗
arXiv2026

ECO-ID: Event-Camera based Optical System for Secure Multi-User Ultra-Low Latency Identification

Time-critical interactive systems increasingly require ultra-low-latency device identification for multiple users, yet prevailing approaches such as passwords, QR codes, and RFID/NFC are constrained by human input, frame-based sensing, or near-contact range. This paper presents ECO-ID, an event-camera-based optical system for multi-user, ultra-low-latency identification over visible light communication (VLC). Leveraging microsecond-resolution, asynchronous observations of brightness transitions, ECO-ID employs a spatiotemporal coding design: disjoint LED subsets provide spatial separation among users, while user-specific timing delays encode identities without inter-user synchronization. The optical channel and event-driven sensing reduce full-scene capture relative to frame cameras and limit the RF attack surface, while enabling rapid token verification with freshness and replay protection. We implement a prototype and demonstrate that ECO-ID can practically achieve approximately 99.8\% localization and 98.7\% identification with 0.64 ms mean latency, while theoretically supporting identification at the scale of tens of concurrent users. Overall, ECO-ID provides a fast, privacy-conscious, and security-aware alternative for scalable multi-user identification in time-critical interactive environments.

eess.SP↗
arXiv2026

Spin nematic liquid crystal and scalar spin chirality in tetragonal lattice YbMnBi$_2$

A spin nematic order, analogous to the nematic liquid crystal, characterizes the spontaneous breaking of spin-space rotational symmetry while preserving time-reversal ($T$) symmetry. In contrast, scalar spin chirality (SSC), a composite three-spin order, breaks $T$ symmetry and is known to induce an anomalous Hall effect (AHE). Although a spin nematic phase has been suggested in frustrated magnets and the square-lattice iridate, how it might affect magnetotransport properties is unknown. Here we use polarized neutron scattering to show that tetragonal $A$MnBi$_2$ ($A$ = Ca, Yb) is a strictly $c$-axis-aligned collinear antiferromagnet (C-type), with $T_N \approx 270$ K and 290 K, respectively. On cooling from 450 K to $T_N$, low-energy spin excitations in YbMnBi$_2$ spontaneously change from isotropic to anisotropic in spin space within the tetragonal plane, forming a dynamic spin nematic phase around 400 K due to heavy Yb-induced spin-orbit coupling, before gapping out below $T_N$. Similar measurements on CaMnBi$_2$ reveal isotropic paramagnetic scattering without a spin nematic phase above $T_N$. Under an in-plane magnetic field, the Yb$^{3+}$ moments may interact with the dynamic spin nematic phase to induce nonzero SSC, giving rise to AHE and an anomalous Nernst effect (ANE) in YbMnBi$_2$ that are absent in CaMnBi$_2$ above $T_N$. A symmetry-based Ginzburg-Landau analysis shows that coupling terms between the nematic order and SSC are allowed under an external magnetic field, which could explain the rapid increase of AHE with field in YbMnBi$_2$. Our results provide compelling evidence for dynamic SSC-induced AHE and ANE in the paramagnetic phase of a compensated collinear antiferromagnet, opening a new avenue for the physics of composite spin orders and room-temperature spintronics without magnetic order.

cond-mat.str-el↗
arXiv2026

Rethink Before You Execute: Adaptive Execution for World Action Models

World Action Models (WAMs) jointly predict future actions and the evolution of the environment. At each inference, a WAM generates a chunk of actions and the robot executes a fixed prefix before replanning. We argue that this fixed execution horizon is poorly matched to execution dynamics: the chunk reliability varies across task stages, so when to replan depends on the result of accumulated execution, not on the step counts. We propose TempoWAM (Timing Execution by Monitoring Progress Online), a lightweight plug-and-play execution scheme for WAMs. A Recurrent Progress Monitor first estimates task progress from the current observation, task instruction, remaining actions, and execution history; and an Adaptive Execution Protocol then evaluates whether the chunk is advancing the task to decide if replanning is needed. To bridge the training-deployment gap, the protocol is calibrated by a task-dependent calibration factor with online adaptation. Experiments on LIBERO, RoboTwin, and real-world tasks show that TempoWAM consistently improves the efficiency-success trade-off of WAM execution. On real robots, it reduces WAM inferences by 26.9% on easy tasks while maintaining success, and improves success by 13.3 points on difficult tasks.

cs.RO↗
arXiv2026

The self-organized vacancy order in Pr$_9$Ge$_{16}$

In this work, we report the discovery of a new crystal structure on the Ge-rich side of the Pr-Ge binary phase diagram. Using a high-temperature flux technique, we grew single crystals of $Pr_9Ge_{16}$, which adopt a previously unreported orthorhombic $Fdd$2 structure type featuring ordered Ge vacancies. We present the anisotropic magnetic properties and identify the crystallographic $b$ axis perpendicular to the crystal plane as the magnetic easy axis. Temperature-dependent resistivity measurements reveal metallic behavior with a distinct anomaly at $T_{\mathrm{C}}$ = 14.3 K. Hall resistivity data indicate that electron-like carriers dominate, with a carrier concentration on the order of $10^{27}~\mathrm{m}^{-3}$. The magnetic order is readily suppressed by a magnetic field of approximately 0.4 T applied along the easy $b$ axis.

