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Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data. Integrating ensemble-based DA methods with nonlinear deep learning models is computationally expensive and may suffer from large sampling errors. To address these challenges, we introduce a deep learning framework designed to simultaneously provide accurate forecasts and efficient DA. It is named Conditional Gaussian Koopman Network (CGKN), which transforms general nonlinear systems into nonlinear neural differential equations with conditional Gaussian structures. CGKN aims to retain essential nonlinear components while applying systematic and minimal simplifications to facilitate the development of analytic formulae for nonlinear DA. This allows for seamless integration of DA performance into the deep learning training process, eliminating the need for empirical tuning as required in ensemble methods. CGKN compensates for structural simplifications by lifting the dimension of the system, which is motivated by Koopman theory. Nevertheless, CGKN exploits special nonlinear dynamics within the lifted space. This enables the model to capture extreme events and strong non-Gaussian features in joint and marginal distributions with appropriate uncertainty quantification. We demonstrate the effectiveness of CGKN for both prediction and DA on three strongly nonlinear and non-Gaussian turbulent systems: the projected stochastic Burgers--Sivashinsky equation, the Lorenz 96 system, and the El Ni\~no-Southern Oscillation. The results justify the robustness and computational efficiency of CGKN.

We study topologically stable defect structures in systems where the defect line classification in three dimensions and associated algebra of interactions (the fundamental group) are governed by the non-Abelian 8-element group, the quaternions Q_8. The non-Abelian character of the defect algebra leads to a topological rigidity of bound defect pairs, and trivalent junctions which are the building blocks of multi-junction trivalent networks. We realize such structures in laboratory chiral nematics and analyze their behavior analytically, along with numerical modeling.

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub., Comment: 15 pages, 13 figures

The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarking of robust LiDAR SLAM algorithms. To address this gap, we introduce GEODE, a comprehensive multi-LiDAR, multi-scenario dataset specifically designed to include real-world geometrically degenerate environments. GEODE comprises 64 trajectories spanning over 64 kilometers across seven diverse settings with varying degrees of degeneracy. The data was meticulously collected to promote the development of versatile algorithms by incorporating various LiDAR sensors, stereo cameras, IMUs, and diverse motion conditions. We evaluate state-of-the-art SLAM approaches using the GEODE dataset to highlight current limitations in LiDAR SLAM techniques. This extensive dataset will be publicly available at https://geode.github.io, supporting further advancements in LiDAR-based SLAM., Comment: 15 pages, 9 figures, 6 tables. Submitted for IJRR dataset paper

The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure control, aiming to enhance imaging performance in challenging conditions. A lightweight image simulator is developed to facilitate the training process, enabling the diversification of image exposure and sequence trajectory. This setup enables completely offline training, eliminating the need for direct interaction with camera hardware and the real environments. Different levels of reward functions are crafted to enhance the VO systems, equipping the DRL agents with varying intelligence. Extensive experiments have shown that our exposure control agents achieve superior efficiency-with an average inference duration of 1.58 ms per frame on a CPU-and respond more quickly than traditional feedback control schemes. By choosing an appropriate reward function, agents acquire an intelligent understanding of motion trends and anticipate future illumination changes. This predictive capability allows VO systems to deliver more stable and precise odometry results. The codes and datasets are available at https://github.com/ShuyangUni/drl_exposure_ctrl., Comment: 8 pages, 7 figures

In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects. Previous methods either utilized range view (RV) or bird's eye view (BEV) residual maps to capture motion information. Unlike traditional approaches, we propose combining RV and BEV residual maps to exploit a greater potential of motion information jointly. Thus, we introduce CV-MOS, a cross-view model for moving object segmentation. Novelty, we decouple spatial-temporal information by capturing the motion from BEV and RV residual maps and generating semantic features from range images, which are used as moving object guidance for the motion branch. Our direct and unique solution maximizes the use of range images and RV and BEV residual maps, significantly enhancing the performance of LiDAR-based MOS task. Our method achieved leading IoU(\%) scores of 77.5\% and 79.2\% on the validation and test sets of the SemanticKitti dataset. In particular, CV-MOS demonstrates SOTA performance to date on various datasets. The CV-MOS implementation is available at https://github.com/SCNU-RISLAB/CV-MOS

