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Jing Yuan

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22 papers

2 author rows

AAAI Conference 2026 Conference Paper

JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction

Yuhao Zhan
Yuqing Zhang
Jing Yuan
Qixiang Ma
Zhiqi Yang
Yu Gu
Zemin Liu
Fei Wu

Existing Grammatical Error Correction (GEC) systems suffer from limited reference diversity, leading to underestimated evaluation and restricted model generalization. To address this issue, we introduce the Judge of Edit-Level Validity (JELV), an automated framework to validate correction edits from grammaticality, faithfulness, and fluency. Using our proposed human-annotated Pair-wise Edit-level Validity Dataset (PEVData) as benchmark, JELV offers two implementations: a multi-turn LLM-as-Judges pipeline achieving 90% agreement with human annotators, and a distilled DeBERTa classifier with 85% precision on valid edits. We then apply JELV to reclassify misjudged false positives in evaluation and derive a comprehensive evaluation metric by integrating false positive decoupling and fluency scoring, resulting in state-of-the-art correlation with human judgments. We also apply JELV to filter LLM-generated correction candidates, expanding the BEA19's single-reference dataset containing 38,692 source sentences. Retraining top GEC systems on this expanded dataset yields measurable performance gains. JELV provides a scalable solution for enhancing reference diversity and strengthening both evaluation and model generalization.

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AAAI Conference 2026 Conference Paper

Learning to Optimize Job Shop Scheduling Under Structural Uncertainty

Rui Zhang
Jianwei Niu
Xuefeng Liu
Shaojie Tang
Jing Yuan

The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the quality of intermediate products) that cannot be known in advance. Existing methods struggle to address this challenge due to incorrect credit assignment: a high-quality action may be unfairly penalized if it is followed by a time-consuming path. To address this problem, we propose a novel method named UP-AAC. In contrast to conventional actor-critic methods, UP-AAC employs an asymmetric architecture. While its actor receives a standard stochastic state, the critic is crucially provided with a deterministic state reconstructed in hindsight. This design allows the critic to learn a more accurate value function, which in turn provides a lower-variance policy gradient to the actor, leading to more stable learning. In addition, we design an attention-based Uncertainty Perception Model (UPM) to enhance the actor's scheduling decisions. Extensive experiments demonstrate that our method outperforms existing approaches in reducing makespan on benchmark instances.

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TCS Journal 2025 Journal Article

Approximating decision trees with priority hypotheses

Jing Yuan
Shaojie Tang

This paper addresses the problem of creating decision trees for identifying hypotheses, also known as entities, in a setting where the cost of an action is dependent on the true hypothesis. Specifically, we consider the scenario where n hypotheses are divided into m groups based on their priority levels. Taking an action on a higher priority hypothesis incurs a higher cost. This is relevant to many real-world applications where cost-sensitive decisions need to be made. For example, in a medical diagnosis task, the goal is to take a series of actions (such as medical tests) to identify a cause. Each action in this process requires conducting a test on the patient and observing the outcome, which can take anywhere from a few minutes to several weeks depending on the test. In this case, the cost (the result of waiting for the outcome) is higher if the true hypothesis is more time-sensitive. For example, if the true hypothesis is toxic chemical exposure (as opposed to a chronic disease such as diabetes), a delay of a few minutes could significantly increase the patient's risk of mortality. We propose a group greedy algorithm to solve this problem. We demonstrate that under worst-case scenarios, our algorithm has an approximation ratio of O ( m log ⁡ n ). Importantly, when m = 1, meaning there is only one group of hypotheses, our result is consistent with the logarithmic approximation bound for the traditional optimal decision tree problem.

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NeurIPS Conference 2025 Conference Paper

BMW: Bidirectionally Memory bank reWriting for Unsupervised Person Re-Identification

