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Augmented Lagrangian Methods for Convex Matrix Optimization Problems

Journal of the Operations Research Society of China 2022年第2期10卷 305-342页

作者： Ying Cui Chao Ding Xu-Dong Li Xin-Yuan Zhao Department of Industrial and Systems Engineering University of MinnesotaMinneapolis 55411USA Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Data Science Fudan UniversityShanghai 200433China Shanghai Center for Mathematical Sciences Fudan UniversityShanghai 200433China College of Applied Sciences Beijing University of TechnologyBeijing 100124China

In this paper,we provide some gentle introductions to the recent advance in augmented Lagrangian methods for solving large-scale convex matrix optimization problems(cMOP).Specifically,we reviewed two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex matrix optimization problems regularized by nonsmooth spectral *** a mild quadratic growth condition on the dual of cMOP,we further discussed the R-superlinear convergence of the Karush-Kuhn-Tucker(KKT)residuals of the sequence generated by the augmented Lagrangian methods(ALM)for solving convex matrix optimization *** details of the ALM for solving core convex matrix optimization problems are also provided.

关键词： Matrix optimization Spectral functions Quadratic growth conditions Metric subregularity Augmented Lagrangian methods Fast convergence rates Semismooth Newton methods

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Doubly stochastic continuous time random walk

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Physical Review Research 2024年第1期6卷 L012033-L012033页

作者： Maxence Arutkin Shlomi Reuveni School of Chemistry Center for the Physics & Chemistry of Living Systems Tel Aviv University 6997801 Tel Aviv Israel Service d'Epidémiologie-Data-Biostatistiques Délégation à la Recherche Clinique et à l'Innovation Hôpital Foch 92150 Suresnes France Sackler Center for Computational Molecular & Materials Science Ratner Institute for Single Molecule Chemistry Tel Aviv University 6997801 Tel Aviv Israel

Since its introduction some 60 years ago, the Montroll-Weiss continuous time random walk has found numerous applications due its ease of use and ability to describe both regular and anomalous diffusion. Yet, despite its broad applicability and generality, the model cannot account for effects coming from random diffusivity fluctuations, which have been observed in the motion of asset prices and molecules. To bridge this gap, we introduce a doubly stochastic version of the model in which waiting times between jumps are replaced with a fluctuating jump rate. We show that this newly added layer of randomness gives rise to a rich phenomenology while keeping the model fully tractable, allowing us to explore general properties and illustrate them with examples. In particular, we show that the model presented herein provides an alternative pathway to Brownian yet non-Gaussian diffusion, which has been observed and explained via diffusing diffusivity approaches.

关键词： Anomalous diffusion Continuous-time random walk Escape problems Random walks Stochastic processes

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On an Integrated Security Framework for Defense Against Various DDoS Attacks in SDN

On an Integrated Security Framework for Defense Against Vari...

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International Conference on computing, Networking and Communications (ICNC)

作者： Hao Wu Aiqin Hou Weike Nie Chase Wu School of Information Science and Technology Northwest University Xi’an Shaanxi China Department of Data Science New Jersey Institute of Technology Newark NJ USA

As a new network paradigm, software-defined networking (SDN) technology has been increasingly adopted. Unfortunately, SDN-enabled networks are more prone to threats from DDoS attacks than traditional networks due to the nature of centralized management. We propose an integrated defense framework to detect and mitigate various types of DDoS attacks in SDN-enabled networks. The proposed framework deploys two technical modules in the control plane of SDN for defending against high-rate and low-rate DDoS attacks, respectively. The former module consists of three components, which watch out for suspicious traffic, detect attacks using ensemble learning, and intercept malicious packets, respectively. The latter module is designed specifically to defend against the Slow Ternary Content Addressable Memory (TCAM) exhaustion attack (Slow-TCAM) using a new Alleviative Threat for TCAM (ATFT) algorithm. The proposed framework is implemented and tested in simulated networks using Mininet and further evaluated on the CICDDoS2019 dataset. Experimental results illustrate the superior performance of the proposed framework in defending against different types of DDoS attacks in comparison with other state-of-the-art algorithms.

