arXiv subjects

Zhi Yan

Publications and source records attributed to Zhi Yan.

At least 19 recordsLinked to original sources

arXiv2026

Interface-Engineered Giant Multistate Resistance Switching in Altermagnetic CrSb Multiferroic Tunnel Junctions

Altermagnets enable spin-split transport without stray magnetic fields, yet converting their momentum-dependent spin splitting into a strong tunnel-junction response requires interface-selected tunneling channels. Here, using density functional theory combined with nonequilibrium Green's function calculations, we demonstrate giant multistate resistance switching in CrSb/$\alpha$-In$_2$Se$_3$ altermagnetic multiferroic tunnel junctions. The response is governed not by the bulk spin splitting of CrSb alone, but by a symmetry-selected interfacial mechanism in which Cr/Sb terminations and \textit{h}-BN or graphene insertion layers determine spin-channel matching, while ferroelectric polarization reshapes the electrostatic barrier. Symmetric and asymmetric terminations reverse the correspondence between parallel/antiparallel N\'eel-vector configurations and high-/low-resistance states, showing that the actual alignment of interfacial Cr moments selects the dominant tunneling channels. Monolayer-In$_2$Se$_3$ junctions exhibit four nonvolatile resistance states, with tunneling magnetoresistance (TMR) and tunneling electroresistance (TER) reaching 1626\% and 2206\%, respectively, and increasing to 9576\% and 4144\% upon Fermi-level shifting. Finite-bias calculations further reveal robust spin filtering and tunable spin-polarized currents. Extending the barrier to bilayer In$_2$Se$_3$ introduces interlayer polarization coupling, enabling eight resistance states with maximum TMR and TER values of $3.77\times10^{4}\%$ and $4.18\times10^{5}\%$, respectively. These results establish interface symmetry, spin-channel matching, and ferroelectric barrier reconstruction as design principles for stray-field-free multistate spintronic tunnel devices.

cond-mat.mtrl-sci↗

arXiv2026

Towards 3D karst underwater scene reconstruction from rotating sonar data

Karst aquifers provide critical freshwater resources but pose significant hazards due to their complex and poorly understood subsurface geometry. Mapping these environments is challenging because sonar data from underwater exploration is sparse and noisy, while navigation estimates suffer from drift limiting standard 3D reconstruction methods. We present a pipeline for reconstructing underwater karst conduits from a sonar profiler. We combine a continuous-time SLAM approach to correct trajectory drift with a novel two-stage deep learning method for surface reconstruction, producing an immersive and navigable 3D mesh for hydrogeological analysis.

cs.RO↗

arXiv2026

Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability

As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from semantic hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted ``cat''). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demonstrate that this phenomenon is not specific to a single architecture but is a fundamental consequence of Linear Semantic Leakage in high-dimensional embedding spaces. We propose a unified theoretical framework, Linear Semantic Attribution (LSA), which generalizes across discriminative methods. We introduce OSP, a geometric intervention that utilizes the residual property of OMP to disentangle unique semantic signals from shared concepts. We prove theoretically and demonstrate empirically that OSP minimizes hallucination by orthogonalizing the query vector against distractor concepts, rendering the attribution model blind to shared features while preserving fidelity for correct prompts. Our code is available at: https://github.com/emirhanbilgic/Orthogonal-Semantic-Projection

cs.CV↗

arXiv2026

Nonvolatile single-ion memory with picosecond switching

The rapid development of artificial intelligence (AI), Internet of Things (IoT), and edge computing applications has posed severe challenges to conventional memory technologies in terms of density, speed, and energy consumption. Herein, a single-ion transport mechanism is proposed to achieve picosecond (ps) switching capability. For monolayer hexagonal boron nitride (h-BN) with single-atom vacancy defects, first-principles calculations reveal that single-ion penetration across the BN plane dominates the resistive switching. The trapping and release of a single ion correspond to different states of the memory device for one bit of information. Experimentally fabricated single-ion memory exhibits nonvolatile resistive switching with ultra-fast switching speed of 20 ps and ultra-low energy consumption of 310 aJ/bit. This high performance is attributed to the extremely short distance for the single ion to travel through. Such devices pave the way for the realization of high-performance nonvolatile memory with ultra-fast speed, ultra-low energy consumption, and high storage density, that is called the "Unified Memory" long desired by the whole industry.

