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Percolation is a cornerstone concept in physics, providing crucial insights into critical phenomena and phase transitions. In this study, we adopt a kinetic perspective to reveal the scaling behaviors of higher-order gaps in the largest cluster across various percolation models, spanning from latticebased to network systems, encompassing both continuous and discontinuous percolation. Our results uncover an inherent self-similarity in the dynamical process both for critical and supercritical phase, characterized by two independent Fisher exponents, respectively. Utilizing a scaling ansatz, we propose a novel scaling relation that links the discovered Fisher exponents with other known critical exponents. Additionally, we demonstrate the application of our theory to real systems, showing its practical utility in extracting the corresponding Fisher exponents. These findings enrich our understanding of percolation dynamics and highlight the robust and universal scaling laws that transcend individual models and extend to broader classes of complex systems., Comment: 4.5 + 6 pages ; 4 + 12 figures

The applications of superconducting cable or magnet require that the superconductors are made into wires or tapes. For cuprate superconductors, this is a big challenge because of the strong flux motion induced by high anisotropy, very short coherence length and strong thermal fluctuation, etc. One of the ways is to fabricate superconducting films on flexible metallic tapes with oxide buffer layers. The successful one so far is the REBa$_2$Cu$_3$O$_7$ (REBCO, RE=rare earth elements) films in tape form, as called the coated conductors. While the superconducting transition temperature of REBCO system is limited to about 90 K. Here we report the successful fabrication of another new non-toxic superconducting film, namely (Cu,C)Ba$_{2}$Ca$_{2}$Cu$_3$O$_{9+\delta}$ on these flexible metallic tapes with LaMnO$_3$ and CeO$_2$ as the top layers. The onset superconducting transition occurs at 112 K and 110 K, and the zero-resistance transition temperatures are about 96 K and 98 K, respectively. The temperature dependent resistivity under magnetic fields in different directions reveal a relatively small anisotropy. Further optimization of the films will improve the zero resistance transition temperature, thus can also improve the characteristic properties for applications. Our results show that the (Cu,C)Ba$_{2}$Ca$_{2}$Cu$_3$O$_{9+\delta}$ is a promising candidate material for the high power applications in liquid nitrogen temperature region., Comment: 11 pages, 5 figures

High superconducting transition temperature is favorable for the applications of superconductors. Some cuprate superconductors have the transition temperatures above 100 K, such as the Hg- or Tl-based 1223 and 1234 phases, but many of them contain the toxic elements, like Hg and Tl. Meanwhile, the high anisotropy makes the vortices easy to move, thus the irreversibility magnetic field is very low in the liquid nitrogen temperature region. Here we report the successful synthesis of superconducting thin films (Cu,C)Ba$_{2}$Ca$_{2}$Cu$_3$O$_{9+\delta}$ with the zero-resistance transition temperature reaching 99.7 K. The film shows a very good crystallinity with (00l) orientation. The superconducting transitions are rather sharp as revealed by both resistivity and magnetization measurements. Temperature dependent resistivity has been measured under different magnetic fields along c-axis and ab-plane, and the irreversibility lines have been achieved. The resistivity was also measured at different temperatures with the magnetic field rotated in the ac-plane, and the data can be nicely scaled by using the anisotropic Ginzburg-Landau model, yielding a temperature dependent anisotropy which varies from 17 at 110 K to 4 at 77 K. We also measured the magnetization-hysteresis-loops and calculated the critical current density through the Bean critical state model. The critical current density at 77 K is about 6$\times$10$^5$ A/cm$^2$ (zero field), thus the film may be a good candidate for the applications of superconducting cables or the high frequency superconducting filters., Comment: 13 pages, 4 figures