Xiaobin Liu
Jianing Li
Baiwei Guo
Jing Yuan

Recent works show that contrastive learning based on memory banks is an effective framework for unsupervised person Re-IDentification (ReID). In existing methods, memory banks are typically initialized with cluster centroids and rewritten with positive samples via the momentum mechanism along with the model training. However, this mechanism solely focuses on the intra-class compactness by pulling memory banks close to positive samples, neglecting the inter-class separability among different memory banks. Rewriting memory banks with partial constraint limits their discrimination capacities, and hence hinders learning discriminative features based on those memory banks. In this paper, we claim that memory banks should be rewritten with both intra-class and inter-class constraints, and therefore propose a unified memory bank rewriting mechanism, Bidirectionally Memory bank reWriting (BMW), to chase enhanced discrimination capacity. Specifically, BMW formulates the memory bank rewriting as the gradient descent update with two objectives, i. e. , reducing intra-class diversity and enhancing inter-class separability. To effectively enhance the separability of memory banks with limited number of rewriting steps, we further design a novel objective formulation for the inter-class constraint, which is more effective for one step update. BMW enhances both representation and discrimination capacities of memory banks, thus leads to an effective ReID feature optimization. BMW is simple yet effective and can serve as a new paradigm for person ReID methods based on memory banks. Extensive experiments on standard benchmarks demonstrate the effectiveness of our BMW method in unsupervised ReID model training. Specially, BMW even outperforms previous methods that use stronger backbones. Code is available at https: //github. com/liu-xb/BMW.

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ICLR Conference 2025 Conference Paper

CryoFM: A Flow-based Foundation Model for Cryo-EM Densities

Yi Zhou
Yilai Li
Jing Yuan
Quanquan Gu

Cryo-electron microscopy (cryo-EM) is a powerful technique in structural biology and drug discovery, enabling the study of biomolecules at high resolution. Significant advancements by structural biologists using cryo-EM have led to the production of around 40k protein density maps at various resolutions. However, cryo-EM data processing algorithms have yet to fully benefit from our knowledge of biomolecular density maps, with only a few recent models being data-driven but limited to specific tasks. In this study, we present CryoFM, a foundation model designed as a generative model, learning the distribution of high-quality density maps and generalizing effectively to downstream tasks. Built on flow matching, CryoFM is trained to accurately capture the prior distribution of biomolecular density maps. Furthermore, we introduce a flow posterior sampling method that leverages CryoFM as a flexible prior for several downstream tasks in cryo-EM and cryo-electron tomography (cryo-ET) without the need for fine-tuning, achieving state-of-the-art performance on most tasks and demonstrating its potential as a foundational model for broader applications in these fields.

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EAAI Journal 2025 Journal Article

Interpretable adaptive multiwavelet kernel-driven two-dimensional convolutional neural network for mechanical fault diagnosis

Tianheng Hai
Jing Yuan
Huiming Jiang
Qian Zhao

Despite extensive research on convolutional neural networks (CNNs) for intelligent fault diagnosis, several challenges remain, including the limited effectiveness of one-dimensional CNNs, susceptibility to noise, and lack of interpretability. To address these issues, an interpretable adaptive multiwavelet kernel-driven two-dimensional convolutional neural network model called MWKN has been designed for mechanical fault diagnosis in this paper. Specifically, a newly designed adaptive natural convolutional layer based on two-dimensional multiwavelet transform is embedded as a feature extraction module in the shallow layer of a two-dimensional CNN model. Crucially, this novel multiwavelet convolutional layer is jointly optimized with the entire network, enabling the adaptive optimization of its intrinsic multiwavelet convolutional kernel. Additionally, this model incorporates a specifically designed embedded two-dimensional neighboring coefficient shrinkage module to address the issue of CNN susceptibility to strong noise. This study investigates the interpretability of the MWKN model through simulated fault experiments, addressing interpretability deficits observed in CNN. The results demonstrate that the embedded multiwavelet kernel and the inclusive WMKN model as a whole possess synchronized learning and matching rules, substantiating that the learning process of the multiwavelet kernel is neither isolated nor random but follows an inherent adaptive learning mechanism based on the principle of error minimization. Finally, the excellent fault identification capability and robust noise resistance of MWKN are validated through experimental cases of variable speed bearing faults and pump circulation bearing faults under strong noise background. In addition, the industrial applicability and interpretability of the method were further validated in an industrial scenario case.

