arXiv subjects

Yongkang Li

Publications and source records attributed to Yongkang Li.

52 records · Page 3Linked to original sources

arXiv2024

Hybrid-order topology in unconventional magnets of Eu-based Zintl compounds with surface-dependent quantum geometry

The exploration of magnetic topological insulators is instrumental in exploring axion electrodynamics and intriguing transport phenomena, such as the quantum anomalous Hall effect. Here, we report that a family of magnetic compounds Eu$_{2n+1}$In$_{2}$(As,Sb)$_{2n+2}$ ($n=0,1,2$) exhibit both gapless Dirac surface states and chiral hinge modes. Such a hybrid-order topology hatches surface-dependent quantum geometry. By mapping the responses into real space, we demonstrate the existence of chiral hinge modes along the $c$ direction, which originate from the half-quantized anomalous Hall effect on two gapped $ac$/$bc$ facets due to Berry curvature, while the unpinned Dirac surface states on the gapless $ab$ facet generate an intrinsic nonlinear anomalous Hall effect due to the quantum metric. When Eu$_{3}$In$_{2}$As$_{4}$ is polarized to the ferromagnetic phase by an external magnetic field, it becomes an ideal Weyl semimetal with a single pair of type-I Weyl points and no extra Fermi pocket. Our work predicts rich topological states sensitive to magnetic structures, quantum geometry-induced transport and topological superconductivity if proximitized with a superconductor.

cond-mat.mtrl-sci↗

arXiv2024

Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction

Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to improve trajectory prediction in AD. HLTP++ incorporates a novel teacher-student knowledge distillation framework. The "teacher" model equipped with an adaptive visual sector, mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. On the other hand, the "student" model focuses on real-time interaction and human decision-making, drawing parallels to the human memory storage mechanism. Furthermore, we improve the model's efficiency by introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for faster and more precise predictions with fewer parameters. Evaluated using the NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance compared to existing models, which reduces the predicted trajectory error with over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++ demonstrates strong adaptability in challenging environments with incomplete input data. This marks a significant stride in the journey towards fully AD systems.

cs.AI↗

arXiv2024

Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving

Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model's unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.

cs.RO↗

arXiv2024

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel "adaptive visual sector" mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a "dynamic traffic graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs. The code for the proposed model is available at our Github.

cs.RO↗

arXiv2024

A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving

In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorporating human decision-making insights enables AVs to more effectively anticipate the potential actions of other vehicles, significantly improving prediction accuracy and responsiveness in dynamic environments. This paper introduces the Human-Like Trajectory Prediction (HLTP) model, which adopts a teacher-student knowledge distillation framework inspired by human cognitive processes. The HLTP model incorporates a sophisticated teacher-student knowledge distillation framework. The "teacher" model, equipped with an adaptive visual sector, mimics the visual processing of the human brain, particularly the functions of the occipital and temporal lobes. The "student" model focuses on real-time interaction and decision-making, drawing parallels to prefrontal and parietal cortex functions. This approach allows for dynamic adaptation to changing driving scenarios, capturing essential perceptual cues for accurate prediction. Evaluated using the Macao Connected and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighD benchmarks, HLTP demonstrates superior performance compared to existing models, particularly in challenging environments with incomplete data. The project page is available at Github.

cs.AI↗

arXiv2024

Quantum Oscillations in kagome metals CsTi3Bi5 and RbTi3Bi5

We report quantum oscillation measurements on the kagome compounds ATi$_3$Bi$_5$ (A=Rb, Cs) in magnetic fields up to 41.5 T and temperatures down to 350 mK. In addition to the frequencies observed in previous studies, we have observed multiple unreported frequencies above 2000 T in CsTi$_3$Bi$_5$ using a tunnel diode oscillator technique. We compare these results against density functional theory calculations and find good agreement with the calculations in the number of peaks observed, frequency, and the dimensionality of the Fermi surface. For RbTi$_3$Bi$_5$ we have obtained a different quantum oscillation spectrum, although calculated quantum oscillation frequencies for the Rb compound are remarkably similar to the Cs compound, calling for further studies.

