arXiv subjects

Xinran Li

Publications and source records attributed to Xinran Li.

At least 55 records · Page 3Linked to original sources

arXiv2025

SymBridge: A Human-in-the-Loop Cyber-Physical Interactive System for Adaptive Human-Robot Symbiosis

The development of intelligent robots seeks to seamlessly integrate them into the human world, providing assistance and companionship in daily life and work, with the ultimate goal of achieving human-robot symbiosis. This requires robots with intelligent interaction abilities to work naturally and effectively with humans. However, current robotic simulators fail to support real human participation, limiting their ability to provide authentic interaction experiences and gather valuable human feedback essential for enhancing robotic capabilities. In this paper, we introduce SymBridge, the first human-in-the-loop cyber-physical interactive system designed to enable the safe and efficient development, evaluation, and optimization of human-robot interaction methods. Specifically, we employ augmented reality technology to enable real humans to interact with virtual robots in physical environments, creating an authentic interactive experience. Building on this, we propose a novel robotic interaction model that generates responsive, precise robot actions in real time through continuous human behavior observation. The model incorporates multi-resolution human motion features and environmental affordances, ensuring contextually adaptive robotic responses. Additionally, SymBridge enables continuous robot learning by collecting human feedback and dynamically adapting the robotic interaction model. By leveraging a carefully designed system architecture and modules, SymBridge builds a bridge between humans and robots, as well as between cyber and physical spaces, providing a natural and realistic online interaction experience while facilitating the continuous evolution of robotic intelligence. Extensive experiments, user studies, and real robot testing demonstrate the promising performance of the system and highlight its potential to significantly advance research on human-robot symbiosis.

cs.RO↗

arXiv2025

Asymptotic Theory of the Best-Choice Rerandomization using the Mahalanobis Distance

Rerandomization, a design that utilizes pretreatment covariates and improves their balance between different treatment groups, has received attention recently in both theory and practice. From a survey by Bruhn and McKenzie (2009), there are at least two types of rerandomization that are used in practice: the first rerandomizes the treatment assignment until covariate imbalance is below a prespecified threshold; the second randomizes the treatment assignment multiple times and chooses the one with the best covariate balance. In this paper we will consider the second type of rerandomization, namely the best-choice rerandomization, whose theory and inference are still lacking in the literature. In particular, we will focus on the best-choice rerandomization that uses the Mahalanobis distance to measure covariate imbalance, which is one of the most commonly used imbalance measure for multivariate covariates and is invariant to affine transformations of covariates. We will study the large-sample repeatedly sampling properties of the best-choice rerandomization, allowing both the number of covariates and the number of tried complete randomizations to increase with the sample size. We show that the asymptotic distribution of the difference-in-means estimator is more concentrated around the true average treatment effect under rerandomization than under the complete randomization, and propose large-sample accurate confidence intervals for rerandomization that are shorter than that for the completely randomized experiment. We further demonstrate that, with moderate number of covariates and with the number of tried randomizations increasing polynomially with the sample size, the best-choice rerandomization can achieve the ideally optimal precision that one can expect even with perfectly balanced covariates. The developed theory and methods are also illustrated using real field experiments.

stat.ME↗

arXiv2025

Sensitivity Analysis for Observational Studies with Flexible Matched Designs

Observational studies provide invaluable opportunities to draw causal inference, but they may suffer from biases due to pretreatment difference between treated and control units. Matching is a popular approach to reduce observed covariate imbalance. To tackle unmeasured confounding, a sensitivity analysis is often conducted to investigate how robust a causal conclusion is to the strength of unmeasured confounding. For matched observational studies, Rosenbaum proposed a sensitivity analysis framework that uses the randomization of treatment assignments as the ``reasoned basis'' and imposes no model assumptions on the potential outcomes as well as their dependence on the observed and unobserved confounding factors. However, this otherwise appealing framework requires exact matching to guarantee its validity, which is hard to achieve in practice. In this paper we provide an alternative inferential framework that shares the same procedure as Rosenbaum's approach but relies on a different justification. Our framework allows flexible matching algorithms and utilizes alternative source of randomness, in particular random permutations of potential outcomes instead of treatment assignments, to guarantee statistical validity.

