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Open Text Classification Based on Dynamic Boundary Balance 9th

Open Text Classification Based on Dynamic Boundary Balance

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19th International conference on Advanced Data Mining and Applications, ADMA 2023

作者： Xu, Ganlin Feng, Jianzhou Wei, Qikai School of Information Science and Engineering Yanshan University Qinhuangdao066000 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100000 China

ISBN: (纸本)9783031466700

Open classification is the problem where there exist some unseen/unknown classes in the test set, i.e., these unknown/unseen classes don’t appear when the model is trained. Existing work often maps samples to high-dimensional space to make decisions, which leads to unobservable and inexplicable results. To address the issue, we shift perspectives to two-dimensional space and put forward a two-stage learning method built on the dynamic decision boundaries balance. We refer it to open classification with dynamic boundary balance (OCD2B). First, we construct a vanilla classifier via known classes with BERT model. Then, we use the prior knowledge of known classes to dynamically determine the decision boundaries between known classes and unknown classes in low-dimensional space. We propose a novel boundary loss function as a boundary balance strategy to reduce open space risk and empirical risk. Experimental results on two standard datasets show that our method achieves performance gain over existing methods, providing easily observable results. In particular, the larger the ratio of unseen classes is, the more obvious the performance advantage the model achieves. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Proceedings - 2023 32nd International conference on Parallel Architecture and Compilation Techniques, PACT 2023

Proceedings - 2023 32nd International Conference on Parallel...

引用

32nd International conference on Parallel Architecture and Compilation Techniques, PACT 2023

ISBN: (纸本)9798350342543

The proceedings contain 33 papers. The topics discussed include: MBAPIS: multi-level behavior analysis guided program interval selection for microarchitecture studies;automatic code generation for high-performance graph algorithms;SimplePIM: a software framework for productive and efficient processing-in-memory;Drishyam: an image is worth a data prefetcher;architecture-aware currying;PreFlush: lightweight hardware prediction mechanism for cache line flush and writeback;retargeting applications for heterogeneous systems with the tribble source-to-source framework;dynamic allocation of processor cores to graph applications on commodity servers;parallelizing maximal clique enumeration on GPUs;and HugeGPT: storing guest page tables on host huge pages to accelerate address translation.

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ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-high Resolution Segmentation

ISDNet: Integrating Shallow and Deep Networks for Efficient ...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Guo, Shaohua Liu, Liang Gan, Zhenye Wang, Yabiao Zhang, Wuhao Wang, Chengjie Jiang, Guannan Zhang, Wei Yi, Ran Ma, Lizhuang Xu, Ke Shanghai Jiao Tong Univ Shanghai Peoples R China Youtu Lab Tencent Shenzhen Peoples R China East China Normal Univ Shanghai Peoples R China City Univ Hong Kong Hong Kong Peoples R China CATL Ningde Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation. To tackle these issues, most of the previous works follow the global-local refinement pipeline, which pays more attention to the memory consumption but neglects the inference speed. In comparison to the pipeline that partitions the large image into small local regions, we focus on inferring the whole image directly. In this paper, we propose ISDNet, a novel ultra-high resolution segmentation framework that integrates the shallow and deep networks in a new manner, which significantly accelerates the inference speed while achieving accurate segmentation. To further exploit the relationship between the shallow and deep features, we propose a novel Relational-Aware feature Fusion module, which ensures high performance and robustness of our framework. Extensive experiments on Deep-globe, Inria Aerial, and Cityscapes datasets demonstrate our performance is consistently superior to state-of-the-arts. Specifically, it achieves 73.30 mIoU with a speed of 27.70 FPS on Deepglobe, which is more accurate and 172 x faster than the recent competitor.

关键词： Image segmentation computer vision Image resolution Codes Pipelines Memory management Robustness

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high Responsivity and Low Dark Current Phototransistor Based on Perovskite/InZnO Heterostructure

High Responsivity and Low Dark Current Phototransistor Based...

