arXiv subjects

Jian Shen

Publications and source records attributed to Jian Shen.

At least 19 recordsLinked to original sources

arXiv2026

Multiferroicity in the two-dimensional limit in hexagonal LuFeO3 films

Multiferroic oxides, which combine coupled ferroelectric and magnetic orders, are central to understanding correlated quantum phenomena. Yet, as thickness approaches the two-dimensional (2D) limit, both ferroelectricity and magnetism are conventionally expected to vanish due to depolarization fields and finite-size effects, respectively. Here, we demonstrate that hexagonal LuFeO3 (h-LuFeO3) retains coupled ferroelectricity and magnetism at the 2D limit, with a thickness of just one and a half unit cells. Remarkably, the ferroelectric polarization remains comparable to bulk values at room temperature, while long-range magnetism and magnetoelectric coupling persist at low temperatures. We further show that the K3 phonon mode, which mediates the polarization-magnetism coupling, is stable down to the 2D limit. Our results establish h-LuFeO3 as the first oxide system to exhibit genuine 2D-limit multiferroicity, providing a fundamental breakthrough in the long-standing quest to understand and control coupled ferroic orders at the atomic scale.

cond-mat.mtrl-sci↗

arXiv2026

First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education

The most dangerous mistakes a novice counselor makes are not the obvious ones: they are utterances that sound caring while quietly violating professional ethics and leaving vulnerable clients less protected. We build an AI supervisor that does not replace novice counselors, but grows them-teaching them to internalize ethical violations they would otherwise never notice. What makes this supervisor non-trivial is not detection but teaching: it must locate the ethical-violating utterance, diagnose the ethical violation against APA principles, and deliver feedback that explains not just what went wrong, but why it is risky and how to respond differently. The core obstacle is that (1) ethical violations are by nature unlabeled in real clinical data, and (2) existing AI counselors trained only to match correct answers will never learn to teach. We resolve both at once: a controllable AI novice that intentionally enacts predefined mistake categories makes supervision labels a natural byproduct of generation, yielding ETHICSCAFF, a 9,915-instance human-in-the-loop dataset; and GRPO under a Novice Growth Reward (NGR) optimizes the supervisor not for answer correctness but for whether a weaker novice model actually improves after reading its explanation. Experiments show that a novice guided by our supervisor outperforms an unguided peer on clinical metrics, and that teaching-oriented optimization via NGR further sharpens the supervisor's own ethical detection. In a user study with novice counseling-psychology students, participants show significant self-efficacy gains across all eight assessed competencies after receiving AI supervisory feedback, demonstrating that the scaffold transfers from simulation to real-world practice.

cs.CL↗

arXiv2026

Persistent Interfacial Topological Hall Effect Demonstrating Electrical Readout of Topological Spin Structures in Insulators

Conventional topological Hall effects (THE) require conducting magnets, leaving insulating systems largely inaccessible. Here we introduce the interfacial topological Hall effect (ITHE), where the noncoplanar spin textures of insulating magnets are imprinted onto an adjacent heavy metal via the magnetic proximity effect (MPE) and detected electrically. In Pt/h-LuFeO3 bilayers, h-LuFeO3 hosts a topological spin structure robust against high magnetic fields, arising from a 120° triangular spin lattice with small spin canting that yields nontrivial topology but minimal magnetization. This generates a giant Hall response in Pt up to 0.5% of the longitudinal resistivity and a Hall-conductivity/magnetization ratio above 2 V^{-1}, clearly distinguishable from the spin Hall Hanle effect background. Field- and temperature-dependent analysis further reveals that Pt nanoclusters inherit topological textures from h-LuFeO3 via MPE. Unlike the conventional THE narrow peak-and-dip features, ITHE in Pt/h-LuFeO3 persists across a broad magnetic field range up to 14 T, demonstrating the exceptional stability of the underlying topological spin structure. This establishes ITHE as a powerful and sensitive probe for topological magnetism in ultrathin insulating films and paves the way for new spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Rewritable Complementary Nanoelectronics Enabled by Electron-Beam Programmable Ambipolar Doping

