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Collaborative learning environments that support students' problem solving have been shown to promote better decision-making, greater academic achievement, and more reasonable argumentation about controversial issues. In this research, we developed a technology-based critical discussion platform to support middle school students' argumentation, with a focus on evidence-based reasoning and perspective taking. A feasibility study was conducted to examine the patterns of group interaction and individual students' contributions to the critical discussion and their perceptions of the critical discussion activity. We found that more students used text-based communications than audio, but students who used audio collaborated with each other more frequently. In addition, student engagement in argumentative discourse varied greatly across groups as well as individuals. At the end of the discussion, most groups provided a solution that integrated both sides of the controversial issue. Survey and interview results suggest an overall positive experience with this technology-supported critical discussion activity. Using the insights from our research, we develop a conceptual dialogue analysis framework that identifies relevant skills under the argumentation and collaboration dimensions. In this report, we discuss our design considerations, feasibility study results, and implications of engaging students in computer-supported collaborative argumentation.

This report provides a summary of the full "First Look" report, which presents data on students in the United States in kindergarten through grade 12 in a physical or virtual school or are homeschooled for equivalent grades. The focus of the report is on parent and family involvement in the students' education during the 2022-2023 school year, as reported by the students' parents or guardians. It includes the percentage of students who participated in selected family activities. Demographic information about students and families is presented, including students' poverty status and parents' education and language spoken at home, as well as school characteristics, such as school size and school type. These data represent circumstances after the termination of most COVID-19 pandemic safety measures limiting in-person group activities. The data for this report come from the Parent and Family Involvement in Education (PFI) Survey, administered as part of the 2023 National Household Education Surveys Program (NHES:2023).

Stellar-mass and supermassive black holes abound in the Universe, whereas intermediate-mass black holes (IMBHs) of ~10^2-10^5 solar masses in between are largely missing observationally, with few cases found only. Here we report the real-time discovery of a long-duration X-ray transient, EP240222a, accompanied by an optical flare with prominent H and He emission lines revealed by prompt follow-up observations. Its observed properties evidence an IMBH located unambiguously in the halo of a nearby galaxy and flaring by tidally disrupting a star -- the only confirmed off-nucleus IMBH-tidal disruption event so far. This work demonstrates the potential of sensitive time-domain X-ray surveys, complemented by timely multi-wavelength follow-ups, in probing IMBHs, their environments, demographics, origins and connections to stellar-mass and supermassive black holes., Comment: 64 pages, 15 figures, submitted

Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the overall policy distribution, particularly in continuous action spaces. To address these limitations, we propose the Distribution-Aware Projected Gradient Descent attack (DAPGD). DAPGD uses distribution similarity as the gradient perturbation input to attack the policy network, which leverages the entire policy distribution rather than relying on individual samples. We utilize the Bhattacharyya distance in DAPGD to measure policy similarity, enabling sensitive detection of subtle but critical differences between probability distributions. Our experiment results demonstrate that DAPGD achieves SOTA results compared to the baselines in three robot navigation tasks, achieving an average 22.03% higher reward drop compared to the best baseline., Comment: 10 pages, 2 figures, 2 tables

The information loss or distortion caused by single-channel speech enhancement (SE) harms the performance of automatic speech recognition (ASR). Observation addition (OA) is an effective post-processing method to improve ASR performance by balancing noisy and enhanced speech. Determining the OA coefficient is crucial. However, the currently supervised OA coefficient module, called the bridging module, only utilizes simulated noisy speech for training, which has a severe mismatch with real noisy speech. In this paper, we propose training strategies to train the bridging module with real noisy speech. First, DNSMOS is selected to evaluate the perceptual quality of real noisy speech with no need for the corresponding clean label to train the bridging module. Additional constraints during training are introduced to enhance the robustness of the bridging module further. Each utterance is evaluated by the ASR back-end using various OA coefficients to obtain the word error rates (WERs). The WERs are used to construct a multidimensional vector. This vector is introduced into the bridging module with multi-task learning and is used to determine the optimal OA coefficients. The experimental results on the CHiME-4 dataset show that the proposed methods all had significant improvement compared with the simulated data trained bridging module, especially under real evaluation sets.

Secondary cosmic ray fluxes are important probes of the propagation and interaction of high-energy particles in the Galaxy. Recent measurements of primary and secondary cosmic ray nuclei have revealed unexpected spectral features that demand a deeper understanding. In this work we report the direct measurement of the cosmic ray boron spectrum from 10 GeV/n to 8 TeV/n with eight years of data collected by the Dark Matter Particle Explorer (DAMPE) mission. The measured spectrum shows an evident hardening at $182\pm24$ GeV/n with a spectral power index of $\gamma_1 = 3.02 \pm 0.01$ before the break and an index change of $\Delta \gamma = 0.31 \pm 0.05$ after the break. A simple power law model is disfavored at a confidence level of 8$\sigma$. Compared with the hardenings measured in the DAMPE proton and helium spectra, the secondary boron spectrum hardens roughly twice as much as these primaries, which is consistent with a propagation related mechanism to interpret the spectral hardenings of cosmic rays observed at hundreds of GeV/n., Comment: 10 pages, 10 figures, submitted to PRL

Human video synthesis aims to create lifelike characters in various environments, with wide applications in VR, storytelling, and content creation. While 2D diffusion-based methods have made significant progress, they struggle to generalize to complex 3D poses and varying scene backgrounds. To address these limitations, we introduce CFSynthesis, a novel framework for generating high-quality human videos with customizable attributes, including identity, motion, and scene configurations. Our method leverages a texture-SMPL-based representation to ensure consistent and stable character appearances across free viewpoints. Additionally, we introduce a novel foreground-background separation strategy that effectively decomposes the scene as foreground and background, enabling seamless integration of user-defined backgrounds. Experimental results on multiple datasets show that CFSynthesis not only achieves state-of-the-art performance in complex human animations but also adapts effectively to 3D motions in free-view and user-specified scenarios.