引用

International conference on Display Technology, 2024

作者： Lin, Bosi Hu, Rundong Chen, Tong Zhou, Hang School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen Guangdong China

Amorphous metal oxide semiconductors (AOS), especially indium-gallium-zinc oxide (IGZO), have been studied and applied in sensing, display and other industrial fields, including phototransistors, because of their outstanding electronic properties. [1] [2] However, due to its much low visible light response and limited mobility, IGZO could not match the rapidly developing application needs of phototransistor.[3] The construction of photo-TFT (thin film transistor) with better electronical performance and stronger light response has become a key issue. [4] Therefore, indium-zinc oxide (IZO) with relative high mobility (15~20 cm2/V.s) and perovskite with high photosensitivity come into view. [5] In this study, we report a Ruddlesden-Popper (RP) quasi-two-dimensional (quasi-2D) perovskite -IZO phototransistor with heterostructure. A quasi-2D perovskite layer is spin-coated on the high-mobility IZO channel TFT, which enables ultralow off-state dark current of 10-12 A, high responsivity and detectivity under visible light, especially high responsivity and detectivity of 1480 A/W and 2.21×1014 Jones under 525 nm green light illumination. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Perovskite

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RUIE: Retrieval-based Unified Information Extraction using Large Language Model 31

RUIE: Retrieval-based Unified Information Extraction using L...

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31st International conference on Computational Linguistics, COLING 2025

作者： Liao, Xincheng Duan, Junwen Huang, Yixi Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha China

ISBN: (纸本)9798891761964

Unified information extraction (UIE) aims to extract diverse structured information from unstructured text. While large language models (LLMs) have shown promise for UIE, they require significant computational resources and often struggle to generalize to unseen tasks. We propose RUIE (Retrieval-based Unified Information Extraction), a framework that leverages in-context learning for efficient task generalization. RUIE introduces a novel demonstration selection mechanism combining LLM preferences with a keyword-enhanced reward model, and employs a bi-encoder retriever trained through contrastive learning and knowledge distillation. As the first trainable retrieval framework for UIE, RUIE serves as a universal plugin for various LLMs. Experimental results on eight held-out datasets demonstrate RUIE's effectiveness, with average F1-score improvements of 19.22 and 3.22 compared to instruction-tuning methods and other retrievers, respectively. © 2025 Association for Computational Linguistics.

关键词： Data mining

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Machine Learning-Based GPU Energy Prediction for Workload Management in Datacenters 15

Machine Learning-Based GPU Energy Prediction for Workload Ma...

引用

15th IEEE Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Ismalej, Brandon Smith, Matthew Jiang, Xunfei California State University Northridge Department of Computer Science NorthridgeCA91330 United States

ISBN: (纸本)9798331507695

Data centers (DCs) have become a vital component of a digital economy, accounting for approximately 1% of worldwide energy consumption, as the reliance on cloud services and GPU hardware increases for high-performance computing (HPC) tasks. Despite various strategies for improving energy efficiency, there is a lack of research integrating real-world workload traces into the prediction of GPU power consumption, mainly due to the lack of publicly available data. This research addresses this gap by utilizing synthetic data generated to mimic the GPU intensity of real-world workload characteristics and developing GPU power prediction models for integration into the GPUCloudSim Plus simulator. We conducted a statistical analysis of Alibaba and Helios workload traces and ran experiments with diverse GPU-intensive tasks to emulate real-world workloads. Using task-average metrics, we trained four machine learning (ML) models to predict GPU power consumption, with the best performing XGBoost model yielding an RMSE of 1.217. We propose this modeling design to integrate a GPU power prediction model into the GPUCloudSim Plus simulator. Energy-aware simulations for GPU-intensive workloads could be verified on workloads from real data centers, advancing the study of workload management. © 2025 IEEE.

关键词： Prediction models

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Optimization Design of 15kW Induction Motor for IE4 Level CNC machine tools with NAGA-II 26

Optimization Design of 15kW Induction Motor for IE4 Level CN...

