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Jing Chen

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44 papers

2 author rows

EAAI Journal 2026 Journal Article

Coconut germination precise prediction via multimodal fusion with Co-attention networks: A non-destructive precision agriculture and food engineering solution

Anum Mehmood
Zemin Wu
Yu Zhang
Xinpeng Bai
Chengxu Sun
Uzair Aslam Bhatti
Mengxing Huang
Shenghuang Lin

Coconut is the fruit of the coconut palm. Due to its characteristics of a long growth cycle and low germination rate, accurate prediction of its developmental status is particularly important. Traditional research primarily relies on the sectioning method to observe its internal structure. Although this approach can reveal morphological characteristics, its destructive nature prevents continuous monitoring of the internal developmental processes. Recent advancements in Computed Tomography (CT)-based nondestructive imaging and artificial intelligence have enabled novel approaches for investigating internal coconut morphology. However, current methodologies frequently overlook the impact of field environmental factors on coconut germination processes, consequently constraining prediction accuracy. To address this issue, this study proposes a Transformer-based multimodal feature fusion predictive model. Through the integration of CT images and environmental data, the model achieves precise prediction of coconut developmental status. Initially, the enhanced Deeplab V3+ model extracts deep semantic features from coconut CT images, while Fourier positional encoding is applied to amplify periodic features in environmental data (e. g. , temperature, humidity). Subsequently, a cross-modal multi-head attention mechanism is designed to achieve comprehensive fusion between CT-derived semantic features and field data characteristics, thoroughly exploring their correlations. Ultimately, to further enhance model performance, this study incorporates supervised contrastive loss functions and implements intra-class feature aggregation coupled with inter-class feature separation strategies for feature space optimization. The experimental results demonstrate that the proposed model achieves superior performance in coconut developmental stage prediction tasks: compared with conventional unimodal approaches, it improves prediction accuracy and F1-score by 9 % and 8 %, respectively, thereby validating the effectiveness of multimodal data fusion and the rationality of the model design.

AAAI Conference 2026 Conference Paper

DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting

Daojun Liang
Jing Chen
Xiao Wang
Yinglong Wang
Shuo Li

Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first analysing concept drift through a bias-variance lens and proving that weighted ensemble reduces variance without increasing bias. These insights motivate DeepBooTS, a novel end-to-end dual-stream residual-decreasing boosting method that progressively reconstructs the intrinsic signal. In our design, each block of a deep model becomes an ensemble of learners with an auxiliary output branch forming a highway to the final prediction. The block‑wise outputs correct the residuals of previous blocks, leading to a learning‑driven decomposition of both inputs and targets. This method enhances versatility and interpretability while substantially improving robustness to concept drift. Extensive experiments, including those on large-scale datasets, show that the proposed method outperforms existing methods by a large margin, yielding an average performance improvement of 15.8% across various datasets, establishing a new benchmark for TS forecasting.

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JBHI Journal 2026 Journal Article

Few-Shot Personalized Blood Pressure Estimation from Photoplethysmography and Physiological Priors via Low-Rank Adaptation

Meitong Li
Jing Chen
Dawei Shi
Yuanting Zhang
Xiao Wang

Noninvasive continuous blood pressure (BP) monitoring has become a critical requirement for effective health management in the general population. To address the challenge of accurate few-shot personalized BP estimation, a photoplethysmography (PPG)-based framework built on the unified multi-task time series model with a Transformer backbone is proposed. The framework comprises population-level pretraining and personalized fine tuning with a pulse pressure segmented penalty (PPSP) loss. The PPSP couples systolic BP (SBP) and diastolic BP (DBP) outputs by penalizing pulse pressure values outside clinically accepted ranges, which enforces physiological consistency. In addition, a sampling-rate-robust low-rank adaptation (SRR-LoRA) is introduced to improve estimation accuracy when low-frequency PPG signals are employed. After rate alignment, SRR-LoRA prioritizes measurements over interpolated points, suppresses interpolation noise, and preserves cross-device generalization. Model performance was evaluated on the UCI cuffless BP estimation dataset, the University of Queensland vital signs dataset, and the CAS-BP dataset. 113, 812 samples from 2, 405 subjects were used for pretraining, and data from 316 subjects (each with 50 samples) were included for few-shot fine tuning. The proposed method achieved mean absolute errors of 1. 52/1. 07 mmHg for SBP/DBP. These results fulfill the Association for the Advancement of Medical Instrumentation BP standard and correspond to Grade A performance according to the British Hypertension Society standard and IEEE 1708 standard, which demonstrates the framework's potential for practical personalized wearable BP monitoring.

AAAI Conference 2026 System Paper

GeoProblem Factory: A Visual Interaction System for Solvable and Controllable Geometric Problem Generation by Leveraging Symbolic Deduction Engine

Zhuoxuan Jiang
Yanpeng Li
Tianyang Zhang
Jing Chen
Yong Li
Mo Guang
Wen Si
Shaohua Zhang

We propose a novel system, GeoProblem Factory, designed to effectively generate high-quality geometry problems for intelligent education. The system enables to efficiently produce batches of geometry problems for teachers and students, either to save time and manual effort or to support personalized learning. Generating geometry problems is particularly challenging, as it requires ensuring both solvability and controllability from a pedagogical perspective. To address these issues, we adopt a state-of-the-art pipeline method based on a symbolic deduction engine and develop a visual interaction demo. This demo allows users to easily refine the generated problems through visual operations. It provides two modes for inputting controllable information: specifying knowledge points or supplying a reference problem. Moreover, the system can automatically generate a preliminary geometric diagram corresponding to each problem for further refinement. Through human–machine interaction, the system can more efficiently produce high-quality geometry problems than ever.

