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Monocular visual localization plays a pivotal role in advanced driver assistance systems and autonomous driving by estimating a vehicle's ego-motion from a single pinhole camera. Nevertheless, conventional monocular visual odometry encoun-ters challenges in scale estimation due to the absence of depth information during projection. Previous methodologies, whether rooted in physical constraints or deep learning paradigms, con-tend with issues related to computational complexity and the management of dynamic objects. This study extends our prior research, presenting innovative strategies for ego-motion estima-tion and the selection of ground points. Striving for a nuanced equilibrium between computational efficiency and precision, we propose a hybrid method that leverages the SegNeXt model for real-time applications, encompassing both ego-motion estimation and ground point selection. Our methodology incorporates dy-namic object masks to eliminate unstable features and employs ground plane masks for meticulous triangulation. Furthermore, we exploit Geometry-constraint to delineate road regions for scale recovery. The integration of this approach with the mo-nocular version of ORB-SLAM3 culminates in the accurate esti-mation of a road model, a pivotal component in our scale recov-ery process. Rigorous experiments, conducted on the KITTI da-taset, systematically compare our method with existing monocu-lar visual odometry algorithms and contemporary scale recovery methodologies. The results undeniably confirm the superior ef-fectiveness of our approach, surpassing state-of-the-art visual odometry algorithms. Our source code is available at https://git hub.com/bFr0zNq/MVOSegScale.

Photonic crystal fibers (PCFs) that trap and guide light using photonic bandgaps have revolutionized modern optics with enormous scientific innovations and technological applications spanning many disciplines. Recently, inspired by the discovery of topological phases of matter, Dirac-vortex topological PCFs have been theoretically proposed with intriguing topological properties and unprecedented opportunities in optical fiber communications. However, due to the substantial challenges of fabrication and characterization, experimental demonstration of Dirac-vortex topological PCFs has thus far remained elusive. Here, we report the experimental realization of a Dirac-vortex topological PCF using the standard stack-and-draw fabrication process with silica glass capillaries. Moreover, we experimentally observe that Dirac-vortex single-polarization single-mode bounds to and propagates along the fiber core in the full communication window (1260-1675nm). Our study pushes the research frontier of PCFs and provides a new avenue to enhance their performance and functionality further.

Accurate segmentation of 3D vascular structures is essential for various medical imaging applications. The dispersed nature of vascular structures leads to inherent spatial uncertainty and necessitates location awareness, yet most current 3D medical segmentation models rely on the patch-wise training strategy that usually loses this spatial context. In this study, we introduce the Coordinate-aware Modulated Mamba Network (COMMA) and contribute a manually labeled dataset of 570 cases, the largest publicly available 3D vessel dataset to date. COMMA leverages both entire and cropped patch data through global and local branches, ensuring robust and efficient spatial location awareness. Specifically, COMMA employs a channel-compressed Mamba (ccMamba) block to encode entire image data, capturing long-range dependencies while optimizing computational costs. Additionally, we propose a coordinate-aware modulated (CaM) block to enhance interactions between the global and local branches, allowing the local branch to better perceive spatial information. We evaluate COMMA on six datasets, covering two imaging modalities and five types of vascular tissues. The results demonstrate COMMA's superior performance compared to state-of-the-art methods with computational efficiency, especially in segmenting small vessels. Ablation studies further highlight the importance of our proposed modules and spatial information. The code and data will be open source at https://github.com/shigen-StoneRoot/COMMA.

In rehabilitation, powered, and teleoperation exoskeletons, connecting the human body to the exoskeleton through binding attachments is a common configuration. However, the uncertainty of the tightness and the donning deviation of the binding attachments will affect the flexibility and comfort of the exoskeletons, especially during high-speed movement. To address this challenge, this paper presents a flexible exoskeleton control approach with binding alignment and full-arm coordination. Firstly, the sources of the force interaction caused by donning offsets are analyzed, based on which the interactive force data is classified into the major, assistant, coordination, and redundant component categories. Then, a binding alignment strategy (BAS) is proposed to reduce the donning disturbances by combining different force data. Furthermore, we propose a full-arm coordination mechanism (FCM) that focuses on two modes of arm movement intent, joint-oriented and target-oriented, to improve the flexible performance of the whole exoskeleton control during high-speed motion. In this method, we propose an algorithm to distinguish the two intentions to resolve the conflict issue of the force component. Finally, a series of experiments covering various aspects of exoskeleton performance (flexibility, adaptability, accuracy, speed, and fatigue) were conducted to demonstrate the benefits of our control framework in our full-arm exoskeleton.

