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- research-articleAugust 2024
Rethinking Order Dispatching in Online Ride-Hailing Platforms
- Zhaoxing Yang,
- Haiming Jin,
- Guiyun Fan,
- Min Lu,
- Yiran Liu,
- Xinlang Yue,
- Hao Pan,
- Zhe Xu,
- Guobin Wu,
- Qun Li,
- Xiaotong Wang,
- Jiecheng Guo
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2024, Pages 3863–3873https://doi.org/10.1145/3637528.3672028Achieving optimal order dispatching has been a long-standing challenge for online ride-hailing platforms. Early methods would make shortsighted matchings as they only consider order prices alone as the edge weights in the driver-order bipartite graph, ...
- short-paperJuly 2024
Masked Graph Transformer for Large-Scale Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 2502–2506https://doi.org/10.1145/3626772.3657971Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, the quadratic space and time complexity hinders the scalability of Graph ...
- research-articleJuly 2024
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 774–784https://doi.org/10.1145/3626772.3657695Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: ...
- research-articleJuly 2024
Trust it or not: Confidence-guided automatic radiology report generation
- Yixin Wang,
- Zihao Lin,
- Zhe Xu,
- Haoyu Dong,
- Jie Luo,
- Jiang Tian,
- Zhongchao Shi,
- Lifu Huang,
- Yang Zhang,
- Jianping Fan,
- Zhiqiang He
AbstractMedical imaging plays a pivotal role in diagnosis and treatment in clinical practice. Inspired by the significant progress in automatic image captioning, various deep learning (DL)-based methods have been proposed to generate radiology reports ...
- brief-reportApril 2024
Tiny polyp detection from endoscopic video frames using vision transformers
Pattern Analysis & Applications (PAAS), Volume 27, Issue 2Jun 2024https://doi.org/10.1007/s10044-024-01254-3AbstractDeep learning techniques can be effective in helping doctors diagnose gastrointestinal polyps. Currently, processing video frame sequences containing a large amount of spurious noise in polyp detection suffers from elevated recall and mean average ...
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- research-articleMarch 2024
Unsupervised domain adaptation of dynamic extension networks based on class decision boundaries
AbstractIn response to the problems of inaccurate feature alignment, loss of source domain information, imbalanced sample distribution, and biased class decision boundaries in traditional unsupervised domain adaptation methods, this paper proposes a class ...
- research-articleMarch 2024
What's the story in EBS glory: evolutions and lessons in building cloud block store
- Weidong Zhang,
- Erci Xu,
- Qiuping Wang,
- Xiaolu Zhang,
- Yuesheng Gu,
- Zhenwei Lu,
- Tao Ouyang,
- Guanqun Dai,
- Wenwen Peng,
- Zhe Xu,
- Shuo Zhang,
- Dong Wu,
- Yilei Peng,
- Tianyun Wang,
- Haoran Zhang,
- Jiasheng Wang,
- Wenyuan Yan,
- Yuanyuan Dong,
- Wenhui Yao,
- Zhongjie Wu,
- Lingjun Zhu,
- Chao Shi,
- Yinhu Wang,
- Rong Liu,
- Junping Wu,
- Jiaji Zhu,
- Jiesheng Wu
FAST '24: Proceedings of the 22nd USENIX Conference on File and Storage TechnologiesFebruary 2024, Article No.: 17, Pages 277–292In this paper, we qualitatively and quantitatively discuss the design choices, production experience, and lessons in building the Elastic Block Storage (EBS) at ALIBABA CLOUD over the past decade. To cope with hardware advancement and users' demands, we ...
- research-articleFebruary 2024
Decentralized graph-based multi-agent reinforcement learning using reward machines
AbstractIn multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks. The difficulties lie in computational complexity and how to learn the high-level ideas behind reward functions. ...
- research-articleJanuary 2024
Class-Incremental Unsupervised Domain Adaptation via Pseudo-Label Distillation
IEEE Transactions on Image Processing (TIP), Volume 332024, Pages 1188–1198https://doi.org/10.1109/TIP.2024.3357258Class-Incremental Unsupervised Domain Adaptation (CI-UDA) requires the model can continually learn several steps containing unlabeled target domain samples, while the source-labeled dataset is available all the time. The key to tackling CI-UDA problem is ...