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AAAI Conference 2026 Conference Paper

Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis

Rui Zou
Mengqi Wei
Yutao Zhu
Jirong Wen
Xin Zhao
Jing Chen

Large Language Models (LLMs) excel in reasoning and generation across domains, but still struggle with identifying and diagnosing complex errors. This stems mainly from training objectives that prioritize correct answers, limiting exposure to and learning from errors. While recent studies have begun to address this by introducing error signals, most rely on shallow, static errors, restricting improvement in deep diagnostic ability. To overcome this, we propose Hide and Seek Game (HSG), a dynamic adversarial framework for error generation and diagnosis, and evaluate it on mathematical problem-solving. HSG involves two adversarial roles: Sneaky, which hides by generating subtle, deceptive reasoning errors, and Diagnosis, which seeks to accurately detect them. Through adversarial co-evolution, both error stealth and diagnostic precision are enhanced. Experiments on three mathematical reasoning datasets demonstrate that HSG significantly boosts error diagnosis, achieving 16.8%-31.4% higher accuracy than baselines like GPT-4o. We also release a challenging dataset of deceptive errors and diagnostic annotations as a benchmark for future research.

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AAAI Conference 2026 Conference Paper

Information Elicitation Mechanisms for Bayesian Auctions (Abstract Reprint)

Jing Chen
Bo Li
Yingkai Li

In this paper we design information elicitation mechanisms for Bayesian auctions. While in Bayesian mechanism design the distributions of the players’ private types are often assumed to be common knowledge, information elicitation considers the situation where the players know the distributions better than the decision maker. To weaken the information assumption in Bayesian auctions, we consider an information structure where the knowledge about the distributions is arbitrarily scattered among the players. In such an unstructured information setting, we design mechanisms for unit-demand auctions and additive auctions that aggregate the players’ knowledge, generating revenue that are constant approximations to the optimal Bayesian mechanisms with a common prior. Our mechanisms are 2-step dominant-strategy truthful, and the approximation ratios improve gracefully with the amount of knowledge the players collectively have.

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EAAI Journal 2026 Journal Article

Reinforcement learning enhanced evolutionary algorithms for inverse design of electric cargo truck frames

Hongli Chen
Dengfeng Wang
Wenchao Xu
Shang Zhang
Zifeng Zhang
Zihao Meng
Jing Chen

In the field of engineering, inverse design offers considerable advantages such as shortened development cycles and reduced design costs. However, it still faces challenges including complex nonlinear mappings and limited prediction accuracy. To tackle these issues, this paper introduces a Q-learning-based parameter adaptation strategy embedded within evolutionary algorithms, with the aim of improving the efficiency and precision of inverse design for engineering structures. The inverse design process is decomposed into forward modeling and inverse optimization, where target performance values are predefined. Based on this formulation, the problem is framed as either single- or multi-objective optimization, and new evaluation metrics for solution sets are introduced. To enhance optimization performance, a Q-learning-driven dynamic parameter control mechanism is developed for both Differential Evolution (DE) and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Experimental studies on multiple large-scale datasets and a real-world engineering case involving an electric cargo truck frame demonstrate that the proposed QL-DE and MOEA/D-QL algorithms significantly improve optimization effectiveness and convergence behavior. The results also offer insights into the suitability of single-versus multi-objective formulations for different types of inverse problems. This study presents a novel approach for tackling inverse design challenges and highlights the promising synergy between reinforcement learning and evolutionary algorithms, indicating broad applicability in engineering design.

JBHI Journal 2025 Journal Article

ACEA-Net: Weakly Supervised Prostate 3D MRI Image Segmentation via Advanced Prompt Points

Jie Zou
Mengxing Huang
Yu Zhang
Zhiyuan Zhang
Wenjie Zhou
Uzair Aslam Bhatti
Jing Chen
Zhiming Bai

In prostate 3D MRI image segmentation methods, it is usually necessary to annotate each slice, and these annotations are generally time-consuming and specialized. In this study, we generate pseudo-labels using an annotation method with one foreground seed point and six edge relaxation points. We design a weakly supervised semantic learning segmentation framework, ACEA-Net. This segmentation framework solves the under-expansion problem due to the lack of semantic affinity of the seed point pixels in the pseudo-labeling generation process. We design a Seed Cluster Geodesic Distance Transform (SeedGeo) seed expansion strategy to provide a more complete supervised signal. In the segmentation model training phase, Adaptive Convolutional Normalization (ACN) and Enhanced Simple Parameter-Free Attention Module (SimAM) are utilized to smooth the convolutional layer's output in the U-Net baseline model to suppress noisy labels. The proposed segmentation framework achieves excellent segmentation results on the MSD prostate and PROMISE12 prostate datasets, with Dice similarity coefficients (Dice) of 87. 23% and 81. 00% for the two segmentation tasks, and Average Symmetry Surface Distances (ASSD) of 1. 73mm and 2. 02mm, respectively, which are superior to the current state-of-the-art method.

JBHI Journal 2025 Journal Article

Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning

Bokai Hu
Han Zhang
Caixia Jia
Ke Chen
Xiangjiang Tang
Da He
Luni Zhang
Shiyao Gu

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a valuable tool for assessing IPN by evaluating the location and quantity of microbubbles within the carotid plaque. However, this task is typically performed by experienced radiologists. Here we propose a deep learning-based multi-task model for the automatic segmentation and IPN grade classification of carotid plaque on CEUS images and videos. We also compare the performance of our model with that of radiologists. To simulate the clinical practice of radiologists, who often use CEUS videos with dynamic imaging to track microbubble flow and identify IPN, we develop a workflow for plaque vulnerability assessment using CEUS videos. Our multi-task model outperformed individually trained segmentation and classification models, achieving superior performance in IPN grade classification based on CEUS images. Specifically, our model achieved a high segmentation Dice coefficient of 84. 64% and a high classification accuracy of 81. 67%. Moreover, our model surpassed the performance of junior and medium-level radiologists, providing more accurate IPN grading of carotid plaque on CEUS images. For CEUS videos, our model achieved a classification accuracy of 80. 00% in IPN grading. Overall, our multi-task model demonstrates great performance in the automatic, accurate, objective, and efficient IPN grading in both CEUS images and videos. This work holds significant promise for enhancing the clinical diagnosis of plaque vulnerability associated with IPN in CEUS evaluations.

