Article
Multidisciplinary Sciences
Hiroki Saishu, Kota Kudo, Yuichi Takano
Summary: This paper introduces a mixed-integer optimization approach for sparse Poisson regression, allowing to find the best subset of explanatory variables. By applying a piecewise-linear approximation to the log-likelihood function, a mixed-integer quadratic optimization formulation is derived and can be solved to optimality using optimization software. Experimental results show that our method outperforms conventional greedy algorithms in selecting tangent lines and provides better out-of-sample prediction performance in low-noise situations.
Article
Computer Science, Information Systems
Vinicius Layter Xavier
Summary: This paper proposes a new regression model called hyperbolic regression, which is suitable for outcome variables ranging from [0,1] or measured on a binary scale. The model is constructed based on the hyperbolic penalty method and can be solved using either the least squares or maximum likelihood methods. Experimental results show that the hyperbolic regression outperforms logistic regression in terms of accuracy and F-1 score.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen
Summary: This study investigates the performance of ReLU neural networks for quantile regression. We derive an upper bound on the mean squared error of ReLU networks used for estimating quantiles and show tight bounds for certain function classes. Empirical simulations demonstrate the application of theoretical results to practical implementations of ReLU networks.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Seyed Hossein Ghafarian
Summary: This article introduces Probabilistic Minimax Active Learning (PMAL) and its approximation method for logistic regression as the likelihood function. Experimental results show that the proposed algorithm effectively asks queries and achieves superior performance compared to existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biotechnology & Applied Microbiology
Nicholas P. Howard, Michela Troggio, Charles-Eric Durel, Helene Muranty, Caroline Denance, Luca Bianco, John Tillman, Eric van de Weg
Summary: This study assessed the concordance and accuracy of Illumina Infinium and Affymetrix Axiom SNP array data in apple genomes. The majority of the data was found to be compatible, although intense data filtering and curation were required for data integration. This in-depth analysis may provide valuable insights for future work on SNP array data integration and interpretation, as well as for probe/platform development.
Article
Biochemistry & Molecular Biology
Maria Radanova, Mariya Levkova, Galya Mihaylova, Rostislav Manev, Margarita Maneva, Rossen Hadgiev, Nikolay Conev, Ivan Donev
Summary: There is a growing interest in studying single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes, as they may be associated with susceptibility, prognosis, and treatment response in colorectal cancer (CRC). These miRNA-SNPs could serve as non-invasive biomarkers for early detection of CRC. However, contradictory findings have been reported when different research groups investigated the same SNP in a gene for a specific miRNA, highlighting the need for more case-control studies involving participants from different ethnic backgrounds. According to our review, three miRNA-SNPs - miR-146a rs2910164, miR-27a rs895819, and miR-608 rs4919510 - appear to be promising prognostic, diagnostic, and predictive biomarkers for CRC.
Article
Biochemical Research Methods
Xueli Song, Rongpeng Li, Kaiming Wang, Yuntong Bai, Yuzhu Xiao, Yu-Ping Wang
Summary: The study proposes a novel model called JSCoReg, which extracts class-specific features from different health conditions/disease classes. The model outperforms similar models in terms of feature selection accuracy. Applied to the analysis of a schizophrenia dataset, JSCoReg enables better identification of biologically and statistically significant biomarkers.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Ling Dai, Guangyun Zhang, Jinqi Gong, Rongting Zhang
Summary: This paper proposes a data-driven method for hyperspectral remotely sensed data, which can autonomously extract key features and interactively learn feature indexes, providing a more flexible and creative framework compared to traditional methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Nuttanan Wichitaksorn, Yingyue Kang, Faqiang Zhang
Summary: Random subspace logistic regression is a regression model that improves classification or prediction accuracy by randomly selecting features. It can be applied to both standard and lasso logistic regression models. The proposed method shows promising results in simulations and empirical data, indicating its potential as an alternative for traditional feature selection approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Agriculture, Dairy & Animal Science
Peng Wang, Wentao Li, Ziyi Liu, Xiaoyun He, Rong Lan, Yufang Liu, Mingxing Chu
Summary: This study analyzed the association between PPP2R5C and SLC39A5 gene polymorphisms and litter size in Yunshang black goats. The results showed that the polymorphisms PPP2R5C g.65977743C>T and SLC39A5 g.50676693T>C were significantly associated with the litter size of the third parity in Yunshang black goats. Further experiments confirmed that individuals with CC genotype of PPP2R5C and TT genotype of SLC39A5 had higher expression of these genes in goat ovarian tissues. These findings provide new insights into candidate gene selection for marker-assisted selection in goats.
Article
Cell Biology
Giuseppe Silvestri, Carla Canedo-Ribeiro, Maria Serrano-Albal, Remi Labrecque, Patrick Blondin, Steven G. Larmer, Gabriele Marras, Desmond A. R. Tutt, Alan H. Handyside, Marta Farre, Kevin D. Sinclair, Darren K. Griffin
Summary: The study demonstrated that screening for aneuploid embryos using PGT-A can significantly improve pregnancy and live birth rates in cattle IVP programs. Analyzing 1713 cattle embryos revealed that aneuploid embryos have lower chances of establishing pregnancies and giving rise to live births compared to euploid embryos.
