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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Quan, Quan Tang, Fenghe Xu, Zikang Zhu, Heqin Zhou, S. Kevin Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China

The Segment Anything Model (SAM) has achieved a notable success in two-dimensional image segmentation in natural images. However, the substantial gap between medical and natural images hinders its direct application to medical image segmentation tasks. Particularly in 3D medical images, SAM struggles to learn contextual relationships between slices, limiting its practical applicability. Moreover, applying 2D SAM to 3D images requires prompting the entire volume, which is time- and label-consuming. To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window. It firstly takes three slices from a 3D volume and point or bounding box prompts on the central slice as inputs to predict segmentation masks for all three slices. Subsequently, the masks of the top and bottom slices are then used to generate new prompts for adjacent slices. Finally, step-wise prediction can be achieved by sliding the prediction window forward or backward through the entire volume. Our model is trained on multiple public and private medical datasets and demonstrates its effectiveness through extensive 3D segmetnation experiments, with the help of minimal prompts. Code is available at https://***/Curli-quan/Slide-SAM. © 2024 CC-BY 4.0, Q. Quan, F. Tang, Z. Xu, H. Zhu & S. Zhou.

关键词： Image segmentation

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Evolving Knowledge Distillation with Large Language Models and Active Learning 30

Evolving Knowledge Distillation with Large Language Models a...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Chengyuan Kang, Yangyang Zhao, Fubang Kuang, Kun Jiang, Zhuoren Sun, Changlong Wu, Fei College of Computer Science and Technology Zhejiang University China Institute for Intelligent Computing Alibaba Group China School of Public Affairs Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China

ISBN: (纸本)9782493814104

Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model). Different from previous work, we actively analyze the student model's weaknesses, and then synthesize labeled samples based on the analysis. In addition, we provide iterative feedback to the LLMs regarding the student model's performance to continuously construct diversified and challenging samples. Experiments and analysis on different NLP tasks, namely, text classification and named entity recognition show the effectiveness of EvoKD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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The Past, Present, and Future of Storage Technologies (Part 1 of 2)

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ACM TRANSACTIONS ON STORAGE 2025年第1期21卷 1-3页

作者： Kuenning, Geoff Won, Youjip Zhao, Ming Computer Science Harvey Mudd College Claremont United States Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea (the Republic of) School of Computing and Information Sciences Arizona State University Tempe United States Computer Science Stony Brook University Stony Brook United States

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TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes 27

TransFeat-TPP: An Interpretable Deep Covariate Temporal Poin...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Meng, Zizhuo Li, Boyu Fan, Xuhui Li, Zhidong Wang, Yang Chen, Fang Zhou, Feng Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)9781643685489

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://***/waystogetthere/***. © 2024 The Authors.

关键词： Spatio-temporal data

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Towards Efficient MIT query in Trajectory Data 39

Towards Efficient MIT query in Trajectory Data

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Chen, Jian Gao, Hong Zhang, Kaiqi Wang, Jiachi Luo, Yubo Wu, Zhenqing Li, Jianzhong Harbin Institute of Technology Faculty of Computing Harbin China Zhejiang Normal University College of Mathematics and Computer Science Jinhua China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9798350322279

Maximizing Influence (Max-Inf) query is a fundamental operation in spatial data management. Given a set of weighted objects, this query aims to find an optimal location from a candidate set to maximize its influence, which is the total weight of its reverse nearest neighbors. Existing work commonly assumes that every object is in a fixed location. In real life, however, there are a wide variety of drive-in services (e.g., food joints, fuel stations, ATMs, etc.) that are widely accessed by mobile users (i.e., trajectories) instead of the fixed ones. It is urgent and challenging to solve the Max-Inf query in trajectory data (MIT). In this paper, we first define the MIT query which aims to find the optimal location to maximize the total weight of influenced trajectories. We propose a novel index structure, QB-tree to hierarchically group trajectories with similar activity regions together for subsequent unified processing, and classify trajectories inside the same node into multiple buckets according to their motion patterns. For each bucket, we construct a rectilinear polygon using the trajectories in it to exclude some irrelevant areas in the minimum boundary rectangle. Moreover, we develop a branch-and-bound approach called BBM to efficiently solve the MIT query. The algorithm adaptively partitions the candidates into disjoint regions and prunes the regions without containing optimal results. Then, by exploiting the QB-tree, the upper and lower bounds are efficiently computed with three-level pruning technique. Finally, we conduct extensive experiments on real and synthetic datasets to evaluate our index and algorithms, and the experimental results demonstrate that our algorithm has high performance in terms of efficiency, scalability, and genericity. © 2023 IEEE.

