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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Qi, Huayi Xu, Minghui Cheng, Xiuzhen Lyu, Weifeng Shandong University School of Computer Science and Technology China Beihang University State Key Laboratory of Software Development Environment China

ISBN: (纸本)9798350334142

Blockchain systems can become overwhelmed by a large number of transactions, leading to increased latency. As a consequence, latency-sensitive users must bid against each other and pay higher fees to ensure that their transactions are processed in priority. However, most of the time of a blockchain system (78% in Ethereum), there is still a lot of unused computational power, with few users sending transactions. To address this issue and reduce latency for users, we propose the latency-first smart contract model in this paper, which optimistically accepts committed transactions. This allows users to submit a commitment during times of high demand, and then complete the rest of the work at a lower priority. From the perspective of the blockchain, this temporarily "overclocks"the system. We have developed a programming tool for our model, and our experiments show that the proposed latency-first smart contract model can greatly reduce latency during the periods of high demand. © 2023 IEEE.

关键词： Smart contract

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Effective Training of PINNs by Combining CMA-ES with Gradient Descent 13

Effective Training of PINNs by Combining CMA-ES with Gradien...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Lin Yuan, Yuan School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beijing100191 China

ISBN: (纸本)9798350308365

Physics-Informed Neural Networks (PINNs) have recently received increasing attention, however, optimizing the loss function of PINNs is notoriously difficult, where the landscape of the loss function is often highly non-convex and rugged. Local optimization methods based on gradient information can converge quickly but are prone to being trapped in local minima for training PINNs. Evolutionary algorithms (EAs) are well known for the global search ability, which can help escape from local minima. It has been reported in the literature that EAs show some advantages over gradient-based methods in training PINNs. Inspired by the Memetic Algorithm, we combine global-search based EAs and local-search based batch gradient descent in order to make the best of both word. In addition, since the PINN loss function is composed of multiple terms, balancing these terms is also a challenging issue. Therefore, we also attempt to combine EAs with multiple-gradient descent algorithm for multi-objective optimization. Our experiments provide strong evidence for the superiority of the above algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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New Intent Discovery with Attracting and Dispersing Prototype 30

New Intent Discovery with Attracting and Dispersing Prototyp...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Shun Yang, Jian Bai, Jiaqi Yan, Chaoran Li, Tongliang Yan, Zhao Li, Zhoujun State Key Lab of Software Development Environment Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Beijing Information Science and Technology University China Tencent Youtu Lab China

ISBN: (纸本)9782493814104

New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data. The task is addressed as a feature-clustering problem and recent studies augment instance representation. However, existing methods fail to capture cluster-friendly representations, since they show less capability to effectively control and coordinate within-cluster and between-cluster distances. Tailored to the NID problem, we propose a Robust and Adaptive Prototypical learning (RAP) framework for globally distinct decision boundaries for both known and new intent categories. Specifically, a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype, achieving greater within-cluster compactness. To attain larger between-cluster separation, another adaptive prototypical dispersing learning (APDL) method is devised to maximize the between-cluster distance from the prototype-to-prototype perspective. Experimental results evaluated on three challenging benchmarks (CLINC, BANKING, and StackOverflow) of our method with better cluster-friendly representation demonstrate that RAP brings in substantial improvements over the current state-of-the-art methods (even large language model) by a large margin (average +5.5% improvement). © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Learning systems

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HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification

HILL: Hierarchy-aware Information Lossless Contrastive Learn...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhu, He Wu, Junran Liu, Ruomei Hou, Yue Yuan, Ze Li, Shangzhe Pan, Yicheng Xu, Ke State Key Lab of Software Development Environment Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China School of Statistics and Mathematics Central University of Finance and Economics Beijing100081 China

ISBN: (纸本)9798891761148

Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information. In this paper, we tend to investigate the feasibility of a contrastive learning scheme in which the semantic and syntactic information inherent in the input sample is adequately reserved in the contrastive samples and fused during the learning process. Specifically, we propose an information lossless contrastive learning strategy for HTC, namely Hierarchy-aware Information Lossless contrastive Learning (HILL), which consists of a text encoder representing the input document, and a structure encoder directly generating the positive sample. The structure encoder takes the document embedding as input, extracts the essential syntactic information inherent in the label hierarchy with the principle of structural entropy minimization, and injects the syntactic information into the text representation via hierarchical representation learning. Experiments on three common datasets are conducted to verify the superiority of HILL. ©2024 Association for Computational Linguistics.