cond-mat.str-el↗

arXiv2024

Chirality induced spin selectivity in chiral crystals

Chirality is a fundamental property of great importance in physics, chemistry, and biology, and has recently been found to generate unexpected spin polarization for electrons passing through organic molecules, known as chirality-induced spin selectivity (CISS). CISS shows promising application potential in spintronic devices, spin-controlled chemistry, and enantiomer separation. It focuses on organic molecules that exhibit poor electronic conductivity and inherent complexities, such as the debated role of SOC at the molecule-metal interface. In this work, we go beyond organic molecules and study chiral solids with excellent electrical conductivity, intrinsic SOC, and topological electronic structures. We demonstrate that electrons exhibit both spin and orbital polarization as they pass through chiral crystals. Both polarization increases with material thickness before saturating to the bulk values. The spin polarization is proportional to intrinsic SOC while the orbital polarization is insensitive to SOC. The large spin polarization comes with strong electrical magnetochiral anisotropy in the magneto-transport of these chiral crystals (e.g., RhSi). Our work reveals the interplay of chirality, electron spin, and orbital in chiral crystals, paving the way for developing chiral solids for chirality-induced phenomena.

cond-mat.mes-hall↗

arXiv2023

Hyper-Relational Knowledge Graph Neural Network for Next POI

With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods.

cs.AI↗

arXiv2023

Complex semiclassical theory for non-Hermitian quantum systems

Non-Hermitian quantum systems exhibit fascinating characteristics such as non-Hermitian topological phenomena and skin effect, yet their studies are limited by the intrinsic difficulties associated with their eigenvalue problems, especially in larger systems and higher dimensions. In Hermitian systems, the semiclassical theory has played an active role in analyzing spectrum, eigenstate, phase, transport properties, etc. Here, we establish a complex semiclassical theory applicable to non-Hermitian quantum systems by an analytical continuation of the physical variables such as momentum, position, time, and energy in the equations of motion and quantization condition to the complex domain. Further, we propose a closed-orbit scheme and physical meaning under such complex variables. We demonstrate that such a framework straightforwardly yields complex energy spectra and quantum states, topological phases and transitions, and even the skin effect in non-Hermitian quantum systems, presenting an unprecedented perspective toward nontrivial non-Hermitian physics, even with larger systems and higher dimensions.

cond-mat.mes-hall↗

arXiv2023

Secure HARQ-IR-Aided Terahertz Communications

Terahertz (THz) communication is one of the most promising candidates to accommodate high-speed mobile data services. This paper proposes a secure hybrid automatic repeat request with incremental redundancy (HARQ-IR) aided THz communication scheme, where the transmission secrecy is ensured by confusing the eavesdropper with dummy messages. The connection and secrecy outage probabilities are then derived in closed-form. Besides, the tail behaviour of the connection outage probability in high signal-to-noise ratio (SNR) is examined by carrying out the asymptotic analysis, and the upper bound of the secrecy outage probability is obtained in a simple form by capitalizing on large deviations. With these results, we take a step further to investigate the secrecy long term average throughput (LTAT). By noticing that HARQ-IR not only improves the reliability of the legitimate user, but also increases the probability of being eavesdropped, a robust rate adaption policy is finally proposed to maximize the LTAT while restricting the connection and secrecy outage probabilities within satisfactory requirements.

cs.IT↗

arXiv2023

Quantum oscillations with topological phases in a kagome metal CsTi$_3$Bi$_5$

Quantum oscillations can reveal Fermi surfaces and their topology in solids and provide a powerful tool for understanding transport and electronic properties. It is well established that the oscillation frequency maps the Fermi surface area by Onsager's relation. However, the topological phase accumulated along the quantum orbit remains difficult to estimate in calculations, because it includes multiple contributions from the Berry phase, orbital and spin moments, and also becomes gauge-sensitive for degenerate states. In this work, we develop a gauge-independent Wilson loop scheme to evaluate all topological phase contributions and apply it to CsTi$_3$Bi$_5$, an emerging kagome metal. We find that the spin-orbit coupling dramatically alters the topological phase compared to the spinless case. Especially, oscillation phases of representative quantum orbits demonstrate a strong 3D signature despite their cylinder-like Fermi surface geometry. Our work reveals the Fermi surface topology of CsTi$_3$Bi$_5$ and paves the way for the theoretical investigation of quantum oscillations in realistic materials.

cond-mat.str-el↗

arXiv2023

Emergent topological quantum orbits in the charge density wave phase of kagome metal CsV$_3$Sb$_5$