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TCS Journal 2025 Journal Article

Submodular participatory budgeting

Jing Yuan
Shaojie Tang

Participatory budgeting refers to the practice of allocating public resources by collecting and aggregating individual preferences. Most existing studies in this field often assume an additive utility function, where each individual holds a private utility for each candidate project, and the total utility of a set of funded projects is simply the sum of the utilities of all projects. We argue that this assumption does not always hold in reality. For example, building two playgrounds in the same neighborhood does not necessarily lead to twice the utility of building a single playground. To address this, we extend the existing study by proposing a submodular participatory budgeting problem, assuming that the utility function of each individual is a monotone and submodular function over funded projects. We propose and examine three preference elicitation methods and analyze their performances in terms of distortion. Notably, if the utility function is additive, our aggregation rule designed for threshold approval votes achieves a better distortion than the state-of-the-art approach.

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NeurIPS Conference 2025 Conference Paper

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Xinghao Wu
Xuefeng Liu
Jianwei Niu
Guogang Zhu
Mingjia Shi
Shaojie Tang
Jing Yuan

Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model’s performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to process an individual model adapted to its local distribution. Many existing methods assume that certain global model parameters are difficult to train effectively in a collaborative manner under heterogeneous data. Consequently, they localize or fine-tune these parameters to obtain personalized models. In this paper, we reveal that both the feature extractor and classifier of the global model are inherently strong, and the primary cause of its suboptimal performance is the mismatch between local features and the global classifier. Although existing methods alleviate this mismatch to some extent and improve performance, we find that they either (1) fail to fully resolve the mismatch while degrading the feature extractor, or (2) address the mismatch only post-training, allowing it to persist during training. This increases inter-client gradient divergence, hinders model aggregation, and ultimately leaves the feature extractor suboptimal for client data. To address this issue, we propose FedPFT, a novel framework that resolves the mismatch during training using personalized prompts. These prompts, along with local features, are processed by a shared self-attention-based transformation module, ensuring alignment with the global classifier. Additionally, this prompt-driven approach offers strong flexibility, enabling task-specific prompts to incorporate additional training objectives (\eg, contrastive learning) to further enhance the feature extractor. Extensive experiments show that FedPFT outperforms state-of-the-art methods by up to 5. 07%, with further gains of up to 7. 08% when collaborative contrastive learning is incorporated.

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AAAI Conference 2024 Conference Paper

Non-monotone Sequential Submodular Maximization

Shaojie Tang
Jing Yuan

In this paper, we study a fundamental problem in submodular optimization known as sequential submodular maximization. The primary objective of this problem is to select and rank a sequence of items to optimize a group of submodular functions. The existing research on this problem has predominantly concentrated on the monotone setting, assuming that the submodular functions are non-decreasing. However, in various real-world scenarios, like diversity-aware recommendation systems, adding items to an existing set might negatively impact the overall utility. In response, we propose to study this problem with non-monotone submodular functions and develop approximation algorithms for both flexible and fixed length constraints, as well as a special case with identical utility functions. The empirical evaluations further validate the effectiveness of our proposed algorithms in the domain of video recommendations.

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TCS Journal 2023 Journal Article

Streaming adaptive submodular maximization

Shaojie Tang
Jing Yuan

Adaptive submodular maximization has been extensively studied in the literature. However, most of existing studies in this field focus on pool-based setting, where one is allowed to pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.

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AAMAS Conference 2023 Conference Paper

Worst-Case Adaptive Submodular Cover

Jing Yuan
Shaojie Tang

In this paper, we study the adaptive submodular cover problem under the worst-case setting. This problem generalizes many previously studied problems, namely, the pool-based active learning and the stochastic submodular set cover. The input of our problem is a set of items (e. g. , medical tests) and each item has a random state (e. g. , the outcome of a medical test), whose realization is initially unknown. One must select an item at a fixed cost in order to observe its realization. There is a utility function which maps a subset of items and their states to a non-negative real number. We aim to sequentially select a group of items to achieve a “target value” while minimizing the maximum cost across realizations (a. k. a. worst-case cost). To facilitate our study, we assume that the utility function is worst-case submodular, a property that is commonly found in many machine learning applications. With this assumption, we develop a tight (log(𝑄/𝜂) + 1)-approximation policy, where 𝑄 is the “target value” and 𝜂 is the smallest difference between 𝑄 and any achievable utility value ˆ 𝑄 < 𝑄. We also study a worst-case maximum-coverage problem, a dual problem of the minimum-costcover problem, whose goal is to select a group of items to maximize its worst-case utility subject to a budget constraint. To solve this problem, we develop a (1 − 1/𝑒)/2-approximation solution.

