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Finding Simplex Items in data Streams

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Finding Simplex Items in Data Streams

International Conference on data engineering

作者： Zhuochen Fan Jiarui Guo Xiaodong Li Tong Yang Yikai Zhao Yuhan Wu Bin Cui Yanwei Xu Steve Uhlig Gong Zhang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Hong Kong SAR China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.

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Experimental Study on the Zebra Crossing Behavior of Mixed Bicycles and Pedestrians

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SSRN 2022年

作者： Ma, Jian Wang, Qiao Chen, Juan Jiang, Rui Song, Weiguo Li, Ruoyu Lian, Liping School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu610031 China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China State Key Laboratory of Fire Science University of Science and Technology of China Hefei230027 China School of Urban Planning and Design Peking University Shenzhen Graduate School School of Architectural Engineering Shenzhen Polytechnic Shenzhen518055 China

Slow transportation mode, consisting of walking and cycling, plays an important role in urban traffic system. Based on the fact that bicycles and pedestrians often share the common road facility, we conducted bicycle-pedestrian mixed flow experiments to investigate the interaction effect among individuals in a 10 m-long, 3 m-wide passageway, mimicking the zebra crossing area. The goal is to explore the influence of bicycles proportion ( p ) on traffic efficiency and investigate the spatial distribution of agents in the mixed flow. Our analyses showed that the increase of bicycle proportion results in longer passing time and less travel time because of large personal space requirements of cyclists. When the bicycle proportion is small or large, the majority pedestrians (bicycles) were divided into several parts by minor bicycles (pedestrians) along the motion direction. We also recorded the following- and lateral- distance between two nearest individuals to calculate the theoretical formula of the passing time for unidirectional bicycle-pedestrian mixed flow based on the existing formula. Comparing the passing time between numerical calculation and experimental data, the effect coefficients for bicycle and pedestrian were obtained. Focusing on the overtaking bicycles, the through gap, lateral and headway distance that decide the overtaking behavior were analyzed. These findings are the first step toward the development of bicycle-pedestrian mixed flow on zebra crossing, which is contribute to provide practical implications for the planning and design of bicycle and pedestrian shared infrastructures. © 2022, The Authors. All rights reserved.

关键词： Travel time

Experimental Study on the Zebra Crossing Behavior of Mixed Bicycles and Pedestrians

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SSRN 2022年

关键词： Travel time

Where to look and how to describe: Fashion image retrieval with an attentional heterogeneous Bilinear network

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arXiv

arXiv 2020年

作者： Su, Haibo Wang, Peng Liu, Lingqiao Li, Hui Li, Zhen Zhang, Yanning School of Computer Science Northwestern Polytechnical University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide Australia MinSheng FinTech Corp. Ltd China

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., "how to describe") and localization (i.e., "where to look") information, and their interactions. To this end, we propose a biologically inspired framework for image-based fashion product retrieval, which mimics the hypothesized two-stream visual processing system of human brain. The proposed attentional heterogeneous bilinear network (AHBN) consists of two branches: a deep CNN branch to extract fine-grained appearance attributes and a fully convolutional branch to extract landmark localization information. A joint channel-wise attention mechanism is further applied to the extracted heterogeneous features to focus on important channels, followed by a compact bilinear pooling layer to model the interaction of the two streams. Our proposed framework achieves satisfactory performance on three image-based fashion product retrieval benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

Deep Learning Classification of Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma with Four-Phase Ct Images

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SSRN 2024年

作者： Zhu, Wentao Wu, Yuanfeng Feng, Zhan Cui, Heng Rao, Fan Ni, Yangfan Chen, Geng Xia, Yong Zhejiang Lab Hangzhou China Department of Radiology College of Medicine The First Affiliated Hospital Zhejiang University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

Liver cancer is one of the leading causes of cancer-related death globally. The differentiation of hepatic tumors is critical for designing an accurate treatment plan and improving the five-year survival rate. Recently, multiphase CT images have become the first-line examination approach for clinical diagnosis. Existing methods have been proposed to distinguish hepatic tumors based on deep learning using multiphase CT. However, CNN-based models fail to explicitly establish temporal relationships among different phases, and RNN-based methods rely on expensive calculations due to the recurrent mechanism. In this paper, we proposed a transformer-based deep learning method for identifying intrahepatic cholangiocarcinoma and hepatocellular carcinoma using four-phase CT images as input. Specifically, a difference attention module was developed to improve the feature representation by using the phase difference as interphase temporal information. Moreover, a transformer-based structure was used for classification to explore the long-term context and capture the temporal relationship among phases using a multihead self-attention module. Experiments with clinical datasets are implemented to evaluate the performance of the proposed method and mainstream methods. In the experiments, the proposed method demonstrated superior performance in classifying ICC and HCC on the test set, with an accuracy of 83.72%, a sensitivity of 0.7511, a specificity of 0.9200 and an AUC of 0.90. The results indicate that the proposed method outperforms the traditional deep learning based methods. The architecture can be utilized in other researches using multiple images as input. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

