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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiangmeng Li Wenwen Qiang Yanan Zhang Wenyi Mo Changwen Zheng Bing Su Hui Xiong University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (纸本)9781713871088

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.1

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Multivariate Time Series Forecasting with Transfer Entropy Graph

arXiv

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arXiv 2020年

作者： Duan, Ziheng Xu, Haoyan Huang, Yida Feng, Jie Wang, Yueyang The School of Big Data and Software Engineering Chongqing University Chongqing401331 China The Department of Control Science and Engineering Zhejiang University Hangzhou310027 China

Multivariate time series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help decision-making. To date, many MTS forecasting methods have been proposed and widely applied. However, these methods assume that the predicted value of a single variable is affected by all other variables, which ignores the causal relationship among variables. To address the above issue, we propose a novel end-to-end deep learning model, termed graph neural network with Neural Granger Causality (CauGNN) in this paper. To characterize the causal information among variables, we introduce the Neural Granger Causality graph in our model. Each variable is regarded as a graph node, and each edge represents the casual relationship between variables. In addition, convolutional neural network (CNN) filters with different perception scales are used for time series feature extraction, which is used to generate the feature of each node. Finally, Graph Neural Network (GNN) is adopted to tackle the forecasting problem of graph structure generated by MTS. Three benchmark datasets from the real world are used to evaluate the proposed CauGNN. The comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Fast extraction of word embedding from Q-contexts

arXiv

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arXiv 2021年

作者： Kong, Junsheng Li, Weizhao Liu, Zeyi Liao, Ben Qiu, Jiezhong Hsieh, Chang-Yu Cai, Yi Zhang, Shengyu School of Software Engineering South China University of Technology China University of Cambridge United Kingdom Tencent Quantum Lab China Tsinghua University China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education

The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally challenging for most existing methods. In this work, we show that with merely a small fraction of contexts (Q-contexts) which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors. Mutual information between contexts and words can be encoded canonically as a sampling state, thus, Q-contexts can be fast constructed. Furthermore, we present an efficient and effective WEQ method, which is capable of extracting word embedding directly from these typical contexts. In practical scenarios, our algorithm runs 11∼ 13 times faster than well-established methods. By comparing with well-known methods such as matrix factorization, word2vec, GloVe and fasttext, we demonstrate that our method achieves comparable performance on a variety of downstream NLP tasks, and in the meanwhile maintains run-time and resource advantages over all these baselines. © 2021, CC BY.

关键词： Embeddings

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Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Xiong, Haoyi Duan, Lixin Tan, Mingkui Yang, Weihua Liu, Huiying Xu, Yanwu Baidu Inc. Beijing China School of Computer Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Software Engineering South China University of Technology Guangdong Guangzhou China Big Data and Artificial Intelligence Institute Shenzhen Eye Hospital Jinan University Guangdong Shenzhen China Institute for Infocomm Research A*STAR Singapore Singapore

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the difference between the grader expertness. In this paper, we consider the task of predicting the segmentation with the calibrated inter-observer uncertainty. We note that in clinical practice, the medical image segmentation is usually used to assist the disease diagnosis. Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis First segmentation Framework (DiFF) is proposed to estimate diagnosis-first segmentation from the raw images. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis First Ground-truth (DF-GT). Then, we further propose Take and Give Model (T&G Model) to segment DF-GT from the raw image. In this way, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF is able to significantly facilitate the corresponding disease diagnosis (gla

关键词： Diagnosis

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Loophole-free test of macroscopic realism via high-order correlations of measurement

arXiv

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arXiv 2024年

作者： Wang, Ping Chen, Chong Liao, Hao Vorobyov, Vadim V. Wrachtrup, Jörg Liu, Ren-Bao College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Centre for Quantum Coherence The Chinese University of Hong Kong New Territories Shatin Hong Kong The Hong Kong Institute of Quantum Information Science and Technology The Chinese University of Hong Kong New Territories Shatin Hong Kong National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China 3rd Institute of Physics Research Center SCoPE and IQST University of Stuttgart Stuttgart70569 Germany New Cornerstone Science Laboratory The Chinese University of Hong Kong New Territories Shatin Hong Kong

Test of macroscopic realism (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the non-invasive measurability loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of raw data recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing quantum coherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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Construction and optimization of a file distribution model for all-to-all comparison of big dataset

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Ingenierie des Systemes d'Information 2019年第3期24卷 337-342页

作者： Li, Leixiao Gao, Jing Wang, Hui Deng, Dan Lin, Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry Hohhot010018 China

This paper probes into the all-to-all comparison of large dataset, and gives a formal mathematical description of the problem. Then, a multi-objective file distribution model was constructed based on the LP, aiming to localize the data, balance node storage and loads, minimize the storage occupation, and control the occupied storage within the storage limit of each node. To save storage space, the established model was further optimized, and the file distribution algorithm was designed for the distributed environment. Experimental results show that our model and algorithm successfully balanced the storage occupation and loads between computing nodes, and minimized the occupation of node storage. © 2019 International Information and engineering Technology Association. All rights reserved.

