arXiv subjects

Junjie Luo

Publications and source records attributed to Junjie Luo.

At least 19 recordsLinked to original sources

arXiv2026

StabQ: Quantum Program Analysis via Weighted Stabilizer Representations

Quantum program analysis remains challenging due to the exponentially large state space of quantum programs and the difficulty of precisely characterizing their execution behavior. In particular, non-Clifford operations introduce additional complexity that limits the applicability of stabilizer-based techniques. Although stabilizer representations provide compact descriptions for Clifford circuits, their limited expressiveness prevents them from directly supporting general quantum program analysis. In this work, we propose StabQ, a symbolic execution framework for quantum program analysis based on stabilizer representations. StabQ extends stabilizer-based symbolic execution beyond Clifford-only programs by introducing a symbolic state representation that captures and propagates quantum state evolution while preserving execution semantics. Based on this representation, StabQ constructs a Tableau Chain that represents the evolution of intermediate symbolic states throughout program execution and enables reusable analysis of quantum program executions. Furthermore, StabQ incorporates tableau consolidation and global-phase recovery mechanisms to mitigate symbolic state growth during execution. Building upon the Tableau Chain, StabQ supports multiple quantum program analysis tasks, including quantum state reconstruction, entanglement analysis, and Clifford-property detection. We evaluate StabQ on three benchmark suites---Algorithms, MQT Bench, and QASMBench. The results demonstrate that StabQ constructs semantically consistent symbolic models, accurately preserves quantum state evolution, and effectively supports downstream analysis tasks across diverse quantum programs.

cs.SC↗

arXiv2026

FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation

As CGM-based AI tools approach clinical deployment, whether their accuracy is equitable across patient demographics remains insufficiently tested. To enable this evaluation, we constructed FairGlucose, a 300-patient CGM cohort balanced across 12 demographic strata (age x gender x type 1/type 2 diabetes), with 132,480 forecasting samples and 3,945 unique behavioral events (meals, exercise, medication) logged by 81 patients. Benchmarking 33 models across four families on 2-hour glucose forecasting, we find that population-level external validation can conceal substantial subgroup disparities. Aggregate out-of-distribution metrics appear stable (approximately 1.0), yet subgroup-level ratios range from 0.8 to 1.4, with T1D patients showing 6 mg/dL higher prediction error than T2D (p < 0.001). This disparity persists across all 33 models, suggesting a property of the prediction task rather than any single architecture. Further analysis shows that subgroup performance gaps align with the proportion of clinically hard cases, and that input-length sensitivity varies across demographics, motivating personalized configurations. Frontier LLMs underperform specialized neural models by 1-6 mg/dL; behavioral events contribute negligibly (approximately 0.1 mg/dL) even under oracle event access. These findings establish that population-level validation alone is insufficient for equity assessment of digital health AI, motivating subgroup-disaggregated reporting as a default standard.

cs.CY↗

arXiv2026

Beyond One-shot: AI Agents for Learning in Field Experiments

Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design. Significant barriers exist to extracting actionable knowledge from prior experimental data to inform new interventions. We study whether tool-augmented agentic AI can automatically learn from experimental data to generate new interventions in subsequent experiments. Through two-stage field experiments in healthcare prescription messaging (693,139 patient visits), we compare a Human + Chatbot method (Stage 1: behavioral experts with conversational AI co-designing 13 message variants, 444,691 patient visits) against a Tool-Augmented Agentic AI method (Stage 2: AI autonomously extracting principles from Stage 1 data to generate 17 new variants, 248,448 patient visits). The Agentic AI method, equipped with analytical tools, structured Data-Information-Knowledge-Wisdom (DIKW) reasoning agents, and transparent evidence chains, produces superior interventions: the best AI-generated message achieved a 69.8% CTR (+6.5 percentage points over baseline). Critically, our results suggest that the value comes from domain-specific experimental data, not from general reasoning ability: frontier LLMs operating without experimental data failed to predict which interventions would succeed. The field experiments also revealed that general-purpose behavioral theories used for intervention design do not extend uniformly to specific healthcare contexts, motivating an agentic AI approach to theory audits at field-experiment scale. Our research shows that tool-augmented AI can learn from experimental data and generate improved domain-relevant interventions, transforming behavioral experimentation from one-shot evaluation into a scalable system for cumulative design learning.

