arXiv subjects

Jiaxin Yu

Publications and source records attributed to Jiaxin Yu.

At least 19 recordsLinked to original sources

arXiv2026

DREAM Technical Report

Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across modules, rely on rigid rules, and have limited awareness of real-time intent, leaving session-level shifts among browsing, comparison, and purchase insufficiently addressed. We present DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them. DREAM has two core components. First, a three-tier Intent Engine fuses on-device signals into structured L0/L1/L2 intent representations; its edge-cloud trigger chain reduces reporting volume to approximately 8.7%. Second, a Meta Engine uses a MetaModel for layered M1-to-M2-to-M3 reasoning: intent summarization, strategy planning informed by Strategy Memory, and parameter translation. It dispatches the resulting parameters through a unified outlet with safety guardrails. A Reward Dual Loop continuously optimizes both components by combining offline simulation for strategy-space exploration with online feedback for outcome calibration, forming a cycle of generation, execution, evaluation, and experience accumulation. Large-scale A/B tests on Taobao's homepage feed show that re-ranking control alone improves IPV by 2.06%, Core IPV by 2.39%, and GMV by 0.88%. Extending control to fine ranking raises these gains to 2.71%, 3.06%, and 1.31%, respectively, while consistently improving PV by more than 1%. These gains require neither replacement of pipeline models nor compromise of serving stability, supporting agentic meta-control as a viable paradigm for industrial recommendation.

cs.IR↗

arXiv2026

Importance sampling and active subspace in quasi-Monte Carlo

The quasi-Monte Carlo method is widely used in computational finance, whose efficiency strongly depends on the smoothness and effective dimension of the integrand. In this work, we investigate the combination of importance sampling and the active subspace method under the quasi-Monte Carlo framework and propose a three-step approach, referred to as the IS-AS-preintegration method, which sequentially applies importance sampling, active subspace, and preintegration. The proposed method is applied to the option pricing and sensitivity analysis problems in finance, and its performance is evaluated through extensive numerical experiments. The results demonstrate that the proposed method is highly competitive compared with existing popular methods. In particular, for out-of-the-money and deep out-of-the-money options, the proposed approach overcomes the limitations of the preintegration via active subspace method and achieves superior variance reduction, while maintaining comparable performance for other moneyness cases.

math.NA↗

arXiv2026

Self-referenced, drift-tolerant dipole-resolved population inversion using degeneracy-lifted dual quasinormal modes

Photoluminescence intensity is widely used to infer exciton populations, yet the detected signal inherently convolves occupancy with radiative-rate modification and collection efficiency, making quantitative inversion vulnerable to pump and system drifts. Here we realize a dual-channel self-referenced scheme enabled by two nearly degenerate quasinormal modes in a hybrid microcavity. Their shared optical path provides common-mode observables (i.e., overall spectral and intensity drift) that track global thermo-optic and pump fluctuations, while their differential-mode observables (i.e., spectral splitting and mode-contrasted emission) remain highly sensitive to local gap dielectric perturbations and dipole-dependent radiative weights. Using temperature as a control parameter in monolayer WSe$ _2 $, we exploit this common/differential-mode framework to robustly invert the relative populations of excitons with out-of-plane ($ \perp $) and in-plane ($ \parallel $) dipole transitions without external absolute calibration. At the temperature of $\sim$50 K, we obtain $ N_\perp/N_\parallel \approx 200 $, coincident with the expected accumulation in the out-of-plane-emitting dark manifold. This internally referenced approach provides a practical route to drift-tolerant, dipole-resolved population metrology in nanogap photonic systems.

physics.optics↗

arXiv2026

Population Metrology of a Hidden Exciton Reservoir: Quasi-Thermalization versus Localization

Long-lived dark states can dominate the lowest-energy manifold of optically driven quantum materials, yet their occupation remains difficult to quantify, leaving it unclear whether it reflects thermal redistribution or kinetic trapping. We combine microsphere-enabled far-field access with quantitative optical-response calibration to retrieve dark-to-bright population ratios in monolayer WSe2. Temperature-dependent measurements and controlled defect enhancement separate mobile and localized contributions. Near room temperature, the mobile dark-to-bright ratio reaches approximately 65% of its Boltzmann limit, indicating substantial but incomplete quasi-thermalization, whereas the low-temperature excess is dominated by defect-assisted localization. Dark-state dominance alone therefore does not establish equilibration, a distinction essential for interpreting transport and collective phases in optically hidden quasiparticle reservoirs.

physics.optics↗

arXiv2026

Spatial-VLN: Zero-Shot Vision-and-Language Navigation With Explicit Spatial Perception and Exploration