Quantitative MRI (qMRI) estimates tissue properties of interest from measured MRI signals. This process is conventionally achieved by computationally expensive model fitting, which limits qMRI's clinical use, motivating recent development based on machine learning. Self-supervised approaches are particularly popular as they avoid the pitfall of distributional shift that affects supervised methods. However, it is unknown how they would behave if similar signals can result from multiple tissue properties, a common challenge known as model degeneracy. Understanding this is crucial for ascertaining the scope within which self-supervised approaches may be applied. To this end, this work makes two contributions. First, we demonstrate that model degeneracy will compromise self-supervised approaches, motivating the development of mitigating strategies. Second, we propose such a mitigating solution based on applying appropriate constraining transforms on the output of the bottleneck layer of the autoencoder network typically employed in self-supervised approaches. We illustrate both contributions using the estimation of proton density fat fraction and $R_2^*$ from chemical shift-encoded MRI, an ideal exemplar due to its exhibition of degeneracy across the full parameter space. The results from both simulation and $\textit{in vivo}$ experiments demonstrate that the proposed strategy resolves model degeneracy.

The influence of various damping on the performance of Schwarz methods for time-harmonic waves is visualized by Fourier analysis., Comment: 8 pages, submitted to the 28th International Conference on Domain Decomposition Methods

Graphs are able to model interconnected entities in many online services, supporting a wide range of applications on the Web. This raises an important question: How can we train a graph foundational model on multiple source domains and adapt to an unseen target domain? A major obstacle is that graphs from different domains often exhibit divergent characteristics. Some studies leverage large language models to align multiple domains based on textual descriptions associated with the graphs, limiting their applicability to text-attributed graphs. For text-free graphs, a few recent works attempt to align different feature distributions across domains, while generally neglecting structural differences. In this work, we propose a novel Structure Alignment framework for text-free Multi-domain Graph Pre-Training and cross-domain adaptation (SAMGPT). It is designed to learn multi-domain knowledge from graphs originating in multiple source domains, which can then be adapted to address applications in an unseen target domain. Specifically, we introduce a set of structure tokens to harmonize structure-based aggregation across source domains during the pre-training phase. Next, for cross-domain adaptation, we design dual prompts, namely, holistic prompts and specific prompts, which adapt unified multi-domain structural knowledge and fine-grained, domain-specific information, respectively, to a target domain. Finally, we conduct comprehensive experiments on seven public datasets to evaluate and analyze the effectiveness of SAMGPT., Comment: Accepted by WWW2025 Main Track

Map construction task plays a vital role in providing precise and comprehensive static environmental information essential for autonomous driving systems. Primary sensors include cameras and LiDAR, with configurations varying between camera-only, LiDAR-only, or camera-LiDAR fusion, based on cost-performance considerations. While fusion-based methods typically perform best, existing approaches often neglect modality interaction and rely on simple fusion strategies, which suffer from the problems of misalignment and information loss. To address these issues, we propose MapFusion, a novel multi-modal Bird's-Eye View (BEV) feature fusion method for map construction. Specifically, to solve the semantic misalignment problem between camera and LiDAR BEV features, we introduce the Cross-modal Interaction Transform (CIT) module, enabling interaction between two BEV feature spaces and enhancing feature representation through a self-attention mechanism. Additionally, we propose an effective Dual Dynamic Fusion (DDF) module to adaptively select valuable information from different modalities, which can take full advantage of the inherent information between different modalities. Moreover, MapFusion is designed to be simple and plug-and-play, easily integrated into existing pipelines. We evaluate MapFusion on two map construction tasks, including High-definition (HD) map and BEV map segmentation, to show its versatility and effectiveness. Compared with the state-of-the-art methods, MapFusion achieves 3.6% and 6.2% absolute improvements on the HD map construction and BEV map segmentation tasks on the nuScenes dataset, respectively, demonstrating the superiority of our approach.

We propose an approach based on Fourier analysis to wavenumber explicit sharp estimation of absolute and relative errors of finite difference methods for the Helmholtz equation. We use the approach to analyze the classical centred scheme for the Helmholtz equation with a general smooth source term and Dirichlet boundary conditions in 1D. For the Fourier interpolants of the discrete solution with homogeneous (or inhomogeneous) Dirichlet conditions, we show rigorously that the worst case attainable convergence order of the absolute error is k2h2 (or k3h2) in the L2-norm and k3h2 (or k4h2) in the H1-semi-norm, and that of the relative error is k3h2 in both L2- and H1-semi-norms. Even though the classical centred scheme is well-known, it is the first time that such sharp estimates of absolute and relative errors are obtained. We show also that the Fourier analysis approach can be used as a convenient visual tool for evaluating finite difference schemes in presence of source terms, which is beyond the scope of dispersion analysis., Comment: 30 pages, 21 figures