cs.AI↗

arXiv2026

Depth from Dual Differential Defocus and Stereo Consensus

We introduce D^3S Consensus, a physics-based, closed-form algorithm that unifies depth-from-defocus (DfD) and stereo to achieve highly accurate depth estimation throughout an extended working range beyond the depth-of-field (DoF) of cameras. Given a pair of dual-defocus stereo images, the method estimates an overdetermined set of depth using a novel DfD theory, Dual Differential Defocus (D^3), and (S)tereo in a coupled fashion. It then picks the most confident depth prediction from the set by enforcing consensus between these physically independent cues to reject unreliable estimates. Analysis shows that D^3S achieves a comparable working range under the same error tolerance with 10x smaller baseline than previous triangulation-based depth estimation systems. This enables compact passive binocular rangefinders with substantially smaller form factors than conventional stereo and DfD designs. We demonstrate the first D^3S prototype with only 4 mm baseline and 12 mm EFL. It generates up to 900 x 1800-pixel depth maps with 1-cm mean absolute error over 0.3-1.64 m from a snapshot acquisition. This has surpassed the reported accuracy of certain commercially available stereo cameras with much larger form factors.

eess.IV↗

arXiv2026

Compact single-shot ranging and near-far imaging using metasurfaces

We present a metasurface imaging system capable of simultaneously capturing two images at close range (1-2~cm) and an additional image at long range (about 40~cm) on a shared photosensor. The close-range image pair focuses at 1.4~cm and 2.0~cm, respectively, which forms a focal stack, enabling passive ranging with an accuracy of $\pm$1~mm from 12~mm to 20~mm through a computationally efficient depth-from-defocus algorithm for a simplified scenario. The entire system is compact, with a total track length of 15~mm, making it suitable for seamless integration into edge platforms for defense and other resource-constrained applications.

eess.IV↗

arXiv2026

QEMI: A Quantum Software Stacks Testing Framework via Equivalence Modulo Inputs

As quantum algorithms and hardware continue to evolve, ensuring the correctness of the quantum software stack (QSS) has become increasingly important. However, testing QSSes remains challenging due to the oracle problem, i.e., the lack of a reliable ground truth for expected program behavior. Existing metamorphic testing approaches often rely on equivalent circuit transformations, backend modifications, or parameter tuning to address this issue. In this work, inspired by Equivalence Modulo Inputs (EMI), we propose Quantum EMI (QEMI), a new testing approach for QSSes. Our key contributions include: (1) a random quantum program generator that produces code with dead code based on quantum control-flow structures, and (2) an adaptation of the EMI technique from classical compiler testing to generate variants by removing dead code. By comparing the behavior of these variants, we can detect potential bugs in QSS implementations. We applied QEMI to Qiskit, Q#, and Cirq, and successfully identified 11 crash bugs and 1 behavioral inconsistency. QEMI expands the limited set of testing techniques available for quantum software stacks by going beyond structural transformations and incorporating semantics-preserving ones into quantum program analysis.