Multiperiod Location–Allocation Optimization of Large-Scale Project Construction Logistics Centers in Complex Environmental Regions

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SSRN 2024年

作者： Shen, Hao Zhang, Jin Sun, Wenjie Yang, Wenguang Li, Guoqi School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Sichuan Chengdu611756 China National United Engineering Laboratory of Integrated and Intelligent Transportation Sichuan Chengdu611756 China School of Economics and Management Xinjiang University Xinjiang Urumqi844099 China

As an efficient management pattern to improve logistics efficiency and reduce logistics costs through intensive management of construction materials, construction logistic centers (CLCs) have received active attention from academics and practitioners. However, the CLC location–allocation problem, which considers periodic demand and transportation risk, has not been adequately solved. This work provides an approach to integrating transportation path risk into multi-period CLC location–allocation optimization for large-scale projects in complex environmental regions. For this purpose, we formulate a hybrid nonlinear integer planning model to define this location–allocation problem and minimize the total cost of construction logistics and transportation risk. The total cost includes transportation costs for project materials, construction, opening, closing, operating, and storage costs for the CLC. The model also incorporates critical features from realistic scenarios, including CLC’s service coverage, capacity constraints and minimum utilization limits, and transportation path risk. Then, we designed a NSGA-II based on endocrine hormone regulation (EHR-NSGA-II) to solve the model. Finally, a large-scale railroad construction project in complex environmental regions is used as an example to prove the effectiveness of the model and algorithm. Compared with the single-period model, the multi-period model designed in this paper provides a total cost reduction of 8.11% for the CLC location–allocation scheme, of which the operating cost is reduced by 63.72%. In addition, analyzing several key parameters provides valuable insights for managers to design more reliable construction logistics networks when facing different scenarios. © 2024, The Authors. All rights reserved.

关键词： Integer programming

Joint optimization of train blocking and shipment path: An integrated model and a sequential algorithm

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arXiv

arXiv 2019年

作者： Chen, Chongshuang Zhao, Jun School of Mathematics Southwest Jiaotong University ChengduSichuan611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology ChengduSichuan611756 China School of Transportation and Logistics Southwest Jiaotong University ChengduSichuan611756 China

Graph Condensation for Inductive Node Representation Learning

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arXiv

arXiv 2023年

作者： Gao, Xinyi Chen, Tong Zang, Yilong Zhang, Wentao Hung Nguyen, Quoc Viet Zheng, Kai Yin, Hongzhi The University of Queensland Brisbane Australia Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph. Copyright © 2023, The Authors. All rights reserved.

关键词： Condensation

SDGFormer: An Efficient Convolution Network Structurally Similar to Transformer

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SDGFormer: An Efficient Convolution Network Structurally Sim...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chaohao Wen Xun Gong School of Computing and Artificial Intelligence SWJTU Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology SWJTU Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Chengdu China

Deep Neural Networks (DNN) have achieved extraordinary success in many visual recognition tasks. Visual Transformer (ViT), which is derived from Natural Language Processing (NLP), has achieved state-of-the-art (SOTA) results on many tasks due to its capability of capturing long-range dependencies in visual data. However, Existing ViT models are challenging to deploy on devices due to their massive computational consumption, huge memory overhead, and reliance on large datasets. In this work, we address these issues by replacing some computationally expensive and memory-intensive modules in ViT with standard Convolutional Neural Network (CNN) modules. Firstly, we propose an efficient Self-Attention module called SDG-Attention (SDGA) with linear space and time complexity, and an economical FeedForward Network (FFN) composed of group convolution and shuffle channel (SFFN). Then, we develop a lightweight CNN model with SDGA and SFFN, SDGFormer, which embraces several priors of ViT and is LayerNorm-Free. We evaluate SDGFormer on ImageNet-1K and Mini-ImageNet, and the SDGFormer-S achieves a comparable top-1 accuracy of 77.6% on ImageNet-1K with 9.1M parameters and 1.6 GFlops regimes. Moreover, our SDGFormer-T achieves SOTA performance on Mini-ImageNet with 83.3% accuracy, demonstrating good generalization on small datasets without extra data.

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