stat.ME↗

arXiv2025

Robust Sensitivity Analysis via Augmented Percentile Bootstrap under Simultaneous Violations of Unconfoundedness and Overlap

The identification of causal effects in observational studies typically relies on two standard assumptions: unconfoundedness and overlap. However, both assumptions are often questionable in practice: unconfoundedness is inherently untestable, and overlap may fail in the presence of extreme unmeasured confounding. While various approaches have been developed to address unmeasured confounding and extreme propensity scores separately, few methods accommodate simultaneous violations of both assumptions. In this paper, we propose a sensitivity analysis framework that relaxes both unconfoundedness and overlap, building upon the marginal sensitivity model. Specifically, we allow the bound on unmeasured confounding to hold for only a subset of the population, thereby accommodating heterogeneity in confounding and allowing treatment probabilities to be zero or one. Moreover, unlike prior work, our approach does not require bounded outcomes and focuses on overlap-weighted average treatment effects, which are both practically meaningful and robust to non-overlap. We develop computationally efficient methods to obtain worst-case bounds via linear programming, and introduce a novel augmented percentile bootstrap procedure for statistical inference. This bootstrap method handles parameters defined through over-identified estimating equations involving unobserved variables and may be of independent interest. Our work provides a unified and flexible framework for sensitivity analysis under violations of both unconfoundedness and overlap.

stat.ME↗

arXiv2025

Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) is a practical and challenging task. This paper proposes a novel multimodal approach, the Long-Short Distance Graph Neural Network (LSDGNN). Based on the Directed Acyclic Graph (DAG), it constructs a long-distance graph neural network and a short-distance graph neural network to obtain multimodal features of distant and nearby utterances, respectively. To ensure that long- and short-distance features are as distinct as possible in representation while enabling mutual influence between the two modules, we employ a Differential Regularizer and incorporate a BiAffine Module to facilitate feature interaction. In addition, we propose an Improved Curriculum Learning (ICL) to address the challenge of data imbalance. By computing the similarity between different emotions to emphasize the shifts in similar emotions, we design a "weighted emotional shift" metric and develop a difficulty measurer, enabling a training process that prioritizes learning easy samples before harder ones. Experimental results on the IEMOCAP and MELD datasets demonstrate that our model outperforms existing benchmarks.

cs.LG↗

arXiv2025

TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving

Multi-task learning (MTL) can advance assistive driving by exploring inter-task correlations through shared representations. However, existing methods face two critical limitations: single-modality constraints limiting comprehensive scene understanding and inefficient architectures impeding real-time deployment. This paper proposes TEM^3-Learning (Time-Efficient Multimodal Multi-task Learning), a novel framework that jointly optimizes driver emotion recognition, driver behavior recognition, traffic context recognition, and vehicle behavior recognition through a two-stage architecture. The first component, the mamba-based multi-view temporal-spatial feature extraction subnetwork (MTS-Mamba), introduces a forward-backward temporal scanning mechanism and global-local spatial attention to efficiently extract low-cost temporal-spatial features from multi-view sequential images. The second component, the MTL-based gated multimodal feature integrator (MGMI), employs task-specific multi-gating modules to adaptively highlight the most relevant modality features for each task, effectively alleviating the negative transfer problem in MTL. Evaluation on the AIDE dataset, our proposed model achieves state-of-the-art accuracy across all four tasks, maintaining a lightweight architecture with fewer than 6 million parameters and delivering an impressive 142.32 FPS inference speed. Rigorous ablation studies further validate the effectiveness of the proposed framework and the independent contributions of each module. The code is available on https://github.com/Wenzhuo-Liu/TEM3-Learning.