Oxygen ion conductors are crucial for enhancing the efficiency of various clean energy technologies, including fuel cells, batteries, electrolyzers, membranes, sensors, and more. In this study, LaBi2O4Cl is identified as an ultra-fast oxygen conductor from the MBi2O4X (M=rare-earth element, X=halogen element) family, discovered by a structure-similarity analysis of >60k oxygen-containing compounds. Ab initio studies reveal that LaBi2O4Cl has an ultra-low migration barrier of 0.1 eV for oxygen vacancy, significantly lower than 0.6-0.8 eV for interstitial oxygen. Frenkel pairs are the dominant defects in intrinsic LaBi2O4Cl, facilitating notable oxygen diffusion primarily through vacancies at higher temperatures. LaBi2O4Cl with extrinsic oxygen vacancies (2.8%) exhibits a conductivity of 0.3 S/cm at 25{\deg}C, maintains a 0.1 eV diffusion barrier up to 1100{\deg}C, and transitions from extrinsic to mixed extrinsic and intrinsic behavior as the Frenkel pair concentration increases at higher temperatures. Experimental results on synthesized LaBi2O4Cl and Sr-doped LaBi2O4Cl demonstrate comparable or higher oxygen conductivity than YSZ and LSGM below 400 {\deg}C, with lower activation energies. Further experimental optimization of LaBi2O4Cl, including aliovalent doping and microstructure refinement, could significantly enhance its performance and efficiency, facilitating fast oxygen conduction approaching room temperature.

Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals individually, ignoring the rich contextual information in the object relationships between the neighbor proposals. In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection. Specifically, we create an inter-object relationship graph based on proposals in a frame via the graph generator to connect each proposal with its neighbor proposals. Afterward, the GNN module extracts edge features from the generated graph and iteratively refines proposal features with the captured edge features. Ultimately, we leverage the refined features as input to the detection head to obtain detection results. Our approach improves upon the baseline PV-RCNN on the KITTI validation set for the car class across easy, moderate, and hard difficulty levels by 0.82%, 0.74%, and 0.58%, respectively. Additionally, our method outperforms the baseline by more than 1% under the moderate and hard levels BEV AP on the test server.

Earth system tipping elements have been predominantly investigated for their potential to trigger \textit{negative} ecological, climatic, and societal shifts. Yet, an overlooked but seminal avenue exists in the form of \textit{positive} tipping phenomena, whose underlying mechanisms and benefits remain largely underexplored. To bridge this gap, our research introduces a fundamental percolation-based framework to assess the criticality and resilience of planetary terrestrial vegetation systems. Leveraging high-resolution satellite data, we focus on greening-induced positive tipping dynamics driven by global warming. We feature the Qinghai-Tibetan Plateau (QTP) and the Sahel region as contrasting yet analogous case studies. Our analysis uncovers an intriguing phenomenon where vegetation fragmentation aligns with a percolation threshold, exhibiting a scale-invariant pattern characterized by nearly perfect power laws with three critical exponents. Remarkably, contrary to conventional destructive tipping elements, these regions act as favorable tipping elements, transitioning from fragmented to cohesive vegetation patterns due to anthropogenic climate change and afforestation efforts. Furthermore, we propose an \textit{optimal resilience enhancement model} to reinforce vegetation robustness while minimizing socio-economic costs. This study provides valuable insights into the favorable aspects of tipping elements under climate change and offers effective strategies for enhancing ecological resilience against environmental threats.