Zero-shot Vision-and-Language Navigation (VLN) agents leveraging Large Language Models (LLMs) excel in generalization but suffer from insufficient spatial perception. Focusing on complex continuous environments, we categorize key perceptual bottlenecks into three spatial challenges: door interaction,multi-room navigation, and ambiguous instruction execution, where existing methods consistently suffer high failure rates. We present Spatial-VLN, a perception-guided exploration framework designed to overcome these challenges. The framework consists of two main modules. The Spatial Perception Enhancement (SPE) module integrates panoramic filtering with specialized door and region experts to produce spatially coherent, cross-view consistent perceptual representations. Building on this foundation, our Explored Multi-expert Reasoning (EMR) module uses parallel LLM experts to address waypoint-level semantics and region-level spatial transitions. When discrepancies arise between expert predictions, a query-and-explore mechanism is activated, prompting the agent to actively probe critical areas and resolve perceptual ambiguities. Experiments on VLN-CE demonstrate that Spatial VLN achieves state-of-the-art performance using only low-cost LLMs. Furthermore, to validate real-world applicability, we introduce a value-based waypoint sampling strategy that effectively bridges the Sim2Real gap. Extensive real-world evaluations confirm that our framework delivers superior generalization and robustness in complex environments. Our codes and videos are available at https://yueluhhxx.github.io/Spatial-VLN-web/.

cs.CV↗

arXiv2025

Direct observation of room-temperature exciton condensation

Exciton condensation--an interaction-driven, macroscopically coherent paired-fermion state--offers the prospect for dissipationless energy transport in solids, akin to that in superconductivity. Although their light effective mass and strong Coulomb binding favour high transition temperatures, convincing demonstrations of pure-exciton condensation have hitherto been limited to cryogenic conditions. Here, we report the direct observation of quasi-equilibrium condensation of dark excitons in monolayer tungsten diselenide at 300 K and ambient pressure. We achieve this by creating nanoscale spacing-graded Stark traps to confine free excitons, setting the finite-size scale, non-resonant off-axis optical injection to control the local density-temperature trajectory, and employing surface plasmon polariton-enhanced microsphere-assisted microscopy to boost dark-exciton emission and directly image first-order spatial coherence with sub-diffraction resolution. We observe a sharp degeneracy threshold and a clear phase transition, evidenced by extended first-order spatial coherence with algebraic decay and a critical exponent consistent with the universal Berezinskii-Kosterlitz-Thouless criterion. Identical condensation signatures are observed in over 30 independent samples. Our work establishes a room-temperature excitonic platform for exploring strongly correlated many-body physics and advancing near-dissipationless, coherent quantum technologies.

physics.optics↗

arXiv2025

Robust Estimation of Moderated Causal Excursion Odds Ratio in Micro-Randomized Trials

Micro-randomized trials (MRTs) have become increasingly popular for developing and evaluating mobile health interventions that promote healthy behaviors and manage chronic conditions. The recently proposed causal excursion effects have become the standard measure for interventions' marginal and moderated effect in MRTs. Existing methods for MRTs with binary outcomes focus on causal excursion effects on the relative risk scale. However, a causal excursion effect on the odds ratio scale is attractive for its interpretability and valid predicted probabilities, making it a valuable supplement to causal excursion relative risk. In this paper, we propose two novel estimators for the moderated causal excursion odds ratio for MRTs with longitudinal binary outcomes. When the prespecified moderator fully captures the way interventions are sequentially randomized, we propose a doubly robust estimator that remains consistent if either of two nuisance models is correctly specified. For more general settings in which treatment randomization depends on variables beyond the chosen moderator, we propose a general estimator that incorporates an association nuisance model. We further establish the general estimator's robustness to the misspecification of the association nuisance model under no causal effect, and extend the general estimator to accommodate any link functions. We establish the consistency and asymptotic normality of both estimators and demonstrate their performance through simulation studies. We apply the methods to Drink Less, a 30-day MRT for developing mobile health interventions to help reduce alcohol consumption, where the proximal outcome is whether the user opens the app in the hour following the notification.

stat.ME↗

arXiv2025

Partial-Wetting Phenomena in Active Matter

Abundant interfacial phenomena in nature, such as water droplets on lotus leaves and water transport in plant vessels, originate from partial-wetting phenomena, which can be well described by Young's equation. It remains an intriguing question whether similar behaviors exist in active matter. In this letter, we present a clear demonstration of the partial-wetting phenomenon in a ternary laning system, which is a typical active system. A phase diagram is constructed in which the relative drift velocities of different components govern the transitions among drying, partial wetting, and complete wetting states. The mechanical balance on the contact lines of the partial-wetting phase described by Young's equation is verified. A theoretical picture is proposed to explain the analogy of partial wetting in the laning system to that in the equilibrium system.