Nonlocal kinetic energy density functionals (KEDFs) with density-dependent kernels are currently the most accurate functionals available for orbital-free density functional theory (OF-DFT) calculations. However, despite advances in numerical techniques and using only (semi)local density-dependent kernels, nonlocal KEDFs still present substantial computational costs in OF-DFT, limiting their application in large-scale material simulations. To address this challenge, we propose an efficient framework for reconstructing nonlocal KEDFs by incorporating the density functional tight-binding approach, in which the energy functionals are simplified through a first-order functional expansion based on the superposition of free-atom electron densities. This strategy allows the computationally expensive nonlocal kinetic energy and potential calculations to be performed only once during the electron density optimization process, significantly reducing computational overhead while maintaining high accuracy. Benchmark tests using advanced nonlocal KEDFs, such as revHC and LDAK-MGPA, on standard structures including Li, Mg, Al, Ga, Si, III-V semiconductors, as well as Mg$_{50}$ and Si$_{50}$ clusters, demonstrate that our method achieves orders-of-magnitude improvements in efficiency, providing a cost-effective balance between accuracy and computational speed. Additionally, the reconstructed functionals exhibit improved numerical stability for both bulk and finite systems, paving the way for developing more sophisticated KEDFs for realistic material simulations using OF-DFT., Comment: 16 pages, 4 figures

This report provides the most recent national indicators on school crime and safety. The information presented in this report serves as a reference for policymakers and practitioners so that they can develop effective programs and policies aimed at violence and school crime prevention. Accurate information about the nature, extent, and scope of the problem being addressed is essential for developing effective programs and policies. The report is organized into five sections: elementary and secondary student and teacher victimization; school environment; fights and weapons; safety, security, and mental health practices; and postsecondary campus safety and security. Each section begins with a set of key findings. In this report, where available, data on victimization that occurred away from school are offered as a point of comparison for data on victimization that occurred at school. Indicators of crime and safety are compared across different population subgroups and over time. All data reflect the most current data available at the time the report was produced. Data throughout this report represent the 50 states and the District of Columbia. Findings described with comparative language (e.g., higher, lower, increase, and decrease) are statistically significant at the 0.05 level.

Simultaneous Localization and Mapping (SLAM) is essential for precise surgical interventions and robotic tasks in minimally invasive procedures. While recent advancements in 3D Gaussian Splatting (3DGS) have improved SLAM with high-quality novel view synthesis and fast rendering, these systems struggle with accurate depth and surface reconstruction due to multi-view inconsistencies. Simply incorporating SLAM and 3DGS leads to mismatches between the reconstructed frames. In this work, we present Endo-2DTAM, a real-time endoscopic SLAM system with 2D Gaussian Splatting (2DGS) to address these challenges. Endo-2DTAM incorporates a surface normal-aware pipeline, which consists of tracking, mapping, and bundle adjustment modules for geometrically accurate reconstruction. Our robust tracking module combines point-to-point and point-to-plane distance metrics, while the mapping module utilizes normal consistency and depth distortion to enhance surface reconstruction quality. We also introduce a pose-consistent strategy for efficient and geometrically coherent keyframe sampling. Extensive experiments on public endoscopic datasets demonstrate that Endo-2DTAM achieves an RMSE of $1.87\pm 0.63$ mm for depth reconstruction of surgical scenes while maintaining computationally efficient tracking, high-quality visual appearance, and real-time rendering. Our code will be released at github.com/lastbasket/Endo-2DTAM., Comment: Accepted by ICRA 2025

Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages like Chinese. Existing methods primarily focus on enhancing model comprehension and improving data diversity to boost performance. However, these approaches still struggle with inadequate model applicability and semantic distribution biases in domain-specific contexts. To overcome these limitations, we propose a novel framework that combines an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction (KnowFREE) model. Our workflow employs explanation prompts to generate precise contextual interpretations of target entities, effectively mitigating semantic biases and enriching the model's contextual understanding. The KnowFREE model further integrates extension label features, enabling efficient nested entity extraction without relying on external knowledge during inference. Experiments on multiple Chinese domain-specific sequence labeling datasets demonstrate that our approach achieves state-of-the-art performance, effectively addressing the challenges posed by low-resource settings.

Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model, it is important to identify its semantically coherent error slices that are easy to interpret, which is referred to as the error slice discovery problem. However, there is no proper metric of slice coherence without relying on extra information like predefined slice labels. Current evaluation of slice coherence requires access to predefined slices formulated by metadata like attributes or subclasses. Its validity heavily relies on the quality and abundance of metadata, where some possible patterns could be ignored. Besides, current algorithms cannot directly incorporate the constraint of coherence into their optimization objective due to the absence of an explicit coherence metric, which could potentially hinder their effectiveness. In this paper, we propose manifold compactness, a coherence metric without reliance on extra information by incorporating the data geometry property into its design, and experiments on typical datasets empirically validate the rationality of the metric. Then we develop Manifold Compactness based error Slice Discovery (MCSD), a novel algorithm that directly treats risk and coherence as the optimization objective, and is flexible to be applied to models of various tasks. Extensive experiments on the benchmark and case studies on other typical datasets demonstrate the superiority of MCSD.

