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Yi Sun

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40

JBHI Journal 2026 Journal Article

$\text{P}^\text{2}$RS: A Quantitative Rating Scale for Pain Assessment based on Pulse Wave Characterization

  • Yue He
  • Yi Sun
  • Ke Sun
  • Wei Bin
  • Quan Wang
  • Heng Yang
  • Xinxin Li

For pain intensity assessment, currently there are mainly 11 rating scales, from primitive Visual Analog Scale (VAS) to elaborate Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). However, they all depend on a self-report mechanism, making their results so subjective that the consistency, comparability and reference value are barely satisfactory. Inspired by the phenomenon that discomfort may give rise to the throbbing of radial artery, we develop an objective rating scale innovatively, quantifying the severity of pain by the degree of “lateral instability” of an arterial pulse wave. In attempting to monitor this lateral instability, a sort of ultra-small piezoresistive pressure sensor is fabricated in an area of 0. 4 × 0. 4 $\text{mm}^\text{2}$. With 18 of such sensors, we build a flexible tactile sensing dense-array with a pitch of only 0. 65 mm. Overlying the radial artery perpendicularly to the blood flow direction, the dense-array succeeds in observing the cross-section of a pulse wave. The barycenter of the cross-section of each wave cycle is taken as the feature point to represent its lateral shape and drift. The standard deviation of the barycenters' horizontal coordinates is thereby calculated as the pulsatile perceptual rating scale ( $\text{P}^\text{2}$ RS) to reflect the degree of lateral instability, that is, our scale of pain intensity. Among 86 clinical samples, the pain threshold is 0. 11, which is concluded by a binary classification model based on a support vector machine. In terms of its consistency with previous rating scales, the average correlation coefficient reaches 0. 804 among 43 pain samples.

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EAAI Journal 2026 Journal Article

A customized computed tomography image segmentation framework based on Segment Anything Model for power battery electrode

  • Jingmin Lian
  • Xuanheng Li
  • Zijun Zhang
  • Jianlong Yu
  • Yi Sun

Any structural defects in new energy batteries may lead to serious safety hazards. Industrial computed tomography (CT) combined with image segmentation offers an effective solution to accurate defect detection, but narrow electrode gaps, cross-device grayscale variation, CT noise, and limited training data make the task challenging. Existing segmentation methods struggle to achieve both satisfying segmentation accuracy and generalization capability. While Segment Anything Model (SAM) demonstrates excellent feature extraction and generalization capabilities on natural images, its direct application to CT image segmentation remains challenging. To address the above issues, we propose BE-SAM, a SAM-based customized encoder–decoder framework for CT image segmentation of power battery electrodes. The encoder employs a dual-branch design, where the SAM encoder branch extracts global contextual features, and the Strip Edge Encoder (SEE) branch built on multi-scale wavelet strip convolutions captures fine-grained edge details. Centered on the Multi-Scale Robust Adaptive Module (MSRA), the decoder effectively integrates multi-scale hybrid features and enhances robustness against CT noise. To evaluate BE-SAM, we construct three real datasets consisting of CT images of battery electrodes collected from different production lines and annotated by experts. Experiments demonstrate that BE-SAM achieves highly accurate segmentation while exhibiting strong generalization and robustness.

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AAAI Conference 2026 Short Paper

Can Large Language Models Grasp 3D Medical Anatomy Shapes? (Student Abstract)

  • Yao Gao
  • Feng Li
  • Jeroen Van Dessel
  • Yi Sun
  • Robin Willaert

What if the next generation of human-computer interaction is not a screen... but a conversation? Large Language Models (LLMs) offer a new paradigm for interacting with computers through text, but they lack shape reasoning capabilities. We introduce Textual Anatomy Encoding (TAE), a workflow that connects LLMs with 3D anatomies. TAE employs clinician-validated semantic annotations and rule-based prompts to achieve deterministic and interpretable landmark localization. The results indicate that TAE enables LLMs to move beyond textual knowledge, achieving an accurate understanding of anatomical localization. This framework opens opportunities for diagnosis, surgical planning, and scalable medical annotation, positioning LLMs as a foundation for next-generation human–computer interaction in healthcare.

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JBHI Journal 2026 Journal Article

CFRAFN: A Cross-Feature Residual Attention Fusion Network for Major Depressive Disorder Prediction Using Clinical Voice Recordings

  • Rumo Pan
  • Sidu Feng
  • Yi Sun
  • Jinqiu Xu
  • Tianzhang Zhai
  • Xiaochun Wu
  • Liangliang Tan
  • Yonggui Yuan

Major depressive disorder (MDD) is a prevalent mental disorder with a significant burden on individuals and society, and timely identification and intervention are essential for effective management. Voice data have been used as behavioral indicators of MDD, offering valuable insights into an individual's mental state. In this study, we collected voice data from 221 patients diagnosed with MDD at the inpatient ward of the Department of Psychiatry and Psychosomatics, Zhongda Hospital, Southeast University, alongside 113 healthy controls, to construct the Chinese depressive voice dataset. We proposed the cross-feature residual attention fusion network (CFRAFN), which leverages extended Geneva minimalistic acoustic parameter set features along with high-dimensional embeddings extracted from the pretrained VGGish model to effectively capture MDD-associated phonetic patterns. Specifically, CFRAFN utilizes differentiated residual blocks to maintain training stability in deep hierarchical structure. Furthermore, the self-attention fusion strategy dynamically weighted the significance of each feature modality, ensuring effective feature integration and consequently improving MDD prediction accuracy. Experimental results demonstrated that CFRAFN achieved an excellent predictive performance with an area under the receiver operating characteristic curve of 0. 924 in an independent test set, and significantly outperformed 11 baseline models across 5-fold cross-validation.