JBHI Journal 2025 Journal Article

DRL-HNet: A Deep Residual Learning Framework for Microbe-Drug Associations Prediction Using Heterogeneous Network Feature

Jing Chen
Leyang Zhang
Yifei Wang
Susu Cui
Zhipan Liang
Xu Lu

In the field of biomedicine, predicting microbe-drug associations (MDAs) is crucial for advancing drug discovery and personalized therapy. However, traditional experimental approaches often fall short in meeting requirements for accuracy and scalability. Previous studies have primarily relied on feature similarities to predict microbe-drug associations, largely ignoring the complex interdependencies essential for improved prediction. In this paper, we propose a novel framework named Deep Residual Learning Framework Using Heterogeneous Network Feature (DRL-HNet) for MDAs prediction. DRL-HNet constructs a heterogeneous network representation by integrating relationships and features from multiple data sources for both microbes and drugs. The model incorporates deep residual learning with bottleneck layers to effectively reduce computational complexity while enhancing network expressiveness. Multi-source feature fusion is leveraged to capture complex interaction patterns, while residual connections mitigate overfitting and enhance training efficiency. Extensive cross-validation experiments demonstrate that DRL-HNet outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.

JBHI Journal 2025 Journal Article

Efficient Click-Based Interactive Segmentation for Medical Image With Improved Plain-ViT

Mengxing Huang
Jie Zou
Yu Zhang
Uzair Aslam Bhatti
Jing Chen

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88. 11% and Number of Clicks(NoC) at 80% are 7. 03 clicks.

AIIM Journal 2025 Journal Article

Generalized aggregation index based collaborative fusion for medical diagnosis

Weimin Peng
Aihong Chen
Wenyuan Huang
Jing Chen
Haitao Xu

It is critical for data fusion and its decision applications to define the similarity relationships between data units. Compared with the traditional similarity relationship driven by data, this paper proposes a new concept of generalized aggregation index (GAI) driven by data and knowledge. The concept of GAI defines the cumulative representation of the generalized relationships between data units. The generalized relationship involves not only data attributes but also the decision effect knowledge behind data attributes, and is a more accurate relationship representation. Based on data units' GAIs, a new GAI based collaborative fusion method for multi-source medical data is proposed to get high quality fusion results and precise decision conclusions. In the fusion process, the data units in different datasets attract each other and aggregate into entity subsets for fusion collaboratively based on the data units' attraction capabilities measured by data units' GAIs. The experimental analysis shows that the proposed classical and quantum-inspired GAI based methods can get high quality fusion results and highly precise diagnosis conclusions.

NeurIPS Conference 2025 Conference Paper

GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification

Qiao Li
Jie Li
Yukang Zhang
Lei Tan
Jing Chen
Jiayi Ji

Aerial-Ground person re-identification (AG-ReID) is an emerging yet challenging task that aims to match pedestrian images captured from drastically different viewpoints, typically from unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task poses significant challenges due to extreme viewpoint discrepancies, occlusions, and domain gaps between aerial and ground imagery. While prior works have made progress by learning cross-view representations, they remain limited in handling severe pose variations and spatial misalignment. To address these issues, we propose a Geometric and Semantic Alignment Network (GSAlign) tailored for AG-ReID. GSAlign introduces two key components to jointly tackle geometric distortion and semantic misalignment in aerial-ground matching: a Learnable Thin Plate Spline (LTPS) Transformation Module and a Dynamic Alignment Module (DAM). The LTPS module adaptively warps pedestrian features based on a set of learned keypoints, effectively compensating for geometric variations caused by extreme viewpoint changes. In parallel, the DAM estimates visibility-aware representation masks that highlight visible body regions at the semantic level, thereby alleviating the negative impact of occlusions and partial observations in cross-view correspondence. Extensive experiments on the challenging CARGO benchmark demonstrate the effectiveness of GSAlign, achieving significant improvements of +18. 8\% in mAP and +16. 8\% in Rank-1 accuracy over previous state-of-the-art methods.

JBHI Journal 2025 Journal Article

HGBHAN: A Novel Framework for Microbe-Drug Interaction Prediction Using Heterogeneous Graphs and Bi-LSTM With Hierarchical Attention

Jing Chen
Leyang Zhang
Yifei Wang
Susu Cui
Zhipan Liang
Xu Lu

Predicting microbe–drug associations (MDAs) is vital for accelerating drug discovery and optimizing clinical interventions in biomedical research. Traditional laboratory-based methods, though reliable, are constrained by high costs and limited scalability. While many computational approaches have utilized feature similarities to infer MDAs, they often overlook the complex and heterogeneous relationships inherent in biological networks, as well as the challenge posed by imbalanced datasets. In this study, we propose HGBHAN, a novel framework for MDAs prediction using heterogeneous graphs and bidirectional long short-term memory (Bi-LSTM) with hierarchical attention, for robust MDAs prediction. HGBHAN constructs a comprehensive heterogeneous network by integrating microbe and drug similarities with known association information, capturing multi-level structural and sequential dependencies. The model employs Bi-LSTM modules and a hierarchical attention mechanism to learn discriminative node embeddings, while residual connections are incorporated to address the over-smoothing issue in graph neural networks. Extensive experiments conducted on three public benchmark datasets demonstrate that HGBHAN outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe–drug associations.

JAAMAS Journal 2025 Journal Article

Information elicitation mechanisms for Bayesian auctions

Jing Chen
Bo Li
Yingkai Li

Abstract In this paper we design information elicitation mechanisms for Bayesian auctions. While in Bayesian mechanism design the distributions of the players’ private types are often assumed to be common knowledge, information elicitation considers the situation where the players know the distributions better than the decision maker. To weaken the information assumption in Bayesian auctions, we consider an information structure where the knowledge about the distributions is arbitrarily scattered among the players. In such an unstructured information setting, we design mechanisms for unit-demand auctions and additive auctions that aggregate the players’ knowledge, generating revenue that are constant approximations to the optimal Bayesian mechanisms with a common prior. Our mechanisms are 2-step dominant-strategy truthful and the approximation ratios improve gracefully with the amount of knowledge the players collectively have.

