arXiv subjects

Kun Jiang

Publications and source records attributed to Kun Jiang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Unconventional Coherence Peak in Cuprate Superconductors

The Hebel-Slichter coherence peak, observed in the spin-lattice relaxation rate $1/T_1$ just below the critical temperature $T_{\rm c}$, serves as a crucial experimental validation of the Bardeen-Cooper-Schrieffer pairing symmetry in conventional superconductors. However, no coherence peak in $1/T_1$ has been observed in unconventional superconductors like cuprates. In this study, an unconventional coherence peak is identified for the first time using nuclear quadrupole resonance on YBa$_2$Cu$_4$O$_8$, pointing to a distinctive pairing symmetry. The spin-lattice relaxation rate in nuclear quadrupole resonance and nuclear magnetic resonance with nuclear spin $I>1/2$ comprises the magnetic relaxation rate $1/T_{1}^{\rm mag}$, which probes magnetic fluctuations, and the quadrupole relaxation rate $1/T_{1}^{\rm quad}$, which probes charge fluctuations. By utilizing $^{63}$Cu and $^{65}$Cu isotopes, we successfully distinguish $1/T_{1}^{\rm mag}$ and $1/T_{1 }^{\rm quad}$ of YBa$_2$Cu$_4$O$_8$ and reveal the presence of the coherence peak in $1/T_{1 }^{\rm quad}$ but not in $1/T_{1}^{\rm mag}$, in contrast to conventional superconductors. Our finding demonstrates that unconventional superconductors do not exhibit a coherence peak in $1/T_{1}$ when the relaxation is due to fluctuations of the hyperfine field. Conversely, a coherence peak is expected when the relaxation is caused by electric field gradient fluctuations, due to the different coherence factors between charge and magnetic fluctuations. Our successful measurements of $1/T_{1}$ for the chains of YBa$_2$Cu$_4$O$_8$ suggest that, should the conditions for predominant quadrupole relaxation be satisfied, this phenomenon could provide a novel approach to exploring the unconventional nature of the pairing mechanism in other superconductors.

cond-mat.supr-con↗

arXiv2024

Low-temperature mean valence of nickel ions in pressurized La$_3$Ni$_2$O$_7$

The discovery of high critical temperature (Tc) superconductivity in pressurized La$_3$Ni$_2$O$_7$ has ignited renewed excitement in the search of novel high-Tc superconducting compounds with 3d transition metals. Compared to other ambient-pressure superconductors, such as copper-oxide and iron-oxypnictides, unraveling the mechanisms of the pressure-induced superconductivity poses significant and unique challenges. A critical factor in this phenomenon seems to be related to the electronic configuration of 3d orbitals, which may play a fundamental role in driving high-Tc superconductivity. However, the pressure effects on the mixed-valence states of 3d-orbital cations and their influence on the emergence of high-Tc superconductivity remain poorly understood. Here, we use high-pressure (P) and low-temperature synchrotron X-ray absorption spectroscopy to investigate the influence of pressure on the mean valence change of Ni ions in La$_3$Ni$_2$O$_7$. Our results demonstrate that at a low-temperature of 20 K, the mean valence remains relatively stable across the pressures range from 1 atm to 40 GPa. Based on analyzing the absorption data, we find that, at a critical pressure, the ambient-pressure ordered phases disappear and both the structural and the superconducting phase transition occur. The pressure-induced structural phase transition revealed by our absorption results is consistent with that determined by X-ray diffraction, offering new information for a comprehensive understanding on the pressure-induced superconductivity in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2024

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reasoning capabilities, but their computational demands pose challenges for real-time decision-making and precise planning. This paper presents FASIONAD, a novel dual-system framework inspired by the cognitive model "Thinking, Fast and Slow." The fast system handles routine navigation tasks using rapid, data-driven path planning, while the slow system focuses on complex reasoning and decision-making in challenging or unfamiliar situations. A dynamic switching mechanism based on score distribution and feedback allows seamless transitions between the two systems. Visual prompts generated by the fast system enable human-like reasoning in the slow system, which provides high-quality feedback to enhance the fast system's decision-making. To evaluate FASIONAD, we introduce a new benchmark derived from the nuScenes dataset, specifically designed to differentiate fast and slow scenarios. FASIONAD achieves state-of-the-art performance on this benchmark, establishing a new standard for frameworks integrating fast and slow cognitive processes in autonomous driving. This approach paves the way for more adaptive, human-like autonomous driving systems.

cs.RO↗

arXiv2024

The electronic and magnetic structures of bilayer La$_3$Ni$_2$O$_7$ at ambient pressure