cs.CV↗

arXiv2025

New Constructions of Full Flag Codes Based on Partial Spreads

Flag codes are a class of multishot network codes comprising sequences of nested subspaces (flags) within the vector space $\mathbb{F}_q^n$, where $q$ is a prime power. In this paper, we propose a family of constructions for full flag codes based on partial spreads. The distances of this family include maximum distance (optimum distance flag codes), second-maximum distance (quasi-optimum distance flag codes), as well as other feasible values. The structure of these flag codes resembles that of a \textquotedblleft sandwich", consisting of one layer of companion matrix and two layers of partial spreads. Furthermore, we present an efficient decoding algorithm for these codes.

cs.IT↗

arXiv2025

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments

Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and evolve both before and during test time, equipping them with environment-relevant knowledge for better planning and enhanced communication for improved cooperation. Inspired by centralized training with decentralized execution in multi-agent reinforcement learning, we propose a \textit{Learn as Individuals, Evolve as a Team (LIET)} paradigm for multi-agent LLMs adaptation. At the individual level, LLM agents learn a local utility function from exploratory datasets to better comprehend the embodied environment, which is then queried during test time to support informed decision-making. At the team level, LLM agents collaboratively and iteratively maintain and update a shared cooperation knowledge list based on new experiences, using it to guide more effective communication. By combining individual learning with team evolution, LIET enables comprehensive and flexible adaptation for LLM agents. Our experiments on Communicative Watch-And-Help and ThreeD-World Multi-Agent Transport benchmarks demonstrate that LIET, instantiated with both LLaMA and GPT-4o, outperforms existing baselines and exhibits strong cooperative planning abilities.

cs.MA↗

arXiv2025

CheX-DS: Improving Chest X-ray Image Classification with Ensemble Learning Based on DenseNet and Swin Transformer

The automatic diagnosis of chest diseases is a popular and challenging task. Most current methods are based on convolutional neural networks (CNNs), which focus on local features while neglecting global features. Recently, self-attention mechanisms have been introduced into the field of computer vision, demonstrating superior performance. Therefore, this paper proposes an effective model, CheX-DS, for classifying long-tail multi-label data in the medical field of chest X-rays. The model is based on the excellent CNN model DenseNet for medical imaging and the newly popular Swin Transformer model, utilizing ensemble deep learning techniques to combine the two models and leverage the advantages of both CNNs and Transformers. The loss function of CheX-DS combines weighted binary cross-entropy loss with asymmetric loss, effectively addressing the issue of data imbalance. The NIH ChestX-ray14 dataset is selected to evaluate the model's effectiveness. The model outperforms previous studies with an excellent average AUC score of 83.76\%, demonstrating its superior performance.

cs.CV↗

arXiv2025

Classifying and characterizing the evolution of minimum orbit intersection distance for near-Earth asteroids

In this paper, the minimum orbit intersection distances (MOIDs) of near-Earth asteroids (NEAs) over the next 200 years were computed and analyzed in detail. It was shown that the MOID of a NEA relative to the Earth-Moon barycenter (EMB) is usually a superior metric for predicting a potential impact than that relative to the Earth. Subsequently, a novel MOID Evolution Index (MEI) spanning from 0.0 to 9.9 was proposed and the orbits of NEAs are classified into 100 distinct categories by considering the variations of the MOID over time, which is useful for quickly screening and prioritizing hazardous asteroids for future research. Furthermore, it was demonstrated that a linear fitting to the MOID evolution provides a simple yet valid approach for most of the NEAs, which is useful for quickly estimating the MOID value without the need to perform an orbit propagation. As a result, a scheme with several parameters was proposed to characterize the MOID variations as well as the relative position information of the critical points along the orbits associated with the minimum distances. A database incorporating these parameters and the MEI values was therefore established for the cataloged NEAs, enabling the derivation of statistically constrained upper bounds for secular MOID drift rate as function of the semi-major axes. Finally, some special orbital configurations and dynamical mechanisms that may lead to a large deviation from the linear fit or multiple orbit crossings were also investigated, indicating the intricate nature in the patterns of MOID evolution for some NEAs.

astro-ph.EP↗

arXiv2025

Low Energy Backgrounds and Excess Noise in a Two-Channel Low-Threshold Calorimeter

