arXiv subjects

Xu Shi

Publications and source records attributed to Xu Shi.

At least 37 records · Page 2Linked to original sources

arXiv2025

Leveraging Channel Knowledge Map for Multi-User Hierarchical Beam Training Under Position Uncertainty

Channel knowledge map (CKM) emerges as a promising framework to acquire location-specific channel information without consuming wireless resources, creating new horizons for advanced wireless network design and optimization. Despite its potential, the practical application of CKM in beam training faces several challenges. On one hand, the user's precise location is typically unavailable prior to beam training, which limits the utility of CKM since its effectiveness relies heavily on accurate input of position data. On the other hand, the intricate interplay among CKM, real-time observations, and training strategies has not been thoroughly studied, leading to suboptimal performance and difficulties in practical implementation. In this paper, we present a framework for CKM-aided beam training that addresses these limitations. For single-user scenario, we propose a reward-motivated beam-potential hierarchical strategy which integrates partial position information and CKM. This strategy models the user equipment (UE) position uncertainty and formulates the hierarchical searching process as a pruned binary search tree. An optimal hierarchical searching strategy with minimal overhead is derived by evaluating the weights and rewards of potential codewords. Furthermore, a low-complexity two-layer lookahead scheme is designed to balance overhead and computational demands. For multi-user scenario, we develop a correlation-driven position-pruning training scheme, where sidelobe gains from inter-user interference are exploited to provide additional side information for overhead reduction, allowing all users to be simultaneously assigned their respective supportive beams. Simulations validate the superior performances of proposed approaches in advancing 6G beam training.

cs.IT↗

arXiv2025

BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems

The channel knowledge map (CKM) enables efficient construction of high-fidelity mapping between spatial environments and channel parameters via electromagnetic information analysis. Nevertheless, existing studies are largely confined to single-antenna systems, failing to offer dedicated guidance for multi-antenna communication scenarios. To address the inherent conflict between traditional real-value pathloss map and multi-degree-of-freedom (DoF) coherent beamforming in B5G/6G systems, this paper proposes a novel concept of BeamCKM and CKMTransUNet architecture. The CKMTransUNet approach combines a UNet backbone for multi-scale feature extraction with a vision transformer (ViT) module to capture global dependencies among encoded linear vectors, utilizing a composite loss function to characterize the beam propagation characteristics. Furthermore, based on the CKMTransUNet backbone, this paper presents a methodology named M3ChanNet. It leverages the multi-modal learning technique and cross-attention mechanisms to extract intrinsic side information from environmental profiles and real-time multi-beam observations, thereby further improving the map construction accuracy. Simulation results demonstrate that the proposed method consistently outperforms state-of-the-art (SOTA) interpolation methods and deep learning (DL) approaches, delivering superior performance even when environmental contours are inaccurate. For reproducibility, the code is publicly accessible at https://github.com/github-whh/BeamCKM.

eess.SP↗

arXiv2025

InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy

Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the first evidence that synthetic data alone can match the performance of the strongest $π$-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation. The resulting model also exhibits surprisingly zero-shot sim-to-real transfer on several challenging tasks. Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables long-horizon skill composition, flexible task assembly, and heterogeneous embodiments with minimal manual tuning. Using the same architecture as $π_0$, we pre-train a model entirely on InternData-A1 and find that it matches the official $π_0$ across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks. We release the dataset and will open-source the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.

cs.RO↗

arXiv2025

An Adaptive Algorithm for Bilevel Optimization on Riemannian Manifolds

Existing methods for solving Riemannian bilevel optimization (RBO) problems require prior knowledge of the problem's first- and second-order information and curvature parameter of the Riemannian manifold to determine step sizes, which poses practical limitations when these parameters are unknown or computationally infeasible to obtain. In this paper, we introduce the Adaptive Riemannian Hypergradient Descent (AdaRHD) algorithm for solving RBO problems. To our knowledge, AdaRHD is the first method to incorporate a fully adaptive step size strategy that eliminates the need for problem-specific parameters in RBO. We prove that AdaRHD achieves an $\mathcal{O}(1/ε)$ iteration complexity for finding an $ε$-stationary point, thus matching the complexity of existing non-adaptive methods. Furthermore, we demonstrate that substituting exponential mappings with retraction mappings maintains the same complexity bound. Experiments demonstrate that AdaRHD achieves comparable performance to existing non-adaptive approaches while exhibiting greater robustness.

math.OC↗

arXiv2025

Identification and estimation of vaccine effectiveness in the test-negative design under equi-confounding