cs.SE↗

arXiv2026

Group-Sensitive Offline Contextual Bandits

Offline contextual bandits allow one to learn policies from historical/offline data without requiring online interaction. However, offline policy optimization that maximizes overall expected rewards can unintentionally amplify the reward disparities across groups. As a result, some groups might benefit more than others from the learned policy, raising concerns about fairness, especially when the resources are limited. In this paper, we study a group-sensitive fairness constraint in offline contextual bandits, reducing group-wise reward disparities that may arise during policy learning. We tackle the following common-parity requirements: the reward disparity is constrained within some user-defined threshold or the reward disparity should be minimized during policy optimization. We propose a constrained offline policy optimization framework by introducing group-wise reward disparity constraints into an off-policy gradient-based optimization procedure. To improve the estimation of the group-wise reward disparity during training, we employ a doubly robust estimator and further provide a convergence guarantee for policy optimization. Empirical results in synthetic and real-world datasets demonstrate that our method effectively reduces reward disparities while maintaining competitive overall performance.

cs.LG↗

arXiv2025

Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries

Extracting depth information from photon-limited, defocused images is challenging because depth from defocus (DfD) relies on accurate estimation of defocus blur, which is fundamentally sensitive to image noise. We present a novel approach to robustly measure object depths from photon-limited images along the defocused boundaries. It is based on a new image patch representation, Blurry-Edges, that explicitly stores and visualizes a rich set of low-level patch information, including boundaries, color, and smoothness. We develop a deep neural network architecture that predicts the Blurry-Edges representation from a pair of differently defocused images, from which depth can be calculated using a closed-form DfD relation we derive. The experimental results on synthetic and real data show that our method achieves the highest depth estimation accuracy on photon-limited images compared to a broad range of state-of-the-art DfD methods.

cs.CV↗

arXiv2025

Achieving Coordination in Non-Cooperative Joint Replenishment Games

We analyze an infinite-horizon deterministic joint replenishment model from a non-cooperative game-theoretical approach. In this model, a group of retailers can choose to jointly place an order, which incurs a major setup cost independent of the group, and a minor setup cost for each retailer. Additionally, each retailer is associated with a holding cost. Our objective is to design cost allocation rules that minimize the long-run average system cost while accounting for the fact that each retailer independently selects its replenishment interval to minimize its own cost. We introduce a class of cost allocation rules that distribute the major setup cost among the associated retailers in proportion to their predefined weights. For these rules, we establish a monotonicity property of agent better responses, which enables us to prove the existence of a payoff dominant pure Nash equilibrium that can also be computed efficiently. We then analyze the efficiency of these equilibria by examining the price of stability (PoS), the ratio of the best Nash equilibrium's system cost to the social optimum, across different information settings. In particular, our analysis reveals that one rule, which leverages retailers' own holding cost rates, achieves a near-optimal PoS of 1.25, while another rule that does not require access to retailers' private information also yields a favorable PoS.

cs.GT↗

arXiv2025

Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs

Understanding how patients perceive their physicians is essential to improving trust, communication, and satisfaction. Patients increasingly consult large language models (LLMs) to summarize physician reviews and shape provider choices, yet the national landscape of patient-perceived physician traits remains poorly characterized. We present an LLM-based pipeline that extracts ten patient-perceived physician trait scores from review text: five Big-Five-style and five patient-oriented dimensions. From one million U.S. physicians, we analyze 4.1 million reviews of 226,999 physicians. We validate the pipeline through multi-model comparison and human expert benchmarking. LLM and human-rater trait scores from reviews are consistent. Trait scores correlate strongly with review rating scores yet retain substantial independent variance. Two national-scale patterns emerge: male physicians receive higher trait scores across all traits, with the largest gap in clinical competence; specialty differences are driven by encounter context, with surgical specialties leading interpersonal qualities and psychiatry lowest. Cluster analysis identifies four physician archetypes, from "Uniform High" (33.8%, high across traits) to "Uniform Low" (22.6%, low across traits). This map of LLM-derived physician traits exposes how LLMs read the U.S. clinical workforce. Pending clinical validation, it opens future research on fairness, bias, and how LLM-mediated provider search shapes patient choice.