We introduce SimBench, a benchmark designed to evaluate the proficiency of student large language models (S-LLMs) in generating digital twins (DTs) that can be used in simulators for virtual testing. Given a collection of S-LLMs, this benchmark enables the ranking of the S-LLMs based on their ability to produce high-quality DTs. We demonstrate this by comparing over 20 open- and closed-source S-LLMs. Using multi-turn interactions, SimBench employs a rule-based judge LLM (J-LLM) that leverages both predefined rules and human-in-the-loop guidance to assign scores for the DTs generated by the S-LLM, thus providing a consistent and expert-inspired evaluation protocol. The J-LLM is specific to a simulator, and herein the proposed benchmarking approach is demonstrated in conjunction with the Chrono multi-physics simulator. Chrono provided the backdrop used to assess an S-LLM in relation to the latter's ability to create digital twins for multibody dynamics, finite element analysis, vehicle dynamics, robotic dynamics, and sensor simulations. The proposed benchmarking principle is broadly applicable and enables the assessment of an S-LLM's ability to generate digital twins for other simulation packages. All code and data are available at https://github.com/uwsbel/SimBench.

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications., Comment: 40 pages, 8 figures

Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. However, existing approaches often struggle with data redundancy, robustness, and accuracy in complex environments. To address these challenges, we present MS-Mapping, an novel multi-session LiDAR mapping system that employs an incremental mapping scheme for robust and accurate map assembly in large-scale environments. Our approach introduces three key innovations: 1) A distribution-aware keyframe selection method that captures the subtle contributions of each point cloud frame to the map by analyzing the similarity of map distributions. This method effectively reduces data redundancy and pose graph size, while enhancing graph optimization speed; 2) An uncertainty model that automatically performs least-squares adjustments according to the covariance matrix during graph optimization, improving mapping precision, robustness, and flexibility without the need for scene-specific parameter tuning. This uncertainty model enables our system to monitor pose uncertainty and avoid ill-posed optimizations, thereby increasing adaptability to diverse and challenging environments. 3) To ensure fair evaluation, we redesign baseline comparisons and the evaluation benchmark. Direct assessment of map accuracy demonstrates the superiority of the proposed MS-Mapping algorithm compared to state-of-the-art methods. In addition to employing public datasets such as Urban-Nav, FusionPortable, and Newer College, we conducted extensive experiments on such a large \SI{855}{m}$\times$\SI{636}{m} ground truth map, collecting over \SI{20}{km} of indoor and outdoor data across more than ten sequences..., Comment: 18 pages, 22 figures

Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale separation, deterministic and local closure models often lack enough generalization capability, which limits their performance in many real-world applications. In this work, we propose a data-driven modeling framework for constructing stochastic and non-local closure models via conditional diffusion model and neural operator. Specifically, the Fourier neural operator is incorporated into a score-based diffusion model, which serves as a data-driven stochastic closure model for complex dynamical systems governed by partial differential equations (PDEs). We also demonstrate how accelerated sampling methods can improve the efficiency of the data-driven stochastic closure model. The results show that the proposed methodology provides a systematic approach via generative machine learning techniques to construct data-driven stochastic closure models for multiscale dynamical systems with continuous spatiotemporal fields.