The recently discovered kagome materials $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) attract intense research interest in intertwined topology, superconductivity, and charge density waves (CDW). Although the in-plane $2\times2$ CDW is well studied, its out-of-plane structural correlation with the Fermi surface properties is less understood. In this work, we advance the theoretical description of quantum oscillations and investigate the Fermi surface properties in the three-dimensional CDW phase of CsV$_3$Sb$_5$. We derived Fermi-energy-resolved and layer-resolved quantum orbits that agree quantitatively with recent experiments in the fundamental frequency, cyclotron mass, and topology. We reveal a complex Dirac nodal network that would lead to a $π$ Berry phase of a quantum orbit in the spinless case. However, the phase shift of topological quantum orbits is contributed by the orbital moment and Zeeman effect besides the Berry phase in the presence of spin-orbital coupling (SOC). Therefore, we can observe topological quantum orbits with a $π$ phase shift in otherwise trivial orbits without SOC, contrary to common perception. Our work reveals the rich topological nature of kagome materials and paves a path to resolve different topological origins of quantum orbits.

cond-mat.str-el↗

arXiv2022

TIER-A: Denoising Learning Framework for Information Extraction

With the development of deep neural language models, great progress has been made in information extraction recently. However, deep learning models often overfit on noisy data points, leading to poor performance. In this work, we examine the role of information entropy in the overfitting process and draw a key insight that overfitting is a process of overconfidence and entropy decreasing. Motivated by such properties, we propose a simple yet effective co-regularization joint-training framework TIER-A, Aggregation Joint-training Framework with Temperature Calibration and Information Entropy Regularization. Our framework consists of several neural models with identical structures. These models are jointly trained and we avoid overfitting by introducing temperature and information entropy regularization. Extensive experiments on two widely-used but noisy datasets, TACRED and CoNLL03, demonstrate the correctness of our assumption and the effectiveness of our framework.

cs.CL↗

arXiv2021

An Empirical Study on Relation Extraction in the Biomedical Domain

Relation extraction is a fundamental problem in natural language processing. Most existing models are defined for relation extraction in the general domain. However, their performance on specific domains (e.g., biomedicine) is yet unclear. To fill this gap, this paper carries out an empirical study on relation extraction in biomedical research articles. Specifically, we consider both sentence-level and document-level relation extraction, and run a few state-of-the-art methods on several benchmark datasets. Our results show that (1) current document-level relation extraction methods have strong generalization ability; (2) existing methods require a large amount of labeled data for model fine-tuning in biomedicine. Our observations may inspire people in this field to develop more effective models for biomedical relation extraction.

cs.CL↗

arXiv2020

Achievable Rate of Multi-Antenna WSRNs with EH Constraint in the presence of a Jammer

In this paper, the rate-energy region is studied for the wireless sensor relay network (WSRN) with energy harvesting in the presence of a jammer. In the model, a source communicates to a destination equipped with a single antenna with energy harvesting constraint through a multi-antenna cooperative relay under beamforming. Meanwhile, there is a jammer intended to disturb the communication. The relay works in half-duplex mode and knows all the channel state information (CSI). When beamforming is employed at the relay, the network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). We characterize the achievable rate-energy region. Since the problem is non-convex, we present three methods to transform it into a semi-definite programming problem (SDP), and the closed-form expression for two special boundary points of the rate-energy region is obtained. Finally, the simulations show the rate-energy region and the anti-jamming performance of the proposed scheme.

cs.IT↗

arXiv2017

Elastic Local Breakout Strategy and Implementation for Delay-Sensitive Packets with Local Significance

Explosion of mobile traffic will bring a heavy burden to the core network, and rapid growth of mobile devices, as well as increasing demand for delay-sensitive services poses severe challenges to future wireless communication systems. In this regard, local breakout is a promising solution to save core network load and, more importantly, to reduce end-to-end (e2e) delay of packets with local significance. However, the capacity of local breakout link is limited, resulting in excessive delay when the traffic load through the local link is high. Therefore, the decision on whether the traffic flows should be transmitted through core network or by local breakout link has great practical significance. In this paper, we propose and implement a novel local breakout framework to deliver low e2e delay packets with local significance. A real-time local breakout rule based on the solution to a Markov decision process is given, showing that some packets with local significance should pass through core network rather than being delivered by local breakout link to meet the delay requirements. To test our proposed framework, a long-term-evolution (LTE) based test-bed with virtual base stations is implemented, by which we show the proposed framework is feasible and the e2e delay is significantly reduced.

cs.IT↗