JBHI Journal 2025 Journal Article

Infusing Multi-Hop Medical Knowledge Into Smaller Language Models for Biomedical Question Answering

Jing Chen
Zhihua Wei
Wen Shen
Rui Shang

MedQA-USMLE is a challenging biomedical question answering (BQA) task, as its questions typically involve multi-hop reasoning. To solve this task, BQA systems should possess not only extensive medical professional knowledge but also strong medical reasoning capabilities. While state-of-the-art larger language models, such as Med-PaLM 2, have overcome this challenge, smaller language models (SLMs) still struggle with it. To bridge this gap, we introduces a multi-hop medical knowledge infusion (MHMKI) procedure to endow SLMs with medical reasoning capabilities. Specifically, we categorize MedQA-USMLE questions into distinct reasoning types, then tailor pre-training instances for each type of questions using the semi-structured information and hyperlinks of Wikipedia articles. To enable SLMs to efficiently capture the multi-hop knowledge contained in these instances, we design a reasoning chain masked language model to further pre-train BERT models. Moreover, we convert the pre-training instances into a composite question answering dataset for intermediate fine-tuning of GPT models. We evaluate MHMKI on six SLMs across five datasets spanning three BQA tasks. The results demonstrate that MHMKI consistently improves SLMs' performance, particularly on tasks requiring substantial medical reasoning. For instance, the accuracy of MedQA-USMLE shows a significant increase of 5. 3% on average.

YNICL Journal 2025 Journal Article

Large-scale functional network connectivity mediate the associations of white matter lesions with executive functions and information processing speed in asymptomatic cerebral small vessels diseases

Jing Chen
Weiwei lu
Zhangyang Wang
Mingfang Shi
Zhang Shi
Weibin Shi

OBJECTIVE: To examine the role of the large-scale functional network connectivity between white matter lesions (WMLs) and cognitive behaviors in patients of asymptomatic cerebral small vascular diseases (CSVD). METHODS: The study sample consisted of 211 asymptomatic CSVD patients with WMLs. Large-scale internetwork and intranetwork functional connectivity (FC) were calculated using a combination of resting-state functional MRI data and independent component analysis. Neuropsychological tests involve cognitive functions were also measured. Then, potential correlations between WMLs, functional network connectivity and cognitive behaviors were tested. Mediation analysis was used to explore the role of functional network connectivity between WMLs and cognitive behaviors. RESULTS: We successfully identified fourteen meaningful resting-state functional networks. Internetwork FC between dorsal sensorimotor network (dSMN) and right frontoparietal network (rFPN), dSMN and left frontoparietal network (lFPN), auditory network (AN) and posterior default network (pDMN), AN and executive control network (ECN), ECN and salience network (SN), dorsal attention network (DAN) and ECN were significant correlated with volumes of WMLs. Executive function were associated with internetwork FC between AN and pDMN, ECN and SN. Moreover, internetwork FC between AN and pDMN, ECN and SN mediated the relations of WMLs with executive function (for AN and pDMN, indirect effect: -0.0371, 95% CI: -0.0829 to -0.0073; for ECN and SN, indirect effect: -0.03191, 95% CI: -0.0807 to -0.0047). Moreover, left inferior parietal lobule in rFPN, right precentral gyrus in anterior default network (aDMN), right paracentral lobue in pDMN and left precunues in ECN were related to volumes of WMLs. There is a significant association of WMLs with intranetwork FC in left precunues, which could mediate the link between WMLs and information processing speed (indirect effect: -0.0437, 95% CI: -0.1055 to -0.0081). CONCLUSION: WMLs in asymptomatic CSVD patients may induce large-scale connectivity changes including the internetwork FC and intranetwork FC, which might further influence executive function and information processing speed.

JBHI Journal 2025 Journal Article

Multi-Perturbation Consistency Learning for Semi-Supervised Medical Image Segmentation

Zhiyuan Zhang
Yu Zhang
Jing Chen
Wenlong Feng
Zihao Zhou
Jie Zou
Uzair Aslam Bhatti
Gang Wang

Existing semi-supervised learning (SSL) methods primarily rely on consistency learning to enhance model performance. However, most current approaches only validate the effectiveness of consistency learning under single perturbations, while introducing multiple perturbations may lead to the failure of consistency learning and degrade model performance. To address this issue and effectively leverage multiple perturbations for consistency learning, we propose a semi-supervised medical image segmentation method based on multi-perturbation consistency learning. Specifically, we design a cross-teaching framework integrating sparsely annotated 3D and 2D networks, introducing network perturbations through multidimensional architectures while combining strong and weak data augmentation techniques to achieve input perturbations. Furthermore, to address the instability issue in multi-perturbation consistency learning, we develop two complementary uncertainty-aware correction algorithm targeting labeled and unlabeled data. These designs effectively enhance the model's robustness to both labeled and unlabeled data, overcoming the instability problem in multi-perturbation consistency learning. To validate the proposed method, we conducted experiments on four datasets(ProstateX, HPH55, ACDC, and LA). Experimental results demonstrate that our algorithm outperforms existing methods across all validation datasets and exhibits strong generalization capabilities. This indicates that our approach can maintain excellent performance with limited annotated data while achieving efficient medical image segmentation. The project code will be made publicly available upon acceptance.

EAAI Journal 2025 Journal Article

Portable fair decision making through modular approach

Fengyu Wu
Ayong Ye
Qiuling Chen
Huang Zhang
Jing Chen

Influenced by real-world biases, machine learning based decision systems are prone to discriminatory outcomes, which has garnered considerable attention, and led to the proposal of numerous bias mitigation methods, such as adversarial debiasing and fair representation learning. However, relying on predefined fairness standards prevents these methods from adapting to evolving fairness requirements. To address this issue, we propose a modular fairness enhancement approach. In our approach, each fairness requirement is modeled as a distinct optimization task, with the corresponding model parameters encapsulated within an independent sub-module. In addition, a main module is designed to capture the shared classification features across all fairness tasks. This multi-task learning architecture enables the decision making system to meet multiple fairness requirements without retraining. Experimental evaluations on four real datasets, by comparing portability and four fairness metrics with state-of-the-art methods. The results demonstrate that our method achieves superior portability in addressing various fairness requirements compared to the existing methods.