The ability to reversibly and site-selectively tune ambipolar doping in a single semiconductor is crucial for reconfigurable electronics beyond silicon, but remains highly challenging. Here, we present a rewritable architecture based on electron-beam programmable field-effect transistors (FETs). Using WSe$_2$ as a model system, we demonstrate electron-beam-induced doping that enables reversible, precisely controlled carrier modulation exceeding $10^{13}$ cm$^{-2}$. The in-situ writing, erasing, and rewriting of ambipolar doping of nanoscale patterns was directly visualized by scanning microwave impedance microscopy. This mask-free, lithography-compatible approach can achieve precise band engineering within individual channels, yielding near-ideal subthreshold swings (~ 60 mV/dec) and finely tunable threshold voltages for both carrier types without specialized contact engineering. These capabilities allow on-demand realization of high performance logic, including CMOS inverters with high voltage gains and low power consumption, as well as NAND-to-NOR transitions on the same device via direct polarity rewriting. Our platform offers a scalable and versatile route for rapid prototyping of complementary electronics.

cond-mat.mtrl-sci↗

arXiv2025

Giant field-tunable nonlinear Hall effect by Lorentz skew scattering in a graphene moire superlattice

The nonlinear Hall effect (NHE) can enable rectification and energy harvesting, and its control by external fields, including gate, strain and magnetic field, has been pursued intensively. However, existing tuning pathways rely predominantly on fully quantum mechanical effects and are typically inefficient, resulting in weak NHE signals that limit further progress. In this work, we report the discovery of a distinct type of NHE in a graphene-hBN moire superlattice, which arises from a classical-quantum cooperative effect called Lorentz skew scattering (LSK), induced by a perpendicular magnetic field. This field-driven NHE exhibits a linear dependence on magnetic field and a pronounced unidirectional angular dependence. Remarkably, its magnitude reaches up to 32% of the linear Hall signal. We show that this giant, field-tunable NHE originating from LSK follows a unique quartic scaling law and produces a record-high nonlinear Hall conductivity (36000 μmV-1Ω-1) near van Hove singularities of moire minibands, which is over an order of magnitude larger than all previously reported NHEs. Our findings establish an efficient, magnetic-field-driven route to giant Hall rectification in high-mobility materials, offering a broadly applicable paradigm for modulating the NHE beyond electrostatic gating.

cond-mat.mes-hall↗

arXiv2025

FEWT: Frequency-Enhanced Wavelet-based Transformer for Multimodal Wheeled Bimanual Manipulation

Embodied intelligence bridges the physical world and information spaces, with robots demonstrating immense potential through imitation learning algorithms. In this study, a custom-built wheeled bimanual robotic platform equipped with an exoskeleton-style teleoperation system was utilized to realize intuitive remote manipulation and the efficient collection of anthropomorphic action data. To overcome the representation mismatch between spatial visual semantics and localized high-frequency physical dynamics, we propose a lightweight frequency-aligned imitation-learning framework, termed the Frequency-Enhanced Wavelet-based Transformer (FEWT). FEWT integrates two primary modules: Frequency-Enhanced Efficient Multi-Scale Attention (FE-EMA) and Time-Series Discrete Wavelet Transform (TS-DWT) to explicitly extract and align multi-scale features, improving the compatibility between spatial visual representations and temporal-frequency recalibration. Crucially, for real-world deployment, this framework is further extended into a multimodal system by seamlessly integrating a self-developed Smart Tactile Fabric (STF) sensor into the physical end-effectors, providing local contact-stress information that complements proprioceptive and chassis-motion cues in the shared multimodal representation. Experimental evaluations demonstrate that the core FEWT architecture significantly improves the success rate over the widely used Action Chunking with Transformers baseline, particularly during the most challenging phases of simulated bimanual insertion tasks. Furthermore, in complex real-world mobile and desktop manipulation tasks, the full STF-enhanced system effectively adapts to microscopic dynamic perturbations, yielding substantial performance enhancements.

cs.RO↗

arXiv2025

Defending Saltwater Intrusion: The Freshwater Pushback

Saltwater Intrusion (SWI) threatens freshwater availability, agriculture, and ecosystem resilience in coastal regions. While sea-level rise (SLR) is a known driver of long-term salinization, the counteracting role of freshwater discharge remains underexamined. Here, we combine long-term observations with numerical modeling and machine learning reconstruction to quantify the buffering capacity of freshwater outflows across the U.S. coastline. In systems such as Delaware Bay and parts of the Gulf and South Atlantic coasts, the salt front has shifted seaward in recent decades, linked to increased discharge, despite SLR over that time period. We show that a 10 - 35% increase in freshwater flow can offset the salinity impact of 0.5 m of SLR, though regional variation is significant. With future discharge trends diverging spatially, SWI responses will be highly uneven. These results highlight the critical role of freshwater management in mitigating salinity risks under climate change, with implications for water resource resilience, coastal planning, and long-term adaptation strategies.