cond-mat.soft↗

arXiv2025

Simulated Bifurcation with High-dimensional Expansion for Traffic Signal Optimization on Real-world Networks

With accelerating urbanization and worsening traffic congestion, optimizing traffic signal systems to improve road throughput and alleviate congestion has become a critical issue. This study proposes a short-term traffic prediction model based on real-world road topologies and a typical four-way, eight-phase traffic signal control scheme. The model accounts for traffic flow disparities across directions and signal phase change frequencies, integrating these factors into an optimization objective for global traffic optimization. The structure of this objective function is similar to spin-glass systems in statistical physics. A Simulated Bifurcation optimization algorithm is introduced, with traditional simulated annealing as a benchmark. The results show that Simulated Bifurcation outperforms simulated annealing in both efficiency and effectiveness. Using real traffic flow and road network data from Beijing, we initialized the model and conducted numerical optimization experiments. The results indicate that Simulated Bifurcation significantly outperforms simulated annealing in computational efficiency, effectively solving combinatorial optimization problems with multiple spin interactions, and reducing the time complexity to $O(N^{1.35})$. This solution addresses the NP-hard problem of global traffic signal optimization. Importantly, the signal phase patterns generated by Simulated Bifurcation align with the operational requirements of real traffic signal systems, showcasing its potential in optimizing signal control for large, complex urban traffic networks. This work provides solid theoretical and practical foundations for future urban traffic management and intelligent transportation systems.

physics.soc-ph↗

arXiv2024

Doubly Robust Estimation of Causal Excursion Effects in Micro-Randomized Trials with Missing Longitudinal Outcomes

Micro-randomized trials (MRTs) are increasingly utilized for optimizing mobile health interventions, with the causal excursion effect (CEE) as a central quantity for evaluating interventions under policies that deviate from the experimental policy. However, MRT often contains missing data due to reasons such as missed self-reports or participants not wearing sensors, which can bias CEE estimation. In this paper, we propose a two-stage, doubly robust estimator for CEE in MRTs when longitudinal outcomes are missing at random, accommodating continuous, binary, and count outcomes. Our two-stage approach allows for both parametric and nonparametric modeling options for two nuisance parameters: the missingness model and the outcome regression. We demonstrate that our estimator is doubly robust, achieving consistency and asymptotic normality if either the missingness or the outcome regression model is correctly specified. Simulation studies further validate the estimator's desirable finite-sample performance. We apply the method to HeartSteps, an MRT for developing mobile health interventions that promote physical activity.

stat.ME↗

arXiv2024

Modeling Time-Varying Effects of Mobile Health Interventions Using Longitudinal Functional Data from HeartSteps Micro-Randomized Trial

To optimize mobile health interventions and advance domain knowledge on intervention design, it is critical to understand how the intervention effect varies over time and with contextual information. This study aims to assess how a push notification suggesting physical activity influences individuals' step counts using data from the HeartSteps micro-randomized trial (MRT). The statistical challenges include the time-varying treatments and longitudinal functional step count measurements. We propose the first semiparametric causal excursion effect model with varying coefficients to model the time-varying effects within a decision point and across decision points in an MRT. The proposed model incorporates double time indices to accommodate the longitudinal functional outcome, enabling the assessment of time-varying effect moderation by contextual variables. We propose a two-stage causal effect estimator that is robust against a misspecified high-dimensional outcome regression nuisance model. We establish asymptotic theory and conduct simulation studies to validate the proposed estimator. Our analysis provides new insights into individuals' change in response profiles (such as how soon a response occurs) due to the activity suggestions, how such changes differ by the type of suggestions received, and how such changes depend on other contextual information such as being recently sedentary and the day being a weekday.

stat.AP↗

arXiv2024

MetaBGM: Dynamic Soundtrack Transformation For Continuous Multi-Scene Experiences With Ambient Awareness And Personalization

This paper introduces MetaBGM, a groundbreaking framework for generating background music that adapts to dynamic scenes and real-time user interactions. We define multi-scene as variations in environmental contexts, such as transitions in game settings or movie scenes. To tackle the challenge of converting backend data into music description texts for audio generation models, MetaBGM employs a novel two-stage generation approach that transforms continuous scene and user state data into these texts, which are then fed into an audio generation model for real-time soundtrack creation. Experimental results demonstrate that MetaBGM effectively generates contextually relevant and dynamic background music for interactive applications.