We carry out a systematic study of the electronic and magnetic structure of the ambient-pressure bilayer La$_3$Ni$_2$O$_7$. Employing the hybrid exchange-correlation functional, we show that the exchange-correlation pushes the bonding $d_{z^2}$ bands below the Fermi level to be fully occupied. The calculated Fermi surfaces and the correlation normalized band structure match well with the experimental findings at ambient pressure. Moreover, the electronic susceptibility calculated for this new band structure features nesting-induced peaks near the wave vector $Q=(π/2, π/2)$, suggesting a possible density wave instability in agreement with recent experiments. Through a mean field study and DFT+U calculation introducing a Hubbard U interaction within conventional density functional theory, we confirm the spin-charge intertwined double stripe order is the magnetic ground state. Our results provide a faithful description for the low-pressure La$_3$Ni$_2$O$_7$ electronic structure.

cond-mat.supr-con↗

arXiv2024

Electronic structure and superconducting properties of LaNiO$_2$

Motivated by recent photoemission measurements on the La$_{0.8}$Sr$_{0.2}$NiO$_2$, we carry out a systematic study of the infinite-layer nickelate using both dynamical mean-field theory and density matrix embedding theory. The renormalized electronic structure and Fermi surface of correlated La$_{0.8}$Sr$_{0.2}$NiO$_2$ are studied in an effective two-band model through the dynamical mean-field calculation. We find the correlation effects reflect mainly on the Ni $d$ band, which is consistent with the experimental findings. We further study the ground state including magnetism and superconductivity through the density matrix embedding theory. Within the experimental doping range and rigid-band approximation, we show that the $d$-wave superconductivity is the lowest energy state, while the static magnetism is absent except very close to zero doping. These findings provide a new understanding of infinite-layer nickelate superconductivity.

cond-mat.str-el↗

arXiv2024

Interfering Josephson diode effect in Ta2Pd3Te5 asymmetric edge interferometer

Edge states in topological systems have attracted great interest due to their robustness and linear dispersions. Here a superconducting-proximitized edge interferometer is engineered on a topological insulator Ta2Pd3Te5 with asymmetric edges to realize the interfering Josephson diode effect (JDE), which hosts many advantages, such as the high efficiency as much as 73% at tiny applied magnetic fields with an ultra-low switching power around picowatt. As an important element to induce such JDE, the second-order harmonic in the current-phase relation is also experimentally confirmed by half-integer Shapiro steps. The interfering JDE is also accompanied by the antisymmetric second harmonic transport, which indicates the corresponding asymmetry in the interferometer, as well as the polarity of JDE. This edge interferometer offers an effective method to enhance the performance of JDE, and boosts great potential applications for future superconducting quantum devices.

cond-mat.mes-hall↗

arXiv2024

Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

Connected and automated vehicles (CAVs) are considered a potential solution for future transportation challenges, aiming to develop systems that are efficient, safe, and environmentally friendly. However, CAV control presents significant challenges due to the complexity of interconnectivity and coordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL's application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms. Lastly, it examines the current challenges in deploying MARL for CAV control, including macro-micro optimization, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL.

cs.RO↗

arXiv2024

Paramagnetic contribution in superconductors with different-mass Cooper pairs

Cooper pairs formed by two electrons with different effective masses are common in multiband superconductors, pair density wave states, and other superconducting systems with multiple degrees of freedom. In this paper, we show that there are paramagnetic contributions to the superfluid stiffness in superconductors with different-mass Cooper pairs. This paramagnetic response is owing to the relative motion between two electrons with different mass. Additionally, we identify a particle-hole dichotomy effect related to this mass difference. We investigate the paramagnetic contributions based on the linear-response theory in two-band superconductors with interband pairings and in pair density wave states, respectively. Our results offer a different perspective of the electromagnetic superfluid stiffness in unconventional superconductors beyond the traditional BCS response.

cond-mat.supr-con↗

arXiv2024

High Definition Map Mapping and Update: A General Overview and Future Directions

Along with the rapid growth of autonomous vehicles (AVs), more and more demands are required for environment perception technology. Among others, HD mapping has become one of the more prominent roles in helping the vehicle realize essential tasks such as localization and path planning. While increasing research efforts have been directed toward HD Map development. However, a comprehensive overview of the overall HD map mapping and update framework is still lacking. This article introduces the development and current state of the algorithm involved in creating HD map mapping and its maintenance. As part of this study, the primary data preprocessing approach of processing raw data to information ready to feed for mapping and update purposes, semantic segmentation, and localization are also briefly reviewed. Moreover, the map taxonomy, ontology, and quality assessment are extensively discussed, the map data's general representation method is presented, and the mapping algorithm ranging from SLAM to transformers learning-based approaches are also discussed. The development of the HD map update algorithm, from change detection to the update methods, is also presented. Finally, the authors discuss possible future developments and the remaining challenges in HD map mapping and update technology. This paper simultaneously serves as a position paper and tutorial to those new to HD map mapping and update domains.

