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IEEE Symposium on High-Performance Computer Architecture

作者： Haifeng Li Ke Liu Ting Liang Zuojun Li Tianyue Lu Hui Yuan Yinben Xia Yungang Bao Mingyu Chen Yizhou Shan Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology University of Chinese Academy of Sciences Huawei Technologies Huawei Cloud

Memory disaggregation is a promising direction to mitigate memory contention in datacenters. To make memory disaggregation practical, prior efforts expose remote memory to applications transparently via virtual memory subsystem’s swapping interface. However, due to the semantic gap between OS and applications – OS cannot know the memory accessing sequences of an application but via page faults. This approach has two limitations. First, it learns little from page faults’ access history, which leads to sub-optimal prefetching predictions. Second, a page fault can still occur even if there is a prefetch-hit which leads to a large kernel *** address such limitations, our key insight is to decouple the address capturing from page faults by collecting full memory access traces in the memory controller. Using this idea, we build HoPP – a hardware-software co-designed prefetching framework. HoPP adds hardware modules to the memory controller to feed sufficient hot pages to OS in real-time, which has three benefits in HoPP ’s software design: 1) it improves existing prefetching algorithms with simple revamps, also offers more insights to build better policies; 2) the prefetch algorithm can run as a separate data path alongside the normal remote data path via page faults, potentially hiding the swap latency from applications, and enabling fine-grained control over prefetching behaviors; 3) the prefetch-hit overhead can be eliminated by early page table entry (PTE) injection, i.e., inject PTE for the prefetched page as soon as it returns. We implemented a proof-of-concept prototype using commodity servers along with a hardware-based memory tracking tool called HMTT to emulate a modified memory controller. Results show that compared to Fastswap and Leap, HoPP -optimized prefetching algorithm achieves over 90% accuracy and coverage, which leads to up to 59% completion time improvement for various datacenter applications.

关键词： Software design Prefetching Software algorithms Semantics Prototypes Real-time systems Hardware

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A Category Agnostic Model for Visual Rearrangment

A Category Agnostic Model for Visual Rearrangment

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuyi Liu Xinhang Song Weijie Li Xiaohan Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper presents a novel category agnostic model for visual rearrangement task, which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture, completing the rearrangement task by aligning the scene changes of the goal and shuffled configuration, according to the semantic scene graphs. However, constructing scene graphs requires the inference of category labels, which not only causes the accuracy drop of the entire task but also limits the application in real world scenario. In this paper, we delve deep into the essence of visual re-arrangement task and focus on the two most essential issues, scene change detection and scene change matching. We utilize the movement and the protrusion of point cloud to accurately identify the scene changes and match these changes depending on the similarity of category agnostic appearance feature. Moreover, to assist the agent to explore the environment more efficiently and comprehensively, we propose a closer-aligned-retrace exploration policy, aiming to observe more details of the scene at a closer distance. We conduct extensive experiments onAI2THOR Rearrangement Challenge based on RoomR dataset and a new multi-room multi-instance dataset MrMiR collected by us. The experimental results demonstrate the effectiveness of our proposed method.

关键词： Point cloud compression Visualization Computer vision Accuracy Semantics Feature extraction Pattern recognition

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Fact-Preserved Personalized News Headline Generation

Fact-Preserved Personalized News Headline Generation

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Yang Junhong Lian Xiang Ao Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Suzhou CAS

Personalized news headline generation, aiming at generating user-specific headlines based on readers’ preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoder-decoder headline generator to make the output personalized, while the factual consistency of headlines is inadequate to be verified. In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency. In FPG, the similarity between the candidate news to be exposed and the historical clicked news is used to give different levels of attention to key facts in the candidate news, and the similarity scores help to learn a fact-aware global user embedding. Besides, an additional training procedure based on contrastive learning is devised to further enhance the factual consistency of generated headlines. Extensive experiments conducted on a real-world benchmark PENS 1 validate the superiority of FPG, especially on the tradeoff between personalization and factual consistency. 1 https://***/***

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Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning

Masked Video Distillation: Rethinking Masked Feature Modelin...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rui Wang Dongdong Chen Zuxuan Wu Yinpeng Chen Xiyang Dai Mengchen Liu Lu Yuan Yu–Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Cloud + AI

Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel values. In this paper, we propose masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning: firstly we pretrain an image (or video) model by recovering low-level features of masked patches, then we use the resulting features as targets for masked feature modeling. For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks. Visualization analysis also indicates different teachers produce different learned patterns for students. To leverage the advantage of different teachers, we design a spatial-temporal co-teaching method for MVD. Specifically, we distill student models from both video teachers and image teachers by masked feature modeling. Extensive experimental results demonstrate that video transformers pre-trained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets. Our MVD with vanilla ViT achieves state-of-the-art performance compared with previous methods on several challenging video downstream tasks. For example, with the ViT-Large model, our MVD achieves 86.4% and 76.7% Top-1 accuracy on Kinetics-400 and Something-Something-v2, outperforming VideoMAE by 1.2% and 2.4% respectively. When a larger ViT-Huge model is adopted, MVD achieves the state-of-the-art performance with 77.3% Top-1 accuracy on Something-Something-v2. Code will be available at https://***/ruiwang2021/mvd.