Article
Biochemistry & Molecular Biology
Hui-Jeong An, Sung-Hwan Cho, Han-Sung Park, Ji-Hyang Kim, Young-Ran Kim, Woo-Sik Lee, Jung-Ryeol Lee, Seong-Soo Joo, Eun-Hee Ahn, Nam-Keun Kim
Summary: This study investigated the genetic association between recurrent pregnancy loss (RPL) and microRNA (miRNA) polymorphisms in miR-10aA>T, miR-30cA>G, miR-181aT>C, and miR-499bA>G in Korean women. The study identified specific genotypes of miR-10a and miR-499 that are associated with increased RPL risk.
Article
Nutrition & Dietetics
Yu-Min Huang, Weu Wang, Po-Pin Hsieh, Hsin-Hung Chen
Summary: The genetic effect of obesity plays a significant role in the development of an obesogenic environment. Genes rs712221 and rs2016520 are associated with obesity and show a synergistic effect, increasing the risk of obesity.
Article
Plant Sciences
Xi-ou Xiao, Ning Zhang, Hui Jin, Huaijun Si
Summary: This study evaluated the genetic diversity and population structure of 135 autotetraploid potatoes using SLAF-seq methods. Analysis of 3,397,137 high-quality SNPs revealed that the classification of these potatoes based on SNP profiles did not correlate with their geographical origins. Furthermore, 71 PARMS-SNP markers were successfully used to analyze 190 autotetraploid potato varieties. Overall, these novel SNP markers provide a solid foundation for potato genetic diversity analysis, DUS testing, and plant variety protection.
Article
Engineering, Electrical & Electronic
Cassio F. Dantas, Emmanuel Soubies, Cedric Fevotte
Summary: The Gap safe screening technique is a powerful tool for accelerating the convergence of sparse optimization solvers. It is based on determining the smallest sphere that contains the dual solution, achieved through an inner sphere refinement loop. In this work, we show that this refinement loop converges to the solution of a fixed-point equation, allowing for the development of a non-iterative and theoretically-grounded variant of the sphere refinement step.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Computer Science, Information Systems
Xu Chen, Ya Zhang, Ivor W. Tsang, Yuangang Pan, Jingchao Su
Summary: This article discusses cross-domain recommendation in scenarios where different domains have the same set of users but no overlapping items. Most existing methods focus on shared-user representation, but fail to capture domain-specific features. In this article, an equivalent transformation learner (ETL) is proposed to preserve both domain-specific and overlapped features by modeling the joint distribution of user behaviors across domains.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Engineering, Industrial
Mingxing Li, Daqiang Guo, Ming Li, Ting Qu, George Q. Huang
Summary: The widespread adoption of Industry 4.0 technologies is revolutionising manufacturing operations. This paper introduces a novel concept of operations twins (OT) for achieving synchronisation between production and intralogistics (PiL) through the use of Industry 4.0 technologies and innovative operations management strategies.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xiaowei Zhou, Ivor W. Tsang, Jie Yin
Summary: Deep Neural Networks have achieved great success in classification tasks, but they are vulnerable to adversarial attacks. Adversarial training is an effective strategy to improve the robustness of DNN models, but existing methods fail to generalize well to standard test data. To achieve a better trade-off between standard accuracy and adversarial robustness, a novel adversarial training framework called LADDER is proposed, which generates high-quality adversarial examples through perturbations on latent features.
