arXiv subjects

Kai Yang

Publications and source records attributed to Kai Yang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Unveiling Fiber Networks and Core Formation in the DR21 South Filament

We present high-resolution ($\sim$1000 AU) 3 mm observations with the NOrthern Extended Millimeter Array toward the DR21 South Filament, aiming to reveal its internal fragmentation and search for deeply embedded star-forming activities. Both the continuum and molecular line emissions align well with the filament axis traced by the low-resolution ($\sim$18$^{\prime\prime}$) column density map. The 3 mm continuum, CS (2$-$1), and HCO$^+$ (1$-$0) emissions reveal continuous and diffuse structures with measured FWHM widths of 0.054, 0.029, and 0.030 pc, respectively. In contrast, the H$^{13}$CO$^+$ (1$-$0) emission appears more clumpy and localized. The non-thermal motion in the filament is predominantly subsonic to transonic. We detect 13 dense cores in NH$_2$D (1$_{11}-1_{01}$), three of which coincide with continuum peaks; virial analysis suggests most are gravitationally bound. Using a friend-of-friend algorithm, we identify 32, 34, and 22 velocity-coherent fibers from the CS, HCO$^+$, and H$^{13}$CO$^+$ data, respectively. Compared to fibers traced by CS and HCO$^+$, H$^{13}$CO$^+$ fibers are more frequently associated with NH$_2$D cold cores and exhibit higher average mass-per-unit-length values. Differences among CS, HCO$^+$, and H$^{13}$CO$^+$ emissions likely arise from variations in effective critical densities. These results are consistent with a hierarchical structure, in which the 3.6-pc DR21SF contains velocity-coherent fibers and gravitationally bound dense cores.

astro-ph.GA↗

arXiv2025

SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys

To predict the complex chemical evolution in multicomponent alloys, it is highly desirable to have accurate atomistic simulation methods capable of reaching sufficiently large spatial and temporal scales. In this work, we advance the recently proposed SMC-X method through distributed computation on either GPUs or CPUs, pushing both spatial and temporal scales of atomistic simulation of chemically complex alloys to previously inaccessible scales. This includes a record-breaking 128-billion-atom HEA system extending to the micrometer regime in space, and a 1-billion-atom HEA evolved over more than three million Monte Carlo swap steps, approaching the minute regime in time. We show that such large-scale simulations are essential for bridging the gap between experimental observations and theoretical predictions of the nanoprecipitate sizes in HEAs, based on analysis using the Lifshitz-Slyozov-Wagner (LSW) theory for diffusion-controlled coarsening. This work demonstrates the great potential of SMC-X for simulation-driven exploration of the chemical complexity in high-entropy materials at large spatial and temporal scales.

cond-mat.mtrl-sci↗

arXiv2025

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters

The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors.

cond-mat.dis-nn↗

arXiv2025

RoS-Guard: Robust and Scalable Online Change Detection with Delay-Optimal Guarantees

Online change detection (OCD) aims to rapidly identify change points in streaming data and is critical in applications such as power system monitoring, wireless network sensing, and financial anomaly detection. Existing OCD methods typically assume precise system knowledge, which is unrealistic due to estimation errors and environmental variations. Moreover, existing OCD methods often struggle with efficiency in large-scale systems. To overcome these challenges, we propose RoS-Guard, a robust and optimal OCD algorithm tailored for linear systems with uncertainty. Through a tight relaxation and reformulation of the OCD optimization problem, RoS-Guard employs neural unrolling to enable efficient parallel computation via GPU acceleration. The algorithm provides theoretical guarantees on performance, including expected false alarm rate and worst-case average detection delay. Extensive experiments validate the effectiveness of RoS-Guard and demonstrate significant computational speedup in large-scale system scenarios.