关键词： Associative memory Denial-of-service attack Security Ensemble learning Software defined networking Computer crime

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CLPCare: Bootstrapping Multimodal Large Language Models for Ultrasound Cleft Lip and Palate Recognition and Analysis 10

CLPCare: Bootstrapping Multimodal Large Language Models for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Jiaru Luo, Guofeng Yan, Haolun Huang, Shiwang Liao, Zhenghan Zhang, Shiyi Wu, Jinzhun He, Xiaoqin Chen, Longbiao Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen China Xiamen University National Institute for Data Science in Health and Medicine Xiamen China Xiamen University Women and Children's Hospital Xiamen China Xiamen University Xiamen Maternity & Child Healthcare Hospital Women and Children's Hospital Department of Ultrasound Xiamen China

ISBN: (纸本)9798331520861

Cleft lip and palate is a common congenital malformations in fetuses, and early diagnosis is crucial for reducing the physical and psychological impact on pregnant women and their families. Traditional image processing methods for recognizing and analyzing cleft lip and palate rely on manual annotation and have limited interpretability. To address this issue, we propose a method for ultrasound image recognition and analysis of cleft lip and palate based on multimodal large language models. Firstly, we design a specific ultrasound image findings instruction finetuning template and fine-tune the multimodal large language model to enhance the model's recognition capability. Secondly, we conceive a specific retrieval-enhanced prompt template aimed at improving the model's understanding of cleft lip and palate analysis. Lastly, we evaluate our framework using a real dataset from Xiamen Maternal and Child Health Hospital in China. Additionally, we develop a medical system for the recognition and analysis of cleft lip and palate. © 2024 IEEE.

关键词： Ultrasonic imaging

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Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering

arXiv

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arXiv 2024年

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai College of Data Science and Application Inner Mongolia University of Technology Huhhot China Haihe Lab of ITAI Tianjin China Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper study one challenging issue in incomplete multiview clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Big data: A Technical Preview of Technology 4

Big Data: A Technical Preview of Technology

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4th IEEE International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Dhatterwal, Jagjit Singh Gupta, Sonali Malik, Kiran Kaswan, Kuldeep Singh Bharti, Sunil Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to Be University Dehradun India Matu Ram Institute of Engineering and Management Haryana Rohtak India Galgotias University School of Computer Science & Engineering Uttar Pradesh Greater Noida India Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350360165

The spaceless accessibility of data has dramatically changed the environments of decision making in the digital age. 'Big data' technically refers to datasets that are not only large, but also diverse & fast, thus speeding up the process of managing them with traditional procedures & tools. However, with this more abundance of data, making use of these information, and extracting particular utilities from them will have to be explored and studied. Firstly, decisions must be made that give significant insights from such diverse and changing data which move from the level of transactions, consumer comprehension to the network interpersonal. Massive data analytics, which is an implication of large-scale data analysing by firing on huge data collections, brings such value. The objective of this article is to pursue some of the different kinds of analytical methods and software tools that can be used to analyze data, as well as the alternative decision-making facilitation modes which may be achieved through advanced analytics. © 2024 IEEE.

关键词： MapReduce

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Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations

arXiv

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arXiv 2023年

作者： Jentzen, Arnulf Riekert, Adrian von Wurstemberger, Philippe School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Applied Mathematics Institute for Analysis and Numerics University of Münster Germany School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Risklab Department of Mathematics ETH Zurich Switzerland

In this article we propose a new deep learning approach to approximate operators related to parametric partial differential equations (PDEs). In particular, we introduce a new strategy to design specific artificial neural network (ANN) architectures in conjunction with specific ANN initialization schemes which are tailor-made for the particular approximation problem under consideration. In the proposed approach we combine efficient classical numerical approximation techniques with deep operator learning methodologies. Specifically, we introduce customized adaptions of existing ANN architectures together with specialized initializations for these ANN architectures so that at initialization we have that the ANNs closely mimic a chosen efficient classical numerical algorithm for the considered approximation problem. The obtained ANN architectures and their initialization schemes are thus strongly inspired by numerical algorithms as well as by popular deep learning methodologies from the literature and in that sense we refer to the introduced ANNs in conjunction with their tailor-made initialization schemes as Algorithmically Designed Artificial Neural Networks (ADANNs). We numerically test the proposed ADANN methodology in the case of several parametric PDEs. In the tested numerical examples the ADANN methodology significantly outperforms existing traditional approximation algorithms as well as existing deep operator learning methodologies from the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Comparative Analysis of Image Enhancement Techniques Based on Fundus Image for Diabetic Retinopathy 11th