A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interactions with Human-Driven Vehicles (HDVs). To address this challenge, we propose the Recognize then Resolve (RtR) framework. First, a Bilateral Intention Progression Graph (BIPG) is constructed based on CAV-HDV interaction data to model the evolution of interactions and identify potential HDV intentions. Three typical interaction breakdown scenarios are then categorized, and key moments are defined for triggering cooperative conflict resolution. On this basis, a constrained Monte Carlo Tree Search (MCTS) algorithm is introduced to determine the optimal passage order while accommodating HDV intentions. Experimental results demonstrate that the proposed RtR framework outperforms other cooperative approaches in terms of safety and efficiency across various penetration rates, achieving results close to consistent cooperation while significantly reducing computational resources. Our code and data are available at: https://github.com/FanGShiYuu/RtR-Recognize-then-Resolve/.

Three-dimensional (3D) photonic structures enable numerous applications through their unique ability to guide, trap, and manipulate light. Constructing new functional photonic crystals remains a significant challenge since traditional design principles based on band structure calculations require numerous time-consuming computations. Additionally, traditional design is based on enumerated structures making it difficult to find novel functional geometries. Here, we propose an ultra-fast photonic crystal performance prediction method to enable efficient structure optimization of arbitrary 3D photonic crystals even with multiple variable modulation. Our methodology combines Fourier synthesis-enabling the creation of any smooth geometry within a crystallographic space group-with deep learning, which facilitates efficient photonic characterization within the vast parameter space. Over 2 million structures can be explored within 2 hours using a mainstream desktop workstation. The ideal structures with desired band properties, such as large photonic bandgap, specific frequency ranges, etc., could be rapidly discovered. We systematically confirmed the well-documented assumption that the most significant photonic bandgaps are found in minimal surface morphologies, in which the single diamond (dia net) with Fd3m (227) symmetry reigns supreme among known photonic structures, followed by the chiral single gyroid (srs net) with I4132 (214) symmetry. Additionally, a less well-known 3D photonic crystal with lcs topology within Ia3d (230) was rediscovered to exhibit a wide complete photonic bandgap, comparable to the diamond and the gyroid net. Our method not only validates the assumed hierarchy of photonic structures but also lays the foundation for the tailored design of functional materials and offers fresh insights into the advancement of next-generation optical devices and information technology.

This paper develops a Mean Group Instrumental Variables (MGIV) estimator for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it avoids the incidental parameters problem, enabling asymptotically valid inferences without requiring bias correction. The Monte Carlo experiments indicate strong finite-sample performance of the MGIV estimator across various sample sizes and parameter configurations. The practical utility of the estimator is illustrated through an application to regional economic growth in Europe. By explicitly incorporating heterogeneity, our approach provides fresh insights into the determinants of regional growth, underscoring the critical roles of spatial and temporal dependencies.

Human evaluation is the gold-standard for evaluating text generation models. It is also expensive, and to fit budgetary constraints, a random subset of the test data is often chosen in practice. The randomly selected data may not accurately represent test performance, making this approach economically inefficient for model comparison. Thus, in this work, we develop a suite of selectors to get the most informative datapoints for human evaluation while taking the evaluation costs into account. We show that selectors based on variance in automated metric scores, diversity in model outputs, or Item Response Theory outperform random selection. We further develop an approach to distill these selectors to the scenario where the model outputs are not yet available. In particular, we introduce source-based estimators, which predict item usefulness for human evaluation just based on the source texts. We demonstrate the efficacy of our selectors in two common NLG tasks, machine translation and summarization, and show that up to only ~50% of the test data is needed to produce the same evaluation result as the entire data. Our implementations are published in the subset2evaluate package.

Excitonic insulators represent a unique quantum phase of matter, providing a rich ground for studying exotic quantum bosonic states. Strongly coupled electron-hole bilayers, which host stable dipolar exciton fluids with an exciton density that can be adjusted electrostatically, offer an ideal platform to investigate correlated excitonic insulators. Based on electron-hole bilayers made of MoSe2/hBN/WSe2 heterostructures, here we study the behavior of excitonic insulators in a perpendicular magnetic field. We report the observation of excitonic quantum oscillations in both Coulomb drag signals and electrical resistance at low to medium magnetic fields. Under a strong magnetic field, we identify multiple quantum phase transitions between the excitonic insulator phase and the bilayer quantum Hall insulator phase. These findings underscore the interplay between the electron-hole interactions and Landau level quantization that opens new possibilities for exploring quantum phenomena in composite bosonic insulators.

Recent point-based differentiable rendering techniques have achieved significant success in high-fidelity reconstruction and fast rendering. However, due to the unstructured nature of point-based representations, they are difficult to apply to modern graphics pipelines designed for structured meshes, as well as to a variety of simulation and editing algorithms that work well with structured mesh representations. To this end, we propose StructuredField, a novel representation that achieves both a structured geometric representation of the reconstructed object and a high-fidelity rendering reconstruction of the object. We employ structured tetrahedral meshes to represent the reconstructed object. We reparameterize the geometric parameters of the tetrahedral mesh into the geometric shape parameters of a 3D Gaussians, thereby achieving differentiable high-fidelity rendering of the tetrahedral mesh. We propose a novel inversion-free homeomorphism to constrain the optimization of the tetrahedral mesh, which strictly guarantees that the tetrahedral mesh is remains both inversion-free and self-intersection-free during the optimization process and the final result. Based on our proposed StructuredField, we achieve high-quality structured meshes and high-fidelity reconstruction. We also demonstrate the applicability of our representation to various applications such as physical simulation and deformation., Comment: Project page: https://structuredfield.github.io