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information 48

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Jicun Li, Xingjian Wang, Tianyang Wang, Shi Cao, Yanan Xu, Chengzhong Dou, Dejing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Big Data Lab Baidu Research China University of Alabama Birmingham United States Institute of Information Engineering Chinese Academy of Sciences China State Key Lab of Iotsc University of Macau China

ISBN: (纸本)9781728163277

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method. © 2023 IEEE.

关键词： Fine-tuning Stability Mutual Information Pre-trained Language Model

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Some Thoughts on the Classification Standard of intelligent Autonomous Level of Measurement and Control System

Some Thoughts on the Classification Standard of Intelligent ...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Guangfu Yuan Liu Yang Haoran Han Yong Li Cong Shao Siyu Zhan Gansu Photoelectric Measurement Technology Research Laboratory Gansu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems.

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Emergent communication for rules reasoning 23

Emergent communication for rules reasoning

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yuxuan Guo Yifan Hao Rui Zhang Enshuai Zhou Zidong Du Xishan Zhang Xinkai Song Yuanbo Wen Yongwei Zhao Xuehai Zhou Jiaming Guo Qi Yi Shaohui Peng Di Huang Ruizhi Chen Qi Guo Yunji Chen University of Science and Technology of China and State Key Lab of Processors Institute of Computing Technology CAS and Cambricon Technologies State Key Lab of Processors Institute of Computing Technology CAS State Key Lab of Processors Institute of Computing Technology CAS and Shanghai Innovation Center for Processor Technologies State Key Lab of Processors Institute of Computing Technology CAS and Cambricon Technologies University of Science and Technology of China Intelligent Software Research Center Institute of Software CAS State Key Lab of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences

Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under perception-oriented environmental settings, that forces agents to describe low-level perceptual features intra image or symbol contexts. In this work, inspired by the classic human reasoning test (namely Raven's Progressive Matrix), we propose the Reasoning Game, a cognition-oriented environment that encourages agents to reason and communicate high-level rules, rather than perceived low-level contexts. Moreover, we propose 1) an unbiased dataset (namely rule-RAVEN) as a benchmark to avoid overfitting, 2) and a two-stage curriculum agent training method as a baseline for more stable convergence in the Reasoning Game, where contexts and semantics are bilaterally drifting. Experimental results show that, in the Reasoning Game, a semantically stable and compositional language emerges to solve reasoning problems. The emerged language helps agents apply the extracted rules to the generalization of unseen context attributes, and to the transfer between different context attributes or even tasks.

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Predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model 12th

Predicting Calibrated Conversion Rate of Online Advertising...

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12th CCF Conference on BigData, BigData 2024

作者： Zhang, Xinyue Guo, Yuyao Ao, Xiang School of Information Engineering China University of Geosciences Beijing China Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9789819610235

Accurately predicting conversion rate (CVR) is paramount in online advertising. However, traditional models may face problems such as delayed feedback, where there is a delay of an indeterminate amount of time between click and conversion. Calibration is an effective way to optimize conversion rate estimates in online advertising. Unlike conversion delays, post-click user behaviors occur rapidly and are informative to conversion rate prediction. Our proposed solution, the Multi-Task Mixture-of-Experts Calibration (MTMEC) framework, integrates multi-task learning and mixture-of-experts models. It modifies CVR prediction using post-click user behavior data, utilizing streaming learning for real-time data access. Each task is weighted by a gating network, enabling adaptive loss functions through multi-task learning. Parametric scaling further minimizes calibration errors, enhancing prediction accuracy without excessive parameters. Experiments on real-world datasets validate the effectiveness of MTMEC. It improves model prediction and reduces calibration errors. This framework offers a robust solution for online advertising, bridging the gap between calibration accuracy and system responsiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships

arXiv

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arXiv 2024年

作者： Wan, Zhang Tang, Sheng Wei, Jiawei Zhang, Ruize Cao, Juan Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Zhengzhou University Zhengzhou China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two limitations: Firstly, control conditions (such as depth maps, 3D Mesh) are difficult for ordinary users to obtain directly. Secondly, it’s challenging to drive multiple objects through complex motions with multiple trajectories simultaneously. In this paper, we introduce DragEntity, a video generation model that utilizes entity representation for controlling the motion of multiple objects. Compared to previous methods, DragEntity offers two main advantages: 1) Our method is more user-friendly for interaction because it allows users to drag entities within the image rather than individual pixels. 2) We use entity representation to represent any object in the image, and multiple objects can maintain relative spatial relationships. Therefore, we allow multiple trajectories to control multiple objects in the image with different levels of complexity simultaneously. Our experiments validate the effectiveness of DragEntity, demonstrating its excellent performance in fine-grained control in video generation. Copyright © 2024, The Authors. All rights reserved.

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