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Yu Luo

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23 papers

2 author rows

JBHI Journal 2026 Journal Article

Dual-Branch Deep Unfolding Network for Compressed Sensing MRI Reconstruction

Yujie Liu
Yu Luo
Jie Ling
Lieqing Lin
Ye Wu
Shun Yao

In the field of compressed sensing magnetic resonance imaging (CS-MRI), deep unfolding networks (DUNs) achieve high interpretability and superior performance. However, existing DUN-based methods often treat different components of the MR image uniformly without considering their respective unique characteristics, leading to insufficient detail capture and suboptimal performance. To address this issue, we propose a Dual-BrancH Deep Unfolding Network (DBH-Net), which employs parallel under-complete (UC) and over-complete (OC) branches to alternately reconstruct different components from the under-sampled MR image. The UC branch focuses on extracting low-frequency features by expanding the receptive field, while the OC branch emphasizes high-frequency features by restricting the receptive field. Besides the independent descriptive abilities of dual-branch, the unique characteristics of DUN facilitate a tighter integration between the two branches. Additionally, we introduce an Auxiliary Information Fusion Block (AIFB) to transfer multi-channel auxiliary information between stages, effectively reducing information loss. Extensive experiments on three datasets demonstrate that our proposed DBH-Net outperforms existing state-of-the-art methods.

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YNIMG Journal 2026 Journal Article

Partial volume correction for quantifying venous oxygen saturation levels using contrast-enhanced MRI

Sagar Buch
Yifan Lv
Mingming Wang
Bo Wu
Ryan M. Smith
Yu Luo
E. Mark Haacke

Quantifying brain oxygenation is crucial for diagnosing and managing neurological conditions like stroke. Quantitative susceptibility mapping (QSM), an MRI technique, can measure venous oxygen saturation (Yv) but is hampered by partial volume effects (PVEs) in small vessels, leading to inaccurate measurements. This study aims to develop a robust method to mitigate these PVEs for the QSM-derived Yv and oxygen extraction fraction (OEF) in small cerebral veins. We integrated QSM with high-resolution, contrast-enhanced T1-weighted imaging to generate regional cerebral blood volume (rCBV) maps, which were used to correct for PVEs in QSM data from 30 stroke patients. The corrected QSM images showed a significant increase in venous susceptibility (Δχ) values compared to the uncorrected images (441.46 ± 61.14 ppb vs. 163.26 ± 19.66 ppb; p < 0.001), translating to a physiologically plausible mean Yv of 70.42 ± 4.09%. The method also improved the distinction of asymmetrically prominent cortical veins (APCVs), revealing lower Yv values in these areas for some cases, consistent with reduced oxygenation. Our findings demonstrate that using contrast-enhanced rCBV maps can correct PVEs in QSM, providing more reliable measurements of Yv and OEF in small cerebral veins. This approach offers valuable clinical insight into assessing cerebral hemodynamics in patients with stroke and other neurological conditions.

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ICML Conference 2025 Conference Paper

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control

Zilin Kang
Chenyuan Hu
Yu Luo
Zhecheng Yuan
Ruijie Zheng
Huazhe Xu

Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias—a tendency to overfit early experiences stored in the replay buffer—which limits an RL agent’s sample efficiency and generalizability. A common existing approach to mitigate this issue is periodically resetting the agent during training. Yet, even after multiple resets, RL agents could still be impacted by early experiences. In contrast, humans are less susceptible to such bias, partly due to infantile amnesia, where the formation of new neurons disrupts early memory traces, leading to the forgetting of initial experiences. Inspired by this dual processes of forgetting and growing in neuroscience, in this paper, we propose Forget and Grow ( FoG ), a new deep RL algorithm with two mechanisms introduced. First, Experience Replay Decay (ER Decay) —"forgetting early experience”—which balances memory by gradually reducing the influence of early experiences. Second, Network Expansion —"growing neural capacity”—which enhances agents’ capability to exploit the patterns of existing data by dynamically adding new parameters during training. Empirical results on four major continuous control benchmarks with more than 40 tasks demonstrate the superior performance of FoG against SoTA existing deep RL algorithms, including BRO, SimBa and TD-MPC2.