$P_c(4457)$ has been discovered over five years, but the parity of this particle remains undetermined. In this letter we propose a new interpretation for $P_c(4457)$, which is the state generated from the coupled-channel $\bar{D}^0\Lambda_c^{+}(2595)$ and $\pi^0 P_c(4312)$ since they can exchange an almost on-shell $\Sigma_c^+$. In this scenario, the parity of $P_c(4457)$ will be positive, which is different from the candidate of the bound state of $\bar{D}^*\Sigma_c$. The main decay channel of $P_c(4457)$ in this model is $P_c(4312)\pi$. We propose three processes $\Lambda_b^0 \to J/\psi K_s p \pi^-$, $\Lambda_b^0 \to J/\psi K^- p \pi^0$, and $\Lambda_b^0 \to J/\psi p \pi^- \pi^+ K^-$ to verify $P_c(4457)\to P_c(4312)\pi$., Comment: 6 Pages, 2 figures

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. \citet{zhou2019deep} further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments confirm the theoretical findings.

Despite advancements in robotic-assisted surgery, automating complex tasks like suturing remain challenging due to the need for adaptability and precision. Learning-based approaches, particularly reinforcement learning (RL) and imitation learning (IL), require realistic simulation environments for efficient data collection. However, current platforms often include only relatively simple, non-dexterous manipulations and lack the flexibility required for effective learning and generalization. We introduce SurgicAI, a novel platform for development and benchmarking addressing these challenges by providing the flexibility to accommodate both modular subtasks and more importantly task decomposition in RL-based surgical robotics. Compatible with the da Vinci Surgical System, SurgicAI offers a standardized pipeline for collecting and utilizing expert demonstrations. It supports deployment of multiple RL and IL approaches, and the training of both singular and compositional subtasks in suturing scenarios, featuring high dexterity and modularization. Meanwhile, SurgicAI sets clear metrics and benchmarks for the assessment of learned policies. We implemented and evaluated multiple RL and IL algorithms on SurgicAI. Our detailed benchmark analysis underscores SurgicAI's potential to advance policy learning in surgical robotics. Details: \url{https://github.com/surgical-robotics-ai/SurgicAI

Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available., Comment: 3 pages, 2 figures, Accepted by the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)

Approximate Computing (AC) has emerged as a promising technique for achieving energy-efficient architectures and is expected to become an effective technique for reducing the electricity cost for cloud service providers (CSP). However, the potential misuse of AC has not received adequate attention, which is a coming crisis behind the blueprint of AC. Driven by the pursuit of illegal financial profits, untrusted CSPs may deploy low-cost AC devices and deceive clients by presenting AC services as promised accurate computing products, while falsely claiming AC outputs as accurate results. This misuse of AC will cause both financial loss and computing degradation to cloud clients. In this paper, we define this malicious attack as DisHonest Approximate Computing (DHAC) and analyze the technical challenges faced by clients in detecting such attacks. To address this issue, we propose two golden model free detection methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC provides clients a low-cost approach to infer the residual class to which a legitimate accurate output should belong. By comparing the residual class of the returned result, clients can determine whether a computing service contains any AC elements. FBC detects potential DHAC by computing an invertible check branch using the intermediate values of the program. It compares the values before entering and after returning from the check branch to identify any discrepancies. Both RCC and FBC can be executed concurrently with real computing tasks, enabling real-time DHAC detection with current inputs. Our experimental results show that both RCC and FBC can detect over 96%-99% of DHAC cases without misjudging any legitimate accurate results., Comment: 12 pages, 9 figures

Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and real-time efficiency.

Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undesired ghost tracks that are mixed up with the static environment. Existing dynamic removal methods normally fail to balance the performance in computational efficiency and accuracy. In response, we present BeautyMap to efficiently remove the dynamic points while retaining static features for high-fidelity global maps. Our approach utilizes a binary-encoded matrix to efficiently extract the environment features. With a bit-wise comparison between matrices of each frame and the corresponding map region, we can extract potential dynamic regions. Then we use coarse to fine hierarchical segmentation of the $z$-axis to handle terrain variations. The final static restoration module accounts for the range-visibility of each single scan and protects static points out of sight. Comparative experiments underscore BeautyMap's superior performance in both accuracy and efficiency against other dynamic points removal methods. The code is open-sourced at https://github.com/MKJia/BeautyMap., Comment: The first two authors are co-first authors. 8 pages, accepted by RA-L