关键词： Trajectories

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Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

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Journal of Electronic Science and technology 2024年第2期22卷 48-68页

作者： Gracieth Cavalcanti Batista Johnny Oberg Osamu Saotome Haroldo F.de Campos Velho Elcio Hideiti Shiguemori Ingemar Soderquist Division of Electronic and Embedded Systems KTH Royal Institute of TechnologyStockholm 16440Sweden Electronic Engineering Division Aeronautics Institute of TechnologySao Josédos Campos SP 12228-900Brazil Laboratory of Applied Computing and Mathematics National Institute for Space ResearchSao Josédos Campos SP 12227-900Brazil Department of C4ISR Institute of Advanced StudiesSao Josédos Campos SP 12228-001Brazil Saab AB Linkoping 58188Sweden

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,*** key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)***,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or *** alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced *** a high effort is required for image edge *** a support vector regression(SVR)model is proposed to reduce this computational load and processing *** dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable *** show that the implementation in hardware is 68 times faster than that in *** architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without *** is also the lowest power consumption in current machine learning hardware ***,the circuitry area is 41 times *** with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning *** system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

关键词： Dynamic partial reconfiguration(DPR) Field programmable gate array(FPGA)implementation Image edge detection Support vector regression(SVR) Unmanned aerial vehicle(UAV) pose estimation

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Lattice Menu: A Low-Error Gaze-Based Marking Menu Utilizing Target-Assisted Gaze Gestures on a Lattice of Visual Anchors 22

Lattice Menu: A Low-Error Gaze-Based Marking Menu Utilizing ...

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2022 CHI Conference on Human Factors in computing Systems, CHI 2022

作者： Kim, Taejun Ham, Auejin Ahn, Sunggeun Lee, Geehyuk Hci Lab School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450391573

We present Lattice Menu, a gaze-based marking menu utilizing a lattice of visual anchors that helps perform accurate gaze pointing for menu item selection. Users who know the location of the desired item can leverage target-assisted gaze gestures for multilevel item selection by looking at visual anchors over the gaze trajectories. Our evaluation showed that Lattice Menu exhibits a considerably low error rate (∼1%) and a quick menu selection time (1.3-1.6 s) for expert usage across various menu structures (4 × 4 × 4 and 6 × 6 × 6) and sizes (8, 10 and 12°). In comparison with a traditional gaze-based marking menu that does not utilize visual targets, Lattice Menu showed remarkably (∼5 times) fewer menu selection errors for expert usage. In a post-interview, all 12 subjects preferred Lattice Menu, and most subjects (8 out of 12) commented that the provisioning of visual targets facilitated more stable menu selections with reduced eye fatigue. © 2022 ACM.

关键词： Eye tracking

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Deep Learning Video Analytics Solutions for Ocean Surveillance Systems 14

Deep Learning Video Analytics Solutions for Ocean Surveillan...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Harikrishnan, V.S. Dixit, Shivam Pal Amutha, K. Kamnani, Jitesh Ramasundaram, S. Venkatesan, R. Centre for Development of Advanced Computing Chennai India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9781510657564

Moored data buoys are floating platforms at sea. These buoys serve as in-situ Weather, Ocean and Tsunami observatories. These buoys transmit real-time data through 3G/GSM/GPRS and satellite telemetry. Damage to the buoy systems by humans, boats, ships etc., intentional or otherwise, causes loss of data, and inhibits early warning systems. It also has financial implications due to the loss of the instruments, repair & reinstallation charges, and the time a ship spends to fix the buoy. Challenges arise while analyzing the video footage as they are unstable and shaky, due to the continuous movements of floating ocean buoy platforms caused by the state of the sea. This paper explores object detection algorithms for detecting eight different objects commonly found in the camera video footage transmitted by the buoy platforms at sea. The object detection training implementation gave us a best accuracy of 0.867MAP@0.5IOU. The object detection will help in solutions like Object Search, detection of floating marine plastic debris, understanding the direction of motion of ships, boats etc. In a broader perspective, it can help in Surveillance, Market Survey and Fish Detection in underwater cameras for fish abundance study. © 2022 SPIE.

关键词： Ships

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QuadStretch: A Forearm-wearable Multi-dimensional Skin Stretch Display for Immersive VR Haptic Feedback

QuadStretch: A Forearm-wearable Multi-dimensional Skin Stret...

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2022 CHI Conference on Human Factors in computing Systems, CHI EA 2022

作者： Shim, Youngbo Aram Kim, Taejun Lee, Geehyuk Hci Lab School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450391566

This demonstration presents QuadStretch, a multidimensional skin stretch display that is worn on the forearm for VR interaction. QuadStretch realizes a light and flexible form factor without a large frame that grounds the device on the arm and provides rich haptic feedback through high expressive performance of stretch modality and various stimulation sites around the forearm. In the demonstration, the presenter lets participants experience six VR interaction scenarios with QuadStretch feedback: Boxing, Pistol, Archery, Slingshot, Wings, and Climbing. In each scenario, the user's actions are mapped to the skin stretch parameters and fed back, allowing users to experience QuadStretch's large output space that enables an immersive VR experience. © 2022 Owner/Author.

关键词： Wearable technology

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Brain MR Images Super-Resolution with the Consistent Features 2022

Brain MR Images Super-Resolution with the Consistent Feature...

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14th International Conference on Machine Learning and computing, ICMLC 2022

作者： You, Senrong Shen, Yanyan Wu, Guocheng Wang, Shuqiang Center for Biomedical Information Technology Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Internet of Things Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Data Recovery Key Laboratory of Sichuan Province College of Mathematics and Information Science Neijiang Normal University China

ISBN: (纸本)9781450395700

Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively. © 2022 Owner/Author.

关键词： Generative adversarial networks

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