physics.geo-ph↗

arXiv2025

Observation of ferron transport in ferroelectrics

Ferroelectrics feature spontaneous electric dipolar order reconfigurable via electric fields. Recent theoretical studies of the collective excitations of this electric dipolar order give rise to the hope that "ferron" quasiparticles may complement the magnons of magnetic materials in information and heat management technologies. Yet direct experimental evidence of ferron transport remains elusive. Here we demonstrate efficient ferron injection and detection enabled by ferromagnetic metal contacts, achieving nonlocal signal transmission over micrometer distances in a prototypical ferroelectric PMN-PT. The transmission efficiency can be switched by external magnetic fields that couple to the contacts and gate electric fields that control the ferron excitations. Ferron-based devices open new power saving strategies that employ ferroelectric materials in a future sustainable information society.

physics.app-ph↗

arXiv2025

Recognizing Salt Wave Events in Coastal Systems

Saltwater intrusion is a critical challenge to coastal ecosystems, impacting freshwater resources, biogeochemical cycles, and habitat stability. While relevant studies often focus on the long-term trends of salinity, its episodic variability and resulting ecological disturbance remains underexplored. Here, we introduce the concept of "coastal salt waves" - periods of elevated salinity anomalies, akin to heat waves - to better characterize extreme salinity events and emphasize their significance. Using cases studies, we show that coastal salt waves exhibit significant spatial and temporal variability based on their duration, intensity, frequency, cumulation, and timing, with drivers and impacts that may differ from gradual saltwater intrusion. In years with similar average salinity, salt waves may still vary greatly in characteristics like intensity, resulting in varying environmental impacts. Furthermore, systems without rising average salinity may still face more frequent or intense salt waves. This framework supports monitoring and management strategies to mitigate coastal salinization risks.

physics.geo-ph↗

arXiv2025

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off

To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic integrity of the model and this disturbance accumulates with increased communication rounds. In this paper, we introduce a novel federated learning framework with rigorous privacy guarantees, named FedCEO, designed to strike a trade-off between model utility and user privacy by letting clients ''Collaborate with Each Other''. Specifically, we perform efficient tensor low-rank proximal optimization on stacked local model parameters at the server, demonstrating its capability to flexibly truncate high-frequency components in spectral space. This capability implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes. Moreover, we improve the SOTA utility-privacy trade-off bound by order of $\sqrt{d}$, where $d$ is the input dimension. We illustrate our theoretical results with experiments on representative datasets and observe significant performance improvements and strict privacy guarantees under different privacy settings. The code is available at https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other.

cs.LG↗

arXiv2025

Optimization of Next-Day Delivery Coverage using Constraint Programming and Random Key Optimizers

We consider the logistics network of an e-commerce retailer, specifically the so-called "middle mile" network, that routes inventory from supply warehouses to distribution stations to be ingested into the terminal ("last mile") delivery network. The speed of packages through this middle mile network is a key determinant for the ultimate delivery speed to the end user. An important target for a retailer is to maximize the fraction of user orders that can be serviced within one day, i.e., next-day delivery. As such, we formulate the maximization of expected next-day delivery coverage within the middle-mile network as an optimization problem, involving a set of temporal and capacity-based constraints on the network and requiring the use of a black-box model to evaluate the objective function. We design both exact constraint programming (CP) and heuristic random-key optimizer (RKO) approaches, the former of which uses a proxy objective function. We perform experiments on large-scale, real-world problem instances and show that both approaches have merit, in that they can match or outperform the baseline solution, a bespoke greedy solver with integrated local search, in expected next-day delivery coverage. Our experiments focus on two high-level problem definitions, starting with a base problem and then adding more complexity, and also explore the generalization of the solvers across a range of problem instance sizes. We find that a hybrid model using RKO and a bespoke local search protocol performs best on the full problem definition with respect to expected next-day delivery (increase of +50 basis points [bps] over baseline) but can take days to run, whereas the hybrid model using CP and local search is slightly less competitive (+20 bps) but takes only hours to run.

math.OC↗

arXiv2025

Sign reversal of Berry curvature triple driven by magnetic phase transition in a ferromagnetic polar metal

While the time-reversal-even (T-even) nonlinear Hall effect has been extensively discussed in nonmagnetic materials, the impact of magnetic phase transition on it remains largely overlooked. Here, we report an abrupt enhancement of the T-even nonlinear Hall effect in non-centrosymmetric SrRuO3(111) thin films during the paramagnetic-ferromagnetic transition. Scaling analysis reveals a sign reversal of both the skew scattering and side jump contributions upon the emergence of magnetism, which we attribute to the sign change of Berry curvature triple on the Fermi surface. Density functional theory calculations support this interpretation, ascribing this behavior to distinctive origins of Berry curvature hot spots in paramagnetic and ferromagnetic phases. Our findings unveil the exchange-induced dramatic nonperturbative change of nonlinear Hall effect, and establish SrRuO3(111) thin films as a promising platform for exploring magnetically tunable nonlinear transport effects.