IROS Conference 2025 Conference Paper

SEI3D: CPU-only 3D Object Tracking Fusing Sparse-flow-filtered Edge and Interior Alignment

Jixiang Chen
Jing Chen
Kai Liu
Ting Lei
Leshan Wang

Monocular 3D object tracking methods are widely employed in robotic applications, however, they often struggle with low-contrast image sequences. In this paper, we introduce a novel approach to filtering redundant edges in images by leveraging sparse interior correspondences. Our method features a sparse-flow-based probability segmentation model that comprises both coarse and fine components. The coarse model evaluates the ratio of interior correspondences within a circular region centered on each pixel, while the fine model employs a binary Gaussian kernel based on the nearest interior correspondences. This probability framework facilitates the identification of control points for object edges. Additionally, we implement a robust gradient consistency-based edge connection algorithm to generate refined object edges. Utilizing these filtered edges, we formulate an edge-based energy function that accounts for object contour shape and noise uncertainty, seamlessly integrating into a multi-feature pose optimization framework. Our multi-feature fusion strategy achieves state-of-the-art performance in both public datasets and real-world applications, operating at 60 Hz using only CPU.

ICLR Conference 2025 Conference Paper

Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression

Sha Guo
Jing Chen
Zixuan Hu
Zhuo Chen 0006
Wenhan Yang
Yu Lin
Xing Jiang
Lingyu Duan

Conventional image compression methods are inadequate for intelligent analysis, as they overemphasize pixel-level precision while neglecting semantic significance and the interaction among multiple tasks. This paper introduces a Taskonomy-Aware Multi-Task Compression framework comprising (1) inter-coherent task grouping, which organizes synergistic tasks into shared representations to improve multi-task accuracy and reduce encoding volume, and (2) a conditional entropy-based directed acyclic graph (DAG) that captures causal dependencies among grouped representations. By leveraging parent representations as contextual priors for child representations, the framework effectively utilizes cross-task information to improve entropy model accuracy. Experiments on diverse vision tasks, including Keypoint 2D, Depth Z-buffer, Semantic Segmentation, Surface Normal, Edge Texture, and Autoencoder, demonstrate significant bitrate-performance gains, validating the method’s capability to reduce system entropy uncertainty. These findings underscore the potential of leveraging representation disentanglement, synergy, and causal modeling to learn compact representations, which enable efficient multi-task compression in intelligent systems.

KER Journal 2024 Journal Article

Artificial intelligence for collective intelligence: a national-scale research strategy

Seth Bullock
Nirav Ajmeri
Mike Batty
Michaela Black
John Cartlidge
Robert Challen
Cangxiong Chen
Jing Chen

Abstract Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

YNIMG Journal 2024 Journal Article

Cortical activation and brain network efficiency during dual tasks: An fNIRS study

Qian Ding
Zitong Ou
Shantong Yao
Cheng Wu
Jing Chen
Junhui Shen
Yue Lan
Guangqing Xu

OBJECTIVE: Dual task (DT) is a commonly used paradigm indicative of executive functions. Brain activities during DT walking is usually measured by portable functional near infrared spectroscopy (fNIRS). Previous studies focused on cortical activation in prefrontal cortex and overlooked other brain regions such as sensorimotor cortices. This study is aimed at investigating the modulations of cortical activation and brain network efficiency in multiple brain regions from single to dual tasks with different complexities and their relationships with DT performance. METHODS: Forty-two healthy adults [12 males; mean age: 27.7 (SD=6.5) years] participated in this study. Participants performed behavioral tasks with portable fNIRS simultaneous recording. There were three parts of behavioral tasks: cognitive tasks while standing (serial subtraction of 3's and 7's), walking alone and DT (walk while subtraction, including serial subtraction of 3's and 7's). Cognitive cost, walking cost and cost sum (i.e., sum of cognitive and walking costs) were calculated for DT. Cortical activation, local and global network efficiency were calculated for each task. RESULTS: The cognitive cost was greater and the walking cost was less during DT with subtraction 3's compared with 7's (P's = 0.032 and 0.019, respectively). Cortical activation and network efficiency were differentially modulated among single and dual tasks (P's < 0.05). Prefrontal activation during DT was positively correlated with DT costs, while network efficiency was negatively correlated with DT costs (P's < 0.05). CONCLUSIONS: Our results revealed prefrontal over-activation and reduced network efficiency in individuals with poor DT performance. Our findings suggest that reduced network efficiency could be a possible mechanism contributing to poor DT performance, which is accompanied by compensatory prefrontal over-activation.

AAAI Conference 2023 Conference Paper

PGSS: Pitch-Guided Speech Separation

Xiang Li
Yiwen Wang
Yifan Sun
Xihong Wu
Jing Chen

Monaural speech separation aims to separate concurrent speakers from a single-microphone mixture recording. Inspired by the effect of pitch priming in auditory scene analysis (ASA) mechanisms, a novel pitch-guided speech separation framework is proposed in this work. The prominent advantage of this framework is that both the permutation problem and the unknown speaker number problem existing in general models can be avoided by using pitch contours as the primary means to guide the target speaker. In addition, adversarial training is applied, instead of a traditional time-frequency mask, to improve the perceptual quality of separated speech. Specifically, the proposed framework can be divided into two phases: pitch extraction and speech separation. The former aims to extract pitch contour candidates for each speaker from the mixture, modeling the bottom-up process in ASA mechanisms. Any pitch contour can be selected as the condition in the second phase to separate the corresponding speaker, where a conditional generative adversarial network (CGAN) is applied. The second phase models the effect of pitch priming in ASA. Experiments on the WSJ0-2mix corpus reveal that the proposed approaches can achieve higher pitch extraction accuracy and better separation performance, compared to the baseline models, and have the potential to be applied to SOTA architectures.

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IJCAI Conference 2022 Conference Paper

Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations

Run Wang
Ziheng Huang
Zhikai Chen
Li Liu
Jing Chen
Lina Wang

DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR. In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. Code is available at https: //github. com/AbstractTeen/AntiForgery.