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IROS Conference 2025 Conference Paper

Bayesian Morphology Optimization for Musculoskeletal Systems

Jing Zhao
Yubo Yang
Yinsong Wang
Shuyuan Zhang
Liangjun Huang
Yu Luo
Huaping Liu

In this study, we focus on enhancing the policy of a musculoskeletal arm to develop grasping abilities for objects of varying weights. The agent is modeled using MyoSuite, a platform with realistic biomechanics where muscles drive skeletal movement. We observed that optimizing only the control policy is insufficient for handling heavy object grasping, highlighting the limitations of traditional control-focused approaches. To address this issue, we shift our focus to muscle development by optimizing the arm’s muscle parameters. However, this remains challenging for two main reasons. First, the high dimensionality of the muscle parameter space makes it difficult to find optimal designs. Second, evaluating new muscle configurations requires training a control policy, leading to high computational costs. To tackle these challenges, we adopt two strategies. First, we simplify the problem by optimizing only the stiffness parameters, as they have the greatest impact on grasping performance. Second, we apply the Bayesian Morphology Optimization Method (BMO) to efficiently search the parameter space. Compared to genetic algorithms(GA), BMO finds better solutions with fewer evaluations. Experimental results show that BMO achieves similar rewards with 20% fewer iterations than GA and improves the success rate by 10%. In summary, muscle optimization provides an effective solution for grasping tasks, and BMO demonstrates efficient, robust, and generalizable performance in optimizing muscle parameters for such tasks.

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NeurIPS Conference 2025 Conference Paper

Flow-Based Policy for Online Reinforcement Learning

Lei Lyu
Yunfei Li
Yu Luo
Fuchun Sun
Tao Kong
Jiafeng Xu
Xiao Ma

We present $\textbf{FlowRL}$, a novel framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. We argue that in addition to training signals, enhancing the expressiveness of the policy class is crucial for the performance gains in RL. Flow-based generative models offer such potential, excelling at capturing complex, multimodal action distributions. However, their direct application in online RL is challenging due to a fundamental objective mismatch: standard flow training optimizes for static data imitation, while RL requires value-based policy optimization through a dynamic buffer, leading to difficult optimization landscapes. FlowRL first models policies via a state-dependent velocity field, generating actions through deterministic ODE integration from noise. We derive a constrained policy search objective that jointly maximizes Q through the flow polciy while bounding the Wasserstein-2 distance to a behavior-optimal policy implicitly derived from the replay buffer. This formulation effectively aligns the flow optimization with the RL objective, enabling efficient and value-aware policy learning despite the complexity of the policy class. Empirical evaluations on DMControl and Humanoidbench demonstrate that FlowRL achieves competitive performance in online reinforcement learning benchmarks.

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AIIM Journal 2025 Journal Article

Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions

Kai Sun
Siyan Xue
Fuchun Sun
Haoran Sun
Yu Luo
Ling Wang
Siyuan Wang
Na Guo

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.

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ICRA Conference 2025 Conference Paper

Multi-Segment Soft Robot Control Via Deep Koopman-Based Model Predictive Control

Lei Lv
Lei Liu 0076
Lei Bao
Fuchun Sun 0001
Jiahong Dong
Jianwei Zhang 0001
Xuemei Shan
Kai Sun

Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot “Chordata” demonstrate that DK-MPC could achieve highprecision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://pinkmoon-io.github.io/DKMPC/.

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ICML Conference 2025 Conference Paper

Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling

Tianyu Liu
Kai Sun
Fuchun Sun 0001
Yu Luo
Yuanlong Zhang

Temporal sequences, even after stationarization, often exhibit leptokurtic distributions with fat tails and persistent distribution shifts. These properties destabilize feature dynamics, amplify model variance, and hinder model convergence in time series forecasting. To address this, we propose Morphing-Flow (MoF), a framework that combines a spline-based transform layer (Flow) and a test-time-trained method (Morph), which adaptively normalizes non-stationary, fat-tailed distributions while preserving critical extreme features. MoF ensures that inputs remain within a network’s effective activation space—a structured, normal-like distribution—even under distributional drift. Experiments across eight datasets show that MoF achieves state-of-the-art performance: With a simple linear backbone architecture, it matches the performance of state-of-the-art models on datasets such as Electricity and ETTh2. When paired with a patch-based Mamba architecture, MoF outperforms its closest competitor by 6. 3% on average and reduces forecasting errors in fat-tailed datasets such as Exchange by 21. 7%. Moreover, MoF acts as a plug-and-play module, boosting performance in existing models without architectural changes.

