A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as the unlearning target. However, we find that attackers can extract private information, i.e., gender, race, and age, from a trained model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as the unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to degrade attacking performance and make target attributes indistinguishable. In this paper, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance. Specifically, we investigate two types of distinguishability measurements, i.e., user-to-user and distribution-to-distribution. We use the stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed methods

Learning from evolved next release problem instances

International audienceTaking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are either hard or easy for the target heuristic (GRASP in this study), to investigate where a heuristic works well and where it does not, when facing a software engineering problem. Thereafter, we use a feature-based approach to predict the hardness of the evolved instances, with respect to the target heuristic. Experimental results reveal that, the proposed algorithm is able to evolve NRP instances with different hardness. Furthermore, the problem-specific features enables the prediction of the target heuristic's performance

Study on the Informatization Construction of Public Stomatological Medical Institutions in China

With the deepening of healthcare system reform in China, the competition in the oral healthcare market is becoming stronger day by day. The public hospital is the main body of the medical service system in China, its degree of informatization greatly affects rational market competition and then affects the allocation of resources and the quality of medical service. By analyzing the problems existing in the current informatization of China’s public stomatological medical institutions, this paper discusses how to strengthen the informatization of China’s public stomatological medical institutions, and puts forward targeted optimization measures, to provide a reference for the innovation and development of smart hospital construction of the stomatological industry

Using Machine Learning Method to Qualify and Evaluate the Regional Economy

As the economic lifeline of Southwest China, Sichuan Province has contributed to Chinese sustainable economic development, the most prominent Chengdu. Chengdu-Chongqing area has been pivotal in China\u27s regional development plate. In the 14th Five-Year Plan of China, the implementation of the Chengdu-Chongqing double cities economic circle is emphasized from different aspects. This policy can directly stimulate the regional economy, thus driving the economic development of Sichuan. Accordingly, the study takes the GDP in 2018 of 21 cities of Sichuan province as the dependent variable. Except for the traditional financial method or model, the study adopts one of the Machine Learning methods, Principal Component Analysis (PCA), to compare the development level of 21 cities horizontally and vertically. Meanwhile, within the Machine Learning method, the new model\u27s sampling accuracy is 0.803, and the first two principal components could interpret 91.206% of the total variance. Therefore, the study evaluates, analyzes the results of new ranks of 21 cities, exploring the possibility of coordinated economic development of Sichuan province under the background of the construction of twins Chengdu-Chongqing economic circle. Hopefully, the consequence of research provides a theoretical reference for the policy implementation