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NeurIPS Conference 2022 Conference Paper

Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection

Jing Yuan
Panagiotis Barmpoutis
Tania Stathaki

Vision transformers have demonstrated remarkable performance on a variety of computer vision tasks. In this paper, we illustrate the effectiveness of the deformable vision transformer for single-stage pedestrian detection and propose a spatial and multi-scale feature enhancement module, which aims to achieve the optimal balance between speed and accuracy. Performance improvement with vision transformers on various commonly used single-stage structures is demonstrated. The design of the proposed architecture is investigated in depth. Comprehensive comparisons with state-of-the-art single- and two-stage detectors on different pedestrian datasets are performed. The proposed detector achieves leading performance on Caltech and Citypersons datasets among single- and two-stage methods using fewer parameters than the baseline. The log-average miss rates for Reasonable and Heavy are decreased to 2. 6% and 28. 0% on the Caltech test set, and 10. 9% and 38. 6% on the Citypersons validation set, respectively. The proposed method outperforms SOTA two-stage detectors in the Heavy subset on the Citypersons validation set with considerably faster inference speed.

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AAAI Conference 2022 Conference Paper

Optimal Sampling Gaps for Adaptive Submodular Maximization

Shaojie Tang
Jing Yuan

Running machine learning algorithms on large and rapidly growing volumes of data is often computationally expensive, one common trick to reduce the size of a data set, and thus reduce the computational cost of machine learning algorithms, is probability sampling. It creates a sampled data set by including each data point from the original data set with a known probability. Although the benefit of running machine learning algorithms on the reduced data set is obvious, one major concern is that the performance of the solution obtained from samples might be much worse than that of the optimal solution when using the full data set. In this paper, we examine the performance loss caused by probability sampling in the context of adaptive submodular maximization. We consider a simple probability sampling method which selects each data point with probability at least r ∈ [0, 1]. If we set r = 1, our problem reduces to finding a solution based on the original full data set. We define sampling gap as the largest ratio between the optimal solution obtained from the full data set and the optimal solution obtained from the samples, over independence systems. Our main contribution is to show that if the sampling probability of each data point is at least r and the utility function is policywise submodular, then the sampling gap is both upper bounded and lower bounded by 1/r. We show that the property of policywise submodular can be found in a wide range of real-world applications, including pool-based active learning and adaptive viral marketing.

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AAMAS Conference 2021 Conference Paper

Adaptive Cascade Submodular Maximization

Shaojie Tang
Jing Yuan

In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting. The input of our problem is a set of items, each item is in a particular state (i. e. , the marginal contribution of an item) which is drawn from a known probability distribution. However, we can not know its actual state before selecting it. As compared with existing studies on stochastic submodular maximization, one unique setting of our problem is that each item is associated with a continuation probability which represents the probability that one is allowed to continue to select the next item after selecting the current one. Intuitively, this term captures the externality of selecting one item to all its subsequent items in terms of the opportunity of being selected. Therefore, the actual set of items that can be selected by a policy depends on the specific ordering it adopts to select items, this makes our problem fundamentally different from classical submodular set optimization problems. Our objective is to identify the best sequence of selecting items so as to maximize the expected utility of the selected items. We propose a class of stochastic utility functions, adaptive cascade submodular functions, and show that the objective functions in many practical application domains satisfy adaptive cascade submodularity. Then we develop a 0. 12 approximation algorithm to the adaptive cascade submodular maximization problem.

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AAAI Conference 2021 Conference Paper

Variational Fair Clustering

Imtiaz Masud Ziko
Jing Yuan
Eric Granger
Ismail Ben Ayed

We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. Fundamentally different from the existing combinatorial and spectral solutions, our variational multiterm approach enables to control the trade-off levels between the fairness and clustering objectives. We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker’s inequality. Our tight upper bound can be jointly optimized with various clustering objectives, while yielding a scalable solution, with convergence guarantee. Interestingly, at each iteration, it performs an independent update for each assignment variable. Therefore, it can be easily distributed for large-scale datasets. This scalability is important as it enables to explore different trade-off levels between the fairness and clustering objectives. Unlike spectral relaxation, our formulation does not require computing its eigenvalue decomposition. We report comprehensive evaluations and comparisons with state-of-the-art methods over various fair clustering benchmarks, which show that our variational formulation can yield highly competitive solutions in terms of fairness and clustering objectives.