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AIJ Journal 2022 Journal Article

Bayesian auctions with efficient queries

Jing Chen
Bo Li
Yingkai Li
Pinyan Lu

Designing dominant-strategy incentive compatible (DSIC) mechanisms for a seller to generate (approximately) optimal revenue by selling items to players is a fundamental problem in Bayesian mechanism design. However, most existing studies assume that the seller knows the entire distribution from which the players' values are drawn. Unfortunately, this assumption may not hold in reality: for example, when the distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexity of Bayesian mechanisms. The seller only has limited oracle accesses to the players' distributions, via quantile queries and value queries. For single-item auctions, we design mechanisms with logarithmic number of value or quantile queries which achieve almost optimal revenue. We then prove logarithmic lower-bounds, i. e. , logarithmic number of queries are necessary for any constant approximation DSIC mechanisms, even when randomized and adaptive queries are allowed. Thus our mechanisms are almost optimal regarding query complexity. Our lower-bounds can be extended to multi-item auctions with monotone subadditive valuations, and we complement this part with constant approximation mechanisms for unit-demand or additive valuation functions. Our results are robust even if the answers to the queries contain noises. Thus, in those settings the seller needs to access much less than the entire distribution to achieve approximately optimal revenue.

IJCAI Conference 2022 Conference Paper

Bayesian Auctions with Efficient Queries (Extended Abstract)

Jing Chen
Bo Li
Yingkai Li
Pinyan Lu

Designing dominant-strategy incentive compatible (DSIC) mechanisms for a seller to generate (approximately) optimal revenue by selling items to players is a fundamental problem in Bayesian mechanism design. However, most existing studies assume that the seller knows the entire distribution from which the players’ values are drawn. Unfortunately, this assumption may not hold in reality: for example, when the distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexityof Bayesian mechanisms. The seller only has limited oracle accesses to the players’ distributions, via quantile queriesand value queries. For single-item auctions, we design mechanisms with logarithmicnumber of value or quantile queries which achieve almost optimal revenue. We then prove logarithmic lower-bounds, i. e. , logarithmic number of queries are necessary for any constant approximation DSIC mechanisms, even when randomized and adaptive queries are allowed. Thus our mechanisms are almost optimal regarding query complexity. Our lower-bounds can be extended to multi-item auctions with monotone subadditive valuations, and we complement this part with constant approximation mechanisms for unit-demand or additive valuation functions. Our results are robust even if the answers to the queries contain noises.

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NeurIPS Conference 2022 Conference Paper

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models

Wenbo Su
Yuanxing Zhang
Yufeng Cai
Kaixu Ren
Pengjie Wang
Huimin Yi
Yue Song
Jing Chen

High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models. Although synchronous AR training is designed to have higher training efficiency, asynchronous PS training would be a better choice for training speed when there are stragglers (slow workers) in the shared cluster, especially under limited computing resources. An ideal way to take full advantage of these two training modes is to switch between them upon the cluster status. However, switching training modes often requires tuning hyper-parameters, which is extremely time- and resource-consuming. We find two obstacles to a tuning-free approach: the different distribution of the gradient values and the stale gradients from the stragglers. This paper proposes Global Batch gradients Aggregation (GBA) over PS, which aggregates and applies gradients with the same global batch size as the synchronous training. A token-control process is implemented to assemble the gradients and decay the gradients with severe staleness. We provide the convergence analysis to reveal that GBA has comparable convergence properties with the synchronous training, and demonstrate the robustness of GBA the recommendation models against the gradient staleness. Experiments on three industrial-scale recommendation tasks show that GBA is an effective tuning-free approach for switching. Compared to the state-of-the-art derived asynchronous training, GBA achieves up to 0. 2% improvement on the AUC metric, which is significant for the recommendation models. Meanwhile, under the strained hardware resource, GBA speeds up at least 2. 4x compared to synchronous training.

EAAI Journal 2021 Journal Article

A location conversion method for roads through deep learning-based semantic matching and simplified qualitative direction knowledge representation

Ruozhen Cheng
Jing Chen

Qualitative direction knowledge that appears in natural language descriptions of road-related locations could point to the interior of individual roads or associate multiple roads. Interpreting such descriptions to perform location conversion for roads will support intelligent road-related location services. Existing geocoding technologies could perform textual or semantic matching to transform road names to spatial locations, and research on qualitative direction reasoning could perform efficient location conversion based on semantic queries of qualitative direction knowledge between roads. However, research on geocoding lacks the consideration of matching the described internal direction knowledge of a road to a part of the road. Moreover, efficient location conversion based on semantic queries cannot scale to large road datasets due to the retrieval efficiency of a large amount of qualitative direction knowledge between roads. To accomplish this goal, this study proposes a location conversion method for roads, wherein a road ontology is designed to model the interior direction knowledge of the roads, a deep learning-based road semantic matching model is trained to match the internal direction knowledge descriptions and road segments, and a simplified qualitative direction knowledge representation between roads is performed to support rapid location conversion between roads based on efficient semantic queries. The proposed method was implemented on a road dataset of New York State. The results demonstrate that the proposed method can be effectively applied in road location conversion based on descriptions that contain qualitative direction knowledge inside individual roads or between multiple roads, which expands the scope of artificial intelligence applications.

EAAI Journal 2020 Journal Article

A CLSTM-TMN for marketing intention detection

Yufeng Wang
Kun Ma
Laura Garcia-Hernandez
Jing Chen
Zhihao Hou
Ke Ji
Zhenxiang Chen
Ajith Abraham

In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models.

AAAI Conference 2020 Conference Paper

How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

Zewei Chu
Mingda Chen
Jing Chen
Miaosen Wang
Kevin Gimpel
Manaal Faruqui
Xiance Si

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427, 719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13. 2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting. 1

AAAI Conference 2020 Conference Paper

Learning to Deblur Face Images via Sketch Synthesis

Songnan Lin
Jiawei Zhang
Jinshan Pan
Yicun Liu
Yongtian Wang
Jing Chen
Jimmy Ren

The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm ﬁrst estimates motion blur by a deep CNN and then restores latent clear images with the estimated motion blur. However, estimating motion blur from blurry face images is difﬁcult as the textures of the blurry face images are scarce. As most face images share some common global structures which can be modeled well by sketch information, we propose to learn face sketches by a deep CNN so that the sketches can help the motion blur estimation. With the estimated motion blur, we then develop an effective latent image restoration algorithm based on a deep CNN. Although involving the several components, the proposed algorithm is trained in an end-to-end fashion. We analyze the effectiveness of each component on face image deblurring and show that the proposed algorithm is able to deblur face images with favorable performance against state-of-the-art methods.