cs.LG↗

arXiv2025

Blow-up in finite or infinite time of the 2D cubic Zakharov-Kuznetsov equation

We prove that near-threshold negative energy solutions to the 2D cubic ($L^2$-critical) focusing Zakharov-Kuznetsov (ZK) equation blow-up in finite or infinite time. The proof consists of several steps. First, we show that if the blow-up conclusion is false, there are negative energy solutions arbitrarily close to the threshold that are globally bounded in $H^1$ and are spatially localized, uniformly in time. In the second step, we show that such solutions must in fact be exact remodulations of the ground state, and hence, have zero energy, which is a contradiction. This second step, a nonlinear Liouville theorem, is proved by contradiction, with a limiting argument producing a nontrivial solution to a (linear) linearized ZK equation obeying uniform-in-time spatial localization. Such nontrivial linear solutions are excluded by a local-viral space-time estimate. The general framework of the argument is modeled on Merle [29] and Martel & Merle [24], who treated the 1D problem of the $L^2$-critical gKdV equation. Several new features are introduced here to handle the 2D ZK case.

math.AP↗

arXiv2025

CE-Nav: Flow-Guided Reinforcement Refinement for Cross-Embodiment Local Navigation

Generalizing local navigation policies across diverse robot morphologies is a critical challenge. Progress is often hindered by the need for costly and embodiment-specific data, the tight coupling of planning and control, and the "disastrous averaging" problem where deterministic models fail to capture multi-modal decisions (e.g., turning left or right). We introduce CE-Nav, a novel two-stage (IL-then-RL) framework that systematically decouples universal geometric reasoning from embodiment-specific dynamic adaptation. First, we train an embodiment-agnostic General Expert offline using imitation learning. This expert, a conditional normalizing flow model named VelFlow, learns the full distribution of kinematically-sound actions from a large-scale dataset generated by a classical planner, completely avoiding real robot data and resolving the multi-modality issue. Second, for a new robot, we freeze the expert and use it as a guiding prior to train a lightweight, Dynamics-Aware Refiner via online reinforcement learning. This refiner rapidly learns to compensate for the target robot's specific dynamics and controller imperfections with minimal environmental interaction. Extensive experiments on quadrupeds, bipeds, and quadrotors show that CE-Nav achieves state-of-the-art performance while drastically reducing adaptation cost. Successful real-world deployments further validate our approach as an efficient and scalable solution for building generalizable navigation systems. Code is available at https://github.com/amap-cvlab/CE-Nav.

cs.RO↗

arXiv2025

From Sequence to Structure: Uncovering Substructure Reasoning in Transformers

Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual descriptions, LLMs can still effectively answer related questions. This raises a fundamental question: How can a decoder-only Transformer architecture understand underlying graph structures? To address this, we start with the substructure extraction task, interpreting the inner mechanisms inside the transformers and analyzing the impact of the input queries. Specifically, through both empirical results and theoretical analysis, we present Induced Substructure Filtration (ISF), a perspective that captures the substructure identification in the multi-layer transformers. We further validate the ISF process in LLMs, revealing consistent internal dynamics across layers. Building on these insights, we explore the broader capabilities of Transformers in handling diverse graph types. Specifically, we introduce the concept of thinking in substructures to efficiently extract complex composite patterns, and demonstrate that decoder-only Transformers can successfully extract substructures from attributed graphs, such as molecular graphs. Together, our findings offer a new insight on how sequence-based Transformers perform the substructure extraction task over graph data.

cs.CL↗

arXiv2025

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties

The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavily on ab initio calculations, frequently struggles to reproduce the experimentally observed properties that govern real material behavior. To address this challenge, we present a machine learning-driven, active-learning optimization framework for optimizing classical interatomic potentials to reproduce experimental properties. Our method, here showcased on soda-lime borosilicate glasses, targets both global (density) and local (boron coordination) structural features across a wide range of compositions. By combining a surrogate model with iterative active learning, the framework efficiently explores a five-dimensional parameter space using only 400 molecular dynamics simulations over 17 iterations, making it highly data-efficient and eliminating the need for extensive simulation campaigns. Two transferable parameter sets are identified, each demonstrating good agreement with experimental measurements, including glass density, fraction of four-fold boron, and X-ray structure factor. The framework effectively captures and manages inherent trade-offs between structural objectives and compositional regimes, providing insights into the coordination behavior of boron in complex glass networks. The resulting classical force fields are generalizable and do not require reparameterization for individual compositions. Altogether, this work offers a scalable and experimentally grounded approach for developing transferable interatomic potentials suitable for a broad range of materials, including multi-component glass systems, and beyond.