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TCS Journal 2020 Journal Article

A random algorithm for profit maximization in online social networks

Tiantian Chen
Bin Liu
Wenjing Liu
Qizhi Fang
Jing Yuan
Weili Wu

Given a social network G and a positive integer k, the influence maximization problem seeks for k nodes in G that can influence the largest number of nodes. This problem has found important applications, and a large amount of works have been devoted to identifying the few most influential users. But most of existing works only focus on the diffusion of a single idea or product in social networks. However, in reality, one company may produce multiple kinds of products and one user may also have multiple adoptions. For multiple kinds of different products with different activation costs and profits, it is crucial for the company to distribute the limited budget among multiple products in order to achieve profit maximization. The Profit Maximization with Multiple Adoptions (PM2A) problem aims to seek for a seed set within the budget to maximize the overall profit. In this paper, a Randomized Modified Greedy (RMG) algorithm based on the Reverse Influence Sampling (RIS) technique is presented for the PM2A problem, which could achieve a ( 1 − 1 / e − ε ) -approximate solution with high probability and is also the best performance ratio of the PM2A problem. Comprehensive experiments on three real-world social networks are conducted, and the results demonstrate that our RMG algorithm outperforms the algorithm proposed in [16] and other heuristics in terms of profit maximization, and could better allocate the budget.

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IJCAI Conference 2020 Conference Paper

Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding

Zhu Zhang
Zhou Zhao
Zhijie Lin
Baoxing Huai
Jing Yuan

Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of a queried object according to the given sentence. Currently, most existing grounding methods are restricted to well-aligned segment-sentence pairs. In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sentences. This challenging task requires to capture critical object relations to identify the queried target. However, existing approaches cannot distinguish notable objects and remain in ineffective relation modeling between unnecessary objects. Thus, we propose a novel object-aware multi-branch relation network for object-aware relation discovery. Concretely, we first devise multiple branches to develop object-aware region modeling, where each branch focuses on a crucial object mentioned in the sentence. We then propose multi-branch relation reasoning to capture critical object relationships between the main branch and auxiliary branches. Moreover, we apply a diversity loss to make each branch only pay attention to its corresponding object and boost multi-branch learning. The extensive experiments show the effectiveness of our proposed method.

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YNIMG Journal 2018 Journal Article

Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs

Jing Yuan
Xiang Li
Jinhe Zhang
Liao Luo
Qinglin Dong
Jinglei Lv
Yu Zhao
Xi Jiang

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks’ behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.

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IJCAI Conference 2017 Conference Paper

No Time to Observe: Adaptive Influence Maximization with Partial Feedback

Jing Yuan
Shaojie Tang

Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose partial-feedback model, which allows us to select a seed at any intermediate stage. We develop a novel alpha-greedy policy that achieves a bounded approximation ratio.

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YNIMG Journal 2015 Journal Article

3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets

Wu Qiu
Jing Yuan
Martin Rajchl
Jessica Kishimoto
Yimin Chen
Sandrine de Ribaupierre
Bernard Chiu
Aaron Fenster

Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i. e. , post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10–30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patient's fontanels have closed around 12–18months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice.

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TIST Journal 2013 Journal Article

A framework of traveling companion discovery on trajectory data streams

Lu-An Tang
Yu Zheng
Jing Yuan
Jiawei Han
Alice Leung
Wen-Chih Peng
Thomas La Porta

The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions ) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.

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ICRA Conference 2008 Conference Paper

Development of modular and reconfigurable robot with multiple working modes

Guangjun Liu
Xiaojia He
Jing Yuan
Sajan Abdul
Andrew A. Goldenberg

A modular and reconfigurable robot (MRR) with multiple working modes is developed for performing sophisticated tasks in uncontrolled environments. In the proposed MRR design, each joint module can independently work in active modes with position or torque control, or passive modes with friction compensation. Under a federated control system architecture, not only the MRR configuration can be reconfigured to adapt to various tasks, but also the working mode of each module, which can be switched on-line to satisfy the needs to carry out sophisticated tasks. Three joint modules have been developed, and the proposed method of passive working mode implementation with friction compensation has been tested experimentally. Door opening using a mobile manipulator consisting of the developed joint modules is studied as an application case study of the proposed multiple working mode MRR.

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