The test-negative design (TND) is widely used to evaluate vaccine effectiveness in real-world settings. In a TND study, individuals with similar symptoms who seek care are tested, and effectiveness is estimated by comparing vaccination histories of test-positive cases and test-negative controls. The TND is often justified on the grounds that it reduces confounding due to unmeasured health-seeking behavior, although this has not been formally described using potential outcomes. At the same time, concerns persist that conditioning on test receipt can introduce selection bias. We provide a formal justification of the TND under an assumption of odds ratio equi-confounding, where unmeasured confounders affect test-positive and test-negative individuals equivalently on the odds ratio scale. Health-seeking behavior is one plausible example. We also show that these results hold under the outcome-dependent sampling used in TNDs. We discuss the design implications of the equi-confounding assumption and provide alternative estimators for the marginal risk ratio among the vaccinated under equi-confounding, including outcome modeling and inverse probability weighting estimators as well as a semiparametric estimator that is doubly robust. When equi-confounding does not hold, we suggest a straightforward sensitivity analysis that parameterizes the magnitude of the deviation on the odds ratio scale. A simulation study evaluates the empirical performance of our proposed estimators under a wide range of scenarios. Finally, we discuss broader uses of test-negative outcomes to de-bias cohort studies in which testing is triggered by symptoms.

stat.ME↗

arXiv2025

What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research

In this work, we are motivated by the problem of estimating racial disparities in health outcomes, specifically the average controlled difference (ACD) in telomere length between Black and White individuals, using data from the National Health and Nutrition Examination Survey (NHANES). To do so, we build a propensity for race to properly adjust for other social determinants while characterizing the controlled effect of race on telomere length. Propensity score methods are broadly employed with observational data as a tool to achieve covariate balance, but how to implement them in complex surveys is less studied - in particular, when the survey weights depend on the group variable under comparison (as the NHANES sampling scheme depends on self-reported race). We propose identification formulas to properly estimate the ACD in outcomes between Black and White individuals, with appropriate weighting for both covariate imbalance across the two racial groups and generalizability. Via extensive simulation, we show that our proposed methods outperform traditional analytic approaches in terms of bias, mean squared error, and coverage when estimating the ACD for our setting of interest. In our data, we find that evidence of racial differences in telomere length between Black and White individuals attenuates after accounting for confounding by socioeconomic factors and utilizing appropriate propensity score and survey weighting techniques. Software to implement these methods and code to reproduce our results can be found in the R package svycdiff, available through the Comprehensive R Archive Network (CRAN) at cran.r-project.org/web/packages/svycdiff/, or in a development version on GitHub at github.com/salernos/svycdiff.

stat.ME↗

arXiv2025

Adaptive Proximal Causal Inference with Some Invalid Proxies

Proximal causal inference (PCI) is a recently proposed framework to identify and estimate the causal effect of an exposure on an outcome in the presence of hidden confounders, using observed proxies. Specifically, PCI relies on two types of proxies: a treatment-inducing confounding proxy, related to the outcome only through its association with unmeasured confounders (given treatment and covariates), and an outcome-inducing confounding proxy, related to the treatment only through such association (given covariates). These proxies must satisfy stringent exclusion restrictions - namely, the treatment proxy must not affect the outcome, and the outcome proxy must not be affected by the treatment. To improve identification and potentially efficiency, multiple proxies are often used, raising concerns about bias from exclusion violations. To address this, we introduce necessary and sufficient conditions for identifying causal effects in the presence of many proxies, some potentially invalid. Under a canonical proximal linear structural equations model, we propose a LASSO-based median estimator that jointly selects valid proxies and estimates the causal effect, with theoretical guarantees. Recognizing LASSO's limitations in consistently selecting valid treatment proxies, we develop an adaptive LASSO-based estimator with differential penalization. We show that it is root-n consistent and yields valid confidence intervals when a valid outcome proxy is available. We also extend the approach to settings with many potentially invalid outcome proxies. Theoretical results are supported by simulations and an application assessing the effect of right heart catheterization on 30-day survival in ICU patient.

stat.ME↗

arXiv2025

The Effect of Alcohol intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: 1) potentially nonlinear confounding effects from phenomic variables and 2) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized.