cond-mat.dis-nn↗

arXiv2025

Adaptive Group Policy Optimization: Towards Stable Training and Token-Efficient Reasoning

Since DeepSeek-R1 popularized, Group Relative Policy Optimization (GRPO) has become the core part of training Reasoning LLMs. However, we find some deficiency that influences RL stability and inference efficiency, like zero-variance in advantage estimation. Thus, we propose Adaptive Group Policy Optimization (AGPO) which uses a simple but effective method, an adaptive loss function, to mitigate training fluctuation and token inefficiency. The experiments demonstrate our method achieves more stable training and superior performance with significantly fewer tokens in reasoning steps.

cs.CL↗

arXiv2025

A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction

Intraoperative hypotension (IOH) is strongly associated with postoperative complications, including postoperative delirium and increased mortality, making its early prediction crucial in perioperative care. While several artificial intelligence-based models have been developed to provide IOH warnings, existing methods face limitations in incorporating both time and frequency domain information, capturing short- and long-term dependencies, and handling noise sensitivity in biosignal data. To address these challenges, we propose a novel Self-Adaptive Frequency Domain Network (SAFDNet). Specifically, SAFDNet integrates an adaptive spectral block, which leverages Fourier analysis to extract frequency-domain features and employs self-adaptive thresholding to mitigate noise. Additionally, an interactive attention block is introduced to capture both long-term and short-term dependencies in the data. Extensive internal and external validations on two large-scale real-world datasets demonstrate that SAFDNet achieves up to 97.3\% AUROC in IOH early warning, outperforming state-of-the-art models. Furthermore, SAFDNet exhibits robust predictive performance and low sensitivity to noise, making it well-suited for practical clinical applications.

cs.LG↗

arXiv2025

Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer

Prognostic information is essential for decision-making in breast cancer management. Recently trials have predominantly focused on genomic prognostication tools, even though clinicopathological prognostication is less costly and more widely accessible. Machine learning (ML), transfer learning and ensemble integration offer opportunities to build robust prognostication frameworks. We evaluate this potential to improve survival prognostication in breast cancer by comparing de-novo ML, transfer learning from a pre-trained prognostic tool and ensemble integration. Data from the MA.27 trial was used for model training, with external validation on the TEAM trial and a SEER cohort. Transfer learning was applied by fine-tuning the pre-trained prognostic tool PREDICT v3, de-novo ML included Random Survival Forests and Extreme Gradient Boosting, and ensemble integration was realized through a weighted sum of model predictions. Transfer learning, de-novo RSF, and ensemble integration improved calibration in MA.27 over the pre-trained model (ICI reduced from 0.042 in PREDICT v3 to <=0.007) while discrimination remained comparable (AUC increased from 0.738 in PREDICT v3 to 0.744-0.799). Invalid PREDICT v3 predictions were observed in 23.8-25.8% of MA.27 individuals due to missing information. In contrast, ML models and ensemble integration could predict survival regardless of missing information. Across all models, patient age, nodal status, pathological grading and tumor size had the highest SHAP values, indicating their importance for survival prognostication. External validation in SEER, but not in TEAM, confirmed the benefits of transfer learning, RSF and ensemble integration. This study demonstrates that transfer learning, de-novo RSF, and ensemble integration can improve prognostication in situations where relevant information for PREDICT v3 is lacking or where a dataset shift is likely.

