arXiv subjects

Xin Xiong

Publications and source records attributed to Xin Xiong.

At least 19 recordsLinked to original sources

arXiv2026

VeriTrip: A Verifiable Benchmark for Travel Planning Agents over Unstructured Web Corpora

Existing benchmarks have laid the foundation for travel planning agents by establishing API-centric paradigms. However, as the capabilities of Autonomous Agents continue to advance, their evaluation must evolve beyond simple tool execution toward handling the inherent complexities of the open web. Current benchmarks bypass core cognitive hurdles: they fail to account for information noise, ignore multi-source factual contradictions, and overlook the necessity of grounding visual perception into logical planning. We introduce VeriTrip, a verifiable benchmark designed to meet the increasing demands for agent robustness and reliability. VeriTrip shifts the evaluation focus to evidence-grounded reasoning over unstructured multimodal web corpora. It establishes a Multimodal Retrieval Base (MRB) derived from real-world sources, forcing agents to autonomously orchestrate queries across heterogeneous data. A synchronized Verifiable Knowledge Base (VKB) enables a cell-wise verification protocol that precisely quantifies factual reliability, distinguishing systematic reasoning failures from parametric hallucinations. Our evaluations across leading MLLMs reveal a critical \textit{retrieval-reasoning trade-off}: the cognitive load of autonomous retrieval significantly erodes instruction retention. VeriTrip provides the rigorous foundation necessary for the next generation of planning agents capable of operating in unconstrained, multimodal environments.

cs.AI↗

arXiv2026

Dynamic resolution switching for live streaming

Conventional adaptive bitrate (ABR) streaming systems typically rely on static bitrate ladders to optimize Quality of Experience (QoE). While operationally simple, this "one-size-fits-all" approach neglects content-specific characteristics, often compromising streaming efficiency. Per-title optimization methods address this by predicting the rate-distortion convex hull directly from the source content, but their reliance on pre-encoding source analysis can limit their applicability to live streaming. Moreover, the objective video quality metrics (VQMs) they rely on are optimized for overall correlation with subjective scores rather than cross-over accuracy, often yielding inaccurate cross-over predictions and suboptimal ladder construction. To overcome both limitations, we introduce a Dynamic Resolution Switching (DRS) framework for live streaming that remains fully compatible with existing streaming protocols. Our approach augments static ladders with strategically selected representations guided by user bandwidth distributions and cross-over regions. The quality of these representations is then analyzed in real time to construct dynamic ladders. Central to this framework is a lightweight, bitstream-based VQM that ensures computational efficiency while maximizing the accuracy of subjective resolution cross-over prediction through training on Pairwise Comparison (PC) datasets. At each bitrate, the VQM evaluates all candidate representations to identify the resolution maximizing the quality score. This decision process, operating at a configurable granularity (e.g., per segment), drives the dynamic resolution switching mechanism specifically optimized for the metric. Experimental results validate the approach, demonstrating a significant performance gain (approximately 9% BD-rate reduction under the proposed VQM) while maintaining practical feasibility for live streaming.

eess.IV↗

arXiv2026

Representation learning to advance multi-institutional studies with electronic health record data from US and France

The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems.

cs.AI↗

arXiv2026

Rate-Distortion Optimization for Ensembles of Non-Reference Metrics

Non-reference metrics (NRMs) can assess the visual quality of images and videos without a reference, making them well-suited for the evaluation of user-generated content. Nonetheless, rate-distortion optimization (RDO) in video coding is still mainly driven by full-reference metrics, such as the sum of squared errors, which treat the input as an ideal target. A way to incorporate NRMs into RDO is through linearization (LNRM), where the gradient of the NRM with respect to the input guides bit allocation. While this strategy improves the quality predicted by some metrics, we show that it can yield limited gains or degradations when evaluated with other NRMs. We argue that NRMs are highly non-linear predictors with locally unstable gradients that can compromise the quality of the linearization; furthermore, optimizing a single metric may exploit model-specific biases that do not generalize across quality estimators. Motivated by this observation, we extend the LNRM framework to optimize ensembles of NRMs and, to further improve robustness, we introduce a smoothing-based formulation that stabilizes NRM gradients prior to linearization. Our framework is well-suited to hybrid codecs, and we advocate for its use with overfitted codecs, where it avoids iterative evaluations and backpropagation of neural network-based NRMs, reducing encoder complexity relative to direct NRM optimization. We validate the proposed approach on AVC and Cool-chic, using the YouTube UGC dataset. Experiments demonstrate consistent bitrate savings across multiple NRMs with no decoder complexity overhead and, for Cool-chic, a substantial reduction in encoding runtime compared to direct NRM optimization.