Adversarial attacks have proven effective in deceiving machine learning models by subtly altering input images, motivating extensive research in recent years. Traditional methods constrain perturbations within $l_p$-norm bounds, but advancements in Unrestricted Adversarial Examples (UAEs) allow for more complex, generative-model-based manipulations. Diffusion models now lead UAE generation due to superior stability and image quality over GANs. However, existing diffusion-based UAE methods are limited to using reference images and face challenges in generating Natural Adversarial Examples (NAEs) directly from random noise, often producing uncontrolled or distorted outputs. In this work, we introduce VENOM, the first text-driven framework for high-quality unrestricted adversarial examples generation through diffusion models. VENOM unifies image content generation and adversarial synthesis into a single reverse diffusion process, enabling high-fidelity adversarial examples without sacrificing attack success rate (ASR). To stabilize this process, we incorporate an adaptive adversarial guidance strategy with momentum, ensuring that the generated adversarial examples $x^*$ align with the distribution $p(x)$ of natural images. Extensive experiments demonstrate that VENOM achieves superior ASR and image quality compared to prior methods, marking a significant advancement in adversarial example generation and providing insights into model vulnerabilities for improved defense development.

It is conjectured that the Brauer--Manin obstruction is expected to control the existence of 0-cycles of degree 1 on smooth proper varieties over number fields. In this paper, we prove that the existence of Brauer--Manin obstruction to Hasse principle for 0-cycles of degree 1 on the product of smooth (non-necessarily proper) varieties is equivalent to the simultaneous existence of such an obstruction on each factor. We also prove an analogous statement for smooth varieties defined over function fields of $\mathbb{C}((t))$-curves., Comment: 13 pages. Comments are welcome

The structure of grey matter has long been a key focus in neuroscience, as cell morphology varies by type and can be affected by neurological conditions. Understanding these variations is essential for studying brain function and disease. Diffusion-weighted MRI (dMRI) is a powerful non-invasive tool for examining cellular microstructure in vivo. However, for dMRI to accurately reflect cellular features, it is crucial to determine which aspects of morphology influence its measurements. Proper interpretation of dMRI data depends on understanding its sensitivity to different cellular characteristics. Despite growing interest in cellular morphology, there has been no systematic report on the key features defining different neural cell types. To address this, we analyzed over 3,500 three-dimensional cellular reconstructions across three species and nine cell types, establishing reference values for critical morphological traits. These traits fall into three categories: structural features that define the cell's skeletal framework, shape features that describe spatial organization, and topological features that break down cellular structure to distinguish cell types. Beyond reporting these reference values, we examine their relevance for dMRI, identifying which neural features dMRI can detect and which cell types may be distinguishable. This work provides essential benchmarks for grey matter research, offering new guidelines on linking neuroimaging measurements to neurobiology. These reference values will be a valuable resource for neuroscientists and neuroimaging researchers, aiding in the interpretation of imaging data and the refinement of brain tissue models.

We investigate the properties of nucleons at finite temperature and density using a two-flavor quark meson model with Gaussian fluctuations that extend beyond the mean-field approximation. Our findings suggest that Gaussian fluctuations lead to a non-monotonic behavior of the nucleon mass as a function of temperature and density, which may play an important role in the study of the hadronization process of relativistic heavy-ion collisions. Moreover, we observe an increase in the nucleon radius due to Gaussian fluctuations, suggesting an effective repulsive force akin to the Casimir effect, as observed in the gold-bromobenzene-silica system. This study offers new insights into how temperature, density, and quantum fluctuations affect the structure and properties of nucleons under extreme conditions., Comment: 12 pages, 6 figures

We introduce the vector meson $\omega$ into the Quark Meson model, and study the impact of vector interactions on the properties of static hadrons using the mean-field approximation. The short-range repulsive force associated with vector interactions leads to an expansion of the root mean square radius of nucleons. While the mass of hadrons increases, the gap between this mass and the energy of the three free constituent quarks decreases, resulting in the instability of hadrons. Our study of nucleon mass and radius at finite temperature and density has potential applications for particle yield in heavy ion collisions and the mass-radius relationship in compact stars., Comment: 8 pages, 6 figures

Tripartite entanglement as a remarkable resource in quantum information science has been extensively investigated in hybrid quantum systems, whereas it is generally weak and prone to be suppressed by noise, restricting its practical application in quantum technologies. Here, we propose how to enhance the tripartite entanglement among magnons, photons and phonons in a hybrid cavity-magnon optomechanical system by exploiting a synergistic effect of the optical parametric amplification (OPA) and mechanical parametric amplification (MPA). We find that in the case of individually applying the OPA or MPA, the tripartite entanglement can be enhanced by several folds. Remarkably, upon suitably tuning the phase matching of the two parametric fields presented simultaneously, the strengths of the entanglement can be further enhanced due to the constructive interference between the OPA and MPA. We also show that our method can improve the robustness of the entanglement against thermal noise. Our work provides a promising method for manipulating the entanglement with easy tunability and may serve as a useful tool for the enhancement and protection of fragile quantum resources., Comment: 12 pages, 4 figures, accepted for publication in PRA