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NeurIPS Conference 2025 Conference Paper

Towards Robust Zero-Shot Reinforcement Learning

Kexin Zheng
Lauriane Teyssier
Yinan Zheng
Yu Luo
Xianyuan Zhan

The recent development of zero-shot reinforcement learning (RL) has opened a new avenue for learning pre-trained generalist policies that can adapt to arbitrary new tasks in a zero-shot manner. While the popular Forward-Backward representations (FB) and related methods have shown promise in zero-shot RL, we empirically found that their modeling lacks expressivity and that extrapolation errors caused by out-of-distribution (OOD) actions during offline learning sometimes lead to biased representations, ultimately resulting in suboptimal performance. To address these issues, we propose Behavior-REgularizEd Zero-shot RL with Expressivity enhancement (BREEZE), an upgraded FB-based framework that simultaneously enhances learning stability, policy extraction capability, and representation learning quality. BREEZE introduces behavioral regularization in zero-shot RL policy learning, transforming policy optimization into a stable in-sample learning paradigm. Additionally, BREEZE extracts the policy using a task-conditioned diffusion model, enabling the generation of high-quality and multimodal action distributions in zero-shot RL settings. Moreover, BREEZE employs expressive attention-based architectures for representation modeling to capture the complex relationships between environmental dynamics. Extensive experiments on ExORL and D4RL Kitchen demonstrate that BREEZE achieves the best or near-the-best performance while exhibiting superior robustness compared to prior offline zero-shot RL methods. The official implementation is available at: https: //github. com/Whiterrrrr/BREEZE.

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ICML Conference 2024 Conference Paper

ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization

Tianying Ji
Yongyuan Liang
Yan Zeng 0002
Yu Luo
Guowei Xu 0001
Jiawei Guo
Ruijie Zheng
Furong Huang

The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore the causal relationship between different action dimensions and rewards to evaluate the significance of various primitive behaviors during training. We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Furthermore, to prevent excessive focus on specific primitive behaviors, we analyze the gradient dormancy phenomenon and introduce a dormancy-guided reset mechanism to further enhance the efficacy of our method. Our proposed algorithm, ACE: Off-policy A ctor-critic with C ausality-aware E ntropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks spanning 7 domains compared to model-free RL baselines, which underscores the effectiveness, versatility, and efficient sample efficiency of our approach. Benchmark results and videos are available at https: //ace-rl. github. io/.

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RLJ Journal 2024 Journal Article

Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies

Yu Luo
Fuchun Sun
Tianying Ji
Xianyuan Zhan

Hierarchical reinforcement learning (HRL) addresses complex long-horizon tasks by skillfully decomposing them into subgoals. Therefore, the effectiveness of HRL is greatly influenced by subgoal reachability. Typical HRL methods only consider subgoal reachability from the unilateral level, where a dominant level enforces compliance to the subordinate level. However, we observe that when the dominant level becomes trapped in local exploration or generates unattainable subgoals, the subordinate level is negatively affected and cannot follow the dominant level's actions. This can potentially make both levels stuck in local optima, ultimately hindering subsequent subgoal reachability. Allowing real-time bilateral information sharing and error correction would be a natural cure for this issue, which motivates us to propose a mutual response mechanism. Based on this, we propose the Bidirectional-reachable Hierarchical Policy Optimization~(BrHPO)—a simple yet effective algorithm that also enjoys computation efficiency. Experiment results on a variety of long-horizon tasks showcase that BrHPO outperforms other state-of-the-art HRL baselines, coupled with a significantly higher exploration efficiency and robustness.

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RLC Conference 2024 Conference Paper

Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies

Yu Luo
Fuchun Sun
Tianying Ji
Xianyuan Zhan

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ICLR Conference 2024 Conference Paper

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Guowei Xu 0001
Ruijie Zheng
Yongyuan Liang
Xiyao Wang
Zhecheng Yuan
Tianying Ji
Yu Luo
Xiaoyu Liu 0003

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks. To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network. Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals. Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio. Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit. Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations.

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ICML Conference 2024 Conference Paper

Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

Yu Luo
Tianying Ji
Fuchun Sun 0001
Jianwei Zhang 0001
Huazhe Xu
Xianyuan Zhan

Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously collected data for policy learning. However, most existing off-policy RL algorithms fail to maximally exploit the information in the replay buffer, limiting sample efficiency and policy performance. In this work, we discover that concurrently training an offline RL policy based on the shared online replay buffer can sometimes outperform the original online learning policy, though the occurrence of such performance gains remains uncertain. This motivates a new possibility of harnessing the emergent outperforming offline optimal policy to improve online policy learning. Based on this insight, we present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy through value comparison, and uses it as an adaptive constraint to guarantee stronger policy learning performance. Our experiments demonstrate that OBAC outperforms other popular model-free RL baselines and rivals advanced model-based RL methods in terms of sample efficiency and asymptotic performance across 53 tasks spanning 6 task suites.

