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Service decoupling for open and intelligent service-based RAN

Frontiers of Information Technology & Electronic Engineering 2025年第2期26卷 230-245页

作者： Chunjing YUAN Tong LEI Ze XUE Lin TIAN Shuyuan ZHANG Na LI Zhou TONG Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Hubei Intelligent Cloud Network Operation Center China TelecomWuhan 430024China Nanjing Institute of InforSuperBahn Nanjing 210008China Future Research Lab China Mobile Research InstituteBeijing 100053China

Task diversity is one of the biggest challenges for future sixth-generation(6G)*** the task as the center and driving the dynamic 6G radio access network(RAN)with artificial intelligence(AI)are necessary to accurately meet the personalized demands of ***,AI can only configure the parameters of a monolithic RAN and cannot schedule the *** development trend of 6G RANs is to enhance dynamic capability and scheduling *** this paper,we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize networks according to *** analysis shows that the interactive relationship between RAN control plane(CP)functions is complex and needs to be decoupled according to the principles of high cohesion and low *** on the graph theory rather than expert experience,we design a RAN decoupling *** functional connection and interaction of the CP are represented by constructing an undirected weighted graph,followed by achieving decoupling of the CP through a minimum spanning *** an integration decoupling scheme of a RAN-CN(core network)is introduced considering the duplicate and redundant functions of the RAN and *** granularity of decoupling in a service-based RAN is determined by analyzing the flexibility of decoupling,complexity of signaling,and processing *** find that it is more appropriate to decouple the RAN CP into four *** integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface,supporting AI-based global service scheduling.

关键词： Service decoupling Open and intelligent Service-based radio access network(RAN) Graph theory Full-service 6G network

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End States Guided Matching Network for Retrieval-based Multi-turn Conversation 3

End States Guided Matching Network for Retrieval-based Multi...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Weixin Tan Dandan Song Yujin Gao Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information TechnologyBeijing Institute of Technology

ISBN: (纸本)9781665418683

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.

关键词： Information science Computational modeling Computer network reliability Reliability Data mining

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Caseg: Clip-Based Action Segmentation With Learnable Text Prompt

Caseg: Clip-Based Action Segmentation With Learnable Text Pr...

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IEEE International Conference on Image Processing

作者： Suyuan Huang Haoxin Zhang Yanyu Xu Yan Gao Yao Hu Zengchang Qin Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Xiaohongshu Inc. Institute of High Performance Computing A*Star Guangzhou Zhongsuan Cloud Technology Co.. Ltd.

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Video action segmentation aims to identify and localize actions. Existing models have achieved impressive performance with pre-extracted frame-level features, but this may limit zero-shot learning and cross-dataset inference, especially for new actions or scenes. To overcome this problem, we propose a novel end-to-end network designed for robust performance across both familiar and novel action segmentation scenarios. Our approach combines a plug-and-play visual prompt module enhancing CLIP features’ temporal understanding, and a learnable text prompt that enriches label semantics and refines the model’s focus, significantly boosting performance. Our results demonstrate that CLIP features can assist in action segmentation tasks, and prompts can improve task effectiveness. Furthermore, our findings show that CLIP features contain information that i3d features do not. We evaluate the proposed method on several video datasets, including Georgia Tech Egocentric Activities (GTEA), 50Salads, and Breakfast, and the results show that the proposed model outperforms existing SOTA models.

关键词： Image segmentation Visualization Zero-shot learning Surveillance Semantics Refining Human-robot interaction

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The chordata olfactory receptor database

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Protein & Cell 2025年第4期16卷 283-292页

作者： Wei Han Siyu Bao Jintao Liu Yiran Wu Liting Zeng Tao Zhang Ningmeng Chen Kai Yao Shunguo Fan Aiping Huang Yuanyuan Feng Guiquan Zhang Ruiyi Zhang Hongjin Zhu Tian Hua Zhijie Liu Lina Cao Xingxu Huang Suwen Zhao Human Institute ShanghaiTech UniversityShanghai 201210China Research Center for Life Sciences Computing Zhejiang LabHangzhou 311121China Department of Intelligent Edge Cloud China Telecom Cloud Technology Co.Ltd.Shanghai 200120China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China School of Information Science and Technology Shanghai Tech UniversityShanghai 201210China Shanghai Key Laboratory of High-Resolution Electron Microscopy Shanghai Tech UniversityShanghai 201210China Zhejiang Provincial Key Laboratory of Pancreatic Disease The First Affiliated Hospitaland Institute of Translational MedicineZhejiang University School of MedicineHangzhou 310029China Shanghai Clinical Research and Trial Center Shanghai 201210China

Introduction of database Olfaction is one of the oldest chemosensory systems in chordates,playing crucial roles in their foraging,predator evasion,social communication,mating and parental care(Guo et al.,2023;Li and Liberles,2015;Liberles,2014).The initial step of olfaction is the binding and activation of olfactory receptors(ORs)by odorants in a combinatorial way(Malnic et al.,1999).