We describe observations of low energy excess (LEE) events, background events observed in all light dark matter direct detection calorimeters, and noise in a Transition Edge Sensor based two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We measure two distinct LEE populations: ``shared'' multichannel events with a pulse shape consistent with substrate athermal phonon events, and sub-eV events that couple nearly exclusively to a single channel with a significantly faster pulse shape. These ``singles'' are consistent with events occurring within the aluminum athermal phonon collection fins. Similarly, our measured detector noise is higher than the theoretical expectation. Measured noise can be split into an uncorrelated component, consistent with shot noise from small energy depositions within the athermal phonon sensor itself, and a correlated component, consistent with shot noise from energy depositions within the silicon substrate's phonon system.

physics.ins-det↗

arXiv2025

TSConnect: An Enhanced MOOC Platform for Bridging Communication Gaps Between Instructors and Students in Light of the Curse of Knowledge

Knowledge dissemination in educational settings is profoundly influenced by the curse of knowledge, a cognitive bias that causes experts to underestimate the challenges faced by learners due to their own in-depth understanding of the subject. This bias can hinder effective knowledge transfer and pedagogical effectiveness, and may be exacerbated by inadequate instructor-student communication. To encourage more effective feedback and promote empathy, we introduce TSConnect, a bias-aware, adaptable interactive MOOC (Massive Open Online Course) learning system, informed by a need-finding survey involving 129 students and 6 instructors. TSConnect integrates instructors, students, and Artificial Intelligence (AI) into a cohesive platform, facilitating diverse and targeted communication channels while addressing previously overlooked information needs. A notable feature is its dynamic knowledge graph, which enhances learning support and fosters a more interconnected educational experience. We conducted a between-subjects user study with 30 students comparing TSConnect to a baseline system. Results indicate that TSConnect significantly encourages students to provide more feedback to instructors. Additionally, interviews with 4 instructors reveal insights into how they interpret and respond to this feedback, potentially leading to improvements in teaching strategies and the development of broader pedagogical skills.

cs.HC↗

arXiv2025

Low Tc Hafnium Kinetic Inductance Device with High Internal Quality Factor

Kinetic inductance devices (KIDs) are superconducting resonators with high kinetic inductance sensitive to external energy perturbations. KIDs made with superconductors having $T_c$ far below one Kelvin are of particular interest for sensing minuscule signals, such as light dark matter detection and millimeter wave telescopes for astronomy and cosmology. In this work, we report the promising performance of KIDs fabricated with Hafnium from heated sputter depositions. The KIDs have $T_c$ lower than 249$\pm$2 mK, the internal quality factor ($Q_i$) of the resonators exceeds $10^5$ and the temperature dependence of the resonances can be described by the generalized Mattis-Bardeen model with a disorder parameter $Γ=4\times10^{-3}Δ_0$. Post-fabrication annealing at temperatures above the deposition temperature can further reduce the $T_c$ and $Γ$ without reducing $Q_i$, leading to improvements in energy resolution.

hep-ex↗

arXiv2025

Finite-Sample Valid Randomization Tests for Monotone Spillover Effects

Randomization tests have gained popularity for causal inference under network interference because they are finite-sample valid with minimal assumptions. However, existing procedures are limited as they primarily focus on the existence of spillovers through sharp null hypotheses on potential outcomes. In this paper, we expand the scope of randomization procedures in network settings by developing new tests for the monotonicity of spillover effects. These tests offer insights into whether spillover effects increase, decrease, or exhibit ``diminishing returns'' along certain network dimensions of interest. Our approach partitions the network into multiple (possibly overlapping) parts and tests a monotone contrast hypothesis in each sub-network. The test decisions can then be aggregated in various ways depending on how each test is constructed. We demonstrate our method by re-analyzing a large-scale policing experiment in Colombia, which reveals evidence of monotonicity related to the ``crime displacement hypothesis''. Our analysis suggests that crime spillovers on a control street increase with the number of nearby streets receiving more intense policing but diminish at higher exposure levels.