We propose a practical tool for evaluating and comparing the accuracy of FDMs for the Helmholtz equation. The tool based on Fourier analysis makes it easy to find wavenumber explicit order of convergence, and can be used for rigorous proof. It fills in the gap between the dispersion analysis and the actual error with source term., Comment: 6 pages, 34 figures

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by $6.53\%$ and $2.03\%$ in RoI AP and BEV AP, respectively. The code is available at https://github.com/garfield-cpp/HGSFusion., Comment: 12 pages, 8 figures, 7 tables. Accepted by AAAI 2025 , the 39th Annual AAAI Conference on Artificial Intelligence

Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception., Comment: AAAI 2025 (Oral)

The high-temperature and high-pressure equations of states (EOSs) of rhenium up to 3000 K and 900 GPa are predicted by a recently developed method in the framework of statistical ensemble theory with \textit{ab initio} computational precision. The predicted isothermal EOSs are generally consistent with semi-empirical calculations below 150 GPa and 3000 K. Especially, the predicted isobaric EOS at one atmosphere is in good agreement with previous experiments. Moreover, the bulk modulus obtained in this work is closer to the experimental measurements than other theoretical works. Based on our calculations, the disputes between previous experiments are analyzed, and it is expected that the EOSs predicted under extreme conditions might be verified in future experiments., Comment: 9 pages, 6 figures

Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I) generation has been proposed to leverage region-specific positions and descriptions to enable more precise and controllable generation. However, previous methods primarily focus on UNet-based models (e.g., SD1.5 and SDXL), and limited effort has explored Multimodal Diffusion Transformers (MM-DiTs), which have demonstrated powerful image generation capabilities. Enabling MM-DiT for layout-to-image generation seems straightforward but is challenging due to the complexity of how layout is introduced, integrated, and balanced among multiple modalities. To this end, we explore various network variants to efficiently incorporate layout guidance into MM-DiT, and ultimately present SiamLayout. To Inherit the advantages of MM-DiT, we use a separate set of network weights to process the layout, treating it as equally important as the image and text modalities. Meanwhile, to alleviate the competition among modalities, we decouple the image-layout interaction into a siamese branch alongside the image-text one and fuse them in the later stage. Moreover, we contribute a large-scale layout dataset, named LayoutSAM, which includes 2.7 million image-text pairs and 10.7 million entities. Each entity is annotated with a bounding box and a detailed description. We further construct the LayoutSAM-Eval benchmark as a comprehensive tool for evaluating the L2I generation quality. Finally, we introduce the Layout Designer, which taps into the potential of large language models in layout planning, transforming them into experts in layout generation and optimization. Our code, model, and dataset will be available at https://creatilayout.github.io., Comment: 16 pages, 13 figures

Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enabling dexterous robot hands with functional grasping capabilities is crucial for their deployment to accomplish diverse real-world tasks. Recent research in dexterous grasping, however, often focuses on power grasps while overlooking task- and object-specific functional grasping poses. In this paper, we introduce FunGrasp, a system that enables functional dexterous grasping across various robot hands and performs one-shot transfer to unseen objects. Given a single RGBD image of functional human grasping, our system estimates the hand pose and transfers it to different robotic hands via a human-to-robot (H2R) grasp retargeting module. Guided by the retargeted grasping poses, a policy is trained through reinforcement learning in simulation for dynamic grasping control. To achieve robust sim-to-real transfer, we employ several techniques including privileged learning, system identification, domain randomization, and gravity compensation. In our experiments, we demonstrate that our system enables diverse functional grasping of unseen objects using single RGBD images, and can be successfully deployed across various dexterous robot hands. The significance of the components is validated through comprehensive ablation studies. Project page: https://hly-123.github.io/FunGrasp/ ., Comment: Project page: https://hly-123.github.io/FunGrasp/

The solar system planets are benchmarks for the planet formation theory. Yet two paradigms coexist for the four terrestrial planets: the prolonged collisional growth among planetesimals lasting $>100$ million years (Myr) and the fast formation via planetesimals accreting pebbles within 10 Myr. Despite their dramatic difference, we can hardly tell which theory is more relevant to the true history of the terrestrial planets' formation. Here, we show that the Moon's origin puts stringent constraints on the pebble accretion scenario, rendering it less favourable. In the pebble accretion model, the one-off giant impact between proto-Earth and Theia rarely (probability $<$ 1\textperthousand) occurs at the right timing and configuration for the Moon formation. Even if a potential impact happens by chance, giant impact simulations reveal perfect mixing between proto-Earth and Theia, leaving no room for the observed primordial Earth mantle heterogeneity and the compositional difference, though small, between Earth and the Moon. Thus, the Earth-Moon system along other terrestrial planets should preferably form from chaotic collisional growth in the inner solar system., Comment: 8 pages, 6 figures

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.