Gliomas are the most common primary tumors of the central nervous system. Multimodal MRI is widely used for the preliminary screening of gliomas and plays a crucial role in auxiliary diagnosis, therapeutic efficacy, and prognostic evaluation. Currently, the computer-aided diagnostic studies of gliomas using MRI have focused on independent analysis events such as tumor segmentation, grading, and radiogenomic classification, without studying inter-dependencies among these events. In this study, we propose a Glioma Multimodal MRI Analysis System (GMMAS) that utilizes a deep learning network for processing multiple events simultaneously, leveraging their inter-dependencies through an uncertainty-based multi-task learning architecture and synchronously outputting tumor region segmentation, glioma histological subtype, IDH mutation genotype, and 1p/19q chromosome disorder status. Compared with the reported single-task analysis models, GMMAS improves the precision across tumor layered diagnostic tasks. Additionally, we have employed a two-stage semi-supervised learning method, enhancing model performance by fully exploiting both labeled and unlabeled MRI samples. Further, by utilizing an adaptation module based on knowledge self-distillation and contrastive learning for cross-modal feature extraction, GMMAS exhibited robustness in situations of modality absence and revealed the differing significance of each MRI modal. Finally, based on the analysis outputs of the GMMAS, we created a visual and user-friendly platform for doctors and patients, introducing GMMAS-GPT to generate personalized prognosis evaluations and suggestions., Comment: 23 pages, 13 figures

The discovery of superconductivity in pressurized bilayer and trilayer nickelates has generated significant interest. However, their superconducting properties are often dependent on sample quality and pressure conditions, complicating the interpretation of the underlying physics. Finding new systems with optimized bulk superconducting properties is therefore important for advancing our understanding of these materials. Unlike cupates, where trilayer compounds typically exhibit the highest transition temperature (Tc), the bilayer nickelate La3Ni2O7 has thus far outperformed the trilayer La4Ni3O10 in reported Tc. Whether the trilayer nickelates have achieved the optimal Tc remains unclear, with various scenarios suggesting different possibilities. Here, we report the discovery of bulk superconductivity in pressurized Pr4Ni3O10 single crystals, achieving a maximum onset Tc of 40.5 K at 80.1 GPa, significantly exceeding the 30 K observed in La4Ni3O10. The bulk nature of superconductivity is confirmed by zero resistance and a strong diamagnetic response below Tc with a superconducting volume fraction exceeding 80%. These findings establish trilayer nickelates as genuine bulk high-temperature superconductors, provide new insights into the mechanisms driving superconductivity, and point to a promising route toward further enhancing superconducting properties in nickelates., Comment: 16 pages, 4 figures

In the design of unmanned aerial vehicle (UAV) wireless communications, a better understanding of propagation characteristics and an accurate channel model are required. Measurements and comprehensive analysis for the UAV-based air-ground (AG) propagation channel in the vertical dimension are presented in this letter. Based on the measurement data at 1 and 4 GHz, the large-scale and small-scale channel parameters are extracted in the line-of-sight (LOS) and nonLOS case, respectively. The altitude-dependent path loss model is proposed herein. Furthermore, shadow fading and fast fading are statistically analyzed for comprehensively describing the fading behavior. Our results will be useful in the modeling of AG channels and the performance analysis for UAV-enabled wireless communication systems.

Large agent networks are abundant in applications and nature and pose difficult challenges in the field of multi-agent reinforcement learning (MARL) due to their computational and theoretical complexity. While graphon mean field games and their extensions provide efficient learning algorithms for dense and moderately sparse agent networks, the case of realistic sparser graphs remains largely unsolved. Thus, we propose a novel mean field control model inspired by local weak convergence to include sparse graphs such as power law networks with coefficients above two. Besides a theoretical analysis, we design scalable learning algorithms which apply to the challenging class of graph sequences with finite first moment. We compare our model and algorithms for various examples on synthetic and real world networks with mean field algorithms based on Lp graphons and graphexes. As it turns out, our approach outperforms existing methods in many examples and on various networks due to the special design aiming at an important, but so far hard to solve class of MARL problems.

We investigate the higher repulsive branches of one-dimensional (1D) bosonic and fermionic quantum gases beyond the super-Tonks-Girardeau regime, utilizing the Bethe-Ansatz method and exact diagonalization of small trapped clusters. In contrast to the well-studied lowest branches that are characterized by spin-charge separation, we demonstrate the emergence of strong spin-charge correlation in all higher branches with hard-core interactions. This manifests in distinct quasi-momentum distributions and energy spectra for bosons and spin-1/2 fermions, despite their fermionization. Furthermore, trapped fermions in higher branches exhibit novel spin textures, intricately linked to charge excitations, necessitating a coupled multi-chain description beyond single effective spin-chain models. Our findings unveil a rich interplay between spin and charge degrees of freedom in highly excited 1D systems, opening avenues for exploring novel quantum phenomena beyond the conventional paradigm of low-lying states., Comment: 10 pages, 4 figures

We show that any complete minimal hypersurface in the five-dimensional hyperbolic space $\mathbb H^5$, endowed with constant scalar curvature and vanishing Gauss-Kronecker curvature, must be totally geodesic. Cheng-Peng [3] recently conjecture that any complete minimal hypersurface with constant scalar curvature in $\mathbb H^4$ is totally geodesic. Our result partially confirms this conjecture in five dimensional setting., Comment: 11 pages, no figure

Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their further advancements. In this work, we introduce MM-Retinal V2, a high-quality image-text paired dataset comprising CFP, FFA, and OCT image modalities. Then, we propose a novel fundus vision-language pretraining model, namely KeepFIT V2, which is pretrained by integrating knowledge from the elite data spark into categorical public datasets. Specifically, a preliminary textual pretraining is adopted to equip the text encoder with primarily ophthalmic textual knowledge. Moreover, a hybrid image-text knowledge injection module is designed for knowledge transfer, which is essentially based on a combination of global semantic concepts from contrastive learning and local appearance details from generative learning. Extensive experiments across zero-shot, few-shot, and linear probing settings highlight the generalization and transferability of KeepFIT V2, delivering performance competitive to state-of-the-art fundus VLP models trained on large-scale private image-text datasets. Our dataset and model are publicly available via https://github.com/lxirich/MM-Retinal.