We propose new applications of Floquet theory in Rydberg atoms for constructing quantum entangling gates in atomic ground-state manifolds. By dynamically periodically modulating the Rabi frequencies of transitions between ground and Rydberg states of atoms, error-resilient two-qubit entangling gates can be implemented in the regime of Rydberg blockade. According to different degrees of Floquet theory utilization, the fidelity of the resulting controlled gates surpasses that of the original reference, and it exhibits high robustness against Rabi error in two qubits and detuning error in the control qubit. Our method only uses encoding in the ground states, and compared to the original scheme using Rydberg state for encoding, it is less susceptible to environmental interference, making it more practical to implement. Therefore, our approach may have broader applications or potential for further expansion of geometric quantum computation with neutral atoms.

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises $27$ sequences, spanning over $2.5$ hours and collected from four distinct platforms: a handheld suite, a legged robots, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of $38.7km$. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately $0.3km^2$. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The complete dataset, including sensor data, GT, and calibration details, is accessible at https://fusionportable.github.io/dataset/fusionportable_v2., Comment: 21 pages, 17 figures, 7 tables. Accepted by International Journal of Robotics Research (IJRR)

A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeling complex dynamical systems and implementing analytic formulae of the associated data assimilation (DA). In contrast to the standard neural network predictive models, the CGNSDE is designed to effectively tackle both forward prediction tasks and inverse state estimation problems. The CGNSDE starts by exploiting a systematic causal inference via information theory to build a simple knowledge-based nonlinear model that nevertheless captures as much explainable physics as possible. Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution. These analytic formulae are used as an additional computationally affordable loss to train the neural networks that directly improve the DA accuracy. This DA loss function promotes the CGNSDE to capture the interactions between state variables and thus advances its modeling skills. With the DA loss, the CGNSDE is more capable of estimating extreme events and quantifying the associated uncertainty. Furthermore, crucial physical properties in many complex systems, such as the translate-invariant local dependence of state variables, can significantly simplify the neural network structures and facilitate the CGNSDE to be applied to high-dimensional systems. Numerical experiments based on chaotic systems with intermittency and strong non-Gaussian features indicate that the CGNSDE outperforms knowledge-based regression models, and the DA loss further enhances the modeling skills of the CGNSDE.

Dense atom ensembles with Rydberg excitations display intriguing collective effects mediated by their strong, long-range dipole-dipole interactions. These collective effects, often modeled using Rydberg superatoms, have gained significant attention across various fields due to their potential applications in quantum information processing and quantum optics. In this review article, we delve into the theoretical foundations of Rydberg interactions and explore experimental techniques for their manipulation and detection. We also discuss the latest advancements in harnessing Rydberg collective effects for quantum computation and optical quantum technologies. By synthesizing insights from theoretical studies and experimental demonstrations, we aim to provide a comprehensive overview of this rapidly evolving field and its potential impact on the future of quantum technologies., Comment: Revised according to the comments of referees

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong., Comment: 6 pages, 2 figures, 1 table

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at mias.group/GCM.

We presented the multi-filter light curves of CSS_J154915.7+375506 inaugurally, which were observed by the 1.5 m AZT-22 telescope at Maidanak Astronomical Observatory. A low-resolution spectrum obtained by LAMOST reveals it is an A-type close binary. By analyzing the BVRI total-eclipse light curves, we are able to derive a reliable photometric solution for this system, which indicates that CSS_J154915.7+375506 is an extremely low-mass-ratio (q=0.138) marginal contact binary system. The location in the HR diagram shows that its secondary component with a much smaller mass is the more evolved one, indicating the mass ratio reversal occurred. The present secondary component had transferred a significant amount of mass to the present primary one. By the combination of a total of 20 times of minimum, we investigated its O-C curve. A periodic oscillation and a possible period decrease have been detected. As the period decreases, the system will evolve towards the contact phase. This makes CSS\_J154915.7+375506 a valuable case to study the formation scenario of contact binaries through mass reversal. The periodic oscillation suggested a third body with a minimal mass of $0.91\,M_{\odot}$, which is larger than that of the less massive component in the central binary. This implies that the secondary body was not replaced by the third body during early stellar interactions, indicating that it is a fossil system and retains its original dynamical information., Comment: 8 pages, 6 figures