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AAAI Conference 2026 Conference Paper

Dexterous Manipulation Transfer via Progressive Kinematic-Dynamic Alignment

  • Wenbin Bai
  • Qiyu Chen
  • Xiangbo Lin
  • Jw L
  • Quancheng Li
  • Hejiang Pan
  • Yi Sun

The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy learning. To address this challenge, we present a hand-agnostic manipulation transfer system. It efficiently converts human hand manipulation sequences from demonstration videos into high-quality dexterous manipulation trajectories without requirements of massive training data. To tackle the multi-dimensional disparities between human hands and dexterous hands, as well as the challenges posed by high-degree-of-freedom coordinated control of dexterous hands, we design a progressive transfer framework: first, we establish primary control signals for dexterous hands based on kinematic matching; subsequently, we train residual policies with action space rescaling and thumb-guided initialization to dynamically optimize contact interactions under unified rewards; finally, we compute wrist control trajectories with the objective of preserving operational semantics. Using only human hand manipulation videos, our system automatically configures system parameters for different tasks, balancing kinematic matching and dynamic optimization across dexterous hands, object categories, and tasks. Extensive experimental results demonstrate that our framework can automatically generate smooth and semantically correct dexterous hand manipulation that faithfully reproduces human intentions, achieving high efficiency and strong generalizability with an average transfer success rate of 73%, providing an easily implementable and scalable method for collecting robot dexterous manipulation data. Refer to the arXiv version for the appendix.

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YNICL Journal 2026 Journal Article

Distinct neurologic state in patients with traumatic brain injury and hemorrhagic stroke during the stage of acute disorders of consciousness and the correlation with the neurological prognosis: A multi-modal PET/rs-fMRI study

  • Danjing Yu
  • Kemeng Gao
  • Xiefeng Wang
  • Lin Zhao
  • Yi Sun
  • Zhiyan Shen
  • Yu Wang
  • Ying Wang

PURPOSE: The exact mechanisms underlying the distinct neurological outcomes between Traumatic Brain Injury (TBI) and Hemorrhagic Stroke (HS) remain unclear. Our objective is to assess distinct features of neurologic state between comatose patients with TBI and HS during the stage of acute disorder of consciousness (aDoC) and to identify the correlation of neurologic features with prognosis. METHODS: Data were analyzed from TBI and HS patients examined by positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) simultaneously. Primary clinical outcomes consisted of the state of consciousness and neurological prognosis. The regional neural activity was assessed by the amplitude of fractional low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) on rs-fMRI scans. The standardized uptake value (SUV) on PET scans quantified neural metabolism. Functional connectivity (FC) and graph theoretic approach (GTA) were employed to compare the FC patterns between TBI and HS. Correlations of PET/rs-fMRI indicators with the prognosis of HS and TBI were identified. RESULTS: Muti-modal PET/rs-fMRI analysis showed more active local neurological state in TBI patients than HS patients, specifically in the right precentral gyrus (PreCG.R), right postcentral gyrus (PoCG.R), right superior temporal gyrus (STG.R) and right middle temporal gyrus (MTG.R). TBI patients demonstrated significantly higher clustering coefficient and nodal efficiency of the sensorimotor network (SMN) along with lower connectivity and network efficiency in the default network (DMN) compared to HS patients. PET/rs-fMRI indicators significantly correlated with the neurological prognosis of TBI and HS. CONCLUSIONS: This study elucidated the underlying mechanisms contributing to the distinct neurologic prognosis between comatose TBI and HS patients, and may contribute to the development of early targeted intervention strategies for specific diseases.

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AAAI Conference 2026 Conference Paper

HiLoMix: Robust High- and Low-Frequency Graph Learning Framework for Mixing Address Association

  • Xiaofan Tu
  • Tiantian Duan
  • Shuyi Miao
  • Hanwen Zhang
  • Yi Sun

As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main challenges: label noise and label scarcity, leading to suboptimal performance and limited generalization. To address these, we propose HiLoMix, a graph-based learning framework specifically designed for mixing address association. First, we construct the Heterogeneous Attributed Mixing Interaction Graph (HAMIG) to enrich the topological structure. Second, we introduce frequency-aware graph contrastive learning that captures complementary structural signals from high- and low-frequency graph views. Third, we employ weak supervised learning that assigns confidence-based weights to noisy labels. Then, we jointly train high-pass and low-pass GNNs using both unsupervised contrastive signals and confidence-based supervision to learn robust node representations. Finally, we adopt a stacking framework to fuse predictions from multiple heterogeneous models, further improving generalization and robustness. Experimental results demonstrate that HiLoMix outperforms existing methods in mixing address association.

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EAAI Journal 2025 Journal Article

A single-demonstration guided manipulation learning with dexterous hand

  • Jianwen Li
  • Yinglan Lv
  • Xiangbo Lin
  • Jinglue Hang
  • Xuanheng Li
  • Yi Sun

Demonstration assisted reinforcement learning has been proven to be an extremely effective method for solving complex multi-fingered dexterous hand manipulation tasks. It usually requires costly and time-consuming expert demonstrations for each task, affecting the learning efficiency of the dexterous manipulation policy. To overcome this drawback, this paper devotes to use only one human demonstration per task to obtain a generalizable dexterous manipulation policy. And a novel ‘Basics-before-Extension’ policy learning strategy (BBE) is proposed for this purpose. It consists of two learning stages. In the ‘basics learning stage’, the dexterous hand extracts hand-object contact points as key clues from demonstration, facilitating a quick learning of the expert basic skill. While in ‘extension learning stage’, the designed joint policy training scheme enables the expert knowledge to be transferred and adapted to new environments, outputting generalizable policy. We present the distinctive overall framework from the low-cost demonstration data collection to the policy learning process. Meanwhile, BBE strategy has been experimentally validated on typical grasp and manipulation tasks, including relocating objects, opening door, hammering nails and functional grasp. The results indicate that the proposed BBE strategy can empower the multi-fingered dexterous hand with the intelligence of learning typical grasp and manipulation efficiently and accurately from a single demonstration.