Article
Computer Science, Artificial Intelligence
Peiyao Zhao, Yuangang Pan, Xin Li, Xu Chen, Ivor W. Tsang, Lejian Liao
Summary: Inspired by the success of contrastive learning, graph augmentation strategies have been used to learn node representations. Existing methods add perturbations to construct contrastive samples. However, they ignore the prior information assumption, leading to decreased similarity and increased discrimination among nodes. In this article, a general ranking framework is proposed to incorporate these prior information into contrastive learning. Experimental results on benchmark datasets show the effectiveness of the proposed algorithm compared to supervised and unsupervised models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yiming Xu, Lin Chen, Lixin Duan, Ivor W. Tsang, Jiebo Luo
Summary: This article studies the problem of open set domain adaptation and proposes a method that performs soft rejection of unknown target classes and simultaneously matches the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Mianjie Li, Yuan Liu, Zhihong Tian, Chun Shan
Summary: In this article, an information hiding method based on multidimensional feature fusion for privacy protection in 6G networks is proposed. The method is based on the strong attack resistance of carriers carrying private information, and it involves studying multidimensional feature fusion arbitration methods and constructing an efficient and accurate feature-based information hiding technology.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jiangchao Yao, Bo Han, Zhihan Zhou, Ya Zhang, Ivor W. Tsang
Summary: Learning with noisy labels is important in the Big Data era to save costs. Previous noise-transition-based methods achieved good performance but relied on impractical anchor sets. Our approach introduces a Bayesian framework for parameterizing the noise transition and solves the problem of ill-posed stochastic learning in back-propagation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bing Li, Wei Cui, Le Zhang, Ce Zhu, Wei Wang, Ivor W. Tsang, Joey Tianyi Zhou
Summary: Time series analysis is crucial in various fields such as economics, finance, and surveillance. However, traditional Transformer models have limitations in representing nuanced patterns in time series data. To overcome these challenges, we propose a novel Transformer architecture called DifFormer, which incorporates a multi-resolutional differencing mechanism. DifFormer outperforms existing models in classification, regression, and forecasting tasks, while also exhibiting efficiency and lower time consumption.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Defu Liu, Wen Li, Lixin Duan, Ivor W. Tsang, Guowu Yang
Summary: Deep models have achieved impressive performance in various visual recognition tasks, but their generalization ability is compromised by noisy labels. This paper presents a dynamic label learning algorithm that allows the use of different loss functions for classification in the presence of label noise, ensuring that the search for the optimal classifier of noise-free samples is not hindered by label noise.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yongshun Gong, Zhibin Li, Wei Liu, Xiankai Lu, Xinwang Liu, Ivor W. W. Tsang, Yilong Yin
Summary: Many real-world problems involve data with missing values, which can hinder learning achievements. Existing methods use a universal model for all incomplete data, resulting in suboptimal models for each missingness pattern. This paper proposes a general model that can adjust to different missingness patterns, minimizing competition between data. The model is based on observable features and does not rely on data imputation, and a low-rank constraint is introduced to improve generalization ability.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hui Xu, Changyu Li, Yan Zhang, Lixin Duan, Ivor W. Tsang, Jie Shao
Summary: Cold-start is crucial for recommendations, especially when there is limited user-item interaction data. Meta-learning-based approaches have shown success in addressing this issue by leveraging their strong generalization capabilities to quickly adapt to new tasks in cold-start settings. However, these methods are prone to meta-overfitting when trained with single and sparse ratings because a single rating cannot capture a user's diverse interests under different circumstances. To overcome this, a meta-augmentation technique is proposed to convert non-mutually-exclusive (Non-ME) tasks into mutually-exclusive (ME) tasks without changing inputs, thus relieving the issue of meta-overfitting. Inspired by this technique, this paper proposes a cross-domain meta-augmentation technique for content-aware recommendation systems (MetaCAR) to construct ME tasks in the recommendation scenario. The proposed method consists of two stages: meta-augmentation and meta-learning. In the meta-augmentation stage, domain adaptation is performed using a dual conditional variational autoencoder (CVAE) with a multi-view information bottleneck constraint, and the learned CVAE is applied to generate ratings for users in the target domain. In the meta-learning stage, both the true and generated ratings are used to construct ME tasks, enabling meta-learning recommendations to avoid meta-overfitting. Experiments conducted on real-world datasets demonstrate the significant superiority of MetaCAR over competing baselines, including cross-domain, content-aware, and meta-learning-based recommendations, in dealing with the cold-start user issue.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang
Summary: Multi-variate time series (MTS) data is a common type of data abstraction in the real world, generated from a hybrid dynamical system. MTS data can be categorized into spatial and temporal attributes, and can be analyzed from the spatial view or temporal view. A novel multi-view multi-task (MVMT) learning framework is proposed to extract hidden MVMT information from MTS data while predicting. The framework improves effectiveness and efficiency of canonical architectures according to extensive experiments on three datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofeng Cao, Ivor W. Tsang
Summary: Machine teaching is a reverse problem of machine learning, aiming to guide the student towards its target hypothesis using known learning parameters. Previous studies focused on balancing teaching risk and cost to find the best teaching examples. However, when the student doesn't disclose any cue of the learning parameters, the optimization solver becomes ineffective. This article presents a distribution matching-based machine teaching strategy that iteratively shrinks teaching cost to eliminate boundary perturbations, providing an effective solution.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuhao Liu, Qing Guo, Lan Fu, Zhanghan Ke, Ke Xu, Wei Feng, Ivor W. Tsang, Rynson W. H. Lau
Summary: In this paper, a novel structure-informed shadow removal network (StructNet) is proposed to address the problem of shadow remnants in existing deep learning-based methods. StructNet reconstructs the structure information of the input image without shadows and uses it to guide the image-level shadow removal. Two main modules, MSFE and MFRA, are developed to extract image structural features and regularize feature consistency. Additionally, an extension called MStructNet is proposed to exploit multi-level structure information and improve shadow removal performance.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
Summary: This paper addresses the problem of data-efficient learning from scratch in scenarios where data or labels are expensive to collect. It proposes the MHEAL algorithm based on active learning on homeomorphic tubes of spherical manifolds, and provides comprehensive theoretical guarantees. Empirical results demonstrate the effectiveness of MHEAL in various applications for data-efficient learning.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