Two families of periodic traveling waves exist in the focusing mKdV (modified Korteweg-de Vries) equation. Spectral stability of these waveforms with respect to co-periodic perturbations of the same period has been previously explored by using spectral analysis and variational formulation. By using tools of integrability such as a relation between squared eigenfunctions of the Lax pair and eigenfunctions of the linearized stability problem, we revisit the spectral stability of these waveforms with respect to perturbations of arbitrary periods. In agreement with previous works, we find that one family is spectrally stable for all parameter configurations, whereas the other family is spectrally unstable for all parameter configurations. We show that the onset of the co-periodic instability for the latter family changes the instability bands from figure-$8$ (crossing at the imaginary axis) into figure-$\infty$ (crossing at the real axis)., Comment: 20 pages, 7 figures

Using $\psi(3686)\rightarrow \pi^{0} h_{c}$ decays from a data sample of $(27.12\pm0.14)\times10^{8}$ $\psi(3686)$ events collected by the BESIII detector at the BEPCII collider, $h_c$ radiative decays to $\gamma\pi^{+}\pi^{-},~\gamma\pi^{+}\pi^{-}\eta,~\gamma2(\pi^{+}\pi^{-})$, and $\gamma p\bar{p}$ are observed for the first time, each with a significance greater than $5\sigma$. The corresponding branching fractions are measured. Furthermore, intermediate states below 2.8 GeV/$c^{2}$ are investigated, leading to the first observation of the decay process of $h_c\rightarrow\gamma f_{2}(1270)\rightarrow\gamma\pi^{+}\pi^{-}$ with a significance of $5.5\,\sigma$. This observation represents the first instance of $h_c$ radiative decay to a tensor state.

Aerospace manufacturing demands exceptionally high precision in technical parameters. The remarkable performance of Large Language Models (LLMs), such as GPT-4 and QWen, in Natural Language Processing has sparked industry interest in their application to tasks including process design, material selection, and tool information retrieval. However, LLMs are prone to generating "hallucinations" in specialized domains, producing inaccurate or false information that poses significant risks to the quality of aerospace products and flight safety. This paper introduces a set of evaluation metrics tailored for LLMs in aerospace manufacturing, aiming to assess their accuracy by analyzing their performance in answering questions grounded in professional knowledge. Firstly, key information is extracted through in-depth textual analysis of classic aerospace manufacturing textbooks and guidelines. Subsequently, utilizing LLM generation techniques, we meticulously construct multiple-choice questions with multiple correct answers of varying difficulty. Following this, different LLM models are employed to answer these questions, and their accuracy is recorded. Experimental results demonstrate that the capabilities of LLMs in aerospace professional knowledge are in urgent need of improvement. This study provides a theoretical foundation and practical guidance for the application of LLMs in aerospace manufacturing, addressing a critical gap in the field., Comment: conference paper

This paper gives some exact formulas for the depth of powers of the edge ideal of an edge-weighted integrally closed cycle.

The rapid development of programmable network devices and the widespread use of machine learning (ML) in networking have facilitated efficient research into intelligent data plane (IDP). Offloading ML to programmable data plane (PDP) enables quick analysis and responses to network traffic dynamics, and efficient management of network links. However, PDP hardware pipeline has significant resource limitations. For instance, Intel Tofino ASIC has only 10Mb SRAM in each stage, and lacks support for multiplication, division and floating-point operations. These constraints significantly hinder the development of IDP. This paper presents \quark, a framework that fully offloads convolutional neural network (CNN) inference onto PDP. \quark employs model pruning to simplify the CNN model, and uses quantization to support floating-point operations. Additionally, \quark divides the CNN into smaller units to improve resource utilization on the PDP. We have implemented a testbed prototype of \quark on both P4 hardware switch (Intel Tofino ASIC) and software switch (i.e., BMv2). Extensive evaluation results demonstrate that \quark achieves 97.3\% accuracy in anomaly detection task while using only 22.7\% of the SRAM resources on the Intel Tofino ASIC switch, completing inference tasks at line rate with an average latency of 42.66$\mu s$., Comment: IEEE International Conference on Computer Communications (INFOCOM), 2025

Path Loss (PL) is vital to evaluate the performance of Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs), particularly in urban environments with complex propagation due to various obstacles. Accurately modeling PL requires a generalized Probability of Line-of-Sight (PLoS) that can consider multiple obstructions. While the existing PLoS models mostly assume a simplified Manhattan grid with uniform building sizes and spacing, they overlook the real-world variability in building dimensions. Furthermore, such models do not consider other obstacles, such as trees and streetlights, which may also impact the performance, especially in millimeter-wave (mmWave) bands. This paper introduces a Manhattan Random Simulator (MRS) to estimate PLoS for UAV-based communications in urban areas by incorporating irregular building shapes, non-uniform spacing, and additional random obstacles to create a more realistic environment. Lastly, we present the PL differences with and without obstacles for standard urban environments and derive the empirical PL for these environments., Comment: 9th European Conference on Antennas and Propagation (EuCAP)

Accurate Probability of Line-of-Sight (PLoS) modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing PLoS models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined built-up parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban LoS Simulator (ULS) to model PLoS for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical PLoS for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate PLoS results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks., Comment: 2025 IEEE Wireless Communications and Networking Conference (WCNC): 2025 IEEE Wireless Communications and Networking Conference (WCNC) Workshops - WCNC 2025 WS03: The 3rd International Workshop on the Internet of Time-Critical Things (IoTime 2025)