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JBHI Journal 2025 Journal Article

CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection

  • Liang Zhang
  • Shurong Sheng
  • Xiongfei Wang
  • Jia-Hong Gao
  • Yi Sun
  • Kuntao Xiao
  • Wanli Yang
  • Pengfei Teng

Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significantly reduce the labor and time required for manual annotation of MEG recording data, thereby aiding clinicians in identifying epileptogenic foci and evaluating treatment prognosis. Research in this domain often utilizes the raw, multi-channel signals from MEG scans for spike detection, commonly neglecting the multi-channel spiking patterns from spatially adjacent channels. Moreover, epileptic spikes share considerable morphological similarities with artifact signals within the recordings, posing a challenge for models to differentiate between the two. In this paper, we introduce a multimodal fusion framework that addresses these two challenges collectively. Instead of relying solely on the signal recordings, our framework also mines knowledge from their corresponding topography-map images, which encapsulate the spatial context and amplitude distribution of the input signals. To facilitate more effective data fusion, we present a novel multimodal feature fusion technique called CrossConvPyramid, built upon a convolutional pyramid architecture augmented by an attention mechanism. It initially employs cross-attention and a convolutional pyramid to encode inter-modal correlations within the intermediate features extracted by individual unimodal networks. Subsequently, it utilizes a self-attention mechanism to refine and select the most salient features from both inter-modal and unimodal features, specifically tailored for the spike classification task. Our method achieved the average F1 scores of 92. 88% and 95. 23% across two distinct real-world MEG datasets from separate centers, respectively outperforming the current state-of-the-art by 2. 31% and 0. 88%. We plan to release the code on GitHub later.

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JMLR Journal 2025 Journal Article

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

  • Xiangdong Xie
  • Jiahua Guo
  • Yi Sun

Bayesian networks (BNs) are a powerful tool for knowledge representation and reasoning, especially for complex systems. A critical task in the applications of BNs is conditional inference or inference in the presence of selection bias. However, post-conditioning, the conditional distribution family of a BN can become complex for analysis, and the corresponding induced subgraph may not accurately encode the conditional independencies for the remaining variables. In this work, we first investigate the conditions under which a BN remains closed under conditioning, meaning that the induced subgraph is consistent with the structural information of conditional distributions. Conversely, when a BN is not closed, we aim to construct a new directed acyclic graph (DAG) as a minimal $\mathcal{I}$-map for the conditional model by incorporating directed edges into the original induced graph. We present an equivalent characterization of this minimal $\mathcal{I}$-map and develop an efficient algorithm for its identification. The proposed framework improves the efficiency of conditional inference of a BN. Additionally, the DAG minimal $\mathcal{I}$-map offers graphical criteria for the safe integration of knowledge from diverse sources (subpopulations/conditional distributions), facilitating correct parameter estimation. Both theoretical analysis and simulation studies demonstrate that using a DAG minimal $\mathcal{I}$-map for conditional inference is more effective than traditional methods based on the joint distribution of the original BN. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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JBHI Journal 2025 Journal Article

FBCPM: A Filter Bank Connectome-Based Predictive Modeling Framework for EEG Signals

  • Linze Qian
  • Sujie Wang
  • Ioannis Kakkos
  • Xiaoyu Li
  • Xinyi Xu
  • Mengru Xu
  • George K. Matsopoulos
  • Yi Sun

The human brain connectome has long been recognized as a crucial component for various cognitive functions. While connectome-based predictive modeling (CPM) has been extensively explored for predicting behavior outcomes at the individual-level, its application to electroencephalogram (EEG) remains limited due to the inherent diversity and complexity of EEG frequency information. In the present work, we aim to address this issue by developing a filter bank CPM (FBCPM) framework that leverages narrowband EEG functional connectivity (FC) for individual prediction. Four independent datasets comprising 280 healthy subjects with 392 EEG recordings during the psychomotor vigilance test (PVT), were adopted here. Using the discovery dataset (i. e. , Dataset 1) with 137 recordings, the feasibility of FBCPM was evaluated via predicting mean reaction time (RT) measures within a 15-min PVT task. The results showed that FBCPM framework achieved notable prediction accuracy and outperformed four benchmark approaches. Subsequent comprehensive internal and external validation analyses further affirmed its robustness across various hyper-parameters and generalizability to another three independent datasets (i. e. , Dataset 2 to Dataset 4) with divergent recording or preprocessing settings. Moreover, the FBCPM framework exhibited satisfactory performance when generalized to time-on-task (TOT) effect measures (i. e. , $\mathit {\Delta RT}$ and $\mathit {TOT_{slope}}$ ). Further investigation of contributing features to mean RT prediction indicated the remarkable predictive ability of negative features, manifesting as a pattern of low-frequency (below 8 Hz) predominance and complex topological distributions. Overall, these findings indicated that FBCPM provided a significant methodological advance in EEG-based individual prediction approaches, moving a step forward towards practical application in cognitive neuroscience.

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AAAI Conference 2025 Conference Paper

Multi-fingered Hand Grasps with Visuo-Tactile Fusion via Multi-Agent Deep Reinforcement Learning

  • Peida Jia
  • Xuanheng Li
  • Tianqiang Zhu
  • Rina Wu
  • Xiangbo Lin
  • Yi Sun

Humans achieve contact-rich dexterous grasping through the synergy of visual and tactile information. However, the high-dimensional action space of high DoF multi-fingered hands poses significant challenges to this operation. In this study, we address this complexity by controlling the robotic hand at the reduced dimensional level of individual fingers instead of the entire hand, and develop a finger-based multi-agent deep reinforcement learning strategy by regarding the wrist, arm, and each finger of the hand as intelligent agents. We commence by applying a single-agent reinforcement learning algorithm to guide the whole hand to reach the feasible approaching direction and distance to the object. Then, we develop neuroscience-inspired visuo-tactile fusion networks to train multiple agents to control their assigned fingers by effectively leveraging visual and tactile feedback. This enables dynamic and collaborative adjustments of finger-object interactions, ultimately achieving precise contact with specific areas of the objects. The grasping results on 8 objects show that our approach can achieve stable and compliant grasps. To the best of our knowledge, this is the first work that employs a finger-based multi-agent reinforcement learning approach to control the dexterous grasping process under the guidance of both visual and tactile feedback.