TCS Journal 2019 Journal Article

Algorand: A secure and efficient distributed ledger

Jing Chen
Silvio Micali

A distributed ledger is a tamperproof sequence of data that can be publicly accessed and augmented by everyone, without being maintained by a centralized party. Distributed ledgers stand to revolutionize the way a modern society operates. They can secure all kinds of traditional transactions, such as payments, asset transfers and titles, in the exact order in which the transactions occur; and enable totally new transactions, such as cryptocurrencies and smart contracts. They can remove intermediaries and usher in a new paradigm for trust. As currently implemented, however, distributed ledgers scale poorly and cannot achieve their enormous potential. In this paper we propose Algorand, an alternative, secure and efficient distributed ledger. Algorand is permissionless and works in a highly asynchronous environment. Unlike prior implementations of distributed ledgers based on “proof of work, ” Algorand dispenses with “miners” and requires only a negligible amount of computation. Moreover, its transaction history “forks” only with negligible probability: that is, Algorand guarantees the finality of a transaction the moment the transaction enters the ledger.

IJCAI Conference 2019 Conference Paper

Approximately Maximizing the Broker's Profit in a Two-sided Market

Jing Chen
Bo Li
Yingkai Li

We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a single item to sell and holds a private value on her item, and each buyer has a valuation function over the bundles of the sellers' items. We consider the Bayesian setting where the agents' values/valuations are independently drawn from prior distributions, and aim at designing dominant-strategy incentive-compatible (DSIC) mechanisms that are approximately optimal. Production-cost markets, where each item has a publicly-known cost to be produced, provide a platform for us to study two-sided markets. Briefly, we show how to covert a mechanism for production-cost markets into a mechanism for the broker, whenever the former satisfies cost-monotonicity. This reduction holds even when buyers have general combinatorial valuation functions. When the buyers' valuations are additive, we generalize an existing mechanism to production-cost markets in an approximation-preserving way. We then show that the resulting mechanism is cost-monotone and thus can be converted into an 8-approximation mechanism for two-sided markets.

AAMAS Conference 2019 Conference Paper

Maximin-Aware Allocations of Indivisible Goods

Hau Chan
Jing Chen
Bo Li
Xiaowei Wu

We study envy-free allocations of indivisible goods to agents in settings where each agent is unaware of the bundles (or allocated goods) of other agents. In particular, we propose maximin aware (MMA) fairness measure, which guarantees that every agent, given the bundle allocated to her, is aware that she does not get the worst bundle, even if she does not know how the other goods are distributed. We also introduce two of its relaxations, MMA1 and MMAX. We show that MMA1 and MMAX potentially have stronger egalitarian guarantees than EF1 and are easier to achieve than MMS and EFX. Finally, we present a polynomial-time algorithm, which computes an allocation such that every agent is either 1 2 -approximate MMA or exactly MMAX. Interestingly, the returned allocation is also 1 2 -approximate EFX when all agents have subadditive valuations, which answers an open question left in [Plaut and Roughgarden, SODA 2018].

IJCAI Conference 2019 Conference Paper

Maximin-Aware Allocations of Indivisible Goods

Hau Chan
Jing Chen
Bo Li
Xiaowei Wu

We study envy-free allocations of indivisible goods to agents in settings where each agent is unaware of the goods allocated to other agents. In particular, we propose the maximin aware (MMA) fairness measure, which guarantees that every agent, given the bundle allocated to her, is aware that she does not envy at least one other agent, even if she does not know how the other goods are distributed among other agents. We also introduce two of its relaxations, and discuss their egalitarian guarantee and existence. Finally, we present a polynomial-time algorithm, which computes an allocation that approximately satisfies MMA or its relaxations. Interestingly, the returned allocation is also 1/2-approximate EFX when all agents have sub- additive valuations, which improves the algorithm in [Plaut and Roughgarden, 2018].

JBHI Journal 2018 Journal Article

HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring

Xiaojin Li
Licong Cui
Shiqiang Tao
Jing Chen
Xiang Zhang
Guo-Qiang Zhang

Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features. The correction rules are constructed based on stage transition feature, importing the continuity property of sleep, and characteristic of sleep stage transition. Compared with the gold standard of manual scoring using Rechtschaffen and Kales criterion, the overall accuracy and kappa coefficient applied on 198 subjects has reached 85. 95% and 0. 8046 in our experiment, respectively. The performance of HyCLASS compared favorably to previous work, and it could be integrated with sleep evaluation or sleep diagnosis system in the future.

AIIM Journal 2017 Journal Article

Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine

Qilin Xiang
Bo Liao
Xianhong Li
Huimin Xu
Jing Chen
Zhuoxing Shi
Qi Dai
Yuhua Yao

Objectives In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins. Methods In the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Then, these features are predicted by support vector machine (SVM) and the predictive capability of proposed method is implemented by jackknife test Results The results show that the golden section method is better than no segmentation method. The overall accuracy for ZD98 and CL317 is 98. 98% and 91. 11%, respectively, which indicates that our method can play a complimentary role to the existing methods in the relevant areas. Conclusions The proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.

IJCAI Conference 2016 Conference Paper

A Polynomial Time Optimal Algorithm for Robot-Human Search under Uncertainty

Shaofei Chen
Tim Baarslag
Dengji Zhao
Jing Chen
Lincheng Shen

This paper studies a search problem involving a robot that is searching for a certain item in an uncertain environment (e. g. , searching minerals on Moon) that allows only limited interaction with humans. The uncertainty of the environment comes from the rewards of undiscovered items and the availability of costly human help. The goal of the robot is to maximize the reward of the items found while minimizing the search costs. We show that this search problem is polynomially solvable with a novel integration of the human help, which has not been studied in the literature before. Furthermore, we empirically evaluate our solution with simulations and show that it significantly outperforms several benchmark approaches.