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ICML Conference 2024 Conference Paper

OMPO: A Unified Framework for RL under Policy and Dynamics Shifts

Yu Luo
Tianying Ji
Fuchun Sun 0001
Jianwei Zhang 0001
Huazhe Xu
Xianyuan Zhan

Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overlook the distribution discrepancies induced by policy or dynamics shifts, or rely on specialized algorithms with task priors, thus often resulting in suboptimal policy performances and high learning variances. In this paper, we identify a unified strategy for online RL policy learning under diverse settings of policy and dynamics shifts: transition occupancy matching. In light of this, we introduce a surrogate policy learning objective by considering the transition occupancy discrepancies and then cast it into a tractable min-max optimization problem through dual reformulation. Our method, dubbed Occupancy-Matching Policy Optimization (OMPO), features a specialized actor-critic structure equipped with a distribution discriminator and a small-size local buffer. We conduct extensive experiments based on the OpenAI Gym, Meta-World, and Panda Robots environments, encompassing policy shifts under stationary and non-stationary dynamics, as well as domain adaption. The results demonstrate that OMPO outperforms the specialized baselines from different categories in all settings. We also find that OMPO exhibits particularly strong performance when combined with domain randomization, highlighting its potential in RL-based robotics applications.

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AIIM Journal 2024 Journal Article

Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening

Youyi Song
Ao Zhang
Jinglin Zhou
Yu Luo
Zhizhe Lin
Teng Zhou

Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm’s shape guided by the modeled shape priors We model local shape priors (cytoplasm–level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump–level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.

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ICML Conference 2024 Conference Paper

Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic

Tianying Ji
Yu Luo
Fuchun Sun 0001
Xianyuan Zhan
Jianwei Zhang 0001
Huazhe Xu

Learning high-quality $Q$-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works primarily focus on addressing the value overestimation issue, an outcome of adopting function approximators and off-policy learning. Deviating from the common viewpoint, we observe that $Q$-values are often underestimated in the latter stage of the RL training process, potentially hindering policy learning and reducing sample efficiency. We find that such a long-neglected phenomenon is often related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer. We propose the Blended Exploitation and Exploration (BEE) operator, a simple yet effective approach that updates $Q$-value using both historical best-performing actions and the current policy. Based on BEE, the resulting practical algorithm BAC outperforms state-of-the-art methods in over 50 continuous control tasks and achieves strong performance in failure-prone scenarios and real-world robot tasks. Benchmark results and videos are available at https: //jity16. github. io/BEE/.

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ICRA Conference 2024 Conference Paper

Smooth Computation without Input Delay: Robust Tube-Based Model Predictive Control for Robot Manipulator Planning

Yu Luo
Qie Sima
Tianying Ji
Fuchun Sun 0001
Huaping Liu 0001
Jianwei Zhang 0001

Model Predictive Control (MPC) has exhibited remarkable capabilities in optimizing objectives and meeting constraints. However, the substantial computational burden associated with solving the Optimal Control Problem (OCP) at each triggering instant introduces significant delays between state sampling and control application. These delays limit the practicality of MPC in resource-constrained systems when engaging in complex tasks. The intuition to address this issue in this paper is that by predicting the successor state, the controller can solve the OCP one time step ahead of time thus avoiding the delay of the next action. To this end, we compute deviations between real and nominal system states, predicting forthcoming real states as initial conditions for the imminent OCP solution. Anticipatory computation stores optimal control based on current nominal states, thus mitigating the delay effects. Additionally, we establish an upper bound for linearization error, effectively linearizing the nonlinear system, reducing OCP complexity, and enhancing response speed. We provide empirical validation through two numerical simulations and corresponding real-world robot tasks, demonstrating significant performance improvements and augmented response speed (up to 90%) resulting from the seamless integration of our proposed approach compared to conventional time-triggered MPC strategies.