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AAAI Conference 2024 Conference Paper

DexFuncGrasp: A Robotic Dexterous Functional Grasp Dataset Constructed from a Cost-Effective Real-Simulation Annotation System

  • Jinglue Hang
  • Xiangbo Lin
  • Tianqiang Zhu
  • Xuanheng Li
  • Rina Wu
  • Xiaohong Ma
  • Yi Sun

Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous hands to complete human-like functional grasp, such as grasp the handle of a cup or pressing the button of a flashlight, so as to enable robots to complete subsequent functional manipulation action autonomously, and there is no dataset with functional grasp pose annotations at present. This paper develops a unique Cost-Effective Real-Simulation Annotation System by leveraging natural hand's actions. The system is able to capture a functional grasp of a dexterous hand in a simulated environment assisted by human demonstration in real world. By using this system, dexterous grasp data can be collected efficiently as well as cost-effective. Finally, we construct the first dexterous functional grasp dataset with rich pose annotations. A Functional Grasp Synthesis Model is also provided to validate the effectiveness of the proposed system and dataset. Our project page is: https://hjlllll.github.io/DFG/.

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JBHI Journal 2024 Journal Article

Distributed Medical Data Storage Mechanism Based on Proof of Retrievability and Vector Commitment for Metaverse Services

  • Guowei Fang
  • Yi Sun
  • Mutiq Almutiq
  • Wei Zhou
  • Yekang Zhao
  • Yongjun Ren

The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.

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IROS Conference 2024 Conference Paper

Efficient-PIP: Large-scale Pixel-level Aligned Image Pair Generation for Cross-time Infrared-RGB Translation

  • Jian Li 0003
  • Kexin Fei
  • Yi Sun
  • Jie Wang
  • Bokai Liu
  • Zongtan Zhou
  • Yongbin Zheng
  • Zhenping Sun

Generative models are gaining momentum in both academic and industrial applications driven by the availability of large-scale datasets, especially in tasks involving Image-to-Image Translation. Meanwhile, poor human perception of nighttime environment has led to a demand for translation from night-vision infrared to day-vision RGB images. However, collecting such cross-modal training data at the same time is impossible due to the thermal imaging properties of infrared cameras, the challenge lies in constructing image pairs during the day and at night respectively, where the requirement for data alignment poses significant difficulties. In this paper, we propose a Pixel-level aligned Image Pair generation framework PIP to explore efficient colorization of high-resolution infrared images. Specifically, we first construct a 3D high-precision point cloud map for the purpose of establishing the correlation between day and night scenes. Corresponding point clouds of modal images are collected simultaneously during data acquisition to obtain image sensor poses by Global Matching with the map, which allows us to calculate the transformation relationship from infrared to RGB image coordinate systems based on the sensor parameters and depth information of the map. Leveraging the relationship, the pixel values of RGB image is projected onto the infrared image followed by optimization as the colored image. Accordingly, we present a dataset NUDT-PIP, the first of its kind containing large-scale pixel-level aligned cross-time infrared-RGB image pairs of complicated real road scenes. Experimental results demonstrate the reliability and strong applicability of our dataset in Image-to-Image Translation. Our code will be released at https://github.com/wjjjjyourFA/NUDT-PIP.

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JBHI Journal 2024 Journal Article

SPReCHD: Four-Chamber Semantic Parsing Network for Recognizing Fetal Congenital Heart Disease in Medical Metaverse

  • Sibo Qiao
  • Shanchen Pang
  • Yi Sun
  • Gang Luo
  • Wenjing Yin
  • Yawu Zhao
  • Silin Pan
  • Zhihan Lv

Echocardiography is essential for evaluating cardiac anatomy and function during early recognition and screening for congenital heart disease (CHD), a widespread and complex congenital malformation. However, fetal CHD recognition still faces many difficulties due to instinctive fetal movements, artifacts in ultrasound images, and distinctive fetal cardiac structures. These factors hinder capturing robust and discriminative representations from ultrasound images, resulting in CHD's low prenatal detection rate. Hence, we propose a multi-scale gated axial-transformer network (MSGATNet) to capture fetal four-chamber semantic information. Then, we propose a SPReCHD: four-chamber semantic parsing network for recognizing fetal CHD in the clinical treatment of the medical metaverse, integrating MSGATNet to segment and locate four-chamber arbitrary contours, further capturing distinguished representations for the fetal heart. Comprehensive experiments indicate that our SPReCHD is sufficient in recognizing fetal CHD, achieving a precision of 95. 92%, a recall of 94%, an accuracy of 95%, and a $F_{1}$ score of 94. 95% on the test set, dramatically improving the fetal CHD's prenatal detection rate.

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NeurIPS Conference 2024 Conference Paper

Toward Dynamic Non-Line-of-Sight Imaging with Mamba Enforced Temporal Consistency

  • Yue Li
  • Yi Sun
  • Shida Sun
  • Juntian Ye
  • Yueyi Zhang
  • Feihu Xu
  • Zhiwei Xiong

Dynamic reconstruction in confocal non-line-of-sight imaging encounters great challenges since the dense raster-scanning manner limits the practical frame rate. A fewer pioneer works reconstruct high-resolution volumes from the under-scanning transient measurements but overlook temporal consistency among transient frames. To fully exploit multi-frame information, we propose the first spatial-temporal Mamba (ST-Mamba) based method tailored for dynamic reconstruction of transient videos. Our method capitalizes on neighbouring transient frames to aggregate the target 3D hidden volume. Specifically, the interleaved features extracted from the input transient frames are fed to the proposed ST-Mamba blocks, which leverage the time-resolving causality in transient measurement. The cross ST-Mamba blocks are then devised to integrate the adjacent transient features. The target high-resolution transient frame is subsequently recovered by the transient spreading module. After transient fusion and recovery, a physical-based network is employed to reconstruct the hidden volume. To tackle the substantial noise inherent in transient videos, we propose a wave-based loss function to impose constraints within the phasor field. Besides, we introduce a new dataset, comprising synthetic videos for training and real-world videos for evaluation. Extensive experiments showcase the superior performance of our method on both synthetic data and real world data captured by different imaging setups. The code and data are available at https: //github. com/Depth2World/Dynamic_NLOS.