AAMAS Conference 2016 Conference Paper

Budget Feasible Mechanisms for Dealers

Hau Chan
Jing Chen

We consider the problem of designing budget feasible mechanisms for a dealer, who aims to maximize revenue by buying items from a seller market and selling them to a buyer market that consists of unit-demand buyers. Different from the related literature, the dealer’s “value” for a set of items that he purchased from the seller market is not directly given as a number but it is defined to be the maximum revenue the dealer can obtain from selling the items to the buyers. We aim to design mechanisms that are dominant-strategy truthful for the sellers to report their costs and envy-free for the buyers to purchase their most preferred items (given their prices) in the final outcome, such that the total payment to the sellers does not exceed the dealer’s budget and the dealer’s revenue is (approximately) maximized. First, to understand the structure of the optimal mechanisms, we show that the maximum (envy-free) revenue obtainable by the dealer as a function of the set of purchased items is monotone and subadditive. Thus, existing results on subadditive optimization problems are potentially applicable in solving the mechanism design problem for the dealer. However, a crucial assumption adopted by all previous studies on subadditive functions is that the mechanism or algorithm has access to the value oracle and/or the demand oracle. In the dealer’s problem, instead, we show that (1) the demand oracle can be efficiently simulated by the value oracle and (2) both have efficient O(log n)-approximation algorithms, where n is the number of buyers. This is particularly interesting given the literature, since, in general, the demand oracle can always efficiently simulate the value oracle, and there are cases where the demand oracle is strictly more powerful. Our results show that, for the dealer’s problem, the two oracles are as powerful as each other. Finally, we construct a polynomial-time budget feasible mechanism for the dealer that doesn’t use any oracle and provides an O((log2 n)(log2 m))-approximation of the optimal revenue, where m is the number of sellers.

AAMAS Conference 2016 Conference Paper

Provision-After-Wait with Common Preferences

Hau Chan
Jing Chen

We study the Provision-after-Wait problem in healthcare introduced by Braverman, Chen, and Kannan (2016). In this setting, patients seek a medical procedure, and the procedure can be performed by different hospitals of different costs. Each patient has a value for each hospital, and a budget-constrained government/planner pays for the medical expenses of the patients. The planner’s goal is to find an optimal stable assignment that is envy-free and maximizes the social welfare while keeping the expenses within the budget. In this work, we focus on the settings where the patients have a common preference of the hospitals. We show that computing the optimal stable assignment for maximizing social welfare is NP-hard. Furthermore, we construct a fully polynomial-time approximation scheme (FPTAS) that runs in time O((n + m)n3 m/ ), where m and n are the number of hospitals and patients, respectively. In order to develop the FPTAS, we have defined and studied a new problem, ordered Knapsack. We also consider the setting where the planner uses lottery as a rationing tool. For a large sub-class of our settings, we show the conditions under which the optimal lottery scheme has a simple structure and generates more social welfare than the optimal stable assignment. Moreover, such optimal lottery scheme can be computed by a linear program.

AAAI Conference 2016 Conference Paper

Toward a Better Understanding of Deep Neural Network Based Acoustic Modelling: An Empirical Investigation

Xingfu Wang
Lin Wang
Jing Chen
Litao Wu

Recently, deep neural networks (DNNs) have outperformed traditional acoustic models on a variety of speech recognition benchmarks. However, due to system differences across research groups, although a tremendous breadth and depth of related work has been established, it is still not easy to assess the performance improvements of a particular architectural variant from examining the literature when building DNN acoustic models. Our work aims to uncover which variations among baseline systems are most relevant for automatic speech recognition (ASR) performance via a series of systematic tests on the limits of the major architectural choices. By holding all the other components ﬁxed, we are able to explore the design and training decisions without being confounded by the other inﬂuencing factors. Our experiment results suggest that a relatively simple DNN architecture and optimization technique produces strong results. These ﬁndings, along with previous work, not only help build a better understanding towards why DNN acoustic models perform well or how they might be improved, but also help establish a set of best practices for new speech corpora and language understanding task variants.

TCS Journal 2008 Journal Article

A new framework for the design and analysis of identity-based identification schemes

Guomin Yang
Jing Chen
Duncan S. Wong
Xiaotie Deng
Dongsheng Wang

Constructing an identification scheme is one of the fundamental problems in cryptography, and is very useful in practice. An identity-based identification (IBI) scheme allows a prover to identify himself to a public verifier who knows only the claimed identity of the prover and some public information. In this paper, we propose a new framework for both the design and analysis of IBI schemes. Our approach works in an engineering way. We first identify an IBI scheme as the composition of two building blocks, and then show that, with different security properties of these building blocks, the corresponding IBI schemes can achieve security against impersonation under different levels of attacks, namely, passive attack (id-imp-pa), active attack (id-imp-aa) or concurrent attack (id-imp-ca). In particular, we show that an id-imp-pa secure IBI scheme can be built if there exists a trapdoor weak-one-more relation and an honest verifier zero-knowledge proof with special soundness, while an id-imp-aa and id-imp-ca secure IBI scheme can be built if there exists a trapdoor strong-one-more relation and a Witness Dualism proof with Special Soundness (WD-SS). This new framework can capture IBI construction techniques that are not captured by other known frameworks. It also helps to construct new and efficient schemes. We demonstrate this by proposing two new IBI schemes, one achieving id-imp-pa, and the other one achieving both id-imp-aa and id-imp-ca, and neither of them can be captured by existing frameworks.

YNIMG Journal 2005 Journal Article

Age-dependent brain activation during forward and backward digit recall revealed by fMRI

Xiwen Sun
Xiaochu Zhang
Xiangchuan Chen
Peng Zhang
Min Bao
Daren Zhang
Jing Chen
Sheng He

In this study, brain activation associated with forward and backward digit recall was examined in healthy old and young adults using functional MRI. A number of areas were activated during the recall. In young adults, greater activation was found in the left prefrontal cortex (BA9) and the left occipital visual cortex during backward digit recall than forward digit recall. In contrast, the activation in the right inferior frontal gyrus (BA 44/45) was more extensive in forward digit recall than in backward digit recall. In older adults, backward recall generated stronger activation than forward recall in most areas, including the frontal, the parietal, the occipital, and the temporal cortices. In the backward recall condition, the right inferior frontal gyrus (BA44/45) showed more activation in the old group than in the young group. These results suggest that different neural mechanisms may be involved in forward and backward digit recall and brain functions associated with these two types of recall are differentially affected by aging.