cs.LG↗

arXiv2025

Matrix-variate integer-valued autoregressive processes

In the fields of sociology and economics, the modeling of matrix-variate integervalued time series is urgent. However, no prior studies have addressed the modeling of such data. To address this topic, this paper proposes a novel matrix-variate integer-valued autoregressive model. The key techniques lie in defining two leftand right-matricial thinning operators. The probabilistic and statistical properties of the proposed model are investigated. Furthermore, two estimation methods are developed: projection estimation and iterative least squares estimation. The corresponding asymptotic properties of these estimators are established. Additionally, the order-determination problem is addressed. In the simulation studies, the estimation results are given and the theoretical properties are verified. Finally, it is shown that the matrix-variate integer-valued autoregressive model is superior to the continuous matrix-variate autoregressive and multivariate integer-valued autoregressive models for matrix-variate integer-valued time series data.

math.ST↗

arXiv2025

Contributions to Robust and Efficient Methods for Analysis of High Dimensional Data

A ubiquitous feature of data of our era is their extra-large sizes and dimensions. Analyzing such high-dimensional data poses significant challenges, since the feature dimension is often much larger than the sample size. This thesis introduces robust and computationally efficient methods to address several common challenges associated with high-dimensional data. In my first manuscript, I propose a coherent approach to variable screening that accommodates nonlinear associations. I develop a novel variable screening method that transcends traditional linear assumptions by leveraging mutual information, with an intended application in neuroimaging data. This approach allows for accurate identification of important variables by capturing nonlinear as well as linear relationships between the outcome and covariates. Building on this foundation, I develop new optimization methods for sparse estimation using nonconvex penalties in my second manuscript. These methods address notable challenges in current statistical computing practices, facilitating computationally efficient and robust analyses of complex datasets. The proposed method can be applied to a general class of optimization problems. In my third manuscript, I contribute to robust modeling of high-dimensional correlated observations by developing a mixed-effects model based on Tsallis power-law entropy maximization and discussed the theoretical properties of such distribution. This model surpasses the constraints of conventional Gaussian models by accommodating a broader class of distributions with enhanced robustness to outliers. Additionally, I develop a proximal nonlinear conjugate gradient algorithm that accelerates convergence while maintaining numerical stability, along with rigorous statistical properties for the proposed framework.

math.ST↗

arXiv2025

Topological pumping of light governed by Fibonacci numbers

Topological pumping refers to transfer of a physical quantity governed by the systemtopology, resulting in quantized amounts of the transferred quantities. It is a ubiqui-tous wave phenomenon typically considered subject to exactly periodic adiabatic vari-ation of the system parameters. Recently, proposals for generalizing quasi-periodictopological pumping and identifying possible physical settings for its implementa-tion have emerged. In a strict sense, pumping with incommensurate frequencies canonly manifest over infinite evolution distances, raising a fundamental question aboutits observability in real-world finite-dimensional systems. Here we demonstrate thatbi-chromatic topological pumping with two frequencies, whose ratio is an irrationalnumber, can be viewed as the convergence limit of pumping with two commensuratefrequencies representing the best rational approximations of that irrational number. In our experiment, this phenomenon is observed as the displacement of a light beamcenter in photorefractive crystals induced by two optical lattices. The longitudinalperiods of the lattices, that in the paraxial approximation emulate two pumping fre-quencies, are related as Fibonacci numbers, successively approaching the golden ratio. We observed that a one-cycle displacement of the beam center at each successiveapproximation is determined by the relation between successive Fibonacci numbers,while the average direction of propagation (emulating average pumping velocity) ofthe beam is determined by the golden ratio.

physics.optics↗

arXiv2025

Revealing Nanostructures in High-Entropy Alloys via Machine-Learning Accelerated Scalable Monte Carlo Simulation