- research-articleMarch 2024
Optimization design of collaborative beamforming for heterogeneous UAV swarm
Physical Communication (PHYCOM), Volume 61, Issue CDec 2023https://doi.org/10.1016/j.phycom.2023.102202AbstractCollaborative beamforming (CB) of a virtual antenna array (VAA) is an effective approach to enhance communication performance of UAV swarms. In this paper, the beam optimization problem for a UAV swarm-based VAA in the scenario that involves both ...
- research-articleOctober 2023
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-Link Diagrams
IEEE Transactions on Visualization and Computer Graphics (ITVC), Volume 30, Issue 1Jan. 2024, Pages 562–572https://doi.org/10.1109/TVCG.2023.3326925Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social ...
- ArticleOctober 2023
Towards Expert-Amateur Collaboration: Prototypical Label Isolation Learning for Left Atrium Segmentation with Mixed-Quality Labels
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, Pages 99–109https://doi.org/10.1007/978-3-031-43990-2_10AbstractDeep learning-based medical image segmentation usually requires abundant high-quality labeled data from experts, yet, it is often infeasible in clinical practice. Without sufficient expert-examined labels, the supervised approaches often struggle ...
- ArticleOctober 2023
Category-Level Regularized Unlabeled-to-Labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data
- Zhe Xu,
- Donghuan Lu,
- Jiangpeng Yan,
- Jinghan Sun,
- Jie Luo,
- Dong Wei,
- Sarah Frisken,
- Quanzheng Li,
- Yefeng Zheng,
- Raymond Kai-yu Tong
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, Pages 3–13https://doi.org/10.1007/978-3-031-43901-8_1AbstractSegmenting prostate from MRI is crucial for diagnosis and treatment planning of prostate cancer. Given the scarcity of labeled data in medical imaging, semi-supervised learning (SSL) presents an attractive option as it can utilize both limited ...
- ArticleOctober 2023
Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, Pages 137–147https://doi.org/10.1007/978-3-031-43895-0_13AbstractDeep learning-based segmentation typically requires a large amount of data with dense manual delineation, which is both time-consuming and expensive to obtain for medical images. Consequently, weakly supervised learning, which attempts to utilize ...
- ArticleOctober 2023
You’ve Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-Ray
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, Pages 363–373https://doi.org/10.1007/978-3-031-43907-0_35AbstractChest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods ...
- research-articleOctober 2023
Generalized few-shot node classification: toward an uncertainty-based solution
Knowledge and Information Systems (KAIS), Volume 66, Issue 2Feb 2024, Pages 1205–1229https://doi.org/10.1007/s10115-023-01975-7AbstractFor real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for newly emerging classes. There are many carefully designed ...
- research-articleOctober 2023
Bilateral Relation Distillation for Weakly Supervised Temporal Action Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence (ITPM), Volume 45, Issue 10Oct. 2023, Pages 11458–11471https://doi.org/10.1109/TPAMI.2023.3284853Weakly supervised temporal action localization (WSTAL), which aims to locate the time interval of actions in an untrimmed video with only video-level action labels, has attracted increasing research interest in the past few years. However, a model trained ...
- ArticleAugust 2023
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
- Yash Paliwal,
- Rajarshi Roy,
- Jean-Raphaël Gaglione,
- Nasim Baharisangari,
- Daniel Neider,
- Xiaoming Duan,
- Ufuk Topcu,
- Zhe Xu
Machine Learning and Knowledge ExtractionAug 2023, Pages 123–140https://doi.org/10.1007/978-3-031-40837-3_8AbstractWe study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate ...
- research-articleAugust 2023
Kernel Ridge Regression-Based Graph Dataset Distillation
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, Pages 2850–2861https://doi.org/10.1145/3580305.3599398The huge volume of emerging graph datasets has become a double-bladed sword for graph machine learning. On the one hand, it empowers the success of a myriad of graph neural networks (GNNs) with strong empirical performance. On the other hand, training ...
- research-articleAugust 2023
Node Classification Beyond Homophily: Towards a General Solution
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, Pages 2862–2873https://doi.org/10.1145/3580305.3599446Graph neural networks (GNNs) have become core building blocks behind a myriad of graph learning tasks. The vast majority of the existing GNNs are built upon, either implicitly or explicitly, the homophily assumption, which is not always true and could ...