The concept of Maxwell demon plays an essential role in connecting thermodynamics and information theory, while entanglement and non-locality are fundamental features of quantum theory. Given the rapid advancements in the field of quantum information science, there is a growing interest and significance in investigating the connection between Maxwell demon and quantum correlation. The majority of research endeavors thus far have been directed towards the extraction of work from quantum correlation through the utilization of Maxwell demon. Recently, a novel concept called Maxwell demon-assistant Einstein-Podolsky-Rosen (EPR) steering has been proposed, which suggests that it is possible to simulate quantum correlation by doing work. This seemingly counterintuitive conclusion is attributed to the fact that Alice and Bob need classical communication during EPR steering task, a requirement that does not apply in the Bell test. In this study, we demonstrate Maxwell demon-assistant EPR steering with superconducting quantum circuits. By compiling and optimizing a quantum circuit to be implemented on a 2D superconducting chip, we were able to achieve a steering parameter of $S_{2} = 0.770 \pm 0.005$ in the case of two measurement settings, which surpasses the classical bound of $1/\sqrt{2}$ by 12.6 standard deviations. In addition, experimental observations have revealed a linear correlation between the non-locality demonstrated in EPR steering and the work done by the demon. Considering the errors in practical operation, the experimental results are highly consistent with theoretical predictions. Our findings not only suggest the presence of a Maxwell demon loophole in the EPR steering, but also contribute to a deeper comprehension of the interplay between quantum correlation, information theory, and thermodynamics., Comment: Comments are welcome!

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method., Comment: 8 pages, 10 figures, accepted by 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, e.g., below 400 Celsius. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here, we combine physically-motivated structure and property descriptors, ab initio simulations, and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+d (LMS), a representative member of perrierite/chevkinite family. We found LMS has higher oxygen ionic conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. This work developed and demonstrated a powerful approach for discovering new families of interstitial oxygen conductors and suggests many more such materials remain to be discovered.

Wavefunction is a fundamental concept of quantum theory. Recent studies have shown surprisingly that wavefunction can be directly reconstructed via the measurement of weak value. The weak value based direct wavefunction reconstruction not only gives the operational meaning of wavefunction, but also provides the possibility of realizing holographic imaging with a totally new quantum approach. Here, we review the basic background knowledge of weak value based direct wavefunction reconstruction combined with recent experimental demonstrations. The main purpose of this work focuses on the idea of holographic imaging via direct wavefunction reconstruction. Since research on this topic is still in its early stage, we hope that this work can attract interest in the field of traditional holographic imaging. In addition, the wavefunction holographic imaging may find important applications in quantum information science., Comment: Accepted review paper for Chinese Physics B

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor configuration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to navigate through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely navigate through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient navigation through challenging terrains while conserving energy., Comment: 8 pages, 10 figures, accepted by 2023 IEEE/RSJ International Conference on Intelligent Robots(IROS). This work has been submitted to the IEEE for possible publication

Catching high-speed targets in the flight is a complex and typical highly dynamic task. In this paper, we propose Catch Planner, a planning-with-decision scheme for catching. For sequential decision making, we propose a policy search method based on deep reinforcement learning. In order to make catching adaptive and flexible, we propose a trajectory optimization method to jointly optimize the highly coupled catching time and terminal state while considering the dynamic feasibility and safety. We also propose a flexible constraint transcription method to catch targets at any reasonable attitude and terminal position bias. The proposed Catch Planner provides a new paradigm for the combination of learning and planning and is integrated on the quadrotor designed by ourselves, which runs at 100hz on the onboard computer. Extensive experiments are carried out in real and simulated scenes to verify the robustness of the proposed method and its expansibility when facing a variety of high-speed flying targets., Comment: 11 pages, 8 figures, accepted by IEEE/ASME Transactions on Mechatronics

The rapidly shrinking Arctic sea ice is changing weather patterns and disrupting the balance of nature. Dynamics of Arctic weather variability (WV) plays a crucial role in weather forecasting and is closely related to extreme weather events. Yet, assessing and quantifying the WV for both local Arctic regions and its planetary impacts under anthropogenic climate change is still unknown. Here, we develop a complexity-based approach to systematically evaluate and analyze the dynamic behaviour of WV. We reveal that the WV within and around the Arctic is statistically correlated to the Arctic Oscillation at the intraseasonal time scale. We further find that the variability of the daily Arctic sea ice is increasing due to its dramatic decline under a warming climate. Unstable Arctic weather conditions can disturb regional weather patterns through atmospheric teleconnection pathways, resulting in higher risk to human activities and greater weather forecast uncertainty. A multivariate climate network analysis reveals the existence of such teleconnections and implies a positive feedback loop between the Arctic and global weather instabilities. This enhances the mechanistic understanding of the influence of Arctic amplification on mid-latitude severe weather. Our framework provides a fresh perspective on the linkage of complexity science, WV and the Arctic.