The computational cost of traditional first-principles method quickly becomes prohibitively expensive as the number of atoms increases. This challenge is further amplified by the need to evaluate finite-temperature properties with Monte Carlo (MC) simulations, which is inherently challenging to parallelize due to sequential Markov chain updates. Here, we introduce Scalable Monte Carlo (SMC), an efficient MC simulation method that overcomes the parallelization bottlenecks in conventional MC simulation, reducing the computational complexity of a MC sweep from quadratic to linear. We present a GPU implementation of the SMC method, SMC-GPU, which simultaneously harnesses the thousands of processing cores on a GPU to accelerate the computation. By adopting a data-driven workflow that surrogates the computationally expensive density functional theory (DFT) with ML models, we demonstrate that SMC-GPU is capable of simulating systems of more than one-billion atoms, while maintaining the accuracy of first-principles methods. Using this unprecedented capability, we performed billion-atom MC simulations to investigate the nanostructure evolution of two important high-entropy alloys (HEAs), FeCoNiAlTi and MoNbTaW, in which the nanostructures are believed to be responsible for their superb mechanical properties. Our results reveal a rich diversity of nanostructures, including nanoparticles (NP), 3D-connected NP, and disorder protected nanophases. We quantitatively analyze the size, composition, and morphology of the nanostructures, as well as directly simulate the atom-probe-tomography (APT) needle. The results align well with available experimental observations. This work underscores the promising potential of leveraging large-scale MC simulation to explore the largely uncharted territory of nanostructure evolution in HEAs.

cond-mat.mtrl-sci↗

arXiv2025

Graph Federated Learning for Personalized Privacy Recommendation

Federated recommendation systems (FedRecs) have gained significant attention for providing privacy-preserving recommendation services. However, existing FedRecs assume that all users have the same requirements for privacy protection, i.e., they do not upload any data to the server. The approaches overlook the potential to enhance the recommendation service by utilizing publicly available user data. In real-world applications, users can choose to be private or public. Private users' interaction data is not shared, while public users' interaction data can be shared. Inspired by the issue, this paper proposes a novel Graph Federated Learning for Personalized Privacy Recommendation (GFed-PP) that adapts to different privacy requirements while improving recommendation performance. GFed-PP incorporates the interaction data of public users to build a user-item interaction graph, which is then used to form a user relationship graph. A lightweight graph convolutional network (GCN) is employed to learn each user's user-specific personalized item embedding. To protect user privacy, each client learns the user embedding and the scoring function locally. Additionally, GFed-PP achieves optimization of the federated recommendation framework through the initialization of item embedding on clients and the aggregation of the user relationship graph on the server. Experimental results demonstrate that GFed-PP significantly outperforms existing methods for five datasets, offering superior recommendation accuracy without compromising privacy. This framework provides a practical solution for accommodating varying privacy preferences in federated recommendation systems.

cs.LG↗

arXiv2025

Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the federated distributionally robust optimization (FDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

math.OC↗

arXiv2025

Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches

The development of satellite-augmented low-altitude economy and terrestrial networks (SLAETNs) demands intelligent and autonomous systems that can operate reliably across heterogeneous, dynamic, and mission-critical environments. To address these challenges, this survey focuses on enabling agentic artificial intelligence (AI), that is, artificial agents capable of perceiving, reasoning, and acting, through generative AI (GAI) and large language models (LLMs). We begin by introducing the architecture and characteristics of SLAETNs, and analyzing the challenges that arise in integrating satellite, aerial, and terrestrial components. Then, we present a model-driven foundation by systematically reviewing five major categories of generative models: variational autoencoders (VAEs), generative adversarial networks (GANs), generative diffusion models (GDMs), transformer-based models (TBMs), and LLMs. Moreover, we provide a comparative analysis to highlight their generative mechanisms, capabilities, and deployment trade-offs within SLAETNs. Building on this foundation, we examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks. Finally, we outline key future directions for building scalable, adaptive, and trustworthy generative agents in SLAETNs. This survey aims to provide a unified understanding and actionable reference for advancing agentic AI in next-generation integrated networks.

cs.NI↗