eess.IV↗

arXiv2026

Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI

Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``aging'' effect via frequent retraining is often impractical due to computational costs and privacy constraints. To overcome these hurdles, we introduce Adversarial Drift-Aware Predictive Transfer (ADAPT), a novel framework designed to confer durability against temporal drift with minimal retraining. ADAPT innovatively constructs an uncertainty set of plausible future models by combining historical source models and limited current data. By optimizing worst-case performance over this set, it balances current accuracy with robustness against degradation due to future drifts. Crucially, ADAPT requires only summary-level model estimators from historical periods, preserving data privacy and ensuring operational simplicity. Validated on longitudinal suicide risk prediction using electronic health records from Mass General Brigham (2005--2021) and Duke University Health Systems, ADAPT demonstrated superior stability across coding transitions and pandemic-induced shifts. By minimizing annual performance decay without labeling or retraining future data, ADAPT offers a scalable pathway for sustaining reliable AI in high-stakes healthcare environments.

stat.AP↗

arXiv2025

A Common Pipeline for Harmonizing Electronic Health Record Data for Translational Research

Despite the growing availability of Electronic Health Record (EHR) data, researchers often face substantial barriers in effectively using these data for translational research due to their complexity, heterogeneity, and lack of standardized tools and documentation. To address this critical gap, we introduce PEHRT, a common pipeline for harmonizing EHR data for translational research. PEHRT is a comprehensive, ready-to-use resource that includes open-source code, visualization tools, and detailed documentation to streamline the process of preparing EHR data for analysis. The pipeline provides tools to harmonize structured and unstructured EHR data to standardized ontologies to ensure consistency across diverse coding systems. In the presence of unmapped or heterogeneous local codes, PEHRT further leverages representation learning and pre-trained language models to generate robust embeddings that capture semantic relationships across sites to mitigate heterogeneity and enable integrative downstream analyses. PEHRT also supports cross-institutional co-training through shared representations, allowing participating sites to collaboratively refine embeddings and enhance generalizability without sharing individual-level data. The framework is data model-agnostic and can be seamlessly deployed across diverse healthcare systems to produce interoperable, research-ready datasets. By lowering the technical barriers to EHR-based research, PEHRT empowers investigators to transform raw clinical data into reproducible, analysis-ready resources for discovery and innovation.

stat.ML↗

arXiv2025

Retroreflective Optical ISAC for 6G: Technologies, Applications and Future Directions

Integrated sensing and communication (ISAC) has emerged as a key technological paradigm for sixth generation (6G) mobile networks, aiming to unify sensing and communication in a spectrally efficient and hardware lightweight manner. Radio frequency ISAC (RF-ISAC) is constrained by spectrum crowding, limited sensing resolution, and susceptibility to electromagnetic interference. In contrast, optical ISAC (O-ISAC) leverages the large bandwidth and short wavelength of optical carriers and is regarded as an important complement to RF-ISAC. However, conventional O-ISAC relies on natural optical reflections from target surfaces, which generate weak echoes that are highly dependent on surface materials, thereby limiting the achievable sensing range and sensing accuracy. This article introduces retroreflective optical ISAC (RO-ISAC), which alleviates these limitations by equipping targets with compact retroreflective modules. These modules return incident light approximately back to the source over a useful range of incidence angles, forming a well controlled double pass optical path with strong and stable echoes, and thereby further unlocking the application potential of O-ISAC. The conceptual architecture of RO-ISAC is presented together with the underlying retroreflection mechanism. Key enabling technologies for RO-ISAC systems are discussed, including channel modeling, waveform design, bidirectional transmission, and multi-target sensing and communication, with representative details and experimental validation. The suitability of RO-ISAC is analyzed in indoor, aerial, underwater, and satellite scenarios, and challenges and research directions related to mobility, cooperative networking, intelligent operation, and sustainable deployment are outlined, pointing toward robust and scalable RO-ISAC deployment in future 6G networks.