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26th International conference on Electrical Machines and Systems, ICEMS 2023

作者： Yang, Hye-Won Jang, DoHyun Hwang, Young-Ho Song, In-Seok Jung, Sang-Young Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350317589

This paper is dedicated to the optimal design of an IE4 level Induction Motor (IM), specifically customized to cater to the unique requirements of CNC machine tools, focusing on the augmentation of precision and efficiency. The inherent reliability and low maintenance attributes of IMs make them the preferred choice for powering machine tools. During the process of IM design, a pivotal consideration revolves around the influence of rotor copper losses. These losses contribute to heat generation within the IM, consequently impacting both rotor temperature and motor efficiency. Particularly in the realm of CNC machine tools, which necessitate elevated levels of precision and efficiency, addressing the issue of rotor copper loss emerges as a critical endeavor within the design framework. In this study, a multi-objective optimization approach, specifically NSGA-II, is harnessed to tackle the challenge posed by rotor copper loss. The primary objectives encompass the minimization of both rotor copper loss and current density of the conductor. By achieving these optimizations, the overall performance of the motor is comprehensively improved by effectively reducing the rotor temperature in the end. © 2023 IEEE.

关键词： Multiobjective optimization

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An Approach of Code Summary Generation Using Multi-Feature Fusion Based on Transformer 20th

An Approach of Code Summary Generation Using Multi-Feature ...

引用

Proceedings of the 20th Web Information Systems and Applications conference, WISA 2023

作者： Yang, Chao Wu, Junhua College of Computer and Information Engineering Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9789819962211

The automatic generation of code comments is an important research in program understanding. Code summary describes the function and purpose of the code. It helps developers comprehend the program, and reduces the cost of software maintenance. Many previous approaches only use token sequence of source code as input or only rely on learning code structure information from Abstract Syntax Tree (AST), which will result in extracting source code information and obtaining richer semantic information ineffectually. We propose an approach of code summary generation using Multi-Feature Fusion based on Transformer (MFFT). The Multi-way Tree-LSTM is used to process the AST structure to obtain the semantic information of the code. To extract more code information, we also obtain code token features and combine them with a fusion method to generate a high-quality code summary. The experimental results show that our MFFT method has a good performance improvement. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Learning Foreground-Background Segmentation from Improved Layered GANs 22

Learning Foreground-Background Segmentation from Improved La...

引用

22nd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Yang, Yu Bilen, Hakan Zou, Qiran Cheung, Wing Yin Ji, Xiangyang Tsinghua Univ BNRist Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781665409155

Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a foreground-background segmentation network. In particular, we learn a generative adversarial network that decomposes an image into foreground and background layers, and avoid trivial decompositions by maximizing mutual information between generated images and latent variables. The improved layered GANs can synthesize higher quality datasets from which segmentation networks of higher performance can be learned. Moreover, the segmentation networks are employed to stabilize the training of layered GANs in return, which are further alternately trained with Layered GANs. Experiments on a variety of single-object datasets show that our method achieves competitive generation quality and segmentation performance compared to related methods.

关键词： Training Deep learning Image segmentation computer vision Generative adversarial networks Noise measurement Task analysis

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Subgroup-Adaptive Network for Robust Virtual Patient-Specific Quality Assurance 20

Subgroup-Adaptive Network for Robust Virtual Patient-Specifi...

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20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Zeng, Xiaoyang Ahmed, Awais Tunio, Muhammad Hanif Chengxiao, Yan Hou, Mengshu Xi, Rui School of Computer Science and Engineering University of Electronic Science and Technology of China - UESTC Sichuan611731 China School of Big Data and Artificial Intelligence Chengdu Technological University Sichuan611730 China

ISBN: (纸本)9798350318982

Deep learning-based automatic patient-specific quality assurance (PSQA) alleviates medical resource pressure and ensures the safety of patient treatment plans by predicting the actual dosage difference distribution or metrics. However, deep learning is often affected by extremely unbalanced data distributions, and in clinical practice, the number of normal plans is always much larger than that of abnormal plans. On the other hand, the distribution of patients' lesion sites could be more balanced. The performance of models is often seriously biased in these subgroups, posing a challenge to the reliability of deep learning in the clinical application of PSQA. In this work, we propose a subgroup-adaptive network to address the problem of subgroup drift in PSQA. Our experiments achieved a state-of-the-art (SOTA) MAE of 0.69. Our proposed work can significantly improve the reliability of deep learning in the practical clinical application of PSQA. © 2023 IEEE.

关键词： Quality assurance

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