About [q]-regularity properties of collections of sets

We examine three primal space local Hoelder type regularity properties of finite collections of sets, namely, [q]-semiregularity, [q]-subregularity, and uniform [q]-regularity as well as their quantitative characterizations. Equivalent metric characterizations of the three mentioned regularity properties as well as a sufficient condition of [q]-subregularity in terms of Frechet normals are established. The relationships between [q]-regularity properties of collections of sets and the corresponding regularity properties of set-valued mappings are discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1309.700

An induction theorem and nonlinear regularity models

A general nonlinear regularity model for a set-valued mapping $F:X\times R_+\rightrightarrows Y$, where $X$ and $Y$ are metric spaces, is considered using special iteration procedures, going back to Banach, Schauder, Lusternik and Graves. Namely, we revise the induction theorem from Khanh, J. Math. Anal. Appl., 118 (1986) and employ it to obtain basic estimates for studying regularity/openness properties. We also show that it can serve as a substitution of the Ekeland variational principle when establishing other regularity criteria. Then, we apply the induction theorem and the mentioned estimates to establish criteria for both global and local versions of regularity/openness properties for our model and demonstrate how the definitions and criteria translate into the conventional setting of a set-valued mapping $F:X\rightrightarrows Y$.Comment: 28 page

About uniform regularity of collections of sets

We further investigate the uniform regularity property of collections of sets via primal and dual characterizing constants. These constants play an important role in determining convergence rates of projection algorithms for solving feasibility problems

DIFFICULTIES IN WRITING AN ESSAY OF ENGLISH-MAJORED SOPHOMORES AT TAY DO UNIVERSITY, IN VIETNAM

Writing is an important skill in English that helps people express thoughts, emotion and viewpoint to readers. However, students face some difficulties when writing. Hence, the survey research presents the process of the study about “Difficulties in writing an essay of English-majored sophomores at Tay Do University, in Viet Nam”. It was conducted to find out some difficulties in learning writing (from 200 to 250 - word essay) of 102 the sophomores from Bachelor of English 10 at Tay Do University. Questionnaire, paper interview and essay samples were the instruments of the study. The results showed that sophomores had many problems in writing such as vocabulary, grammar structures, ideas arrangement, background knowledge, and others. Basing on the results, some solutions would be suggested to help students to get a good writing skill. Viết là một kỹ năng quan trọng trong tiếng Anh giúp người viết thể hiện suy nghĩ, cảm xúc và quan điểm với người đọc. Tuy nhiên, sinh viên thường gặp một số khó khăn khi viết. Do đó, nghiên cứu “Khó khăn khi viết bài luận của sinh viên năm thứ hai chuyên ngành Ngôn Ngữ Anh tại Trường Đại học Tây Đô, Việt Nam” được thực hiện nhằm tìm ra một số khó khăn khi học môn viết (bài luận từ 200 đến 250 từ) của 102 sinh viên Cử nhân Tiếng Anh năm thứ hai, khóa 10 của Trường Đại học Tây Đô. Công cụ nghiên cứu gồm bảng câu hỏi, phỏng vấn trên giấy và phân tích bài luận. Kết quả cho thấy sinh viên năm thứ hai gặp nhiều vấn đề về viết như từ vựng, cấu trúc ngữ pháp, sắp xếp ý tưởng, kiến thức nền tảng và những vấn đề khác. Dựa trên kết quả đạt được, một số giải pháp sẽ được đề xuất để giúp sinh viên có được kỹ năng viết tốt hơn. Article visualizations

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this co-training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with $4-8$ times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.Comment: Under review by the IEEE Transactions on Network and Service Managemen

Prevalence and Determinants of Medication Adherence among Patients with HIV/AIDS in Southern Vietnam

This study was conducted to determine the prevalence and determinants of medication adherence among patients with HIV/AIDS in southern Vietnam. METHODS: A cross-sectional study was conducted in a hospital in southern Vietnam from June to December 2019 on patients who began antiretroviral therapy (ART) for at least 6 months. Using a designed questionnaire, patients were considered adherent if they took correct medicines with right doses, on time and properly with food and beverage and had follow-up visits as scheduled. Multivariable logistic regression was used to identify determinants of adherence. KEY FINDINGS: A total of 350 patients (from 861 medical records) were eligible for the study. The majority of patients were male (62.9%), and the dominant age group (≥35 years old) accounted for 53.7% of patients. Sexual intercourse was the primary route of transmission of HIV (95.1%). The proportions of participants who took the correct medicine and at a proper dose were 98.3% and 86.3%, respectively. In total, 94.9% of participants took medicine appropriately in combination with food and beverage, and 75.7% of participants were strictly adherent to ART. The factors marital status (odds ratio (OR) = 2.54; 95%CI = 1.51-4.28), being away from home (OR = 1.7; 95%CI = 1.03-2.78), substance abuse (OR = 2.7; 95%CI = 1.44-5.05), general knowledge about ART (OR = 2.75; 95%CI = 1.67-4.53), stopping medication after improvement (OR = 4.16; 95%CI = 2.29-7.56) and self-assessment of therapy adherence (OR = 9.83; 95%CI = 5.44-17.77) were significantly associated with patients' adherence. CONCLUSIONS: Three-quarters of patients were adherent to ART. Researchers should consider these determinants of adherence in developing interventions in further studies