关键词： Semantics

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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PPO Based Reinforcement Learning Horizontal Pods Auto-Scaling-A Case Study in Simulation environment 4

PPO Based Reinforcement Learning Horizontal Pods Auto-Scalin...

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4th International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Peng, Fang Ji, Xin Zhang, Le Chen, Ruibo Liao, Xingchuang Zhang, Kui State Grid Corporation of China Big Data Center Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing China

ISBN: (纸本)9798350308082

Microservice has become a state-of-art architecture in the software engineering domain. In the big data scenario, the data can be encapsulated by microservice, providing independent service or orchestrating the business procedure. Much research, such as threshold-based, rule-based, and even deep learning-based resource scaling, has been proposed to tackle this problem. However, the model trained on data like log or monitor index limited the intended dynamic resource adjusting, which caused high latency during adjusting. Moreover, the computation complexity and the data latency can still be a hindrance to the problem-solving. This paper proposed a reinforcement learning-based horizontal pods auto-scaling algorithm to study and respond in low latency. Furthermore, a case study to validate the effectiveness was carried out in a simulation environment. The results show that the proposed algorithm can converge after 0.3 million steps, and three groups of experiments were carried out, which shows that the trained reinforcement learning model can adjust the replica number in real-time, which indicates that the proposed algorithm can be applied in a scenario with low resource consumption. © 2023 IEEE.

关键词： Reinforcement learning

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Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-view Stereo 12th

Adaptive Cost Aggregation in Iterative Depth Estimation for...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiang Bai, Xiao Wang, Chen School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China

ISBN: (纸本)9783031463075

The deep multi-view stereo (MVS) approaches generally construct 3D cost volumes to regularize and regress the depth map. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we presented an multi-stage iterative depth map estimation method for MVS. In our network, the cost volume is iteratively processed by lightweight 2D convolution based GRU modules, and the multi-stage coarse-to-fine structure is adopted to speed up the depth estimation process. To further improve the 3D reconstruction quality, we make improvements from two different perspectives of adaptive cost aggregation: a view-adaptive weighting module is proposed to account for the occlusion problem in cost volume fusion, and a spatial-adaptive deformable geometric feature encoding module is introduced to the cost volume feature encoding before feeding into GRUs for stronger modeling capability. Experiments on the DTU dataset demonstrated the effectiveness of the proposed network in accuracy with remarkable efficiency performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Iterative methods

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Quantitative Parameters Analysis for Prenatally Echocardiographic Diagnosis of Atrioventricular Septal Defects

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Congenital Heart Disease 2023年第3期18卷 387-397页

作者： Xiaoxue Zhou Tingyang Yang Ye Zhang Yanping Ruan Jiancheng Han Xiaowei Liu Ying Zhao Xiaoyan Gu Tingting Liu Hairui Wang Yihua He Maternal-Fetal Consultation Center of Congenital Heart Disease Department of EchocardiographyBeijing Anzhen HospitalCapital Medical UniversityBeijingChina State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina

Background:Atrioventricular septal defects(AVSDs)are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography(FE).However,diagnosis on images is heavily depended on sonographers’experience and the quantitative data are rarely ***:This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on ***:One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses *** top six with the highest diagnostic accuracy rate were acquired according to the area under the curve(AUC),and the diagnostic value of six variables was ***:Six parameters obtained on the four-chamber view(4CHV),including the atrial to ventricular length ratio in end-diastole(AVLR-ED),AVLR-ED combined with the atrial to ventricular length ratio in end-systole(AVLR-ES),quantile score(Q score)of AVLR-ED,Q score of AVLR-ES,Q score of ventricle length in end-diastole(VL-ED),and AVLR-ES,were the top six with the highest diagnostic value,and the AUC was 0.99(95%CI 0.99–1.00),0.99(95%CI 0.99–1.00),0.99(95%CI 0.98–1.00),0.95(95%CI 0.91–0.99),0.93(95%CI 0.87–0.99),and 0.91(95%CI 0.83–1.00),*** within the 20%false positive rate,the diagnostic sensitivity was greater than 100%,100%,100%,90%,90%,and 88%,***:Six variables could be used for prenatal diagnosis of *** them,AVLR-ED and Q score of AVLR-ED,obtained on the 4CHV,were more convenient to acquire and had higher diagnostic accuracy.

关键词： AVSDs fetus echocardiography Q score quantitative

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Hyperbolic distance guided multi-positive graph contrastive learning 23

Hyperbolic distance guided multi-positive graph contrastive ...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Liu, Yanxi Lang, Bo State Key Laboratory of Software Development Environment Beihang University Beijing Shi Haidian Qu China Zhongguancun Laboratory Beijing Shi China

ISBN: (纸本)9781450398411

Graph contrastive learning (GCL) aims to obtain embeddings for graph data in an unsupervised manner. Most existing GCL models are designed for homogeneous graphs, whereas the two critical aspects of heterogeneous GCL models are data augmentation and contrastive sample selection. In this paper, we propose a contrastive learning model for heterogeneous graphs. We take advantage of the property that hyperbolic space is suitable for representing highly hierarchical graph data. We use the normalized hyperbolic distance as an indicator to adaptively select edges to remove in data augmentation to obtain different graph views. In addition, we propose a positive example expansion strategy that integrates topological information and attributes information to obtain more highly relevant positive samples and provide more contrasting information. We conduct node classification experiments on several public heterogeneous graph datasets to evaluate the performance of our model. The results show that our model outperforms all unsupervised comparison methods and even outperforms the best-supervised model on several datasets to reach state-of-the-art. © 2023 ACM.

关键词： Graph neural networks

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Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling

Outlier Suppression+: Accurate quantization of large languag...

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Wei, Xiuying Zhang, Yunchen Li, Yuhang Zhang, Xiangguo Gong, Ruihao Guo, Jinyang Liu, Xianglong State Key Lab of Software Development Environment Beihang University China SenseTime Research School of Computer and Communication Sciences EPFL Switzerland Yale University United States UESTC China

ISBN: (纸本)9798891760608

Post-training quantization (PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outliers are concentrated in specific channels and are asymmetric across channels. To address this issue, we propose the Outlier Suppression+ (OS+) framework, which contains the channel-wise shifting for asymmetry and channel-wise scaling for concentration. We show that these operations can be seamlessly migrated into subsequent modules while maintaining equivalence. Second, we propose a fast and stable scheme to calculate effective shifting and scaling values. The channel-wise shifting aligns the center of each channel for removal of outlier asymmetry. The channel-wise scaling quantitatively evaluates changes brought by migration and quantization for better quantization burden balance. We validate our OS+ under both standard and fine-grained quantization settings with models including BERT, OPT, BLOOM, BLOOMZ, and LLaMA. Comprehensive results across various tasks demonstrate the superiority of our approach. Especially, with standard quantization, OS+ can achieve near-floating-point performance on both small models and large language models on 8-bit and 6-bit. Besides, we establish a new state-of-the-art for 4-bit BERT with 15.5% improvement. Our code is available at https://***/ModelTC/Outlier_Suppression_Plus. ©2023 Association for Computational Linguistics.

关键词： Digital arithmetic

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