Among adversarial attacks against sequential recommender systems, model extraction attacks represent a method to attack sequential recommendation models without prior knowledge. Existing research has primarily concentrated on the adversary's execution of black-box attacks through data-free model extraction. However, a significant gap remains in the literature concerning the development of surrogate models by adversaries with access to few-shot raw data (10\% even less). That is, the challenge of how to construct a surrogate model with high functional similarity within the context of few-shot data scenarios remains an issue that requires resolution.This study addresses this gap by introducing a novel few-shot model extraction framework against sequential recommenders, which is designed to construct a superior surrogate model with the utilization of few-shot data. The proposed few-shot model extraction framework is comprised of two components: an autoregressive augmentation generation strategy and a bidirectional repair loss-facilitated model distillation procedure. Specifically, to generate synthetic data that closely approximate the distribution of raw data, autoregressive augmentation generation strategy integrates a probabilistic interaction sampler to extract inherent dependencies and a synthesis determinant signal module to characterize user behavioral patterns. Subsequently, bidirectional repair loss, which target the discrepancies between the recommendation lists, is designed as auxiliary loss to rectify erroneous predictions from surrogate models, transferring knowledge from the victim model to the surrogate model effectively. Experiments on three datasets show that the proposed few-shot model extraction framework yields superior surrogate models.

Quantum evolution is crucial for the understanding of complex quantum systems. However, current implementations of time evolution on quantum photonic platforms face challenges of limited light source efficiency due to propagation loss and merely single-layer complexity. In this work, we present a loop quantum photonic chip (Loop-QPC) designed to efficiently simulate quantum dynamics over multiple time steps in a single chip. Our approach employs a recirculating loop structure to reuse computational resources and eliminate the need for multiple quantum tomography steps or chip reconfigurations. We experimentally demonstrate the dynamics of the spin-boson model on a low-loss Silicon Nitride (SiN) integrated photonic chip. The Loop-QPC achieves a three-step unitary evolution closely matching the theoretical predictions. These results establish the Loop-QPC as a promising method for efficient and scalable quantum simulation, advancing the development of quantum simulation on programmable photonic circuits.

Our review of the lattice chiral fermion delves into some critical areas of lattice field theory. By abandoning Hermiticity, the non-Hermitian formulation circumvents the Nielsen-Ninomiya theorem while maintaining chiral symmetry, a novel approach. Comparing the Wilson and overlap fermions gives insight into how lattice formulations handle chiral symmetry. The Wilson fermion explicitly breaks chiral symmetry to eliminate doublers. In contrast, the overlap fermion restores a modified form of chiral symmetry using the Ginsparg-Wilson relation. We investigate how the (1+1)D Wilson fermion relates to the (1+1)D overlap fermion in the Hamiltonian formulation. This connection could provide a clearer physical understanding of how chiral symmetry manifests at the lattice level. Depending on Hermiticity for efficiency, Monte Carlo methods face unique challenges in a non-Hermitian setting. We investigate how to correctly apply this method to non-Hermitian lattice fermions, which is essential for practical simulations. Finally, the review of topological charge is crucial, as topological features in lattice formulations are strongly connected to chiral symmetry, anomalies, and the index theorem., Comment: 66 pages, 1 figure, minor changes, reference added

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times., Comment: 1 figure, 1 table

The origin of frequently observed ultrahigh electric-induced longitudinal strain, ranging from 1% to 26%, remains an open question. Recent evidence suggests that this phenomenon is linked to the bending deformation of samples, but the mechanisms driving this bending and the strong dependence of nominal strain on sample thickness have yet to be fully understood. Here, we demonstrate that the bending in piezoceramics can be induced by non-zero gradient of d31 acrcoss thickness direction. Our calculations show that in standard perovskite piezoceramics, such as KNbO3, a 0.69% concentration of oxygen vacancies results in a 6.3 pC/N change in d31 by inhibiting polarization rotation, which is sufficient to produce ultrahigh nominal strain in thin samples. The gradients of defect concentration, composition, and stress can all cause sufficient inhomogeneity in the distribution of d31, leading to the bending effect. We propose several approaches to distinguish true electric-induced strain from bending-induced effects. Our work provides clarity on the origin of nominal ultrahigh electricinduced strain and offers valuable insights for advancing piezoelectric materials.