Comparative Analysis of Image Enhancement Techniques Based o...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Meh, Noor Halawati Binti Che Sharif, Nurul Atikah Binti Mohd Harun, Nor Hazlyna Binti Embong, Zunaina Binti Politeknik Tuanku Syed Sirajuddin Arau Perlis02600 Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia

ISBN: (纸本)9789811681288

Enhancement of images plays significant role in certain aspects of sci- entific research. One of the main task is analyzing the abnormal features in retinal fundus image for detection of diabetic retinopathy. However, there are many techniques that have been developed and there is no proof to which technique is the most suitable for retinal fundus image. This paper intended to compare dif- ferent image enhancement techniques in order to obtain a clearer vision of the abnormal features. There are six image enhancement technique have been se- lected which are brightness enhancement, low light image, gray level slicing, me- dian filtering, unsharp masking, and CLAHE. Study for comparing these tech- niques is developed to evaluate the enhancement techniques by obtaining the per- formance metric values in terms of PNSR, MSE, NIQE and entropy. Results show that different enhancement techniques have their own advantages and in this study, CLAHE technique shows the best value for MSE performance evalu- ation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Probing the electric double layer structure at nitrogen-doped graphite electrodes by constant-potential molecular dynamics simulations

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Journal of Energy Chemistry 2024年第6期93卷 299-305,I0008页

作者： Legeng Yu Nan Yao Yu-Chen Gao Zhong-Heng Fu Bo Jiang Ruiping Li Cheng Tang Xiang Chen Tsinghua Center for Green Chemical Engineering Electrification(CCEE) Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China Ordos Carbon Neutral Research and Application Co. Ltd.Ordos 017010Inner Mongoli1China Institute for Carbon Neutrality Tsinghua UniversityBeijing 100084China Center for Next-Generation Energy Materials and School of Chemical Engineering Sungkyunkwan University2066 Seobu-roJangan-guSuwon-siGyeonggi-do 16419Republic of Korea

Electric double layer(EDL)is a critical topic in electrochemistry and largely determines the working performance of lithium ***,atomic insights into the EDL structures on heteroatom-modified graphite anodes and EDL evolution with electrode potential are very ***,a constant-potential molecular dynamics(CPMD)method is proposed to probe the EDL structure under working conditions,taking N-doped graphite electrodes and carbonate electrolytes as an *** interface model was developed,incorporating the electrode potential and atom *** a result,an insightful atomic scenario for the EDL structure under varied electrode potentials has been established,which unveils the important role of doping sites in regulating both the EDL structures and the following electrochemical reactions at the atomic ***,the negatively charged N atoms repel the anions and adsorb Li~+at high and low potentials,*** preferential adsorption suggests that Ndoped graphite can promote Li~+desolvation and regulate the location of Li~+*** CPMD method not only unveils the mysterious function of N-doping from the viewpoint of EDL at the atomic level but also applies to probe the interfacial structure on other complicated electrodes.

关键词： Lithium batteries Graphite N-doping Electric double layer Molecular dynamics Constant potential method Electrode potential

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Linear Distance Metric Learning with Noisy Labels

arXiv

引用

arXiv 2023年

作者： Alishahi, Meysam Little, Anna Phillips, Jeff M. Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Mathematics Utah Center for Data Science University of Utah Salt Lake CityUT84112 United States Kahlert School of Computing Utah Center for Data Science University of Utah Salt Lake CityUT84112 United States

In linear distance metric learning, we are given data in one Euclidean metric space and the goal is to find an appropriate linear map to another Euclidean metric space which respects certain distance conditions as much as possible. In this paper, we formalize a simple and elegant method which reduces to a general continuous convex loss optimization problem, and for different noise models we derive the corresponding loss functions. We show that even if the data is noisy, the ground truth linear metric can be learned with any precision provided access to enough samples, and we provide a corresponding sample complexity bound. Moreover, we present an effective way to truncate the learned model to a low-rank model that can provably maintain the accuracy in the loss function and in parameters - the first such results of this type. Several experimental observations on synthetic and real data sets support and inform our theoretical results. © 2023, CC BY.

关键词： Topology

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