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JBHI Journal 2023 Journal Article

An Effective Co-Support Guided Analysis Model for Multi-Contrast MRI Reconstruction

Yu Luo
Manting Wei
Si Li
Jie Ling
Guobo Xie
Shun Yao

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical diagnosis. However, it is time-consuming to obtain MR data of multi-contrasts and the long scanning time may bring unexpected physiological motion artifacts. To obtain MR images of higher quality within limited acquisition time, we propose an effective model to reconstruct images from under-sampled k-space data of one contrast by utilizing another fully-sampled contrast of the same anatomy. Specifically, multiple contrasts from the same anatomical section exhibit similar structures. Enlightened by the fact that co-support of an image provides an appropriate characterization of morphological structures, we develop a similarity regularization of the co-supports across multi-contrasts. In this case, the guided MRI reconstruction problem is naturally formulated as a mixed integer optimization model consisting of three terms, the data fidelity of k-space, smoothness-enforcing regularization, and co-support regularization. An effective algorithm is developed to solve this minimization model alternatively. In the numerical experiments, T2-weighted images are used as the guidance to reconstruct T1-weighted/T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) images and PD-weighted images are used as the guidance to reconstruct PDFS-weighted images, respectively, from their under-sampled k-space data. The experimental results demonstrate that the proposed model outperforms other state-of-the-art multi-contrast MRI reconstruction methods in terms of both quantitative metrics and visual performance at various sampling ratios.

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YNIMG Journal 2023 Journal Article

The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity

Ke Lv
Yanzhen Liu
Yongsheng Chen
Sagar Buch
Ying Wang
Zhuo Yu
Huiying Wang
Chenxi Zhao

The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.

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NeurIPS Conference 2022 Conference Paper

When to Update Your Model: Constrained Model-based Reinforcement Learning

Tianying Ji
Yu Luo
Fuchun Sun
Mingxuan Jing
Fengxiang He
Wenbing Huang

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

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IROS Conference 2021 Conference Paper

Overlap Displacement Error: Are Your SLAM Poses Map-Consistent?

Christian Mostegel
Jianbo Ye
Yu Luo
Yang Liu

Localization is an essential module that supports many intelligent functions of a mobile robot such as transportation or inspection. However, justifying that a localization module is sufficiently accurate for supporting all downstream tasks is one of the most difficult questions to answer in practice. To overcome this problem, we move away from the traditional calculation of pose errors and propose a new approach that instead evaluates the potential map inconsistency introduced by those pose errors. For this purpose, we propose a new metric, which we call Overlap Displacement Error (ODE). This metric measures the relative displacements between multiple overlapping sensor frustums with respect to the ground truth. All you need to compute this metric are a query trajectory, a ground truth trajectory and the sensor frustum used for mapping. Having the sensor frustum and the map representation as part of the metric, the ODE is customized to the hardware configuration and the mapping strategy. This design allows the analysis of pose accuracy in a space that matters to map creation, and also allows the identification of problems sitting in the interplay between localization and mapping. We demonstrate the potential of this new analysis tool on synthetic and the real-world sequences.

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YNIMG Journal 2018 Journal Article

How acute stress may enhance subsequent memory for threat stimuli outside the focus of attention: DLPFC-amygdala decoupling

Yu Luo
Guillén Fernández
Erno Hermans
Susanne Vogel
Yu Zhang
Hong Li
Floris Klumpers

Stress-related disorders, e. g. , anxiety and depression, are characterized by decreased top-down control for distracting information, as well as a memory bias for threatening information. However, it is unclear how acute stress biases mnemonic encoding and leads to prioritized storage of threat-related information even if outside the focus of attention. In the current study, healthy adults (N = 53, all male) were randomly assigned to stress induction using the socially evaluated cold-pressor test (SECPT) or a control condition. Participants performed a task in which they were required to identify a target letter within a string of letters that were either identical to the target and thereby facilitating detection (low distractor load) or mixed with other letters to complicate the search (high load). Either a fearful or neutral face was presented on the background, outside the focus of attention. Twenty-four hours later, participants were asked to perform a surprise recognition memory test for those background faces. Stress induction resulted in increased cortisol and negative subjective mood ratings. Stress did not affect visual search performance, however, participants in the stress group showed stronger memory compared to the control group for fearful faces in the low attentional load condition. Critically, the stress induced memory bias was accompanied by decoupling between amygdala and DLFPC during encoding, which may represent a mechanism for decreased ability to filter task-irrelevant threatening background information. The current study provides a potential neural account for how stress can produce a negative memory bias for threatening information even if presented outside the focus of attention. Despite of an adaptive advantage for survival, such tendencies may ultimately also lead to generalized fear, a possibility requiring additional investigation.

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