This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confounders and measurement errors with partly interval-censored time-to-event data, where event times are observed exactly for some subjects but left-censored, right-censored, or interval-censored for others. Our method is based on a two-stage Dirichlet process mixture instrumental variable (DPMIV) model which simultaneously models the first-stage random error term for the exposure variable and the second-stage random error term for the time-to-event outcome using a bivariate Gaussian mixture of the Dirichlet process (DPM) model. The DPM model can be broadly understood as a mixture model with an unspecified number of Gaussian components, which relaxes the normal error assumptions and allows the number of mixture components to be determined by the data. We develop an MCMC algorithm for the DPMIV model tailored for partly interval-censored data and conduct extensive simulations to assess the performance of our DPMIV method in comparison with some competing methods. Our simulations revealed that our proposed method is robust under different error distributions and can have superior performance over its parametric counterpart under various scenarios. We further demonstrate the effectiveness of our approach on an UK Biobank data to investigate the causal effect of systolic blood pressure on time-to-development of cardiovascular disease from the onset of diabetes mellitus.

Using data samples collected by the \mbox{BESIII} detector located at the Beijing Electron Positron Collider, the cross sections of the process $e^+e^-\to f_{1}(1285)\pi^+\pi^-$ are measured at forty-five center-of-mass energies from $3.808$ to $4.951 {\rm GeV}$. An investigation on the cross section line shape is performed, and no significant structure is observed.

The mass of galaxy clusters derived from weak-lensing observations is sensitive to projection effects, and, on average, it is biased low with respect to the true cluster mass, with a mass and redshift dependence. In this work, we leverage state-of-the-art hydrodynamical simulations of galaxy clusters carried out with GadgetX and GIZMO-SIMBA as part of the Three Hundred Project. We use them to quantify weak-lensing mass biases with respect also to the results from dark matter-only simulations. We also investigate how the biases of the weak-lensing mass estimates propagate into the richness-mass relation. Future wide-field weak-lensing surveys require per cent accuracy on the characterisation of cluster masses in order to use galaxy cluster number count and clustering as complementary probes for cosmological analyses. We aim to shed light on the effect of the presence of baryons on the weak-lensing mass bias and also whether this bias depends on the galaxy formation recipe; in addition, we seek to model the richness-mass relation that can be used as guidelines for observational experiments for cluster cosmology. We produce weak-lensing simulations of random projections to model the expected excess surface mass density profile of clusters up to redshift $z=1$. We derive the weak-lensing mass-richness relation and find consistency within 1$\sigma$ uncertainties across hydrodynamical simulations. The scatter in observed richness at a fixed weak-lensing mass, or vice versa, increases linearly with redshift at a fixed stellar mass cut. Importantly, we observe that the scatter in richness at a given true mass is smaller than at a given weak-lensing mass. Our results align well with SDSS redMaPPer cluster observations. (Abridged), Comment: submitted to A&A, comments are welcome

Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation (MT) remains challenging due to pretraining on English-centric data and the complexity of reinforcement learning from human feedback (RLHF). Direct Preference Optimization (DPO) has emerged as a simpler and more efficient alternative, but its performance depends heavily on the quality of preference data. To address this, we propose Confidence-Reward driven Preference Optimization (CRPO), a novel method that combines reward scores with model confidence to improve data selection for fine-tuning. CRPO selects challenging sentence pairs where the model is uncertain or underperforms, leading to more effective learning. While primarily designed for LLMs, CRPO also generalizes to encoder-decoder models like NLLB, demonstrating its versatility. Empirical results show that CRPO outperforms existing methods such as RS-DPO, RSO and MBR score in both translation accuracy and data efficiency.

Magnetic measurements under high-pressure conditions are pivotal for the study of superconductivity and magnetic materials but remain challenging due to the micrometer-sized sample in diamond anvil cells (DAC). In this study, we propose a quantum sensing approach utilizing negatively charged boron-vacancy (V$_B^-$) spin defects in two-dimensional hexagonal boron nitride for high resolution magnetic measurements under pressure. The optical and spin properties of VB$^-$ defects were systematically studied under high-pressure conditions, revealing a significant pressure-induced shift in zero-field splitting (ZFS), approximately three times larger than that of nitrogen-vacancy (NV) center. Furthermore, we demonstrate the pressure-dependent magnetic transition and variations in the Curie temperature of van der Waals ferromagnet Fe$_3$GeTe$_2$ flake using V$_B^-$ defects under pressures. Notably, the maximum operational pressure for V$_B^-$ defects was determined to be approximately 11 GPa, attributed to a structural phase transition in hexagonal boron nitride (hBN). This work establishes the way for two-dimensional quantum sensing technologies under high-pressure environments.

Polarization-analyzed small-angle neutron scattering (PASANS) is an advanced technique that enables the selective investigation of magnetic scattering phenomena in magnetic materials and distinguishes coherent scattering obscured by incoherent backgrounds, making it particularly valuable for cutting-edge research. The successful implementation of PASANS in China was achieved for the first time at the newly commissioned Very Small Angle Neutron Scattering (VSANS) instrument at the China Spallation Neutron Source (CSNS). This technique employs a combination of a double-V cavity supermirror polarizer and a radio frequency (RF) neutron spin flipper to manipulate the polarization of the incident neutrons. The scattered neutron polarization is stably analyzed by a specially designed $\textit{in-situ}$ optical pumping $^{3}$He neutron spin filter, which covers a spatially symmetric scattering angle coverage of about 4.8 $^{\circ}$. A comprehensive PASANS data reduction method, aimed at pulsed neutron beams, has been established and validated with a silver behenate powder sample, indicating a maximum momentum transfer coverage of approximately 0.25 {\AA} $^{-1}$.