physics.app-ph↗

arXiv2026

Multi-Agent Pathfinding with Non-Unit Integer Edge Costs via Enhanced Conflict-Based Search and Graph Discretization

Multi-Agent Pathfinding (MAPF) plays a critical role in various domains. Traditional MAPF methods typically assume unit edge costs and single-timestep actions, which limit their applicability to real-world scenarios. MAPFR extends MAPF to handle non-unit costs with real-valued edge costs and continuous-time actions, but its geometric collision model leads to an unbounded state space that compromises solver efficiency. In this paper, we propose MAPFZ, a novel MAPF variant on graphs with non-unit integer costs that preserves a finite state space while offering improved realism over classical MAPF. To solve MAPFZ efficiently, we develop CBS-NIC, an enhanced Conflict-Based Search framework incorporating time-interval-based conflict detection and an improved Safe Interval Path Planning (SIPP) algorithm. Additionally, we propose Bayesian Optimization for Graph Design (BOGD), a discretization method for non-unit edge costs that balances efficiency and accuracy with a sub-linear regret bound. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in runtime and success rate across diverse benchmark scenarios.

cs.AI↗

arXiv2026

SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts

Due to the strong context-awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into smart home assistants to help users manage and adjust their living environments. While LLMs have been shown to effectively understand user needs and provide appropriate responses, most existing studies primarily focus on interpreting and executing user behaviors or instructions. However, a critical function of smart home assistants is the ability to detect when the home environment is in an anomalous state. This involves two key requirements: the LLM must accurately determine whether an anomalous condition is present, and provide either a clear explanation or actionable suggestions. To enhance the anomaly detection capabilities of next-generation LLM-based smart home assistants, we introduce SmartBench, which is the first smart home dataset designed for LLMs, containing both normal and anomalous device states as well as normal and anomalous device state transition contexts. We evaluate 13 mainstream LLMs on this benchmark. The experimental results show that most state-of-the-art models cannot achieve good anomaly detection performance. For example, Claude-Sonnet-4.5 achieves only 66.1% detection accuracy on context-independent anomaly categories, and performs even worse on context-dependent anomalies, with an accuracy of only 57.8%. More experimental results suggest that next-generation LLM-based smart home assistants are still far from being able to effectively detect and handle anomalous conditions in the smart home environment. Our dataset is publicly available at https://github.com/horizonsinzqs/SmartBench.

cs.LG↗

arXiv2025

Giant Tunneling Magnetoresistance in Graphene/$h$-BN Based van der Waals Magnetic Tunnel Junctions via 3$d$ Transition Metal Intercalation

Atomic intercalation offers a powerful route for engineering two-dimensional (2D) materials by precisely tuning interlayer electronic coupling and spin configurations. Here, we propose a generic strategy for the construction of fully 2D magnetic tunnel junctions (MTJs) based on transition metal-intercalated graphene electrodes with $h$-BN barrier layer. First-principles calculations reveal that intercalation not only stabilizes uniform atomic dispersion via steric hindrance but also induces robust ferromagnetism in graphene. Manganese- and vanadium-intercalated systems (Mn-Gr and V-Gr) exhibit exceptional spintronic performance, with tunneling magnetoresistance (TMR) showing a pronounced odd-even oscillation as a function of barrier thickness. A giant TMR of $4.35 \times 10^8\,\%$ is achieved in the Mn-Gr system with a monolayer barrier $h$ -BN ($n=1$), while V-Gr reaches a maximum TMR of $1.86 \times 10^5\,\%$ for a trilayer barrier ($n=3$). Moreover, biaxial strain further enhances the TMR to $10^9\,\%$ and $10^7\,\%$ in Mn-Gr and V-Gr systems, respectively. The devices also exhibit perfect spin filtering and pronounced negative differential resistance, offering new opportunities for high-performance spintronic and memory applications based on 2D van der Waals heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language

Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propose QueryAttack, a novel framework to examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language to bypass the safety alignment mechanisms of LLMs. We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack not only can achieve high attack success rates (ASRs), but also can jailbreak various defense methods. Furthermore, we tailor a defense method against QueryAttack, which can reduce ASR by up to $64\%$ on GPT-4-1106. Our code is available at https://github.com/horizonsinzqs/QueryAttack.

cs.CR↗

arXiv2025

Giant Nonvolatile Multistate Resistance with Fully Magnetically Controlled van der Waals Multiferroic Tunnel Junctions

Ferroelectric polarization switching in electrically controlled van der Waals multiferroic tunnel junctions (vdW-MFTJs) causes atomic migration, compromising device stability and fatigue resistance. Here we propose a fully magnetically controlled vdW-MFTJ based on a $\mathrm{CrBr_3/MnPSe_3/CrBr_3}$ vertical heterostructure, which achieves ferroelectric polarization reversal without relying on atomic migration driven by inversion symmetry breaking. Using first-principles calculations, we investigate the spin-polarized quantum transport properties of the proposed structure. By integrating asymmetric PtTe$_2$/alkali-metal (Li/Na/K)-doped/intercalated CrBr$_3$ electrodes, the device demonstrates exceptional performance, with a maximum tunneling magnetoresistance (TMR) exceeding $8.1\times10^5$\% and tunneling electroresistance (TER) reaching 2499\%, while the spin-filtering channels can be flexibly controlled by the magnetization direction of the magnetic free layer, achieving perfect spin-filtering over a broad bias voltage range. Applying an external bias voltage further enhances these metrics, increasing TMR to $3.6\times 10^7$\% and TER to 9990\%. Notably, a pronounced negative differential resistance (NDR) effect is observed, yielding an unprecedented peak-to-valley ratio (PVR) of $9.55\times10^9$\%, representing the highest value reported for vertical tunnel junctions. These extraordinary characteristics highlight the potential of vdW-MFTJs for ultra-efficient electronic switching, a key feature for next-generation spintronic devices. Our findings provide a solid theoretical foundation for designing and developing high-performance magnetic storage and logic technologies.

cond-mat.mtrl-sci↗

arXiv2024

Magnetic order-dependent giant tunneling magnetoresistance and electroresistance in van der Waals antiferromagnetic-multiferroic tunnel junctions

Antiferromagnetic spintronics exhibits ultra-high operational speed and stability in a magnetic field, holding promise for the realization of next-generation ultra-high-speed magnetic storage. However, theoretical exploration of the electronic transport properties of antiferromagnetic-multiferroic tunnel junction (AMFTJ) devices remains largely unexplored. Here, we design an antiferromagnet/ferroelectric barrier/antiferromagnet van der Waals heterojunction, renamed vdW AMFTJ, using a bilayer MnBi$_2$Te$_4$/In$_2$Se$_3$/bilayer MnBi$_2$Te$_4$ (MBT-2L/IS/MBT-2L) as the prototype. Based on first-principles calculations using the nonequilibrium Green's function method combined with density functional theory, we theoretically investigate the spin-resolved electronic transport properties of this AMFTJ. By manipulating the various possible magnetization directions of the multilayer antiferromagnetic MnBi$_2$Te$_4$ and the ferroelectric polarization direction of the In$_2$Se$_3$ within the junction, sixteen distinct non-volatile resistance states can be revealed and manipulated by applying external biaxial strain and bias voltage. We predict maximum tunneling magnetoresistance (electroresistance) values of $3.79\times10^{4}$\% ($2.41\times10^{5}$\%) in the equilibrium state, which can increase up to $5.01\times10^{5}$\% ($4.97\times10^{5}$\%) under external bias voltage. Furthermore, the perfect spin filtering effect is also present in our AMFTJ. Our results highlight the tremendous potential of the MBT-2L/IS/MBT-2L vdW AMFTJ in non-volatile memory, expanding the application avenues for antiferromagnetic spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Online Context Learning for Socially Compliant Navigation