cs.SD↗

arXiv2024

An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors

Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security analysis tools struggle with poor generalization, high false positive rates, and coarse detection granularity. Large Language Models (LLMs) have been considered promising candidates for addressing those challenges. In this study, we conducted an empirical study to explore the potential of LLMs in detecting security defects during code review. Specifically, we evaluated the performance of seven LLMs under five different prompts and compared them with state-of-the-art static analysis tools. We also performed linguistic and regression analyses for the two top-performing LLMs to identify quality problems in their responses and factors influencing their performance. Our findings show that: (1) In security code review, LLMs significantly outperform state-of-the-art static analysis tools, and the reasoning-optimized LLM performs better than general-purpose LLMs. (2) DeepSeek-R1 achieves the highest performance, followed by GPT-4 provided in the ChatGPT platform. The optimal prompt for DeepSeek-R1 incorporates both the commit message and chain-of-thought (CoT) guidance, while for GPT-4 via ChatGPT, the prompt with a Common Weakness Enumeration (CWE) list works best. (3) GPT-4 via ChatGPT frequently produces vague expressions and exhibits difficulties in accurately following instructions in the prompts, while DeepSeek-R1 more commonly generates inaccurate code details in its outputs. (4) LLMs are more adept at identifying security defects in code files that have fewer tokens and security-relevant annotations. (5) Higher code complexity correlates with enhanced detection capabilities of DeepSeek-R1 for specific security defect types.

cs.SE↗

arXiv2023

Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Modern code generation tools utilizing AI models like Large Language Models (LLMs) have gained increased popularity due to their ability to produce functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Thus, evaluating the quality of generated code, especially its security, is crucial. While prior research explored various aspects of code generation, the focus on security has been limited, mostly examining code produced in controlled environments rather than open source development scenarios. To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot and two other AI code generation tools (i.e., CodeWhisperer and Codeium) from GitHub projects. Our analysis identified 733 snippets, revealing a high likelihood of security weaknesses, with 29.5% of Python and 24.2% of JavaScript snippets affected. These issues span 43 Common Weakness Enumeration (CWE) categories, including significant ones like CWE-330: Use of Insufficiently Random Values, CWE-94: Improper Control of Generation of Code, and CWE-79: Cross-site Scripting. Notably, eight of those CWEs are among the 2023 CWE Top-25, highlighting their severity. We further examined using Copilot Chat to fix security issues in Copilot-generated code by providing Copilot Chat with warning messages from the static analysis tools, and up to 55.5% of the security issues can be fixed. We finally provide the suggestions for mitigating security issues in generated code.

cs.SE↗

arXiv2023

MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings

Although the majority of recent autonomous driving systems concentrate on developing perception methods based on ego-vehicle sensors, there is an overlooked alternative approach that involves leveraging intelligent roadside cameras to help extend the ego-vehicle perception ability beyond the visual range. We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground. However, the roadside camera is installed on a pole with a pitched angle, which makes the existing methods not optimal for roadside scenes. In this paper, we introduce a novel framework for Roadside Monocular 3D object detection with ground-aware embeddings, named MonoGAE. Specifically, the ground plane is a stable and strong prior knowledge due to the fixed installation of cameras in roadside scenarios. In order to reduce the domain gap between the ground geometry information and high-dimensional image features, we employ a supervised training paradigm with a ground plane to predict high-dimensional ground-aware embeddings. These embeddings are subsequently integrated with image features through cross-attention mechanisms. Furthermore, to improve the detector's robustness to the divergences in cameras' installation poses, we replace the ground plane depth map with a novel pixel-level refined ground plane equation map. Our approach demonstrates a substantial performance advantage over all previous monocular 3D object detectors on widely recognized 3D detection benchmarks for roadside cameras. The code and pre-trained models will be released soon.

cs.CV↗

arXiv2023

Latent Dynamic Networked System Identification with High-Dimensional Networked Data

Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems. These systems produce high-dimensional data that reflect the complex interactions among the network nodes with rich sensor measurements. In this paper, we propose a novel algorithm for latent dynamic networked system identification that leverages the network structure and performs dimension reduction for each node via dynamic latent variables (DLVs). The algorithm assumes that the DLVs of each node have an auto-regressive model with exogenous input and interactions from other nodes. The DLVs of each node are extracted to capture the most predictable latent variables in the high dimensional data, while the residual factors are not predictable. The advantage of the proposed framework is demonstrated on an industrial process network for system identification and dynamic data analytics.

eess.SY↗

arXiv2023

Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives.

math.OC↗

arXiv2023

A proposal for detecting the spin of a single electron in superfluid helium

The electron bubble in superfluid helium has two degrees of freedom that may offer exceptionally low dissipation: the electron's spin and the bubble's motion. If these degrees of freedom can be read out and controlled with sufficient sensitivity, they would provide a novel platform for realizing a range of quantum technologies and for exploring open questions in the physics of superfluid helium. Here we propose a practical scheme for accomplishing this by trapping an electron bubble inside a superfluid-filled opto-acoustic cavity.

quant-ph↗