关键词： Linear programming

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Chop chop BERT: Visual question answering by Chopping Visualbert's heads

arXiv

引用

arXiv 2021年

作者： Gao, Chenyu Zhu, Qi Wang, Peng Wu, Qi School of Computer Science Northwestern Polytechnical University Xi'an China School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Adelaide Australia

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA *** Codes 68T45 Copyright © 2021, The Authors. All rights reserved.

关键词： Visual languages

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Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

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Advances in Atmospheric Sciences 2020年第1期37卷 90-104页

作者： Chao LIU Li FU Dan YANG David R.MILLER Junming WANG Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing UniversityChongqing 401331China School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China Department of Natural Resources and Environment University of ConnecticutStorrsCT 06268USA Climate and Atmospheric Science Section Illinois State Water SurveyPrairie Research InstituteUniversity of Illinois at Urbana-ChampaignChampaignIL 61820USA

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian ***,measured surface-layer wind velocities show a strong skewness and *** paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and *** velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian *** show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated *** improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis ***,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle ***,the non-Gaussian model is worth applying to wind field simulation in the surface layer.

关键词： Lagrangian stochastic model wind field simulation non-Gaussian wind velocity surface layer

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Graph-Order Optimization Algorithm Based on Equal-in-Space Distance Model for High-Resolution Image Matting

Graph-Order Optimization Algorithm Based on Equal-in-Space D...

引用

IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Fujian Feng Han Huang Yihui Liang School of Software Engineering South China University of Technology Guangzhou China School of Data Science and Information Engineering Guizhou Minzu University Guiyang China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

ISBN: (纸本)9781665441506

Image matting is an essential image processing technology. optimized-based image matting methods can significantly improve the alpha matte quality of high-resolution images. However, the local information of the foreground may be similar to the background, which causes the inversion problem of the alpha matte in the single-point optimized. In this paper, we propose an image matting mathematical model of the equal-in-space distance. The model transforms the high-resolution image matting problem into several small-scale combinatorial optimization problems according to the similarity among pixel features. Inspired by spanning tree, we propose a graph-order optimization strategy, which generates the optimization sequence of small-scale optimization problems according to the edge weight among graph nodes. In addition, we designed a graph-order optimization algorithm based on optimized information transfer to solve each node in the graph. Experimental results show that the proposed model solves the alpha matte inversion problem of single-point optimization matting. Besides, the proposed algorithm outperforms the state-of-the-art optimization algorithms for the high-resolution image matting problem.

关键词： Cloud computing Image processing Image edge detection Conferences Computational modeling Transforms Information processing

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Predict the Next Location from Trajectory Based on Spatiotemporal Sequence 8

Predict the Next Location from Trajectory Based on Spatiotem...

引用

8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019

作者： Zeng, Jun Tang, Haoran Wu, Yingbo Liu, Ling Hirokawa, Sachio School of Big Data and Software Engineering Chongqing University Chongqing China Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9781728126272

The achievement of wireless communication technology of mobile devices has been witnessed, which produces a large number of trajectory data of mobile users. Processing and analyzing these trajectories could obtain users' movement patterns and behavior rules, leading to provide better location-based services such as point-of-interest recommendation and location prediction. However, enormous volumes of GPS trajectory with high frequency will pose challenges in storage, transmission and computation. Recently, with the rise of social networking sites, more and more users tend to share their geographic locations in real time, thus forming check-in sequences. Hence, this paper proposes a model called SSTLP for predicting next location from trajectory based on spatiotemporal sequence. Firstly, construct location transition probability model by capturing the change of locations in historical trajectory. Secondly, compute distance possibilities of locations by the combination of normal distribution and cosine similarity and then the next location could be figured out. Experiments on real-world data set demonstrate that the proposed model outperforms traditional prediction algorithms. © 2019 IEEE.

关键词： Location

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