Pressure tuning has emerged as a powerful strategy for manipulating the thermoelectric properties of materials by inducing structural and electronic modifications. Herein, we systematically investigate the transport properties and thermoelectric performance concerning lattice distortions induced by hydrostatic pressure in Ag-based chalcopyrite AgXTe2 (X=In, Ga). The findings reveal that the lattice distortion in AgXTe2 exhibits distinct behaviors under lattice compression, diverging from trends observed at ambient pressure. Importantly, the hydrostatic pressure breaks the phenomenally negative correlation between thermal conductivity and lattice distortion. Pressure-induced softening of low-frequency acoustic phonons broadens the low-energy phonon spectrum, enhancing interactions between acoustic and optical phonons. Such broadening substantially increases the number of available three-phonon scattering channels, resulting in a marked reduction in thermal conductivity. Meanwhile, we establish a macroscopic connection between metavalent bonding and anharmonicity, providing an indirect explanation for lattice anharmonicity through pressure-driven transferred charge. Additionally, the applied pressure achieves a notable net increase in the power factor despite the strong coupling of electrical transport parameters, which underscores the potential for bidirectional optimization of transport properties in AgXTe2. As a result, the maximum ZT value of AgInTe2 is nearly doubled, demonstrating that pressure modulation is a powerful strategy for enhancing thermoelectric performance. Our work not only establishes the link between pressure, lattice dynamics, and thermoelectric properties within chalcopyrite AgXTe2, but also inspires the exploration of pressure-related optimization strategies for conventional thermoelectric materials.

The increasing availability of high-resolution satellite imagery has created immense opportunities for various applications. However, processing and analyzing such vast amounts of data in a timely and accurate manner poses significant challenges. The paper presents a new satellite image processing architecture combining edge and cloud computing to better identify man-made structures against natural landscapes. By employing lightweight models at the edge, the system initially identifies potential man-made structures from satellite imagery. These identified images are then transmitted to the cloud, where a more complex model refines the classification, determining specific types of structures. The primary focus is on the trade-off between latency and accuracy, as efficient models often sacrifice accuracy. We compare this hybrid edge-cloud approach against traditional "bent-pipe" method in virtual environment experiments as well as introduce a practical model and compare its performance with existing lightweight models for edge deployment, focusing on accuracy and latency. The results demonstrate that the edge-cloud collaborative model not only reduces overall latency due to minimized data transmission but also maintains high accuracy, offering substantial improvements over traditional approaches under this scenario.

Grain boundaries have extensive influence on the performance of crystal materials. However, the atomic-scale structure and its relation with local and crystallographic symmetries remain elusive in low-symmetry crystals. Herein, we find that the local pseudo-mirror-symmetric atomic layer is the common physical origin of a series of highly coherent grain boundaries in the low-symmetry $\beta$-Ga2O3 crystal. These include the (100) twin boundary and an emerging series of $(h-1'0'2)/(h+1'0'\bar{2})$ coherent asymmetric grain boundaries (CAGBs). Owing to the local pseudo-mirror symmetry and the special geometric relation of the $\beta$-Ga2O3 conventional cell, these CAGBs place 80% of the boundary atoms in pseudo-coincident sites, exhibiting high coherence under the coincident-site lattice model. With a combination of density functional theory calculations, Czochralski growth experiment, and atomic-scale characterizations, the structure and stability of the $(002)/(20\bar{2})$-A CAGB are confirmed, with a boundary energy density as low as 0.36 J/m2. This CAGB is responsible for the spontaneous formation of a twinned defect facet at the surface steps during the epitaxy growth of $\beta$-Ga2O3, warranting a substrate orientation selection rule for $\beta$-Ga2O3. Through this study, we provide insights into the grain boundary physics in the low-symmetry $\beta$-Ga2O3 crystal while emphasizing the importance of the local pseudo-symmetries in the low-symmetry crystals.

The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is to address the issue of losing fidelity by adaptively organizing the rules as a hierarchy rather than reducing them. To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level. Synergized with this hierarchical organization of rules, we develop a matrix-based hierarchical visualization to support exploration at different levels of detail. Our quantitative experiments and case studies demonstrate how our method fosters a deeper understanding of both common and anomalous rules, thereby enhancing interpretability without sacrificing comprehensiveness., Comment: 15 pages, 10 figures

The widespread use of vector graphics creates a significant demand for vectorization methods. While recent learning-based techniques have shown their capability to create vector images of clear topology, filling these primitives with gradients remains a challenge. In this paper, we propose a segmentation-guided vectorization framework to convert raster images into concise vector graphics with radial gradient fills. With the guidance of an embedded gradient-aware segmentation subroutine, our approach progressively appends gradient-filled B\'ezier paths to the output, where primitive parameters are initiated with our newly designed initialization technique and are optimized to minimize our novel loss function. We build our method on a differentiable renderer with traditional segmentation algorithms to develop it as a model-free tool for raster-to-vector conversion. It is tested on various inputs to demonstrate its feasibility, independent of datasets, to synthesize vector graphics with improved visual quality and layer-wise topology compared to prior work.

Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separately due to their distinct characteristics. Thus, when being combined attacked, almost all methods could not deal. Some studies attempt to combine the sparse data from both types of attacks into a single dataset and try to find a common feature space, which is often impractical due to the space is difficult to be found or even non-existent. To overcome these challenges, we propose a novel approach that uses the sparse model to handle sparse data, utilizing different parameter groups to process distinct regions of the sparse feature space. Specifically, we employ the Mixture of Experts (MoE) framework in our model, expert parameters are matched to tokens with varying weights during training and adaptively activated during testing. However, the traditional MoE struggles with the complex and irregular classification boundaries of this problem. Thus, we introduce a flexible self-adapting weighting mechanism, enabling the model to better fit and adapt. In this paper, we proposed La-SoftMoE CLIP, which allows for more flexible adaptation to the Unified Attack Detection (UAD) task, significantly enhancing the model's capability to handle diversity attacks. Experiment results demonstrate that our proposed method has SOTA performance.

We present a microstructure imaging technique for estimating compartment-specific T2 and T2* simultaneously in the human brain. Microstructure imaging with diffusion MRI (dMRI) has enabled the modelling of intra-neurite and extra-neurite diffusion signals separately allowing for the estimation of compartment-specific tissue properties. These compartment-specific properties have been widely used in clinical studies. However, conventional dMRI cannot disentangle differences in relaxations between tissue compartments, causing biased estimates of diffusion measures which also change with TE. To solve the problem, combined relaxometry-diffusion imaging methods have been developed in recent years, providing compartmental T2-diffusion or T2*-diffusion imaging respectively, but not T2 and T2* together. As they provide complementary information, a technique that can estimate both jointly with diffusion is appealing to neuroimaging studies. The aim of this work is to develop a method to map compartmental T2-T2*-diffusion simultaneously. Using an advanced MRI acquisition called diffusion-PEPTIDE, a novel microstructure model is proposed and a multi-step fitting method is developed to estimate parameters of interest. We demonstrate for the first time that compartmental T2, T2* can be estimated simultaneously from in vivo data. we further show the accuracy and precision of parameter estimation with simulation., Comment: 9 pages, 3 figures; presented at ISMRM 2022

Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, the acquisition of iris images typically requires near-infrared illumination and near-infrared band filters, leading to significant and consistent differences in imaging across devices. This underscores the importance of developing cross-domain capabilities in iris anti-spoofing methods. Despite this need, there is no dataset available that comprehensively evaluates the generalization ability of the iris anti-spoofing task. To address this gap, we propose the IrisGeneral dataset, which includes 10 subsets, belonging to 7 databases, published by 4 institutions, collected with 6 types of devices. IrisGeneral is designed with three protocols, aimed at evaluating average performance, cross-racial generalization, and cross-device generalization of iris anti-spoofing models. To tackle the challenge of integrating multiple sub-datasets in IrisGeneral, we employ multiple parameter sets to learn from the various subsets. Specifically, we utilize the Mixture of Experts (MoE) to fit complex data distributions using multiple sub-neural networks. To further enhance the generalization capabilities, we introduce a novel method Masked-MoE (MMoE). It randomly masks a portion of tokens for some experts and requires their outputs to be similar to the unmasked experts, which improves the generalization ability and effectively mitigates the overfitting issue produced by MoE. We selected ResNet50, VIT-B/16, CLIP, and FLIP as representative models and benchmarked them on the IrisGeneral dataset. Experimental results demonstrate that our proposed MMoE with CLIP achieves the best performance on IrisGeneral.

Quantitative magnetic resonance imaging (qMRI) is increasingly investigated for use in a variety of clinical tasks from diagnosis, through staging, to treatment monitoring. However, experiment design in qMRI, the identification of the optimal acquisition protocols, has been focused on obtaining the most precise parameter estimations, with no regard for the specific requirements of downstream tasks. Here we propose SCREENER: A general framework for task-specific experiment design in quantitative MRI. SCREENER incorporates a task-specific objective and seeks the optimal protocol with a deep-reinforcement-learning (DRL) based optimization strategy. To illustrate this framework, we employ a task of classifying the inflammation status of bone marrow using diffusion MRI data with intravoxel incoherent motion (IVIM) modelling. Results demonstrate SCREENER outperforms previous ad hoc and optimized protocols under clinical signal-to-noise ratio (SNR) conditions, achieving significant improvement, both in binary classification tasks, e.g. from 67% to 89%, and in a multi-class classification task, from 46% to 59%. Additionally, we show this improvement is robust to the SNR. Lastly, we demonstrate the advantage of DRL-based optimization strategy, enabling zero-shot discovery of near-optimal protocols for a range of SNRs not used in training. In conclusion, SCREENER has the potential to enable wider uptake of qMRI in the clinic.