Variational quantum algorithms, which consist of optimal parameterized quantum circuits, are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum (NISQ) era. Apart from classical computational resources, different kinds of quantum resources have their contributions to the process of computing, such as information scrambling and entanglement. Characterizing the relation between the complexity of specific problems and quantum resources consumed by solving these problems is helpful for us to understand the structure of VQAs in the context of quantum information processing. In this work, we focus on the quantum approximate optimization algorithm (QAOA), which aims to solve combinatorial optimization problems. We study information scrambling and entanglement in QAOA circuits, respectively, and discover that for a harder problem, more quantum resource is required for the QAOA circuit to obtain the solution in most cases. We note that in the future, our results can be used to benchmark the complexity of quantum many-body problems by information scrambling or entanglement accumulation in the computing process., Comment: 11 pages, 12 figures

The Born rule, which is one of foundational axioms of quantum theory, states that the probability of obtain outcome $a$ for the quantum state $|\psi\rangle$ is determined by $P(a)=|\langle a|\psi\rangle|^{2}$. Despite its great success in predicting the experimental outcomes, there still lacks a direct way to verify the Born rule. Here, we show that the weak value based direct measurement of superposition wavefunction is feasible, which can be used to verify the Born rule directly. The plausible experiment is suggested., Comment: comments are welcome

Tipping elements of the Earth system may shift abruptly and irreversibly from one state to another at tipping points, resulting in a growing threat to our society. Yet, it is not fully clear how to assess and quantify the influence of a tipping element and how to explore the teleconnections between different tipping elements. To fill this knowledge gap, we propose a climate network approach to quantitatively analyze the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that regions, such as, the Tibetan Plateau (TP) and West Antarctic ice sheet, are characterized by higher network weighted links and exhibit strong correlations with the ARA. We then identify a teleconnection propagation path between the ARA and the TP. This path is robust under climate change as simulated by various climate models of CMIP5 and CMIP6. In addition, we detect early warning signals for critical transition in the snow cover extent on the Tibetan Plateau by applying critical slowing down indicators, lag-1 autocorrelation and detrended fluctuation analysis. We find that the snow cover of the TP has been losing stability since 2008, revealing that the TP is operating like a tipping element and approaching a potential tipping point. We further uncover that various climate extremes between the ARA and the TP are significantly synchronized under climate change. Our framework provides new insights into how tipping elements are linked to each other and into the potential predictability of cascading tipping dynamics.

The frequency and magnitude of weather extreme events have increased significantly during the past few years in response to anthropogenic climate change. However, global statistical characteristics and underlying physical mechanisms are still not fully understood. Here, we adopt a statistical physics and probability theory based method to investigate the nature of extreme weather events, particularly the statistics of the day-to-day air temperature differences. These statistical measurements reveal that the distributions of the magnitudes of the extreme events satisfy a universal \textit{Gumbel} distribution, while the waiting time of those extreme events is governed by a universal \textit{Gamma} function. Further finite-size effects analysis indicates robust scaling behaviours. We additionally unveil that the cumulative distribution of logarithmic waiting times between the record events follows an \textit{Exponential} distribution and that the evolution of this climate system is directional where the underlying dynamics are related to a decelerating release of tension. The universal scaling laws are remarkably stable and unaffected by global warming. Counterintuitively, unlike as expected for record dynamics, we find that the number of quakes of the extreme temperature variability does not decay as one over time but with deviations relevant to large-scale climate extreme events. Our theoretical framework provides a fresh perspective on the linkage of universality, scaling, and climate systems. The findings throw light on the nature of the weather variabilities and could guide us to better forecast extreme events.