stat.ME↗

arXiv2025

Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events

During the COVID-19 pandemic, regulatory decision-making was hampered by a lack of timely and high-quality data on rare outcomes. Studying rare outcomes following infection and vaccination requires conducting multi-center observational studies, where sharing individual-level data is a privacy concern. In this paper, we conduct a multi-center observational study of thromboembolic events following COVID-19 and COVID-19 vaccination without sharing individual-level data. We accomplish this by developing a novel distributed learning method for constructing Kaplan-Meier (KM) curves and inverse propensity weighted KM curves with statistical inference. We sequentially update curves site-by-site using the KM influence function, which is a measure of the direction in which an observation should shift our estimate and so can be used to incorporate new observations without access to previous data. We show in simulations that our distributed estimator is unbiased and achieves equal efficiency to the combined data estimator. Applying our method to Beaumont Health, Spectrum Health, and Michigan Medicine data, we find a much higher covariate-adjusted incidence of blood clots after SARS-CoV-2 infection (3.13%, 95% CI: [2.93, 3.35]) compared to first COVID-19 vaccine (0.08%, 95% CI: [0.08, 0.09]). This suggests that the protection vaccines provide against COVID-19-related clots outweighs the risk of vaccine-related adverse events, and shows the potential of distributed survival analysis to provide actionable evidence for time-sensitive decision making.

stat.ME↗

arXiv2025

Fortified Proximal Causal Inference with Many Invalid Proxies

Causal inference from observational data often relies on the assumption of no unmeasured confounding, an assumption frequently violated in practice due to unobserved or poorly measured covariates. Proximal causal inference (PCI) offers a promising framework for addressing unmeasured confounding using a pair of outcome and treatment confounding proxies. However, existing PCI methods typically assume all specified proxies are valid, which may be unrealistic and is untestable without extra assumptions. In this paper, we develop a semiparametric approach for a many-proxy PCI setting that accommodates potentially invalid treatment confounding proxies. We introduce a new class of fortified confounding bridge functions and establish nonparametric identification of the population average treatment effect (ATE) under the assumption that at least $γ$ out of $K$ candidate treatment confounding proxies are valid, for any $γ\leq K$ set by the analyst without requiring knowledge of which proxies are valid. We establish a local semiparametric efficiency bound and develop a class of multiply robust, locally efficient estimators for the ATE. These estimators are thus simultaneously robust to invalid treatment confounding proxies and model misspecification of nuisance parameters. The proposed methods are evaluated through simulation and applied to assess the effect of right heart catheterization in critically ill patients.

stat.ME↗

arXiv2025

Generating Synthetic Electronic Health Record Data: a Methodological Scoping Review with Benchmarking on Phenotype Data and Open-Source Software

We conduct a scoping review of existing approaches for synthetic EHR data generation, and benchmark major methods with proposed open-source software to offer recommendations for practitioners. We search three academic databases for our scoping review. Methods are benchmarked on open-source EHR datasets, MIMIC-III/IV. Seven existing methods covering major categories and two baseline methods are implemented and compared. Evaluation metrics concern data fidelity, downstream utility, privacy protection, and computational cost. 42 studies are identified and classified into five categories. Seven open-source methods covering all categories are selected, trained on MIMIC-III, and evaluated on MIMIC-III or MIMIC-IV for transportability considerations. Among them, GAN-based methods demonstrate competitive performance in fidelity and utility on MIMIC-III; rule-based methods excel in privacy protection. Similar findings are observed on MIMIC-IV, except that GAN-based methods further outperform the baseline methods in preserving fidelity. A Python package, "SynthEHRella", is provided to integrate various choices of approaches and evaluation metrics, enabling more streamlined exploration and evaluation of multiple methods. We found that method choice is governed by the relative importance of the evaluation metrics in downstream use cases. We provide a decision tree to guide the choice among the benchmarked methods. Based on the decision tree, GAN-based methods excel when distributional shifts exist between the training and testing populations. Otherwise, CorGAN and MedGAN are most suitable for association modeling and predictive modeling, respectively. Future research should prioritize enhancing fidelity of the synthetic data while controlling privacy exposure, and comprehensive benchmarking of longitudinal or conditional generation methods.

cs.LG↗

arXiv2025

A new statistical approach for joint modeling of longitudinal outcomes measured in electronic health records with clinically informative presence and observation processes

Biobanks with genetics-linked electronic health records (EHR) have opened up opportunities to study associations between genetic, social, or environmental factors and longitudinal lab biomarkers. However, in EHRs, the timing of patient visits and the recording of lab tests often depend on patient health status, referred to as informative presence (IP) and informative observation (IO), which can bias exposure-biomarker associations. Two gaps remain in EHR-based research: (1) the performance of existing IP-aware methods is unclear in real-world EHR settings, and (2) no existing methods handle IP and IO simultaneously. To address these challenges, we first conduct extensive simulation studies tailored to EHR-specific IP patterns to assess existing methods. We then propose a joint modeling framework, EHRJoint, that simultaneously models the visiting, observation, and longitudinal biomarker processes to address both IP and IO. We develop a computationally efficient estimation procedure based on estimating equations and provide asymptotically valid inference. Simulations show that EHRJoint yields unbiased exposure effect estimates under both IP and IO, while existing methods fail. We apply EHRJoint to the Michigan Genomics Initiative data to examine associations between repeated glucose measurements and two exposures: genetic variants and educational disadvantage.