Robot social navigation needs to adapt to different human factors and environmental contexts. However, since these factors and contexts are difficult to predict and cannot be exhaustively enumerated, traditional learning-based methods have difficulty in ensuring the social attributes of robots in long-term and cross-environment deployments. This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. The proposed method adopts a two-layer structure. The bottom layer is built using a deep reinforcement learning-based method to ensure the output of basic robot navigation commands. The upper layer is implemented using an online robot learning-based method to socialize the control commands suggested by the bottom layer. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones. Experimental results in the most challenging scenarios show that our method improves the performance of the state-of-the-art by 8%. The source code of the proposed method, the data used, and the tools for the per-training step are publicly available at https://github.com/Nedzhaken/SOCSARL-OL.

cs.RO↗

arXiv2024

Giant electrode effect on tunneling magnetoresistance and electroresistance in van der Waals intrinsic multiferroic tunnel junctions using VS2

Van der Waals multiferroic tunnel junctions (vdW-MFTJs) with multiple nonvolatile resistive states are highly suitable for new physics and next-generation storage electronics. However, currently reported vdW-MFTJs are based on two types of materials, i.e., vdW ferromagnetic and ferroelectric materials, forming a multiferroic system. This undoubtedly introduces additional interfaces, increasing the complexity of experimental preparation. Herein, we engineer vdW intrinsic MFTJs utilizing bilayer VS$_2$. By employing the nonequilibrium Green's function combined with density functional theory, we systematically investigate the influence of three types of electrodes (including non-vdW pure metal Ag/Au, vdW metallic 1T-MoS$_2$/2H-PtTe$_2$, and vdW ferromagnetic metallic Fe$_3$GaTe$_2$/Fe$_3$GeTe$_2$) on the electronic transport properties of VS$_2$-based intrinsic MFTJs. We demonstrate that these MFTJs manifest a giant electrode-dependent electronic transport characteristic effect. Comprehensively comparing these electrode pairs, the Fe$_3$GaTe$_2$/Fe$_3$GeTe$_2$ electrode combination exhibits optimal transport properties, the maximum TMR (TER) can reach 10949\% (69\%) and the minimum resistance-area product (RA) is 0.45 $\Omega$$\mu$m$^{2}$, as well as the perfect spin filtering and negative differential resistance effects. More intriguingly, TMR (TER) can be further enhanced to 34000\% (380\%) by applying an external bias voltage (0.1 V), while RA can be reduced to 0.16 $\Omega$$\mu$m$^{2}$ under the influence of biaxial stress (-3\%). Our proposed concept of designing vdW-MFTJs using intrinsic multiferroic materials points towards new avenues in experimental exploration.

cond-mat.mtrl-sci↗

arXiv2023

SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market

The automotive industry plays a critical role in the global economy, and particularly important is the expanding Chinese automobile market due to its immense scale and influence. However, existing automotive sector datasets are limited in their coverage, failing to adequately consider the growing demand for more and diverse variables. This paper aims to bridge this data gap by introducing a comprehensive dataset spanning the years from 2016 to 2022, encompassing sales data, online reviews, and a wealth of information related to the Chinese automotive industry. This dataset serves as a valuable resource, significantly expanding the available data. Its impact extends to various dimensions, including improving forecasting accuracy, expanding the scope of business applications, informing policy development and regulation, and advancing academic research within the automotive sector. To illustrate the dataset's potential applications in both business and academic contexts, we present two application examples. Our developed dataset enhances our understanding of the Chinese automotive market and offers a valuable tool for researchers, policymakers, and industry stakeholders worldwide.

econ.GN↗

arXiv2023

Voltage-tunable giant nonvolatile multiple-state resistance in sliding-interlayer ferroelectric h-BN van der Waals multiferroic tunnel junction