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IJCAI Conference 2023 Conference Paper

SSML-QNet: Scale-Separative Metric Learning Quadruplet Network for Multi-modal Image Patch Matching

  • Xiuwei Zhang
  • Yi Sun
  • Yamin Han
  • Yanping Li
  • Hanlin Yin
  • Yinghui Xing
  • Yanning Zhang

Multi-modal image matching is very challenging due to the significant diversities in visual appearance of different modal images. Typically, the existing well-performed methods mainly focus on learning invariant and discriminative features for measuring the relation between multi-modal image pairs. However, these methods often take the features as a whole and largely overlook the fact that different scale features for a same image pair may have different similarity, which may lead to sub-optimal results only. In this work, we propose a Scale-Separative Metric Learning Quadruplet network (SSML-QNet) for multi-modal image patch matching. Specifically, SSML-QNet can extract both relevant and irrelevant features of imaging modality with the proposed quadruplet network architecture. Then, the proposed Scale-Separative Metric Learning module separately encodes the similarity of different scale features with the pyramid structure. And for each scale, cross-modal consistent features are extracted and measured by coordinate and channel-wise attention sequentially. This makes our network robust to appearance divergence caused by different imaging mechanism. Experiments on the benchmark dataset (VIS-NIR, VIS-LWIR, Optical-SAR, and Brown) have verified that the proposed SSML-QNet is able to outperform other state-of-the-art methods. Furthermore, the cross-dataset transferring experiments on these four datasets also have shown that the proposed method has powerful ability of cross-dataset transferring.

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YNIMG Journal 2023 Journal Article

Vascular-water-exchange MRI (VEXI) enables the detection of subtle AXR alterations in Alzheimer's disease without MRI contrast agent, which may relate to BBB integrity

  • Yifan Zhang
  • Yue Wang
  • Zhaoqing Li
  • Zejun Wang
  • Juange Cheng
  • Xiaoyan Bai
  • Yi-Cheng Hsu
  • Yi Sun

Blood-brain barrier (BBB) impairment is an important pathophysiological process in Alzheimer's disease (AD) and a potential biomarker for early diagnosis of AD. However, most current neuroimaging methods assessing BBB function need the injection of exogenous contrast agents (or tracers), which limits the application of these methods in a large population. In this study, we aim to explore the feasibility of vascular water exchange MRI (VEXI), a diffusion-MRI-based method proposed to assess the BBB permeability to water molecules without using a contrast agent, in the detection of the BBB breakdown in AD. We tested VEXI on a 3T MRI scanner on three groups: AD patients (AD group), mild cognitive impairment (MCI) patients due to AD (MCI group), and the age-matched normal cognition subjects (NC group). Interestingly, we find that the apparent water exchange across the BBB (AXRBBB) measured by VEXI shows higher values in MCI compared with NC, and this higher AXRBBB happens specifically in the hippocampus. This increase in AXRBBB value gets larger and extends to more brain regions (medial orbital frontal cortex and thalamus) from MCI group to the AD group. Furthermore, we find that the AXRBBB values of these three regions is correlated significantly with the impairment of respective cognitive domains independent of age, sex and education. These results suggest VEXI is a promising method to assess the BBB breakdown in AD.

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ICLR Conference 2022 Conference Paper

Bandit Learning with Joint Effect of Incentivized Sampling, Delayed Sampling Feedback, and Self-Reinforcing User Preferences

  • Tianchen Zhou
  • Jia Liu 0002
  • Chaosheng Dong
  • Yi Sun

In this paper, we consider a new multi-armed bandit (MAB) framework motivated by three common complications in online recommender systems in practice: (i) the platform (learning agent) cannot sample an intended product directly and has to incentivize customers to select this product (e.g., promotions and coupons); (ii) customer feedbacks are often received later than their selection times; and (iii) customer preferences among products are influenced and reinforced by historical feedbacks. From the platform's perspective, the goal of the MAB framework is to maximize total reward without incurring excessive incentive costs. A major challenge of this MAB framework is that the loss of information caused by feedback delay complicates both user preference evolution and arm incentivizing decisions, both of which are already highly non-trivial even by themselves. Toward this end, we first propose a policy called ``UCB-Filtering-with-Delayed-Feedback'' (UCB-FDF) policy for this new MAB framework. In our analysis, we consider delayed feedbacks that can have either arm-independent or arm-dependent distributions. In both cases, we allow unbounded support for the random delays, i.e., the random delay can be infinite. We show that the delay impacts in both cases can still be upper bounded by an additive penalty on both the regret and total incentive costs. This further implies that logarithmic regret and incentive cost growth rates are achievable under this new MAB framework. Experimental results corroborate our theoretical analysis on both regret and incentive costs.

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IROS Conference 2022 Conference Paper

Cross-modal Fusion-based Prior Correction for Road Detection in Off-road Environments

  • Yuru Wang
  • Yi Sun
  • Jian Li 0003
  • Meiping Shi

Road detection plays a fundamental role in the visual navigation system of autonomous vehicles. However, it's still challenging to achieve robust road detection in off-road scenarios due to their complicated road appearances and ambiguous road structures. Therefore, existing image-based road detection approaches usually fail to extract the right routes due to the lack of the effective fusion of the image and prior reference paths(road guidances generated via map annotations and GPS localization). Besides, the reference paths are not always reliable because of GPS localization errors and mapping errors. To achieve robust road detection in off-road scenarios, we propose a prior-correction-based road detection network named PR-ROAD via fusing the cross-model information provided by both the reference path and the input image. These two heterogeneous data, prior and image, are deeply fused by a cross-attention module and formulate contextual inter-dependencies. We conduct experiments in our collected rural, off-road and urban datasets. The experimental results demonstrate the effectiveness of the proposed method both on unstructured and structured roads.