The generation of the narrowband strong-correlated biphotons via spontaneous four-wave mixing can be effectively controlled and enhanced by an additional driving field which drives a transition with its upper level being a Rydberg state. We study the properties of the noise of the generated biphotons and show that in the region of weak pumping and low atomic density, a high degree of the photon correlation is maintained with the photon-pair generation rate siginificantly enhanced.

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network's feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets.

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at \url{https://github.com/PengYu-team/Co-LRIO}.

Integrated quantum light source is increasingly desirable in large-scale quantum information processing.~Despite recent remarkable advances, new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitride (GaN) microring based quantum light generation in the telecom C-band, which has potential towards the monolithic integration of quantum light source.~In our demonstration, the GaN microring has a free spectral range of 330 GHz and a near-zero anomalous dispersion region of over 100 nm. The generation of energy-time entangled photon pair is demonstrated with a typical raw two-photon interference visibility of 95.5$\pm$6.5%, which is further configured to generate heralded single photon with a typical heralded second-order auto-correlation $g^{(2)}_{H}(0)$ of 0.045$\pm$0.001. Our results pave the way for developing chip-scale quantum photonic circuit.

We propose a scheme for realizing broadband and tunable transmission non-reciprocity by utilizing two-photon near-resonant transitions in thermal atoms as single-photon far-detuned transitions can be eliminated. Our basic idea is to largely reduce the Doppler broadenings on a pair of two-photon, probe and coupling, transitions and meanwhile make the only four-photon transition Doppler-free (velocity-dependent) for a forward (backward) probe field. One main advantage of this scheme lies in that the transmission non-reciprocity can be realized and manipulated in a frequency range typically exceeding $200$ MHz with isolation ratio above $20$ dB and insertion loss below $1.0$ dB by modulating an assistant field in frequency and amplitude. The intersecting angle between four applied fields also serves as an effective control knob to optimize the nonreciprocal transmission of a forward or backward probe field, e.g. in a much wider frequency range approaching $1.4$ GHz.

Accurately generating ground truth (GT) trajectories is essential for Simultaneous Localization and Mapping (SLAM) evaluation, particularly under varying environmental conditions. This study introduces a systematic approach employing a prior map-assisted framework for generating dense six-degree-of-freedom (6-DoF) GT poses for the first time, enhancing the fidelity of both indoor and outdoor SLAM datasets. Our method excels in handling degenerate and stationary conditions frequently encountered in SLAM datasets, thereby increasing robustness and precision. A significant aspect of our approach is the detailed derivation of covariances within the factor graph, enabling an in-depth analysis of pose uncertainty propagation. This analysis crucially contributes to demonstrating specific pose uncertainties and enhancing trajectory reliability from both theoretical and empirical perspectives. Additionally, we provide an open-source toolbox (https://github.com/JokerJohn/Cloud_Map_Evaluation) for map evaluation criteria, facilitating the indirect assessment of overall trajectory precision. Experimental results show at least a 30\% improvement in map accuracy and a 20\% increase in direct trajectory accuracy compared to the Iterative Closest Point (ICP) \cite{sharp2002icp} algorithm across diverse campus environments, with substantially enhanced robustness. Our open-source solution (https://github.com/JokerJohn/PALoc), extensively applied in the FusionPortable\cite{Jiao2022Mar} dataset, is geared towards SLAM benchmark dataset augmentation and represents a significant advancement in SLAM evaluations., Comment: 11 pages, 8 figures. Accepted by 2024 IEEE/ASME Transactions on Mechatronics (TMECH)

