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Junlin Xiong

Publications and source records attributed to Junlin Xiong.

At least 19 recordsLinked to original sources

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing

Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the non-volatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials.

cond-mat.mtrl-sci

From Transparent Labware Segmentation to Collision Avoidance: A Real-Time Edge-Aware Perception Pipeline

This paper presents an edge-aware instance segmentation framework that enables real-time robotic collision avoidance with transparent laboratory glassware using purely visual perception. Transparent vessels defy conventional segmentation due to refraction, specular reflection, and the absence of stable interior texture, yet their boundary contours remain comparatively reliable visual cues. Exploiting this observation, we augment a one-stage real-time instance segmentation backbone with a lightweight edge-detection branch, edge-guided attention fusion, and a parameter-free SimAM module, and further construct LabGlass-IS, a 3485-image, 21-category instance segmentation dataset of real laboratory glassware. The enhanced model achieves the highest Boundary F-score of 97.80 among compared methods, outperforming the YOLO-prompted FastSAM framework by 18.93 BF points. Furthermore, it maintains an inference speed of 7.1ms per frame and requires only 2.85% of the parameters of the closest accuracy competitor. Multi-view triangulation of mask centroids further provides 3D positions for conservative bounding-volume collision constraints. Real-robot trials achieve a 93.3% collision avoidance success rate, indicating the feasibility of the proposed perception-to-action pipeline for robot collision avoidance among fragile transparent objects. Our code is available at https://github.com/havishamy/TransYOLO_3D. Our video is available at https://havishamy.github.io/paper-videos/.

cs.RO

Reactive Planning based Control for Mobile Robots in Obstacle-Cluttered Environments

This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.

cs.RO

From stacking to function: emergent states and quantum devices in 2D superconductor heterostructures

Two-dimensional (2D) superconductors provide a powerful building block for engineering emergent quantum states shaped by reduced dimensionality, enhanced quantum fluctuations, and interfacial symmetry breaking. In van der Waals heterostructures, atomically sharp and lattice-mismatch-free interfaces enable superconductivity to be deliberately coupled with magnetism, spin orbit interaction, and band topology, allowing collective electronic orders to be combined and reconfigured in ways unattainable in bulk materials. This Review summarizes recent advances in vdW heterostructures of 2D superconductors, focusing on superconductor/magnet, superconductor/topological material, and superconductor/superconductor junctions. We discuss the microscopic mechanisms underlying proximity effects and highlight how interfacial exchange fields, spin orbit coupling, and twist-controlled tunneling give rise to unconventional pairing, long-range spin-triplet supercurrents, nonreciprocal Josephson transport, and topological superconductivity potentially hosting Majorana bound states. Beyond their fundamental significance, the ability to controllably generate topological and nonreciprocal superconducting states positions 2D superconductor heterostructures as promising building blocks for emerging quantum technologies, including ultra-sensitive quantum sensing, programmable superconducting logic, and energy-efficient quantum and neuromorphic computing architectures. Looking forward, advances in materials synthesis, interface engineering, and device integration are expected to further expand the scope and functionality of 2D superconductor heterostructures, reinforcing their role as a central platform for exploring and controlling emergent quantum phases.

cond-mat.mes-hall

All-Electrical Self-Switching of van der Waals Chiral Antiferromagnet

Antiferromagnets have garnered significant attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and ultrafast spintronic applications. Their dual functionality as both spin sources and information carriers could enable all-electrical self-induced switching of antiferromagnetic order, offering great potential for ultra-compact spintronic devices. However, related progress is still elusive. Here, we report the deterministic switching of chiral antiferromagnetic orders induced by charge current at zero external magnetic field in the van der Waals (vdW) magnetically intercalated transition metal dichalcogenide CoTa3S6. This system exhibits strong interactions between cobalt atom magnetic moment lattice and itinerant electrons within the metallic layers, as demonstrated by temperature-dependent angle-resolved photoemission, scanning tunneling spectroscopy, and topological Nernst effect measurements. Notably, the itinerant-localization interactions lead to current-induced chiral spin orbit torques as well as Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange torques that interact with the localized magnetic moments, facilitating all-electrical switching of the chiral magnetic order in the CoTa3S6 flake. Our work opens a promising avenue for manipulating antiferromagnetic orders by delicately engineering the synergistic interactions between magnetic moments and itinerant electrons.

cond-mat.mes-hall

Dimensionality-Driven Anomalous Metallic State with Zero-field Nonreciprocal Transport in Layered Ising Superconductors