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YNIMG Journal 2022 Journal Article

Deciphering the developmental order and microstructural patterns of early white matter pathways in a diffusion MRI based fetal brain atlas

  • Ruike Chen
  • Cong Sun
  • Tingting Liu
  • Yuhao Liao
  • Junyan Wang
  • Yi Sun
  • Yi Zhang
  • Guangbin Wang

White matter (WM) of the fetal brain undergoes rapid development to form early structural connections. Diffusion magnetic resonance imaging (dMRI) has shown to be a useful tool to depict fetal brain WM in utero, and many studies have observed increasing fractional anisotropy and decreasing diffusivity in the fetal brain during the second-to-third trimester, whereas others reported non-monotonic changes. Unbiased dMRI atlases of the fetal brain are important for characterizing the developmental trajectories of WM and providing normative references for in utero diagnosis of prenatal abnormalities. To date, the sole fetal brain dMRI atlas was collected from a Caucasian/mixed population and was constructed based on the diffusion tensor model with limited spatial resolution. In this work, we proposed a fiber orientation distribution (FOD) based pipeline for generating fetal brain dMRI atlases, which showed better registration accuracy than a diffusion tensor based pipeline. Based on the FOD-based pipeline, we constructed the first Chinese fetal brain dMRI atlas using 89 dMRI scans of normal fetuses at gestational age between 24 and 38 weeks. Complex non-monotonic trends of tensor- and FOD-derived microstructural parameters in eight WM tracts were observed, which jointly pointed to different phases of microstructural development. Specifically, we speculated that the turning point of the diffusivity trajectory may correspond to the starting point of pre-myelination, based on which, the developmental order of WM tracts can be mapped and the order was in agreement with the order of myelination from histological studies. The normative atlas also provided a reference for the detection of abnormal WM development, such as that in congenital heart disease. Therefore, the established high-order fetal brain dMRI atlas depicted the spatiotemporal pattern of early WM development, and findings may help decipher the distinct microstructural events in utero.

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JBHI Journal 2022 Journal Article

Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia

  • Yi Sun
  • Zhe Zhang
  • Ioannis Kakkos
  • George K. Matsopoulos
  • Jingjia Yuan
  • John Suckling
  • Luoyi Xu
  • Shuxia Cao

The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.

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AAAI Conference 2022 Conference Paper

Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation

  • Wanli Peng
  • Jianhang Yan
  • Hongtao Wen
  • Yi Sun

Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instancelevel 6D pose estimation. However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects which makes them difficult to be applied in real scenarios. To address this problem, we propose a self-supervised framework for category-level 6D pose estimation in this paper. We leverage DeepSDF as a 3D object representation and design several novel loss functions based on DeepSDF to help the self-supervised model predict unseen object poses without any 6D object pose labels and explicit 3D models in real scenarios. Experiments demonstrate that our method achieves comparable performance with the state-of-the-art fully supervised methods on the category-level NOCS benchmark.

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YNIMG Journal 2022 Journal Article

The direction-dependence of apparent water exchange rate in human white matter

  • Zhaoqing Li
  • Zhenfeng Pang
  • Juange Cheng
  • Yi-Cheng Hsu
  • Yi Sun
  • Evren Özarslan
  • Ruiliang Bai

Transmembrane water exchange is a potential biomarker in the diagnosis and understanding of cancers, brain disorders, and other diseases. Filter-exchange imaging (FEXI), a special case of diffusion exchange spectroscopy adapted for clinical applications, has the potential to reveal different physiological water exchange processes. However, it is still controversial whether modulating the diffusion encoding gradient direction can affect the apparent exchange rate (AXR) measurements of FEXI in white matter (WM) where water diffusion shows strong anisotropy. In this study, we explored the diffusion-encoding direction dependence of FEXI in human brain white matter by performing FEXI with 20 diffusion-encoding directions on a clinical 3T scanner in-vivo. The results show that the AXR values measured when the gradients are perpendicular to the fiber orientation (0. 77 ± 0. 13 s − 1, mean ± standard deviation of all the subjects) are significantly larger than the AXR estimates when the gradients are parallel to the fiber orientation (0. 33 ± 0. 14 s − 1, p < 0. 001) in WM voxels with coherently-orientated fibers. In addition, no significant correlation is found between AXRs measured along these two directions, indicating that they are measuring different water exchange processes. What's more, only the perpendicular AXR rather than the parallel AXR shows dependence on axonal diameter, indicating that the perpendicular AXR might reflect transmembrane water exchange between intra-axonal and extra-cellular spaces. Further finite difference (FD) simulations having three water compartments (intra-axonal, intra-glial, and extra-cellular spaces) to mimic WM micro-environments also suggest that the perpendicular AXR is more sensitive to the axonal water transmembrane exchange than parallel AXR. Taken together, our results show that AXR measured along different directions could be utilized to probe different water exchange processes in WM.