eess.SP↗

arXiv2025

Avoiding Quality Saturation in UGC Compression Using Denoised References

Video-sharing platforms must re-encode large volumes of noisy user-generated content (UGC) to meet streaming demands. However, conventional codecs, which aim to minimize the mean squared error (MSE) between the compressed and input videos, can cause quality saturation (QS) when applied to UGC, i.e., increasing the bitrate preserves input artifacts without improving visual quality. A direct approach to solve this problem is to detect QS by repeatedly evaluating a non-reference metric (NRM) on videos compressed with multiple codec parameters, which is inefficient. In this paper, we re-frame UGC compression and QS detection from the lens of noisy source coding theory: rather than using a NRM, we compute the MSE with respect to the denoised UGC, which serves as an alternative reference (D-MSE). Unlike MSE measured between the UGC input and the compressed UGC, D-MSE saturates at non-zero values as bitrates increase, a phenomenon we term distortion saturation (DS). Since D-MSE can be computed at the block level in the transform domain, we can efficiently detect D-MSE without coding and decoding with various parameters. We propose two methods for DS detection: distortion saturation detection (DSD), which relies on an input-dependent threshold derived from the D-MSE of the input UGC, and rate-distortion saturation detection (RDSD), which estimates the Lagrangian at the saturation point using a low-complexity compression method. Both methods work as a pre-processing step that can help standard-compliant codecs avoid QS in UGC compression. Experiments with AVC show that preventing encoding in the saturation region, i.e., avoiding encoding at QPs that result in QS according to our methods, achieves BD-rate savings of 8%-20% across multiple different NRMs, compared to a naïve baseline that encodes at the given input QP while ignoring QS.

eess.IV↗

arXiv2025

AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation

Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods employ augmentation operations with fixed or random magnitudes throughout the training process. While this fosters data diversity, it can also inevitably introduce uncontrolled variability in augmented data, which could potentially cause misalignment with the evolving training status of the target models. Both theoretical and empirical findings suggest that this misalignment increases the risks of both underfitting and overfitting. To address these limitations, we propose AdaAugment, an innovative and tuning-free adaptive augmentation method that leverages reinforcement learning to dynamically and adaptively adjust augmentation magnitudes for individual training samples based on real-time feedback from the target network. Specifically, AdaAugment features a dual-model architecture consisting of a policy network and a target network, which are jointly optimized to adapt augmentation magnitudes in accordance with the model's training progress effectively. The policy network optimizes the variability within the augmented data, while the target network utilizes the adaptively augmented samples for training. These two networks are jointly optimized and mutually reinforce each other. Extensive experiments across benchmark datasets and deep architectures demonstrate that AdaAugment consistently outperforms other state-of-the-art DA methods in effectiveness while maintaining remarkable efficiency. Code is available at https://github.com/Jackbrocp/AdaAugment.

cs.CV↗

arXiv2025

Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression

Service providers must encode a large volume of noisy videos to meet the demand for user-generated content (UGC) in online video-sharing platforms. However, low-quality UGC challenges conventional codecs based on rate-distortion optimization (RDO) with full-reference metrics (FRMs). While effective for pristine videos, FRMs drive codecs to preserve artifacts when the input is degraded, resulting in suboptimal compression. A more suitable approach used to assess UGC quality is based on non-reference metrics (NRMs). However, RDO with NRMs as a measure of distortion requires an iterative workflow of encoding, decoding, and metric evaluation, which is computationally impractical. This paper overcomes this limitation by linearizing the NRM around the uncompressed video. The resulting cost function enables block-wise bit allocation in the transform domain by estimating the alignment of the quantization error with the gradient of the NRM. To avoid large deviations from the input, we add sum of squared errors (SSE) regularization. We derive expressions for both the SSE regularization parameter and the Lagrangian, akin to the relationship used for SSE-RDO. Experiments with images and videos show bitrate savings of more than 30\% over SSE-RDO using the target NRM, with no decoder complexity overhead and minimal encoder complexity increase.

eess.IV↗

arXiv2025

Domain Adaptation Optimized for Robustness in Mixture Populations

While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced by factors like demographics. Additional challenges in electronic health record (EHR)-based studies include unobserved outcomes and the need to explain population mixtures using broader clinical characteristics than those in standard risk models. To address these challenges under shifts in both covariate distributions and outcome models, we propose a novel framework: Domain Adaptation Optimized for Robustness in Mixture populations (DORM). Leveraging partially labeled source data, DORM constructs an initial target outcome model under a joint source-mixture assumption. To enhance generalizability to future target populations that may deviate from the joint source-mixture approximation, DORM incorporates a group adversarial learning step to derive a final estimate, optimizing its worst-case performance within a convex uncertainty set built around the initial target model. In addition, this robust domain adaptation procedure is assisted by high-dimensional surrogates that enhance transferability in EHR studies. When a small set of gold-standard or noisy labels is available from the target population, a tuning strategy is implemented to refine the uncertainty set, mitigating conservativeness and further improving performance for the specific target population. Statistical convergence and predictive accuracy of our method are quantified through asymptotic studies. Simulation and real-world studies demonstrate the out-performance of our method over existing approaches.