Nowadays, PWM excitation is one of the most common waveforms seen by magnetic components in power electronic converters. Core loss modeling approaches such as the improved Generalized Steinmetz Equation (iGSE) or the loss map based on the composite waveform hypothesis (CWH) generally process the PWM excitation piecewise, which has been proven effective for DC-DC converters. However, an additional challenge arises in DC-AC converters, i.e. the fundamental-frequency sinewave component induces the major loop loss on top of the piecewise high-frequency segments. This major loop loss cannot be modeled on a switching cycle basis by any existing methods. To address this gap, this paper proposes a novel fundamental concept, instantaneous core loss, which is observed empirically for the first time. Enabled by the reactive voltage cancellation method, the instantaneous core loss, which only contains the real power loss, can be measured as a function of time. Building on this concept, a modeling method is proposed to break down the major loop core loss, typically represented as an average value in the literature, into the time domain, enabling cycle-by-cycle modeling as a practical workflow for predicting core losses in PWM converters. Through experiments, the existence of the major loop loss is verified, and generic instantaneous core loss models are extracted for several magnetic components. An example workflow is proposed to extract the cycle-by-cycle core loss at the design stage of a PWM DC-AC converter. This work enhances the fundamental understanding of the core loss process in real time, contributing to the advancement of scientific knowledge.

In the pursuit of robust autonomous driving systems, models trained on real-world datasets often struggle to adapt to new environments, particularly when confronted with corner cases such as extreme weather conditions. Collecting these corner cases in the real world is non-trivial, which necessitates the use of simulators for validation. However,the high computational cost and the domain gap in data distribution have hindered the seamless transition between real and simulated driving scenarios. To tackle this challenge, we propose Retrieval-Augmented Learning for Autonomous Driving (RALAD), a novel framework designed to bridge the real-to-sim gap at a low cost. RALAD features three primary designs, including (1) domain adaptation via an enhanced Optimal Transport (OT) method that accounts for both individual and grouped image distances, (2) a simple and unified framework that can be applied to various models, and (3) efficient fine-tuning techniques that freeze the computationally expensive layers while maintaining robustness. Experimental results demonstrate that RALAD compensates for the performance degradation in simulated environments while maintaining accuracy in real-world scenarios across three different models. Taking Cross View as an example, the mIOU and mAP metrics in real-world scenarios remain stable before and after RALAD fine-tuning, while in simulated environments,the mIOU and mAP metrics are improved by 10.30% and 12.29%, respectively. Moreover, the re-training cost of our approach is reduced by approximately 88.1%. Our code is available at https://github.com/JiachengZuo/RALAD.git.

Experimental characterization of magnetic components has grown to be increasingly important to understand and model their behaviours in high-frequency PWM converters. The BH loop measurement is the only available approach to separate the core loss as an electrical method, which, however, is suspective to the probe phase skew. As an alternative to the regular de-skew approaches based on hardware, this work proposes a novel machine-learning-based method to identify and correct the probe skew, which builds on the newly discovered correlation between the skew and the shape/trajectory of the measured BH loop. A special technique is proposed to artificially generate the skewed images from measured waveforms as augmented training sets. A machine learning pipeline is developed with the Convolutional Neural Network (CNN) to treat the problem as an image-based prediction task. The trained model has demonstrated a high accuracy in identifying the skew value from a BH loop unseen by the model, which enables the compensation of the skew to yield the corrected core loss value and BH loop.

Graph convolutional networks (GCNs) have emerged as a powerful tool for skeleton-based action and gesture recognition, thanks to their ability to model spatial and temporal dependencies in skeleton data. However, existing GCN-based methods face critical limitations: (1) they lack effective spatio-temporal topology modeling that captures dynamic variations in skeletal motion, and (2) they struggle to model multiscale structural relationships beyond local joint connectivity. To address these issues, we propose a novel framework called Dynamic Spatial-Temporal Semantic Awareness Graph Convolutional Network (DSTSA-GCN). DSTSA-GCN introduces three key modules: Group Channel-wise Graph Convolution (GC-GC), Group Temporal-wise Graph Convolution (GT-GC), and Multi-Scale Temporal Convolution (MS-TCN). GC-GC and GT-GC operate in parallel to independently model channel-specific and frame-specific correlations, enabling robust topology learning that accounts for temporal variations. Additionally, both modules employ a grouping strategy to adaptively capture multiscale structural relationships. Complementing this, MS-TCN enhances temporal modeling through group-wise temporal convolutions with diverse receptive fields. Extensive experiments demonstrate that DSTSA-GCN significantly improves the topology modeling capabilities of GCNs, achieving state-of-the-art performance on benchmark datasets for gesture and action recognition, including SHREC17 Track, DHG-14\/28, NTU-RGB+D, and NTU-RGB+D-120., Comment: submit to Neurocomputing

Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize stable features in graph data for classification, based on the premise that these features causally determine the target label, and their influence is invariant to changes in distribution. Along this line, most studies have attempted to pinpoint these stable features by emphasizing explicit substructures in the graph, such as masked or attentive subgraphs, and primarily enforcing the invariance principle in the semantic space, i.e., graph representations. However, we argue that focusing only on the semantic space may not accurately identify these stable features. To address this, we introduce the Unified Invariant Learning (UIL) framework for graph classification. It provides a unified perspective on invariant graph learning, emphasizing both structural and semantic invariance principles to identify more robust stable features. In the graph space, UIL adheres to the structural invariance principle by reducing the distance between graphons over a set of stable features across different environments. Simultaneously, to confirm semantic invariance, UIL underscores that the acquired graph representations should demonstrate exemplary performance across diverse environments. We present both theoretical and empirical evidence to confirm our method's ability to recognize superior stable features. Moreover, through a series of comprehensive experiments complemented by in-depth analyses, we demonstrate that UIL considerably enhances OOD generalization, surpassing the performance of leading baseline methods. Our codes are available at https://github.com/yongduosui/UIL., Comment: Accepted to KDD 2025