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JBHI Journal 2021 Journal Article

EEG Fingerprints of Task-Independent Mental Workload Discrimination

  • Ioannis Kakkos
  • Georgios N. Dimitrakopoulos
  • Yi Sun
  • Jingjia Yuan
  • George K. Matsopoulos
  • Anastasios Bezerianos
  • Yu Sun

In the nascent field of neuroergonomics, mental workload assessment is one of the most important issues and has an apparent significance in real-world applications. Although prior research has achieved efficient single-task classification, scatted studies on cross-task mental workload assessment usually result in unsatisfactory performance. Here, we introduce a data-driven analysis framework to overcome the challenges regarding task-independent workload assessment using a fusion of EEG spectral characteristics and unveil the common neural mechanisms underlying mental workload. Specifically, multi-frequency power spectrum and functional connectivity (FC) were estimated for two workload levels in two working-memory tasks performed by 40 healthy participants, subsequently being fed into a machine learning approach to obtain the importance of each feature vector and evaluate classification performance in a cross-task fashion. Our framework achieved a classification accuracy of 0. 94 for task-independent mental workload discrimination. Further investigation of the designated features in terms of their spectral and localization properties revealed task-independent common patterns in the neural mechanisms governing workload. In particular, increased workload was associated with elevated frontal delta and theta power but reduced parietal alpha power, whereas FC exhibited complex frequency- and region-dependent alterations. By implication, the employment of the EEG feature fusion emphasized their utility in serving as promising indicators for different workload conditions applications.

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NeurIPS Conference 2021 Conference Paper

How Data Augmentation affects Optimization for Linear Regression

  • Boris Hanin
  • Yi Sun

Though data augmentation has rapidly emerged as a key tool for optimization in modern machine learning, a clear picture of how augmentation schedules affect optimization and interact with optimization hyperparameters such as learning rate is nascent. In the spirit of classical convex optimization and recent work on implicit bias, the present work analyzes the effect of augmentation on optimization in the simple convex setting of linear regression with MSE loss. We find joint schedules for learning rate and data augmentation scheme under which augmented gradient descent provably converges and characterize the resulting minimum. Our results apply to arbitrary augmentation schemes, revealing complex interactions between learning rates and augmentations even in the convex setting. Our approach interprets augmented (S)GD as a stochastic optimization method for a time-varying sequence of proxy losses. This gives a unified way to analyze learning rate, batch size, and augmentations ranging from additive noise to random projections. From this perspective, our results, which also give rates of convergence, can be viewed as Monro-Robbins type conditions for augmented (S)GD.

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IJCAI Conference 2021 Conference Paper

Towards Reducing Biases in Combining Multiple Experts Online

  • Yi Sun
  • Iván Ramírez Díaz
  • Alfredo Cuesta Infante
  • Kalyan Veeramachaneni

In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person’s fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where the fairness metric we consider is equalized odds. Our work follows from the classical learning-from-experts scheme, assuming a finite set of classifiers (human experts, rules, options, etc) that cannot be modified. We run separate instances of the algorithm for each label class as well as sensitive groups, where the probability of choosing each instance is optimized for both fairness and regret. Our theoretical results show that approximately equalized odds can be achieved without sacrificing much regret. We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community.

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YNIMG Journal 2020 Journal Article

A 16-channel AC/DC array coil for anesthetized monkey whole-brain imaging at 7T

  • Yang Gao
  • Azma Mareyam
  • Yi Sun
  • Thomas Witzel
  • Nicolas Arango
  • Irene Kuang
  • Jacob White
  • Anna Wang Roe

Functional magnetic resonance imaging (fMRI) in monkeys is important for bridging the gap between invasive animal brain studies and non-invasive human brain studies. To resolve the finer functional structure of the monkey brain, ultra-high-field (UHF) MR is essential, and high-performance, close-fitting RF receive coils are typically desired to fully leverage the intrinsic gains provided by UHF MRI. Moreover, static field (B0) inhomogeneity arising from the tissue susceptibility interface is more severe at UHF, presenting an obstacle to achieving high-resolution fMRI. B0 shim of the monkey head is challenging due to its smaller size and more complex sources of B0 offsets in multi-modal imaging tasks. In the present work, we have customized an array coil for lightly-anesthetized monkey fMRI in the 7T human scanner that combines RF and multi-coil (MC) B0 shim functionality (also referred to as AC/DC coils) to provide high imaging SNR and high-spatial-order, rapidly switchable B0-shim capability. Additional space was retained on the coil to render it compatible with monkey multi-modal imaging studies. Both MC global (whole-volume) and dynamic (slice-optimized) shim methods were tested and evaluated, and the benefits of MC shim for fMRI experiments was also studied. A minor reduction in RF coil performance was found after introducing additional B0 shim circuitry. However, the proposed RF coil provided higher image SNR and more uniform contrast compared to a commercially available coil for human knee imaging. Compared with static 2nd-order shim, the B0 inhomogeneity was reduced by 56.8%, and 95-percentile B0 offset was reduced to within 28.2 Hz through MC shim, versus 68.7 Hz with 2nd-order static shim. As a result, functional image quality could be improved, and brain activation can be better detected using the proposed AC/DC monkey coil.

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YNIMG Journal 2020 Journal Article

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times

  • Ting Gong
  • Qiqi Tong
  • Hongjian He
  • Yi Sun
  • Jianhui Zhong
  • Hui Zhang

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.

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AAAI Conference 2019 Conference Paper

Learning Vine Copula Models for Synthetic Data Generation

  • Yi Sun
  • Alfredo Cuesta-Infante
  • Kalyan Veeramachaneni

A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of loglikelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

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NeurIPS Conference 2015 Conference Paper

From random walks to distances on unweighted graphs

  • Tatsunori Hashimoto
  • Yi Sun
  • Tommi Jaakkola

Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. Despite the significance of this problem, statistical characterization of the proposed metrics has been limited. We introduce and develop a class of techniques for analyzing random walks on graphs using stochastic calculus. Using these techniques we generalize results on the degeneracy of hitting times and analyze a metric based on the Laplace transformed hitting time (LTHT). The metric serves as a natural, provably well-behaved alternative to the expected hitting time. We establish a general correspondence between hitting times of the Brownian motion and analogous hitting times on the graph. We show that the LTHT is consistent with respect to the underlying metric of a geometric graph, preserves clustering tendency, and remains robust against random addition of non-geometric edges. Tests on simulated and real-world data show that the LTHT matches theoretical predictions and outperforms alternatives.