The anomalous metal state (AMS), observed in failed superconductors, provides insights into superconductivity and quantum criticality, with studies revealing unconventional quantum phases like the Bose metal. Recently, layered transition metal dichalcogenide (TMD) superconductors approaching the two-dimensional limit have garnered significant attention for the enhanced phase fluctuations and electronic correlations. Investigating AMS in these systems, particularly in the absence of an external magnetic field, could offer valuable insights into the dimensionality-driven emergence of exotic quantum phenomena, including triplet Cooper pairing, phase fluctuation dynamics, and especially the recently discovered field-free superconducting diode effects. However, the field-free AMS has yet to be observed in TMD superconductors. Here, we report the dimensionality-tunable AMS near the superconducting quantum phase transitions in a layered TMD superconductor 2H-Ta2S3Se. In samples with thicknesses below 10 nm, we demonstrate magnetic field-driven AMS under external magnetic field, characterized by the vanishing of the Hall resistance and the presence of finite longitudinal resistance. Remarkably, an unexpected zero-field AMS emerges as the sample thickness is reduced to 3 nm. This AMS aligns well with the quantum vortex creep model and exhibits non-reciprocal transport behaviors, suggesting the onset of spontaneous time-reversal symmetry breaking accompanied by vortex motion as the system approaches the two-dimensional limit. Our findings open new avenues for exploring dimensionality-driven exotic superconducting quantum critical phases, and pave the way for a deeper understanding of zero-field superconducting diode effects.

cond-mat.supr-con

Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation

Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel in challenging scenarios. These complementary characteristics underscore the potential of integrating frame and event data for optical flow estimation. However, most cross-modal approaches fail to fully utilize the complementary advantages, relying instead on simply stacking information. This study introduces a novel approach that uses a spatially dense modality to guide the aggregation of the temporally dense event modality, achieving effective cross-modal fusion. Specifically, we propose an event-enhanced frame representation that preserves the rich texture of frames and the basic structure of events. We use the enhanced representation as the guiding modality and employ events to capture temporally dense motion information. The robust motion features derived from the guiding modality direct the aggregation of motion information from events. To further enhance fusion, we propose a transformer-based module that complements sparse event motion features with spatially rich frame information and enhances global information propagation. Additionally, a mix-fusion encoder is designed to extract comprehensive spatiotemporal contextual features from both modalities. Extensive experiments on the MVSEC and DSEC-Flow datasets demonstrate the effectiveness of our framework. Leveraging the complementary strengths of frames and events, our method achieves leading performance on the DSEC-Flow dataset. Compared to the event-only model, frame guidance improves accuracy by 10\%. Furthermore, it outperforms the state-of-the-art fusion-based method with a 4\% accuracy gain and a 45\% reduction in inference time.

cs.CV

ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation

Event cameras hold significant promise for high-temporal-resolution (HTR) motion estimation. However, estimating event-based HTR optical flow faces two key challenges: the absence of HTR ground-truth data and the intrinsic sparsity of event data. Most existing approaches rely on the flow accumulation paradigms to indirectly supervise intermediate flows, often resulting in accumulation errors and optimization difficulties. To address these challenges, we propose a residual-based paradigm for estimating HTR optical flow with event data. Our approach separates HTR flow estimation into two stages: global linear motion estimation and HTR residual flow refinement. The residual paradigm effectively mitigates the impacts of event sparsity on optimization and is compatible with any LTR algorithm. Next, to address the challenge posed by the absence of HTR ground truth, we incorporate novel learning strategies. Specifically, we initially employ a shared refiner to estimate the residual flows, enabling both LTR supervision and HTR inference. Subsequently, we introduce regional noise to simulate the residual patterns of intermediate flows, facilitating the adaptation from LTR supervision to HTR inference. Additionally, we show that the noise-based strategy supports in-domain self-supervised training. Comprehensive experimental results demonstrate that our approach achieves state-of-the-art accuracy in both LTR and HTR metrics, highlighting its effectiveness and superiority.

cs.CV

Electrical switching of Ising-superconducting nonreciprocity for quantum neuronal transistor

Nonreciprocal quantum transport effect is mainly governed by the symmetry breaking of the material systems and is gaining extensive attention in condensed matter physics. Realizing electrical switching of the polarity of the nonreciprocal transport without external magnetic field is essential to the development of nonreciprocal quantum devices. However, electrical switching of superconducting nonreciprocity remains yet to be achieved. Here, we report the observation of field-free electrical switching of nonreciprocal Ising superconductivity in Fe3GeTe2/NbSe2 van der Waals (vdW) heterostructure. By taking advantage of this electrically switchable superconducting nonreciprocity, we demonstrate a proof-of-concept nonreciprocal quantum neuronal transistor, which allows for implementing the XOR logic gate and faithfully emulating biological functionality of a cortical neuron in the brain. Our work provides a promising pathway to realize field-free and electrically switchable nonreciprocity of quantum transport and demonstrate its potential in exploring neuromorphic quantum devices with both functionality and performance beyond the traditional devices.