cs.RO↗

arXiv2024

DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model

Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps.

cs.CV↗

arXiv2024

Charge 4$e$ superconductor: a wavefunction approach

We apply a wavefunction approach to study the charge 4$e$ superconductor. The wavefunction takes a similar structure to the BCS wavefunction for charge 2$e$ superconductors. The mean-field Hamiltonian and the Green's functions of charge 4$e$ are constructed. The physical properties including the density of states, gauge invariance, flux quantization, superfluid density and Josephson effects are systematically studied. All these results form a microscopic description of the charge 4$e$ SC and can be served as the hallmarks for experimental verifications.

cond-mat.supr-con↗

arXiv2024

Bulk high-temperature superconductivity in the high-pressure tetragonal phase of bilayer La2PrNi2O7

The Ruddlesden-Popper (R-P) bilayer nickelate, La3Ni2O7, was recently found to show signatures of high-temperature superconductivity (HTSC) at pressures above 14 GPa. Subsequent investigations achieved zero resistance in single- and poly-crystalline samples under hydrostatic pressure conditions. Yet, obvious diamagnetic signals, the other hallmark of superconductors, are still lacking owing to the filamentary nature with low superconducting volume fraction. The presence of a novel "1313" polymorph and competing R-P phases obscured proper identification of the phase for HTSC. Thus, achieving bulk HTSC and identifying the phase at play are the most prominent tasks at present. Here, we address these issues in the praseodymium (Pr)-doped La2PrNi2O7 polycrystalline samples. We find that the substitutions of Pr for La effectively inhibits the intergrowth of different R-P phases, resulting in nearly pure bilayer structure. For La2PrNi2O7, pressure-induced orthorhombic-to-tetragonal structural transition takes place at Pc ~ 11 GPa, above which HTSC emerges gradually upon further compression. The superconducting transition temperatures at 18-20 GPa reach Tconset = 82.5 K and Tczero = 60 K, which are the highest values among known nickelate superconductors. More importantly, bulk HTSC was testified by detecting clear diamagnetic signals below ~75 K corresponding to an estimated superconducting volume fraction ~ 57(5)% at 20 GPa. Our results not only resolve the existing controversies but also illuminate directions for exploring bulk HTSC in the bilayer nickelates.

cond-mat.supr-con↗

arXiv2024

Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs

Cooperative adaptive cruise control (CACC) has been recognized as a fundamental function of autonomous driving, in which platoon stability and energy efficiency are outstanding challenges that are difficult to accommodate in real-world operations. This paper studied the CACC of connected and autonomous vehicles (CAVs) based on the multi-agent reinforcement learning algorithm (MARL) to optimize platoon stability and energy efficiency simultaneously. The optimal use of communication bandwidth is the key to guaranteeing learning performance in real-world driving, and thus this paper proposes a communication-efficient MARL by incorporating the quantified stochastic gradient descent (QSGD) and a binary differential consensus (BDC) method into a fully-decentralized MARL framework. We benchmarked the performance of our proposed BDC-MARL algorithm against several several non-communicative andcommunicative MARL algorithms, e.g., IA2C, FPrint, and DIAL, through the evaluation of platoon stability, fuel economy, and driving comfort. Our results show that BDC-MARL achieved the highest energy savings, improving by up to 5.8%, with an average velocity of 15.26 m/s and an inter-vehicle spacing of 20.76 m. In addition, we conducted different information-sharing analyses to assess communication efficacy, along with sensitivity analyses and scalability tests with varying platoon sizes. The practical effectiveness of our approach is further demonstrated using real-world scenarios sourced from open-sourced OpenACC.

eess.SY↗

arXiv2024

StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation

Predicting the future occupancy states of the surrounding environment is a vital task for autonomous driving. However, current best-performing single-modality methods or multi-modality fusion perception methods are only able to predict uniform snapshots of future occupancy states and require strictly synchronized sensory data for sensor fusion. We propose a novel framework, StreamingFlow, to lift these strong limitations. StreamingFlow is a novel BEV occupancy predictor that ingests asynchronous multi-sensor data streams for fusion and performs streaming forecasting of the future occupancy map at any future timestamps. By integrating neural ordinary differential equations (N-ODE) into recurrent neural networks, StreamingFlow learns derivatives of BEV features over temporal horizons, updates the implicit sensor's BEV features as part of the fusion process, and propagates BEV states to the desired future time point. It shows good zero-shot generalization ability of prediction, reflected in the interpolation of the observed prediction time horizon and the reasonable inference of the unseen farther future period. Extensive experiments on two large-scale datasets, nuScenes and Lyft L5, demonstrate that StreamingFlow significantly outperforms previous vision-based, LiDAR-based methods, and shows superior performance compared to state-of-the-art fusion-based methods.