cs.CL↗

arXiv2025

PAME-AI: Patient Messaging Creation and Optimization using Agentic AI

Messaging patients is a critical part of healthcare communication, helping to improve things like medication adherence and healthy behaviors. However, traditional mobile message design has significant limitations due to its inability to explore the high-dimensional design space. We develop PAME-AI, a novel approach for Patient Messaging Creation and Optimization using Agentic AI. Built on the Data-Information-Knowledge-Wisdom (DIKW) hierarchy, PAME-AI offers a structured framework to move from raw data to actionable insights for high-performance messaging design. PAME-AI is composed of a system of specialized computational agents that progressively transform raw experimental data into actionable message design strategies. We demonstrate our approach's effectiveness through a two-stage experiment, comprising of 444,691 patient encounters in Stage 1 and 74,908 in Stage 2. The best-performing generated message achieved 68.76% engagement compared to the 61.27% baseline, representing a 12.2% relative improvement in click-through rates. This agentic architecture enables parallel processing, hypothesis validation, and continuous learning, making it particularly suitable for large-scale healthcare communication optimization.

cs.AI↗

arXiv2025

A Large Sensor Foundation Model Pretrained on Continuous Glucose Monitor Data for Diabetes Management

Continuous glucose monitoring (CGM) combined with AI offers new opportunities for proactive diabetes management through real-time glucose forecasting. However, most existing models are task-specific and lack generalization across patient populations. Inspired by the autoregressive paradigm of large language models, we introduce CGM-LSM, a Transformer decoder-based Large Sensor Model (LSM) pretrained on 1.6 million CGM records from patients with different diabetes types, ages, and genders. We model patients as sequences of glucose time steps to learn latent knowledge embedded in CGM data and apply it to the prediction of glucose readings for a 2-hour horizon. Compared with prior methods, CGM-LSM significantly improves prediction accuracy and robustness: a 48.51% reduction in root mean square error in one-hour horizon forecasting and consistent zero-shot prediction performance across held-out patient groups. We analyze model performance variations across patient subgroups and prediction scenarios and outline key opportunities and challenges for advancing CGM foundation models.

q-bio.QM↗

arXiv2025

Focal Split: Untethered Snapshot Depth from Differential Defocus

We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480$\times$360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.

cs.CV↗

arXiv2025

Computing Efficient Envy-Free Partial Allocations of Indivisible Goods

Envy-freeness is one of the most prominent fairness concepts in the allocation of indivisible goods. Even though trivial envy-free allocations always exist, rich literature shows this is not true when one additionally requires some efficiency concept (e.g., completeness, Pareto-efficiency, or social welfare maximization). In fact, in such case even deciding the existence of an efficient envy-free allocation is notoriously computationally hard. In this paper, we explore the limits of efficient computability by relaxing standard efficiency concepts and analyzing how this impacts the computational complexity of the respective problems. Specifically, we allow partial allocations (where not all goods are allocated) and impose only very mild efficiency constraints, such as ensuring each agent receives a bundle with positive utility. Surprisingly, even such seemingly weak efficiency requirements lead to a diverse computational complexity landscape. We identify several polynomial-time solvable or fixed-parameter tractable cases for binary utilities, yet we also find NP-hardness in very restricted scenarios involving ternary utilities.

cs.GT↗

arXiv2024

Modeling the Time Evolution of Compact Binary Systems with Machine Learning

This work introduces advanced computational techniques for modeling the time evolution of compact binary systems using machine learning. The dynamics of compact binary systems, such as black holes and neutron stars, present significant nonlinear challenges due to the strong gravitational interactions and the requirement for precise numerical simulations. Traditional methods, like the post-Newtonian approximation, often require significant computational resources and face challenges in accuracy and efficiency. Here, we employed machine learning algorithms, including deep learning models like Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), to predict the future evolution of these systems based on extensive simulation data. Our results demonstrate that employing both LSTM and TCN even as black-box predictors for sequence prediction can also significantly improve the prediction accuracy without PINNs as PDE solvers with prior knowledge or inductive bias. By employing LSTM and TCN, we obtained $R^2$ values of 99.74\% and 99.19\% for the evolutionary orbits of compact binaries dataset, respectively. Our models demonstrate the ability to effectively capture the dynamics of the binaries, achieving high prediction performance with significantly reduced computational overhead by a factor of 40, compared to conventional numerical methods. This study paves the way for more effective and computationally scalable approaches to the understanding of gravitational phenomena and predictive modeling in gravitational-wave astronomy.