cond-mat.mes-hall

Motion Control of Two Mobile Robots under Allowable Collisions

This letter investigates the motion control problem of two mobile robots under allowable collisions. Here, the allowable collisions mean that the collisions do not damage the mobile robots. The occurrence of the collisions is discussed and the effects of the collisions on the mobile robots are analyzed to develop a hybrid model of each mobile robot under allowable collisions. Based on the effects of the collisions, we show the necessity of redesigning the motion control strategy for mobile robots. Furthermore, impulsive control techniques are applied to redesign the motion control strategy to guarantee the task accomplishment for each mobile robot. Finally, an example is used to illustrate the redesigned motion control strategy.

cs.RO

Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure

The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant T-odd SHE in Fe3GeTe2/MoTe2 van der Waals heterostructure, representing a previously unidentified interfacial magnetic spin Hall effect (interfacial-MSHE). Through rigorous symmetry analysis and theoretical calculations, we attribute the interfacial-MSHE to a symmetry-breaking induced spin current dipole at the vdW interface. Furthermore, we show that this linear effect can be used for implementing multiply-accumulate operations and binary convolutional neural networks with cascaded multi-terminal devices. Our findings uncover an interfacial T-odd charge-spin conversion mechanism with promising potential for energy-efficient in-memory computing.

cond-mat.mes-hall

Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure

This paper studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations.

eess.SY

Cascadable in-memory computing based on symmetric writing and read out

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe3GeTe2 and WTe2. We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved by employing unconventional charge to z-spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z-spin to charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing.

cond-mat.mes-hall

Lyapunov Conditions for Input-to-State Stability of Hybrid Systems with Memory

This paper studies input-to-state stability for hybrid systems with memory, which models hybrid dynamics affected by time delays. Using both Lyapunov-Razumikhin functions and Lyapunov-Krasovskii functionals, Lyapunov-based sufficient conditions are established for input-to-state stability. In addition, further extensions and relaxations are proposed for special cases, such as the stable flow/jump cases and the cases that Lyapunov functions do not decrease strictly during flow/jumps. Finally, two examples are used to illustrate the developed results.

eess.SY

$H_2$ model reduction for diffusively coupled second-order networks by convex-optimization

This paper provides an $H_2$ optimal scheme for reducing diffusively coupled second-order systems evolving over undirected networks. The aim is to find a reduced-order model that not only approximates the input-output mapping of the original system but also preserves crucial structures, such as the second-order form, asymptotically stability, and diffusive couplings. To this end, an $H_2$ optimal approach based on a convex relaxation is implemented to reduce the dimension, yielding a lower order asymptotically stable approximation of the original second-order network system. Then, a novel graph reconstruction approach is employed to convert the obtained model to a reduced system that is interpretable as an undirected diffusively coupled network. Finally, the effectiveness of the proposed method is illustrated via a large-scale networked mass-spring-damper system.

math.OC

Scheduler-Pointed False Data Injection Attack for Event-Based Remote State Estimation

In this paper, an attack problem is investigated for event-based remote state estimation in cyber-physical systems. Our objective is to degrade the effect of the event-based scheduler while bypassing a $χ^2$ false data detector. A two-channel scheduler-pointed false data injection attack strategy is proposed by modifying the numerical characteristics of innovation signals. The attack strategy is proved to be always existent, and an algorithm is provided to find it. Under the proposed attack strategy, the scheduler becomes almost invalid and the performance of the remote estimator is degraded. Numerical simulations are used to illustrate our theoretical results.

eess.SY

Stability and $\mathcal{H}_{\infty}$ Performance Analysis of Stochastic Linear Networked and Quantized Control Systems

This paper studies the stability and $\mathcal{H}_{\infty}$ performance analysis problem for linear networked and quantized control systems with both communication delays random packet losses. To deal with the network-induced uncertainties and random packet dropouts, a novel discrete-time stochastic system model is developed for continuous-time networked control systems, and further overapproximated via a polytopic system with norm-bounded uncertainty. Based on the overapproximated system model, sufficient conditions are established for linear networked and quantized control systems in different cases to guarantee input-to-state stability and $\mathcal{H}_{\infty}$ performance with respect to the network-induced errors. Finally, a numerical example is presented to illustrated the developed results.

eess.SY

Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning

This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. By using Q-function, we propose an online learning scheme to estimate the kernel matrix of Q-function and to update the control gain using the data along the system trajectories. The obtained control gain and kernel matrix are proved to converge to the optimal ones. To implement the proposed learning scheme, an online model-free reinforcement learning algorithm is given, where recursive least squares method is used to estimate the kernel matrix of Q-function. A numerical example is presented to illustrate the proposed approach.

eess.SY