cond-mat.mtrl-sci↗

arXiv2025

LiDAR-enhanced 3D Gaussian Splatting Mapping

This paper introduces LiGSM, a novel LiDAR-enhanced 3D Gaussian Splatting (3DGS) mapping framework that improves the accuracy and robustness of 3D scene mapping by integrating LiDAR data. LiGSM constructs joint loss from images and LiDAR point clouds to estimate the poses and optimize their extrinsic parameters, enabling dynamic adaptation to variations in sensor alignment. Furthermore, it leverages LiDAR point clouds to initialize 3DGS, providing a denser and more reliable starting points compared to sparse SfM points. In scene rendering, the framework augments standard image-based supervision with depth maps generated from LiDAR projections, ensuring an accurate scene representation in both geometry and photometry. Experiments on public and self-collected datasets demonstrate that LiGSM outperforms comparative methods in pose tracking and scene rendering.

cs.RO↗

arXiv2025

Observation of giant nonlinear valley Hall effect

The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving nonlinear valley responses undetected and nonlinear valleytronic devices undeveloped. Here, we report the experimental observation of a nonlinear VHE in a graphene-hBN moire superlattice, evidenced by the generation of second-harmonic nonlocal voltages under AC currents. Remarkably, the nonlinear VHE has magnitude surpassing the linear VHE and is highly tunable via a gate voltage, which exhibits a pair of opposite peaks on the two sides of a Dirac gap. The nonlinear signal shows quadratic scaling with driving current and quartic scaling with local resistance, setting it apart from the linear counterpart. These experimental features are consistent with the theoretical picture of nonlocal transport mediated by nonlinear VHE and linear inverse VHE. We further reveal a nonlinear inverse VHE by observing the third- and fourth-harmonic nonlocal voltages. The nonlinear VHE provides a novel mechanism for valley manipulation and enables a novel valleytronic device, the valley rectifier, that converts AC charge current into DC valley current.

cond-mat.mes-hall↗

arXiv2025

Unexpected large electrostatic gating by pyroelectric charge accumulation

Pyroelectricity refers to the accumulation of charges due to changes in the spontaneous polarization of ferroelectric materials when subjected to temperature variations. Typically, these pyroelectric charges are considered unstable and dissipate quickly through interactions with the external environment. Consequently, the pyroelectric effect has been largely overlooked in ferroelectric field-effect transistors. In this work, we leverage the van der Waals interface of hBN to achieve a substantial and long-term electrostatic gating effect in graphene devices via the pyroelectric properties of a ferroelectric LiNbO3 substrate. Upon cooling, the polarization change in LiNbO3 induces high doping concentrations up to 1013 cm-2 in the adjacent graphene. Through a combination of transport measurements and non-contact techniques, we demonstrate that the pyroelectric charge accumulation, as well as its enhancement in electric fields, are responsible for this unexpectedly high doping level. Our findings introduce a novel mechanism for voltage-free electrostatic gating control with long retention.

cond-mat.mtrl-sci↗

arXiv2025

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

physics.app-ph↗

arXiv2025

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↗

arXiv2024

Collective Pinning and Vortex Dynamics in type 2 superconducting thin films with Varying Magnetic Field

A perpendicular magnetic field penetrating a thin type-II superconductor slab produces vortices, with one vortex per flux quantum, h/2e. The vortices interact repulsively and form an ordered array (Abrikosov lattice) in clean systems, while strong disorder changes the lattice into a vortex glass. Here we investigate type-II superconducting films (PdBi2 and NbSe2) with surface acoustic waves (SAWs) at mK temperature. When sweeping the magnetic field at an extremely slow rate, we observe a series of spikes in the attenuation and velocity of the SAW, on average separated in field by approximately Hc1. We suspect the following scenario: The vortex-free region at the edges of the film produces an edge barrier across which the vortices can enter or leave. When the applied field changes, the induced supercurrents flowing along this edge region lowers this barrier until there is an instability. At that point, vortices avalanche into (or out of) the bulk and change the vortex crystal, suggested by the sharp jump in each such spike. The vortices then gradually relax to a new stable pinned configuration, leading to a ~30s relaxation after the jump. Our observation enriches the limited experimental evidence on the important topic of real-time vortex dynamics in superconductors.

cond-mat.supr-con↗