关键词： chordates chemosensory systems introduction database olfaction foraging olfaction binding activation olfactory olfactory receptors odorants

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Why Not Look One Step Ahead in Reinforcement Learning Based Knowledge Graph Reasoning?

SSRN

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SSRN 2024年

作者： Wang, Hao Song, Dandan Wu, Zhijing Tian, YuHang Xu, Jing Laboratory of High-Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Multi-hop reasoning is an effective and explainable method for query answering to improve interpretability by finding reasoning paths over knowledge graphs (KGs). Recent studies apply reinforcement learning-based (RL-based) methods with policy agents to solve this problem. However, they only focus on the sequential reasoning paths and the candidate nodes regardless of the local information of adjacency subgraphs and the semantic correlations between relations in every decision step. In this paper, we propose an RL-based multi-hop reasoning model, Look One Step Ahead (LOSA), which regards more expressive adjacency subgraphs rather than nodes as candidate actions and pays attention to the semantic correlations between relations. Specifically, an adjacency aggregation module encodes the information of subgraphs and transmits the encoding to the policy network to help make decisions, which can reduce backtracking and alleviate sparse-reward issues. Furthermore, a semantic matching module is designed to stress the semantic correlations of relations to improve the rationality of the reasoning paths. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Uncertainty-aware complementary label queries for active learning

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Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1497-1503页

作者： Shengyuan LIU Ke CHEN Tianlei HU Yunqing MAO Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang UniversityHangzhou 310027China State Key Laboratory of Blockchain and Data Security Zhejiang UniversityHangzhou 310027China City Cloud Technology(China)Co. Ltd.Hangzhou 310000China

Many active learning methods assume that a learner can simply ask for the full annotations of some training data from *** methods mainly try to cut the annotation costs by minimizing the number of annotation ***,annotating instances exactly in many realworld classification tasks is still *** reduce the cost of a single annotation action,we try to tackle a novel active learning setting,named active learning with complementary labels(ALCL).ALCL learners ask only yes/no questions in some *** receiving answers from annotators,ALCL learners obtain a few supervised instances and more training instances with complementary labels,which specify only one of the classes to which the pattern does not *** are two challenging issues in ALCL:one is how to sample instances to be queried,and the other is how to learn from these complementary labels and ordinary accurate *** the first issue,we propose an uncertainty-based sampling strategy under this novel setup.

关键词： complementary instance ordinary

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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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CUDA and Applications to Task-based Programming

CUDA and Applications to Task-based Programming

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43rd Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2022

作者： Kerbl, Bernhard Kenzel, Michael Winter, Martin Steinberger, Markus TU Wien Institute of Visual Computing and Human-Centered Technology Austria Saarland University Computer Graphics Lab Germany Intelligent Cloud Rendering Laboratory Huawei Technologies Austria Graz University of Technology Institute of Computer Graphics and Vision Austria

ISBN: (纸本)9783038681724

Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. © 2022 The Author(s)

关键词： Regulatory compliance

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OmniViD: A Generative Framework for Universal Video Understanding

OmniViD: A Generative Framework for Universal Video Understa...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junke Wang Dongdong Chen Chong Luo Bo He Lu Yuan Zuxuan Wu 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 Microsoft Research Asia University of Maryland College Park

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically de-tect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal, different tasks often rely on distinct model architectures and annotation formats. In contrast, natural language processing benefits from a unified output space, i.e., text sequences, which simplifies the training of powerful foundational language models, such as GPT-3, with extensive training cor-pora. Inspired by this, we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way, a variety of video tasks could be formulated as video-grounded token generation. This enables us to address var-ious types of video tasks, including classification (such as action recognition), captioning (covering clip captioning, video question answering, and dense video captioning), and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture, following a generative framework. Through comprehensive experiments, we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or compet-itive results on seven video benchmarks, providing a novel perspective for more universal video understanding. Code is available at https://***/wangjk666/OmniVid.

关键词： Training Location awareness Visualization Vocabulary Computer architecture Benchmark testing Robustness

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Look Before You Match: Instance Understanding Matters in Video Object Segmentation

Look Before You Match: Instance Understanding Matters in Vid...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junke Wang Dongdong Chen Zuxuan Wu Chong Luo Chuanxin Tang Xiyang Dai Yucheng Zhao Yujia Xie 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 Microsoft Research Asia

Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due to the lack of instance understanding ability, the above approaches are oftentimes brittle to large appearance variations or viewpoint changes resulted from the movement of objects and cameras. In this paper, we argue that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, i.e., identifying and segmenting object instances within the video. Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank. We employ the well-learned object queries from IS branch to inject instance-specific information into the query key, with which the instance-augmented matching is further performed. In addition, we introduce a multi-path fusion block to effectively combine the memory readout with multi-scale features from the instance segmentation decoder, which incorporates high-resolution instance-aware features to produce final segmentation results. Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6% and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3% and 86.3%), outperforming alternative methods by clear margins.

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