stat.ME↗

arXiv2024

Federated One-Shot Ensemble Clustering

Cluster analysis across multiple institutions poses significant challenges due to data-sharing restrictions. To overcome these limitations, we introduce the Federated One-shot Ensemble Clustering (FONT) algorithm, a novel solution tailored for multi-site analyses under such constraints. FONT requires only a single round of communication between sites and ensures privacy by exchanging only fitted model parameters and class labels. The algorithm combines locally fitted clustering models into a data-adaptive ensemble, making it broadly applicable to various clustering techniques and robust to differences in cluster proportions across sites. Our theoretical analysis validates the effectiveness of the data-adaptive weights learned by FONT, and simulation studies demonstrate its superior performance compared to existing benchmark methods. We applied FONT to identify subgroups of patients with rheumatoid arthritis across two health systems, revealing improved consistency of patient clusters across sites, while locally fitted clusters proved less transferable. FONT is particularly well-suited for real-world applications with stringent communication and privacy constraints, offering a scalable and practical solution for multi-site clustering.

stat.ML↗

arXiv2024

Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography

Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified.The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.

eess.IV↗

arXiv2023

Distributionally Robust Transfer Learning

Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples. When dealing with a limited amount of target data and a diverse range of source models, our paper introduces a novel approach, Distributionally Robust Optimization for Transfer Learning (TransDRO), that breaks free from strict similarity constraints. TransDRO is designed to optimize the most adversarial loss within an uncertainty set, defined as a collection of target populations generated as a convex combination of source distributions that guarantee excellent prediction performances for the target data. TransDRO effectively bridges the realms of transfer learning and distributional robustness prediction models. We establish the identifiability of TransDRO and its interpretation as a weighted average of source models closest to the baseline model. We also show that TransDRO achieves a faster convergence rate than the model fitted with the target data. Our comprehensive numerical studies and analysis of multi-institutional electronic health records data using TransDRO further substantiate the robustness and accuracy of TransDRO, highlighting its potential as a powerful tool in transfer learning applications.

cs.LG↗

arXiv2023

GIMM: InfoMin-Max for Automated Graph Contrastive Learning

Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task. To solve this problem, we propose InfoMin-Max for automated Graph contrastive learning (GIMM), which prevents GCL from encoding redundant information and losing essential information. GIMM consists of two major modules: (1) automated graph view generator, which acquires the approximation of InfoMin's optimal views through adversarial training without requiring task-relevant information; (2) view comparison, which learns an excellent encoder by applying InfoMax to view representations. To the best of our knowledge, GIMM is the first method that combines the InfoMin and InfoMax principles in GCL. Besides, GIMM introduces randomness to augmentation, thus stabilizing the model against perturbations. Extensive experiments on unsupervised and semi-supervised learning for node and graph classification demonstrate the superiority of our GIMM over state-of-the-art GCL methods with automated and manual data augmentation.

cs.LG↗

arXiv2023

Rate-Distortion Optimization With Alternative References For UGC Video Compression

User generated content (UGC) refers to videos that are uploaded by users and shared over the Internet. UGC may have low quality due to noise and previous compression. When re-encoding UGC for streaming or downloading, a traditional video coding pipeline will perform rate-distortion (RD) optimization to choose coding parameters. However, in the UGC video coding case, since the input is not pristine, quality ``saturation'' (or even degradation) can be observed, i.e., increased bitrate only leads to improved representation of coding artifacts and noise present in the UGC input. In this paper, we study the saturation problem in UGC compression, where the goal is to identify and avoid during encoding, the coding parameters and rates that lead to quality saturation. We proposed a geometric criterion for saturation detection that works with rate-distortion optimization, and only requires a few frames from the UGC video. In addition, we show how to combine the proposed saturation detection method with existing video coding systems that implement rate-distortion optimization for efficient compression of UGC videos.

eess.IV↗

arXiv2022

The state of play of reproducibility in Statistics: an empirical analysis

Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as \textit{Nature} and \textit{Science} and international magazines such as \textit{The Economist}. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this paper, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals utilizing functional magnetic resonance imaging (fMRI) data during the 2010-2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics.

stat.AP↗

arXiv2022

Compression of user generated content using denoised references

Video shared over the internet is commonly referred to as user generated content (UGC). UGC video may have low quality due to various factors including previous compression. UGC video is uploaded by users, and then it is re-encoded to be made available at various levels of quality. In a traditional video coding pipeline the encoder parameters are optimized to minimize a rate-distortion criterion, but when the input signal has low quality, this results in sub-optimal coding parameters optimized to preserve undesirable artifacts. In this paper we formulate the UGC compression problem as that of compression of a noisy/corrupted source. The noisy source coding theorem reveals that an optimal UGC compression system is comprised of optimal denoising of the UGC signal, followed by compression of the denoised signal. Since optimal denoising is unattainable and users may be against modification of their content, we propose encoding the UGC signal, and using denoised references only to compute distortion, so the encoding process can be guided towards perceptually better solutions. We demonstrate the effectiveness of the proposed strategy for JPEG compression of UGC images and videos.

eess.IV↗