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://github.com/SCNU-RISLAB/MF-MOS., Comment: Accepted by ICRA2024

Occupying the intermediate-mass regime of the accretion--jet parameter space, radio continuum emission from active galactic nuclei with black hole mass M_BH <~ 10^6 Msun (low-mass AGNs) is a valuable probe to the physics of relativistic jets. Yet the number of low-mass AGNs with radio detection is rather limited so far (~ 40 in total). In this work we make two efforts to search for radio counterparts for the largest sample of optically selected low-mass AGNs. First, we collect counterparts from the recent data releases of SKA pathfinders such as LOFAR Two-metre Sky Survey (LoTSS). Additionally, we deeply mine in Faint Images of the Radio Sky at Twenty-Centimeters (FIRST), fitting the FIRST images of the optical AGNs with an elaborate procedure optimized to detect faint radio sources. We have obtained 151 radio sources (mainly from the SKA pathfinders), including 102 new reliable sources (S/N >= 5) and 23 new candidates (3.5 <= S/N < 5). The majority of these new sources (119 of 125) have flux densities lower than the threshold of the official FIRST catalog. The new sources have rest-frame 20 cm power (P_20cm) from 1.98 x 10^20 to 1.29 x 10^23 W/Hz. For low-z Seyfert galaxies P_20cm correlates with M_BH intrinsically and positively, yet only marginally with Eddington ratio L/L_EDD. In terms of the logN--logS relation for the expanding Universe, the limiting flux density for the completeness of our LoTSS sources turns out to be 0.45 mJy at 1.4 GHz; i.e., complete to such a flux-density level that is four times deeper than the official FIRST catalog., Comment: Full tables uploaded in the arXiv source files. A bonus: useful parameterized formula of the logN--logS relation for the expanding Universe, with an exponent controlling the abruptness degree of the turnover

Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e.g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important. For example, the small scales of cloud dynamics and droplet formation are crucial for controlling climate, yet are unresolvable in global climate models. Semi-empirical closure models for the effects of unresolved degrees of freedom often exist and encode important domain-specific knowledge. Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge. Here we describe a general approach, principles, and algorithms for learning about structural errors. Key to our approach is to include structural error models inside the models of complex systems, for example, in closure models for unresolved scales. The structural errors then map, usually nonlinearly, to observable data. As a result, however, mismatches between model output and data are only indirectly informative about structural errors, due to a lack of labeled pairs of inputs and outputs of structural error models. Additionally, derivatives of the model may not exist or be readily available. We discuss how structural error models can be learned from indirect data with derivative-free Kalman inversion algorithms and variants, how sparsity constraints enforce a "do no harm" principle, and various ways of modeling structural errors. We also discuss the merits of using non-local and/or stochastic error models. In addition, we demonstrate how data assimilation techniques can assist the learning about structural errors in non-ergodic systems. The concepts and algorithms are illustrated in two numerical examples based on the Lorenz-96 system and a human glucose-insulin model., Comment: 40 pages, 13 figures

Two interacting Rydberg atoms coupled to a waveguide realize a giant-atom platform that exhibits the controllable (phase-dependent) chirality where the direction of nonreciprocal photon scattering can be switched on demand, e.g., by the geometrical tuning of an external driving field. At variance with previous chiral setups, the simplified approach of our proposed platform arises from an optical implementation of the local phase difference between two coupling points of the Rydberg giant atom. Furthermore, employing two or more driving fields, this platform could also be used as a frequency converter with its efficiency exhibiting a strong asymmetry and being significantly enhanced via the chiral couplings. Our results suggest an extendable giant-atom platform that is both innovative and promising for chiral quantum optics and tunable frequency conversion in the optical domain.