astro-ph.CO↗

arXiv2024

Dissipated Correction Map Method with Trapezoidal Rule for the Simulations of Gravitational Waves from Spinning Compact Binary

The correction map method means extended phase-space algorithm with correction map. In our research, we have developed a correction map method, specifically the dissipated correction map method with trapezoidal rule, for numerical simulations of gravitational waves from spinning compact binary systems. This new correction map method, denoted as $CM3$, has shown remarkable performance in various simulation results, such as phase space distance, dissipated energy error, and gravitational waveform, closely resembling the high-order precision implicit Gaussian algorithm. When compared to the previously used midpoint map which denoted as $C_2$, the $CM3$ consistently exhibits a closer alignment with the highly accurate Gaussian algorithm in waveform evolution and orbital trajectory analysis. Through detailed comparisons and analyses, it is evident that $CM3$ outperforms other algorithms, including $CM2$ and $C_2$ mentioned in this paper, in terms of accuracy and precision in simulating spinning compact binary systems. The incorporation of the trapezoidal rule and the optimization with a scale factor $γ$ have significantly enhanced the performance of $CM3$, making it a promising method for future numerical simulations in astrophysics. With the groundbreaking detection of gravitational waves by the LIGO/VIRGO collaboration, interest in this research domain has soared. Our work contributes valuable insights for the application of matched filtering techniques in the analysis of gravitational wave signals, enhancing the precision and reliability of these detection.

astro-ph.IM↗

arXiv2024

Depth from Coupled Optical Differentiation

We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism. It is based on our discovery that per-pixel object distance can be rigorously determined by a coupled pair of optical derivatives of a defocused image using a simple, closed-form relationship. Unlike previous depth-from-defocus (DfD) methods that leverage spatial derivatives of the image to estimate scene depths, the proposed mechanism's use of only optical derivatives makes it significantly more robust to noise. Furthermore, unlike many previous DfD algorithms with requirements on aperture code, this relationship is proved to be universal to a broad range of aperture codes. We build the first 3D sensor based on depth from coupled optical differentiation. Its optical assembly includes a deformable lens and a motorized iris, which enables dynamic adjustments to the optical power and aperture radius. The sensor captures two pairs of images: one pair with a differential change of optical power and the other with a differential change of aperture scale. From the four images, a depth and confidence map can be generated with only 36 floating point operations per output pixel (FLOPOP), more than ten times lower than the previous lowest passive-lighting depth sensing solution to our knowledge. Additionally, the depth map generated by the proposed sensor demonstrates more than twice the working range of previous DfD methods while using significantly lower computation.

cs.CV↗

arXiv2024

Dataflow-Based Optimization for Quantum Intermediate Representation Programs

This paper proposes QDFO, a dataflow-based optimization approach to Microsoft QIR. QDFO consists of two main functions: one is to preprocess the QIR code so that the LLVM optimizer can capture more optimization opportunities, and the other is to optimize the QIR code so that duplicate loading and constructing of qubits and qubit arrays can be avoided. We evaluated our work on the IBM Challenge Dataset, the results show that our method effectively reduces redundant operations in the QIR code. We also completed a preliminary implementation of QDFO and conducted a case study on the real-world code. Our observational study indicates that the LLVM optimizer can further optimize the QIR code preprocessed by our algorithm. Both the experiments and the case study demonstrate the effectiveness of our approach.

cs.PL↗