stat.ME↗

arXiv2025

Beamforming-Codebook-Aware Channel Knowledge Map Construction for Multi-Antenna Systems

Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neural networks (NNs) have achieved efficient CKM generation with reduced computational complexity and real-time processing capabilities. Nevertheless, existing research predominantly focuses on single-antenna systems, failing to address the beamforming requirements inherent to MIMO configurations. Given that appropriate precoding vector selection in MIMO systems can substantially enhance user communication rates, this paper presents a TransUNet-based framework for constructing CKM, which effectively incorporates discrete Fourier transform (DFT) precoding vectors. The proposed architecture combines a UNet backbone for multiscale feature extraction with a Transformer module to capture global dependencies among encoded linear vectors. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) deep learning (DL) approaches, yielding a 17\% improvement in RMSE compared to RadioWNet. The code is publicly accessible at https://github.com/github-whh/TransUNet.

eess.SP↗

arXiv2025

Lightweight and Self-Evolving Channel Twinning: An Ensemble DMD-Assisted Approach

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.

eess.SP↗

arXiv2025

Test-negative designs with various reasons for testing: statistical bias and solution

Test-negative designs are widely used for post-market evaluation of vaccine effectiveness, particularly in cases when randomized trials are not feasible. Differing from classical test-negative designs where only healthcare-seekers with symptoms are included, recent test-negative designs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they introduce bias into the current framework of test-negative designs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical demonstrations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not accounted for. To eliminate this bias, we identify three categories of reasons for testing, including symptoms, mandatory screening, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same vaccine effectiveness parameter, and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. The performance of our proposed method is demonstrated through simulation studies.

stat.ME↗

arXiv2025

Regression-based proximal causal inference for right-censored time-to-event data

Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure (NCE) and outcome (NCO) variables, also known as treatment and outcome confounding proxies. Although regression-based PCI is well developed for binary and continuous outcomes, analogous PCI regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression PCI approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of NCOs, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package 'pci2s'.

stat.ME↗

arXiv2024

mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture

Root cause analysis (RCA) in Micro-services architecture (MSA) with escalating complexity encounters complex challenges in maintaining system stability and efficiency due to fault propagation and circular dependencies among nodes. Diverse root cause analysis faults require multi-agents with diverse expertise. To mitigate the hallucination problem of large language models (LLMs), we design blockchain-inspired voting to ensure the reliability of the analysis by using a decentralized decision-making process. To avoid non-terminating loops led by common circular dependency in MSA, we objectively limit steps and standardize task processing through Agent Workflow. We propose a pioneering framework, multi-Agent Blockchain-inspired Collaboration for root cause analysis in micro-services architecture (mABC), where multiple agents based on the powerful LLMs follow Agent Workflow and collaborate in blockchain-inspired voting. Specifically, seven specialized agents derived from Agent Workflow each provide valuable insights towards root cause analysis based on their expertise and the intrinsic software knowledge of LLMs collaborating within a decentralized chain. Our experiments on the AIOps challenge dataset and a newly created Train-Ticket dataset demonstrate superior performance in identifying root causes and generating effective resolutions. The ablation study further highlights Agent Workflow, multi-agent, and blockchain-inspired voting is crucial for achieving optimal performance. mABC offers a comprehensive automated root cause analysis and resolution in micro-services architecture and significantly improves the IT Operation domain. The code and dataset are in https://github.com/zwpride/mABC.

cs.MA↗

arXiv2024

FG-MDM: Towards Zero-Shot Human Motion Generation via ChatGPT-Refined Descriptions

Recently, significant progress has been made in text-based motion generation, enabling the generation of diverse and high-quality human motions that conform to textual descriptions. However, generating motions beyond the distribution of original datasets remains challenging, i.e., zero-shot generation. By adopting a divide-and-conquer strategy, we propose a new framework named Fine-Grained Human Motion Diffusion Model (FG-MDM) for zero-shot human motion generation. Specifically, we first parse previous vague textual annotations into fine-grained descriptions of different body parts by leveraging a large language model. We then use these fine-grained descriptions to guide a transformer-based diffusion model, which further adopts a design of part tokens. FG-MDM can generate human motions beyond the scope of original datasets owing to descriptions that are closer to motion essence. Our experimental results demonstrate the superiority of FG-MDM over previous methods in zero-shot settings. We will release our fine-grained textual annotations for HumanML3D and KIT.

cs.CV↗