ArXiv最新文献 - Book学术

Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization 将基于变压器的深度强化学习与 Black-Litterman 模型相结合，实现投资组合优化

ArXiv Pub Date : 2024-08-10 DOI: 10.48550/arXiv.2402.16609

Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

引用次数: 0

TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes TinyGC-Net：用于校准 MEMS 陀螺仪的超小型网络

ArXiv Pub Date : 2024-07-26 DOI: 10.48550/arXiv.2403.02618

Chao Cui, Jiankang Zhao

引用次数: 0

Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints 数据传输限制下的短期太阳辐照度预测

ArXiv Pub Date : 2024-07-01 DOI: 10.48550/arXiv.2403.12873

Joshua Edward Hammond, Ricardo A. Lara Orozco, Michael Baldea, B. Korgel

引用次数: 0

F2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis F2Depth：通过光流一致性和特征图合成进行自我监督的室内单目深度估计

ArXiv Pub Date : 2024-07-01 DOI: 10.48550/arXiv.2403.18443

Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li, Baochang Zhang

引用次数: 1

Efficient Constrained k-Center Clustering with Background Knowledge 利用背景知识进行高效受限 k 中心聚类

ArXiv Pub Date : 2024-03-24 DOI: 10.48550/arXiv.2401.12533

Longkun Guo, Chaoqi Jia, Kewen Liao, Zhigang Lu, Minhui Xue

{"title":"Efficient Constrained k-Center Clustering with Background Knowledge","authors":"Longkun Guo, Chaoqi Jia, Kewen Liao, Zhigang Lu, Minhui Xue","doi":"10.48550/arXiv.2401.12533","DOIUrl":"https://doi.org/10.48550/arXiv.2401.12533","url":null,"abstract":"Center-based clustering has attracted significant research interest from both theory and practice. In many practical applications, input data often contain background knowledge that can be used to improve clustering results. In this work, we build on widely adopted k-center clustering and model its input background knowledge as must-link (ML) and cannot-link (CL) constraint sets. However, most clustering problems including k-center are inherently NP-hard, while the more complex constrained variants are known to suffer severer approximation and computation barriers that significantly limit their applicability. By employing a suite of techniques including reverse dominating sets, linear programming (LP) integral polyhedron, and LP duality, we arrive at the first efficient approximation algorithm for constrained k-center with the best possible ratio of 2. We also construct competitive baseline algorithms and empirically evaluate our approximation algorithm against them on a variety of real datasets. The results validate our theoretical findings and demonstrate the great advantages of our algorithm in terms of clustering cost, clustering quality, and running time.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AACP: Aesthetics Assessment of Children's Paintings Based on Self-Supervised Learning AACP：基于自我监督学习的儿童绘画美学评估

ArXiv Pub Date : 2024-03-12 DOI: 10.1609/aaai.v38i3.28030

Shiqi Jiang, Ning Li, Chen Shi, Liping Guo, Changbo Wang, Chenhui Li

{"title":"AACP: Aesthetics Assessment of Children's Paintings Based on Self-Supervised Learning","authors":"Shiqi Jiang, Ning Li, Chen Shi, Liping Guo, Changbo Wang, Chenhui Li","doi":"10.1609/aaai.v38i3.28030","DOIUrl":"https://doi.org/10.1609/aaai.v38i3.28030","url":null,"abstract":"The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Data Management Architecture for Present Quantum Computing 当前量子计算的混合数据管理架构

ArXiv Pub Date : 2024-03-12 DOI: 10.1007/978-981-97-0989-2_14

M. Zajac, U. Störl

引用次数: 0

MineXR: Mining Personalized Extended Reality Interfaces MineXR：挖掘个性化扩展现实界面

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3613904.3642394

Hyunsung Cho, Yukang Yan, Kashyap Todi, Mark Parent, Missie Smith, Tanya R. Jonker, Hrvoje Benko, David Lindlbauer

引用次数: 0

A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce 用于 B2B 电子商务中预测性购买的机器学习和经验贝叶斯方法

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3647750.3647754

Tuhin Subhra De, Pranjal Singh, Alok Patel

{"title":"A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce","authors":"Tuhin Subhra De, Pranjal Singh, Alok Patel","doi":"10.1145/3647750.3647754","DOIUrl":"https://doi.org/10.1145/3647750.3647754","url":null,"abstract":"In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Security-Relevant Methods using Multi-label Machine Learning 利用多标签机器学习检测安全相关方法

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3643796.3648464

Oshando Johnson, Goran Piskachev, Ranjith Krishnamurthy, Eric Bodden

引用次数: 0