stat.ME↗

arXiv2025

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning

In cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performance. Moreover, in large-scale multi-agent systems commonly found in real-world applications, effective communication plays an even more critical role due to the escalated challenge of partial observability compared to smaller-scale setups. In this work, we endeavor to develop a scalable communication protocol for MARL. Unlike previous methods that focus on selecting optimal pairwise communication links-a task that becomes increasingly complex as the number of agents grows-we adopt a global perspective on communication topology design. Specifically, we propose utilizing the exponential topology to enable rapid information dissemination among agents by leveraging its small-diameter and small-size properties. This approach leads to a scalable communication protocol, named ExpoComm. To fully unlock the potential of exponential graphs as communication topologies, we employ memory-based message processors and auxiliary tasks to ground messages, ensuring that they reflect global information and benefit decision-making. Extensive experiments on large-scale cooperative benchmarks, including MAgent and Infrastructure Management Planning, demonstrate the superior performance and robust zero-shot transferability of ExpoComm compared to existing communication strategies. The code is publicly available at https://github.com/LXXXXR/ExpoComm.

cs.MA↗

arXiv2025

Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control

Reinforcement learning (RL) has proven to be well-performed and general-purpose in the inventory control (IC). However, further improvement of RL algorithms in the IC domain is impeded due to two limitations of online experience. First, online experience is expensive to acquire in real-world applications. With the low sample efficiency nature of RL algorithms, it would take extensive time to train the RL policy to convergence. Second, online experience may not reflect the true demand due to the lost sales phenomenon typical in IC, which makes the learning process more challenging. To address the above challenges, we propose a decision framework that combines reinforcement learning with feedback graph (RLFG) and intrinsically motivated exploration (IME) to boost sample efficiency. In particular, we first take advantage of the inherent properties of lost-sales IC problems and design the feedback graph (FG) specially for lost-sales IC problems to generate abundant side experiences aid RL updates. Then we conduct a rigorous theoretical analysis of how the designed FG reduces the sample complexity of RL methods. Based on the theoretical insights, we design an intrinsic reward to direct the RL agent to explore to the state-action space with more side experiences, further exploiting FG's power. Experimental results demonstrate that our method greatly improves the sample efficiency of applying RL in IC. Our code is available at https://anonymous.4open.science/r/RLIMFG4IC-811D/

cs.LG↗

arXiv2025

Hierarchically-Structured Open-Vocabulary Indoor Scene Synthesis with Pre-trained Large Language Model

Indoor scene synthesis aims to automatically produce plausible, realistic and diverse 3D indoor scenes, especially given arbitrary user requirements. Recently, the promising generalization ability of pre-trained large language models (LLM) assist in open-vocabulary indoor scene synthesis. However, the challenge lies in converting the LLM-generated outputs into reasonable and physically feasible scene layouts. In this paper, we propose to generate hierarchically structured scene descriptions with LLM and then compute the scene layouts. Specifically, we train a hierarchy-aware network to infer the fine-grained relative positions between objects and design a divide-and-conquer optimization to solve for scene layouts. The advantages of using hierarchically structured scene representation are two-fold. First, the hierarchical structure provides a rough grounding for object arrangement, which alleviates contradictory placements with dense relations and enhances the generalization ability of the network to infer fine-grained placements. Second, it naturally supports the divide-and-conquer optimization, by first arranging the sub-scenes and then the entire scene, to more effectively solve for a feasible layout. We conduct extensive comparison experiments and ablation studies with both qualitative and quantitative evaluations to validate the effectiveness of our key designs with the hierarchically structured scene representation. Our approach can generate more reasonable scene layouts while better aligned with the user requirements and LLM descriptions. We also present open-vocabulary scene synthesis and interactive scene design results to show the strength of our approach in the applications.

cs.CV↗

arXiv2025

Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach against Moving Reactive Jammer

This paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer's detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the $\varepsilon $-greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90\% increase in normalized throughput.

cs.IT↗