cs.CV↗

arXiv2024

PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving

Vision-centric occupancy networks, which represent the surrounding environment with uniform voxels with semantics, have become a new trend for safe driving of camera-only autonomous driving perception systems, as they are able to detect obstacles regardless of their shape and occlusion. Modern occupancy networks mainly focus on reconstructing visible voxels from object surfaces with voxel-wise semantic prediction. Usually, they suffer from inconsistent predictions of one object and mixed predictions for adjacent objects. These confusions may harm the safety of downstream planning modules. To this end, we investigate panoptic segmentation on 3D voxel scenarios and propose an instance-aware occupancy network, PanoSSC. We predict foreground objects and backgrounds separately and merge both in post-processing. For foreground instance grouping, we propose a novel 3D instance mask decoder that can efficiently extract individual objects. we unify geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into PanoSSC framework and propose new metrics for evaluating panoptic voxels. Extensive experiments show that our method achieves competitive results on SemanticKITTI semantic scene completion benchmark.

cs.CV↗

arXiv2024

Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review

Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception as autonomous vehicles need to accurately perceive highly dynamic, large-scale traffic scenarios and the complexity and computational costs of grid-centric perception are high. In recent years, the rapid development of deep learning techniques and hardware provides fresh insights into the evolution of grid-centric perception. The fundamental difference between grid-centric and object-centric pipeline lies in that grid-centric perception follows a geometry-first paradigm which is more robust to the open-world driving scenarios with endless long-tailed semantically-unknown obstacles. Recent researches demonstrate the great advantages of grid-centric perception, such as comprehensive fine-grained environmental representation, greater robustness to occlusion and irregular shaped objects, better ground estimation, and safer planning policies. There is also a growing trend that the capacity of occupancy networks are greatly expanded to 4D scene perception and prediction and latest techniques are highly related to new research topics such as 4D occupancy forecasting, generative AI and world models in the field of autonomous driving. Given the lack of current surveys for this rapidly expanding field, we present a hierarchically-structured review of grid-centric perception for autonomous vehicles. We organize previous and current knowledge of occupancy grid techniques along the main vein from 2D BEV grids to 3D occupancy to 4D occupancy forecasting. We additionally summarize label-efficient occupancy learning and the role of grid-centric perception in driving systems. Lastly, we present a summary of the current research trend and provide future outlooks.

cs.CV↗

arXiv2024

Majorana zero modes under electron correlation

In this work, we perform a systematic investigation of the correlated topological superconductors (TSCs), especially their non-trivial Majorana zero modes (MZMs). Compared to the non-interacting MZMs, the emerged correlated MZMs become projected MZMs. To prove this, we study the topological superconducting nanowire under the Hubbard and Hatsugai-Kohmoto interactions. Both of them become correlated TSCs under a magnetic field. Their topological properties are numerically computed by the Wilson loop and entanglement spectrum. We successfully extract the projected MZMs connecting the ground state and excited state through exact diagonalization. We also extend our results to the spinful Kitaev chain. Our results can provide a new perspective and understanding of the correlated MZMs.

cond-mat.supr-con↗

arXiv2024

van Hove Singularity-Driven Emergence of Multiple Flat Bands in Kagome Superconductors

The newly discovered Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb and Cs) continue to bring surprises in generating unusual phenomena and physical properties, including anomalous Hall effect, unconventional charge density wave, electronic nematicity and time-reversal symmetry breaking. Here we report an unexpected emergence of multiple flat bands in the AV$_3$Sb$_5$ superconductors. By performing high-resolution angle-resolved photoemission (ARPES) measurements, we observed four branches of flat bands that span over the entire momentum space. The appearance of the flat bands is not anticipated from the band structure calculations and cannot be accounted for by the known mechanisms of flat band generation. It is intimately related to the evolution of van Hove singularities. It is for the first time to observe such emergence of multiple flat bands in solid materials. Our findings provide new insights in revealing the underlying mechanism that governs the unusual behaviors in the Kagome superconductors. They also provide a new pathway in producing flat bands and set a platform to study the flat bands related physics.

cond-mat.mtrl-sci↗