In this work we are going to establish H\"older continuity of harmonic maps from an open set $\Omega$ in an ${\rm RCD}(K,N)$ space valued into a ${\rm CAT}(\kappa)$ space, with the constraint that the image of $\Omega$ via the map is contained in a sufficiently small ball in the target. Building on top of this regularity and assuming a local Lipschitz regularity of the map, we establish a weak version of the Bochner-Eells-Sampson inequality in such a non-smooth setting. Finally we study the boundary regularity of such maps., Comment: Comments are welcome!

Superconducting qubits, a promising candidate for universal quantum computing, currently face limitations in chip size due to reproducibility, wiring complexity, and packaging modes. Distributed quantum modules offer a viable strategy for constructing larger quantum information processing systems, though universal quantum gate operations between remote qubits have yet to be realized. Here, we demonstrate high-fidelity perfect entanglers between two remote superconducting quantum devices over 30 cm distance, leveraging the standing-wave modes in the coaxial cable connecting them. We achieve cross-entropy benchmarking (XEB) fidelities of $(99.15 \pm 0.02)\%$ and $(98.04 \pm 0.04)\%$ for CNOT and CZ gates, respectively, which are more efficient and universal than existing state transfer or feedback-based protocols. This advancement significantly enhances the feasibility of universal distributed quantum information processing, essential for the future development of large-scale quantum systems.

Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measurements are inherently linked to the user. Biocryptosystems represent a key branch of solutions aimed at addressing these issues. However, many existing bio-cryptosystems rely on handcrafted feature extractors and error correction codes (ECC), often leading to performance degradation. To address these challenges and improve the reliability of biometric measurements, we propose a novel biometric cryptosystem named WiFaKey, for generating cryptographic keys from face in unconstrained settings. Speciffcally, WiFaKey ffrst introduces an adaptive random masking-driven feature transformation pipeline, AdaMTrans. AdaMTrans effectively quantizes and binarizes realvalued features and incorporates an adaptive random masking scheme to align the bit error rate with error correction requirements, thereby mitigating the noise gap. Besides, WiFaKey incorporates a supervised learning-based neural decoding scheme called Neural-MS decoder, which delivers a more robust error correction performance with less iteration than non-learning decoders, thereby alleviating the performance degradation. We evaluated WiFaKey using widely adopted face feature extractors on six large unconstrained and two constrained datasets. On the LFW dataset, WiFaKey achieved an average Genuine Match Rate of 85.45% and 85.20% at a 0% False Match Rate for MagFace and AdaFace features, respectively. Our comprehensive comparative analysis shows a signiffcant performance improvement of WiFaKey. The source code of our work is available at github.com/xingbod/WiFaKey.

Monolayer MoTe2 exhibits a variety of derivative structural phases and associated novel electronic properties that enable a wealth of potential applications in future electronic and optoelectronic devices. However, a comprehensive study focusing on the complexities of the controllable phase evolution in this atomically thin film has yet to be performed. This work aims to address this issue by systematically investigating molecular beam epitaxial growth of monolayer Mo-Te compounds on bilayer graphene substrates. By utilizing scanning tunnelling microscopy, we explored a series of thermally driven structural phase evolutions including distinct T'-MoTe2, H-MoTe2, Mo6Te6 nanowires, and multistoichiometric MoTe2-x. Furthermore, we carefully investigated the critical effects of the growth parameters-annealing temperature and time and tellurium concentration-on the controllable and reversible phase transformation within monolayer MoTe2-x. The findings have significant implications for understanding the thin film synthesis and phase transformation engineering inherent to two-dimensional crystals, which can foster further development of high-performance devices., Comment: 17 pages, 5 figures

Preconditioned Proximal Point (PPP) algorithms provide a unified framework for splitting methods in image restoration. Recent advancements with RED (Regularization by Denoising) and PnP (Plug-and-Play) priors have achieved state-of-the-art performance in this domain, emphasizing the need for a meaningful particular solution. However, degenerate PPP algorithms typically exhibit weak convergence in infinite-dimensional Hilbert space, leading to uncertain solutions. To address this issue, we propose the Halpern-type Preconditioned Proximal Point (HPPP) algorithm, which leverages the strong convergence properties of Halpern iteration to achieve a particular solution. Based on the implicit regularization defined by gradient RED, we further introduce the Gradient REgularization by Denoising via HPPP called GraRED-HP3 algorithm. The HPPP algorithm is shown to have the regularity converging to a particular solution by a toy example. Additionally, experiments in image deblurring and inpainting validate the effectiveness of GraRED-HP3, showing it surpasses classical methods such as Chambolle-Pock (CP), PPP, RED, and RED-PRO.