In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. However, despite their potential, existing works face challenges when applying LLMs to medical settings. Strategies relying on training with medical datasets are highly cost-intensive and may suffer from outdated training data. Leveraging external knowledge bases is a suitable alternative, yet it faces obstacles such as limited retrieval precision and poor effectiveness in answer extraction. These issues collectively prevent LLMs from demonstrating the expected level of proficiency in mastering medical expertise. To address these challenges, we introduce Med-R^2, a novel LLM physician framework that adheres to the Evidence-Based Medicine (EBM) process, efficiently integrating retrieval mechanisms as well as the selection and reasoning processes of evidence, thereby enhancing the problem-solving capabilities of LLMs in healthcare scenarios and fostering a trustworthy LLM physician. Our comprehensive experiments indicate that Med-R^2 achieves a 14.87\% improvement over vanilla RAG methods and even a 3.59\% enhancement compared to fine-tuning strategies, without incurring additional training costs.

Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that increasing test-time computational resources can further improve performance. In this work, we introduce Aggregation Fine-Tuning (AFT), a supervised finetuning paradigm where the model learns to synthesize multiple draft responses, referred to as proposals, into a single, refined answer, termed aggregation. At inference time, a propose-and-aggregate strategy further boosts performance by iteratively generating proposals and aggregating them. Empirical evaluations on benchmark datasets show that AFT-trained models substantially outperform standard SFT. Notably, an AFT model, fine-tuned from Llama3.1-8B-Base with only 64k data, achieves a 41.3% LC win rate on AlpacaEval 2, surpassing significantly larger LLMs such as Llama3.1-405B-Instruct and GPT4. By combining sequential refinement and parallel sampling, the propose-and-aggregate framework scales inference-time computation in a flexible manner. Overall, These findings position AFT as a promising approach to unlocking additional capabilities of LLMs without resorting to increasing data volume or model size., Comment: 20 pages; work in progress

Multilingual research has garnered increasing attention, especially in the domain of dialogue systems. The rapid advancements in large language models (LLMs) have fueled the demand for high-performing multilingual models. However, two major challenges persist: the scarcity of high-quality multilingual datasets and the limited complexity of existing datasets in capturing realistic dialogue scenarios. To address these gaps, we introduce XMP, a high-quality parallel Multilingual dataset sourced from Multi-party Podcast dialogues. Each sample in the dataset features at least three participants discussing a wide range of topics, including society, culture, politics, and entertainment.Through extensive experiments, we uncover significant limitations in previously recognized multilingual capabilities of LLMs when applied to such complex dialogue scenarios. For instance, the widely accepted multilingual complementary ability of LLMs is notably impacted. By conducting further experiments, we explore the mechanisms of LLMs in multilingual environments from multiple perspectives, shedding new light on their performance in real-world, diverse conversational contexts.

Recently, the impact of disorder on topological properties has attracted significant attention in photonics, especially the intriguing disorder-induced topological phase transitions in photonic topological Anderson insulators (PTAIs). However, the reported PTAIs are based on time-reversal symmetry broken systems or quasi-three-dimensional time-reversal invariant system, both of which would limit the applications in integrated optics. Here, we realize a time-reversal symmetric two-dimensional PTAI on silicon platform within the near-IR wavelength range, taking the advantageous valley degree of freedom of photonic crystal. A low-threshold topological Anderson phase transition is observed by applying disorder to the critical topologically trivial phase. Conversely, we have also realized extremely robust topologically protected edge states based on the stable topological phase. Both two phenomena are validated through theoretical Dirac Hamiltonian analysis, numerical simulations, and experimental measurements. Our proposed structure holds promise to achieve near-zero topological phase transition thresholds, which breaks the conventional cognition that strong disorder is required to induce the phase transition. It significantly alleviates the difficulty of manipulating disorder and could be extended to other systems, such as condensed matter systems where strong disorder is hard to implement. This work is also beneficial to construct highly robust photonic integrated circuits serving for on-chip photonic and quantum optic information processing. Moreover, this work also provides an outstanding platform to investigate on-chip integrated disordered systems.

This paper studies a wireless networked control system with multiple base stations (BSs) cooperatively coordinating the wireless control of a number of subsystems each consisting of a plant, a sensor, and an actuator. In this system, each sensor first offloads the sensing data to its associated BS, which then employs mobile edge computing (MEC) to process the data and sends the command signals back to the actuator for remote control. We consider the time-division-multiple-access (TDMA) service protocol among different BSs to facilitate the cascaded communication and computation process, in which different BSs implement the uplink data collection and downlink command broadcasting over orthogonal time slots. We also employ the massive multiple-input multiple-output (MIMO) at BSs, based on which each BS serves its associated sensors or actuators over the same time-frequency resources via spatial multiplexing. Under this setup, we jointly design the association between BSs and sensors/actuators as well as the joint communication and computation resource allocation, with the objective of minimizing the closed-loop control latency of the multiple subsystems while ensuring their control stability. The optimization takes into account the transmission uncertainty caused by both the hyper reliable and low-latency communications (HRLLC) and the inter-user interference , as well as the communication and computation resource constraints at distributed nodes. To solve the challenging non-convex joint optimization problem, we develop an efficient algorithm by employing the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed joint BS-sensor/actuator association and resource allocation design significantly outperforms other heuristic schemes and frequency-division-multiple-access (FDMA) counterpart.