cond-mat.mtrl-sci↗
arXiv2026

Shape Ultrasound with Dynamic Microfluidic Lenses

Dynamic shaping of ultrasound into prescribed spatial patterns underlies a broad range of biomedical and engineering applications. However, existing modulation strategies face fundamental limitations: single element transducers paired with acoustic lenses lack reconfigurability, whereas phased arrays require large numbers of independently driven elements, leading to substantial hardware complexity, cost, and rigidity. Here we introduce a microfluidic ultrasound lens system that enables reconfigurable spatial modulation of ultrasonic fields using two orthogonal layers of soft microfluidic channels. Each channel is selectively filled with one of two liquids with distinct sound speeds via an FPGA controlled array of micropumps, generating programmable binary phase patterns. Integrating a 20-row-by-20-column microfluidic lens with a single element transducer, we demonstrate three-dimensional ultrasound focusing with approximately one second reconfiguration time and spatial resolution comparable to that of a 400-element transducer array. The system provides 400 addressable pixels through parallel control of 80 pumps, allowing hardware complexity to scale with the square root of the pixel count. Building on this platform, we demonstrate dynamic ultrasound heating, as well as remote particle manipulation. Furthermore, we demonstrate a cylindrical lens that manipulates ultrasound propagation in the azimuthal direction. Owing to its liquid based, soft architecture, the microfluidic lens offers design flexibility, scalable operation across ultrasound frequencies, low acoustic transmission loss, and stable performance under high acoustic power. Together, these results establish microfluidic phase modulation as a compact, scalable, and flexible approach for dynamic ultrasound field control.

physics.app-ph↗
arXiv2026

From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations

Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity. However, traditional empirical methods face practical limits on control and replication. Meanwhile, LLM-based social simulations are typically behavior-driven and lack theory-aligned environments for modeling Putnam's core propositions. To address these gaps, we introduce SocaSim, an LLM-based multi-agent simulation framework to study Putnam's Social Capital Theory from theoretical blueprint to simulated reality. Specifically, we build an environment integrating social network evolution, trust dynamics, and norm propagation, where agents engage in repeated collective-action experiments, and then apply the three dimensions to analyze adaptation challenges in smart elderly care. Our simulations reproduce Putnam's macro-level patterns and exhibit strong human-agent alignment at the group level. Unlike traditional methods, SocaSim traces micro-level causal pathways of social network, trust, and norms via round-by-round simulations and counterfactual interventions, enabling process-level interpretability. Taken together, these capabilities establish a research paradigm that leverages LLM agents to bridge social science and computer science.

cs.CL↗
arXiv2026

Exploiting RIS Optimization Limits for Multi-User Beamforming and Signal Suppression

This paper presents a unified framework for exploiting the boundaries of reconfigurable intelligent surfaces (RIS) joint optimization in multi-user wireless systems, where a single RIS accommodates diverse objectives.We first propose an adaptive gradient-scaling mechanism that accelerates the convergence of the underlying optimization algorithm while maintaining stable performance across varying channel and system parameters. The proposed mechanism enables the solver to reach a reasonably good solution rapidly without requiring manual tuning of step sizes or algorithmic hyperparameters when system inputs change. We then propose a low-complexity beamformer recovery method tailored for single-user scenarios, which circumvents the full matrix decomposition required by traditional approaches, thereby significantly reducing computational overhead. Building on these foundations, we develop an element allocation strategy that enables user-specific prioritization through assignment of RIS subsets. This is further extended by a modular add-drop mechanism that supports partial-panel optimization in general multi-user settings. The framework is evaluated across three representative scenarios: (i) signal amplification for all users, (ii) signal suppression for all users, and (iii) selective amplification and suppression. To characterize performance limits, we derive power trade-off boundaries using scalarized joint optimization, which closely align with Monte Carlo simulations. Our unified joint optimization method consistently yield solutions near these boundaries, confirming its near-optimality. Extensive simulations under realistic channel models demonstrate that the proposed approach outperforms conventional semidefinite relaxation techniques, offering a scalable and effective RIS control strategy for cooperative and competitive multi-user environments.

eess.SP↗
arXiv2026

TG-DIN: Theory-Guided Demand Inference Network for Generalizable QoS Measurement and Prediction

In this paper, we introduce TG-DIN, a theory-guided demand inference network that infers latent user demand from observable network quality-of-service (QoS) measurements. Rather than directly predicting QoS outcomes using black-box models, TG-DIN explicitly models latent demand as an intermediate variable and links it to observable behavior through a differentiable theory layer grounded in scheduling and queuing principles. This design yields an interpretable, mechanism-consistent representation of user demand that is directly applicable to downstream tasks such as congestion diagnosis, resource allocation, capacity planning, and policy evaluation. The theory layer further enables a principled randomized training regime that exposes the model to diverse yet physically meaningful operating conditions without requiring labeled demand data. Extensive synthetic experiments show that TG-DIN generalizes robustly across capacities, demand levels, and traffic patterns, substantially outperforming purely data-driven baselines under distribution shift. Moreover, when trained exclusively on synthetic data and applied directly to real packet traces, TG-DIN accurately recovers per-user allocation structure in shared-link scenarios. Together, these results demonstrate the effectiveness of theory-guided inductive biases for achieving transferable, deployment-ready inference in dynamic network environments.

cs.NI↗
arXiv2026

Inductance Meets Memory in the Quantum Magnet Mn3Si2Te6

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn3Si2Te6, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.

cond-mat.str-el↗
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

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 N\'eel 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

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↗