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NeurIPS Conference 2014 Conference Paper

Deep Learning Face Representation by Joint Identification-Verification

  • Yi Sun
  • Yuheng Chen
  • Xiaogang Wang
  • Xiaoou Tang

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 features extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 features extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset, 99. 15% face verification accuracy is achieved. Compared with the best previous deep learning result on LFW, the error rate has been significantly reduced by 67%.

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JMLR Journal 2014 Journal Article

Natural Evolution Strategies

  • Daan Wierstra
  • Tom Schaul
  • Tobias Glasmachers
  • Yi Sun
  • Jan Peters
  • Jürgen Schmidhuber

This paper presents Natural Evolution Strategies (NES), a recent family of black-box optimization algorithms that use the natural gradient to update a parameterized search distribution in the direction of higher expected fitness. We introduce a collection of techniques that address issues of convergence, robustness, sample complexity, computational complexity and sensitivity to hyperparameters. This paper explores a number of implementations of the NES family, such as general-purpose multi-variate normal distributions and separable distributions tailored towards search in high dimensional spaces. Experimental results show best published performance on various standard benchmarks, as well as competitive performance on others. [abs] [ pdf ][ bib ] &copy JMLR 2014. ( edit, beta )

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IROS Conference 2013 Conference Paper

Characterization of silicone rubber based soft pneumatic actuators

  • Yi Sun
  • Yun Seong Song
  • Jamie Paik

Conventional pneumatic actuators have been a popular choice due to their decent force/torque output. Nowadays, new generation of pneumatic actuator made out of highly compliant elastomers, which we call soft pneumatic actuators (SPA), are drawing increasing attention due to their ease of fabrication, high customizability and innately softness. However, there is no effective method presented to characterize and understand these actuators, such as to measure the force and torque output, range of motion and the speed of actuation. In this work, we present two types of SPAs: bending and rotary actuators. In addition, we have developed two measurement setups to characterize actuators of different geometries. The measured force/torque outputs of different actuators are presented and analyzed. Step responses to certain pressure input are presented and discussed. A simple model is presented to provide physical insight to the observed behavior of the soft actuators. This work provides the basis for designing customized SPAs with application-specific requirements.

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TCS Journal 2013 Journal Article

Nordhaus–Gaddum-type inequality for the hyper-Wiener index of graphs when decomposing into three parts

  • Guifu Su
  • Liming Xiong
  • Yi Sun
  • Daobin Li

Let k ≥ 2 be an integer, a k -decomposition ( G 1, G 2, ⋯, G k ) of a graph G is a partition of its edge set to form k spanning subgraphs G 1, G 2, …, G k. The hyper-Wiener index W W is one of the recently conceived distance-based graph invariants (Randi 1993 [15]): W W = W W ( G ): = 1 2 W ( G ) + 1 2 W 2 ( G ), where W is the Wiener index (Wiener 1947 [18]) and W 2 is the sum of squares of distance of all pairs of vertices in G. In this paper, we investigate the Nordhaus–Gaddum-type inequality of a 3 -decomposition of K n for the hyper-Wiener index: 7 n 2 ≤ W W ( G 1 ) + W W ( G 2 ) + W W ( G 3 ) ≤ 2 n + 2 4 + n 2 + 4 ( n − 1 ). The corresponding extremal graphs are characterized.

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IROS Conference 2013 Conference Paper

Sensor and actuator integrated low-profile robotic origami

  • Amir Firouzeh
  • Yi Sun
  • Hyunchul Lee
  • Jamie Paik

The robotic origami (Robogami) is a low-profile, sheet-like robot with multi degrees-of-freedom (DoF) that embeds different functional layers. Due to its planar form, it can take advantage of precise 2D fabrication methods usually reserved for micro and nano systems. Not only can these methods reduce fabrication time and expenses, by offering a high precision, they enable us to integrate actuators, sensors and electronic components into a thin sheet. In this research, we study sensors, actuators and fabrication methods for Robogami which can reconfigure into various forms. Our main objective is to develop technologies that can be easily applied to Robogamis consisting of many active folds and DoFs. In this paper, after studying the performance of the proposed sensors and actuators in one fold, we use a design for a crawler robot consisting of four folds to assess the performance of these technologies.

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IROS Conference 2013 Conference Paper

Soft robot for gait rehabilitation of spinalized rodents

  • Yun Seong Song
  • Yi Sun
  • Rubia van den Brand
  • Joachim von Zitzewitz
  • Silvestro Micera
  • Grégoire Courtine
  • Jamie Paik

Soft actuators made of highly elastic polymers allow novel robotic system designs, yet application-specific soft robotic systems are rarely reported. Taking notice of the characteristics of soft pneumatic actuators (SPAs) such as high customizability and low inherent stiffness, we report in this work the use of soft pneumatic actuators for a biomedical use - the development of a soft robot for rodents, aimed to provide a physical assistance during gait rehabilitation of a spinalized animal. The design requirements to perform this unconventional task are introduced. Customized soft actuators, soft joints and soft couplings for the robot are presented. Live animal experiment was performed to evaluate and show the potential of SPAs for their use in the current and future biomedical applications.

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NeurIPS Conference 2010 Conference Paper

Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices

  • Yi Sun
  • Jürgen Schmidhuber
  • Faustino Gomez

We present a new way of converting a reversible finite Markov chain into a nonreversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based on the non-reversible chain is reduced. The method is applicable to any reversible chain whose states are not connected through a tree, and can be interpreted graphically as inserting vortices into the state transition graph. Our result confirms that non-reversible chains are fundamentally better than reversible ones in terms of asymptotic performance, and suggests interesting directions for further improving MCMC.

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JMLR Journal 2010 Journal Article

PyBrain

  • Tom Schaul
  • Justin Bayer
  • Daan Wierstra
  • Yi Sun
  • Martin Felder
  • Frank Sehnke
  • Thomas Rückstieß
  • Jürgen Schmidhuber

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep belief networks. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

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