Multiferroic tunnel junctions (MFTJs) based on two-dimensional (2D) van der Waals heterostructures with sharp and clean interfaces at the atomic scale are crucial for applications in nanoscale multi-resistive logic memory devices. The recently discovered sliding ferroelectricity in 2D van der Waals materials has opened new avenues for ferroelectric-based devices. Here, we theoretically investigate the spin-dependent electronic transport properties of Fe$_3$GeTe$_2$/graphene/bilayer-$h$-BN/graphene/CrI$_3$ (FGT/Gr-BBN-Gr/CrI) all-vdW MFTJs by employing the nonequilibrium Green's function combined with density functional theory. We demonstrate that such FGT/Gr-BBN-Gr/CrI MFTJs exhibit four non-volatile resistance states associated with different staking orders of sliding ferroelectric BBN and magnetization alignment of ferromagnetic free layer CrI$_3$, with a maximum tunnel magnetoresistance (electroresistance) ratio, i.e., TMR (TER) up to $\sim$$3.36\times10^{4}$\% ($\sim$$6.68\times10^{3}$\%) at a specific bias voltage. Furthermore, the perfect spin filtering and remarkable negative differential resistance effects are evident in our MFTJs. We further discover that the TMR, TER, and spin polarization ratio under an equilibrium state can be enhanced by the application of in-plane biaxial strain. This work shows that the giant tunneling resistance ratio, multiple resistance states, and excellent spin-polarized transport properties of sliding ferroelectric BBN-based MFTJs indicate its significant potential in nonvolatile memories.

cond-mat.mes-hall↗

arXiv2023

Towards Long-term Autonomy: A Perspective from Robot Learning

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".

cs.RO↗

arXiv2022

Human-centered Benchmarking for Socially-compliant Robot Navigation

Social compatibility is one of the most important parameters for service robots. It characterizes the quality of interaction between a robot and a human. In this paper, a human-centered benchmarking framework is proposed for socially-compliant robot navigation. In an end-to-end manner, four open-source robot navigation methods are benchmarked, two of which are socially-compliant. All aspects of the benchmarking are clarified to ensure the reproducibility and replicability of the experiments. The social compatibility of robot navigation methods with the Robotic Social Attributes Scale (RoSAS) is measured. After that, the correspondence between RoSAS and the robot-centered metrics is validated. Based on experiments, the extra robot time ratio and the extra distance ratio are the most suitable to judge social compatibility.

cs.RO↗

arXiv2022

Large Rashba Spin-Orbit Coupling and High-Temperature Quantum Anomalous Hall Effect in Re-Intercalated Graphene/CrI$_3$ Heterostructure

In 2010, quantum anomalous Hall effect (QAHE) in graphene was proposed in the presence of Rashba spin-orbit coupling and ferromagnetic exchange field. After a decade's experimental exploration, the anomalous Hall conductance can only reach about 0.25 in the units of $2e^2/h$, which was attributed to the tiny Rashba spin-orbit coupling. Here, we theoretically show that Re-intercalation in graphene/CrI$_3$ heterostructure can not only induce sizeable Rashba spin-orbit coupling ($>$ 40~meV), but also open up large band gaps at valleys $K$ (22.2 meV) and $K' $ (30.3 meV), and a global band gap over 5.5 meV (19.5 meV with random Re distribution) hosting QAHE. A low-energy continuum model is constructed to explain the underlying physical mechanism. We find that Rashba spin-orbit coupling is robust against external stress whereas a tensile strain can increase the global bulk gap. Furthermore, we also show that Re-intercalated graphene with hexagonal boron-nitride can also realize QAHE with bulk gap over 40~meV, indicating the tunability of $5d$-intercalated graphene-based heterostructure. Our finding makes a great leap towards the experimental realization of graphene-based QAHE, and will definitely accelerate the practical application of graphene-based low-power electronics.

cond-mat.mes-hall↗

arXiv2022

3D ToF LiDAR in Mobile Robotics: A Review

In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

cs.RO↗