A Fast Smoothing Newton Method for Bilevel Hyperparameter Optimization for SVC with Logistic Loss

Support Vector Classification with logistic loss has excellent theoretical properties in classification problems where the label values are not continuous. In this paper, we reformulate the hyperparameter selection for SVC with logistic loss as a bilevel optimization problem in which the upper-level problem and the lower-level problem are both based on logistic loss. The resulting bilevel optimization model is converted to a single-level nonlinear programming (NLP) problem based on the KKT conditions of the lower-level problem. Such NLP contains a set of nonlinear equality constraints and a simple lower bound constraint. The second-order sufficient condition is characterized, which guarantees that the strict local optimizers are obtained. To solve such NLP, we apply the smoothing Newton method proposed in \cite{Liang} to solve the KKT conditions, which contain one pair of complementarity constraints. We show that the smoothing Newton method has a superlinear convergence rate. Extensive numerical results verify the efficiency of the proposed approach and strict local minimizers can be achieved both numerically and theoretically. In particular, compared with other methods, our algorithm can achieve competitive results while consuming less time than other methods.Comment: 27 page

A Euclidean Distance Matrix Model for Convex Clustering

Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. The recently proposed sum-of-nums (SON) model by Pelckmans et al. (2005), Lindsten et al. (2011) and Hocking et al. (2011) has received a lot of attention. The advantage of the SON model is the theoretical guarantee in terms of perfect recovery, established by Sun et al. (2018). It also provides great opportunities for designing efficient algorithms for solving the SON model. The semismooth Newton based augmented Lagrangian method by Sun et al. (2018) has demonstrated its superior performance over the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA). In this paper, we propose a Euclidean distance matrix model based on the SON model. An efficient majorization penalty algorithm is proposed to solve the resulting model. Extensive numerical experiments are conducted to demonstrate the efficiency of the proposed model and the majorization penalty algorithm.Comment: 32 pages, 3 figures, 3 table

Impact of the Gate Width of Al 0.27

This paper presents impact of layout sizes of Al0.27Ga0.73N/AlN/Al0.04Ga0.96N/GaN HEMT heterostructure high-mobility transistors (HEMTs) on SiC substrate on its characteristics that include the threshold voltage, the maximum transconductance, characteristic frequency, and the maximum oscillation frequency. The changing parameters include the gate finger number, the gate width per finger. The measurement results based on common-source devices demonstrate that the above parameters have different effects on the threshold voltage, maximum transconductance, and frequency characteristics

Deep-ultraviolet photonics for the disinfection of SARS-CoV-2 and its variants (Delta and Omicron) in the cryogenic environment

Deep-ultraviolet (DUV) disinfection technology provides an expeditious and efficient way to suppress the transmission of coronavirus disease 2019 (COVID-19). However, the influences of viral variants (Delta and Omicron) and low temperatures on the DUV virucidal efficacy are still unknown. Here, we developed a reliable and uniform planar light source comprised of 275-nm light-emitting diodes (LEDs) to investigate the effects of these two unknown factors and delineated the principle behind different disinfection performances. We found the lethal effect of DUV at the same radiation dose was reduced by the cryogenic environment, and a negative-U large-relaxation model was used to explain the difference in view of the photoelectronic nature. The chances were higher in the cryogenic environment for the capture of excited electrons within active genetic molecules back to the initial photo-ionised positions. Additionally, the variant of Omicron required a significantly higher DUV dose to achieve the same virucidal efficacy, and this was thanks to the genetic and proteinic characteristics of the Omicron. The findings in this study are important for human society using DUV disinfection in cold conditions (e.g., the food cold chain logistics and the open air in winter), and the relevant DUV disinfection suggestion against COVID-19 is provided

Mapping the path towards novel treatment strategies: a bibliometric analysis of Hashimoto’s thyroiditis research from 1990 to 2023

BackgroundHashimoto’s thyroiditis (HT), a common form of thyroid autoimmunity, is strongly associated with deteriorating clinical status and impaired quality of life. The escalating global prevalence, coupled with the complexity of disease mechanisms, necessitates a comprehensive, bibliometric analysis to elucidate the trajectory, hotspots, and future trends in HT research.ObjectiveThis study aims to illuminate the development, hotspots, and future directions in HT research through systematic analysis of publications, institutions, authors, journals, references, and keywords. Particular emphasis is placed on novel treatment strategies for HT and its complications, highlighting the potential role of genetic profiling and immunomodulatory therapies.MethodsWe retrieved 8,726 relevant documents from the Web of Science Core Collection database spanning from 1 January 1990 to 7 March 2023. Following the selection of document type, 7,624 articles were included for bibliometric analysis using CiteSpace, VOSviewer, and R software.ResultsThe temporal evolution of HT research is categorized into three distinct phases: exploration (1990-1999), rapid development (1999-2000), and steady growth (2000-present). Notably, the United States, China, Italy, and Japan collectively contributed over half (54.77%) of global publications. Among the top 10 research institutions, four were from Italy (4/10), followed by China (2/10) and the United States (2/10). Recent hotspots, such as the roles of gut microbiota, genetic profiling, and nutritional factors in HT management, the diagnostic dilemmas between HT and Grave’s disease, as well as the challenges in managing HT complicated by papillary thyroid carcinoma and type 1 diabetes mellitus, are discussed.ConclusionAlthough North America and Europe have a considerable academic impact, institutions from emerging countries like China are demonstrating promising potential in HT research. Future studies are anticipated to delve deeper into the differential diagnosis of HT and Grave’s disease, the intricate relationship between gut microbiota and HT pathogenesis, clinical management of HT with papillary thyroid carcinoma or type 1 diabetes, and the beneficial effects of dietary modifications and micronutrients supplementation in HT. Furthermore, the advent of genetic profiling and advanced immunotherapies for managing HT offers promising avenues for future research

Analysis of clinical evidence on traditional Chinese medicine for the treatment of diabetic nephropathy: a comprehensive review with evidence mapping

ObjectiveThis study aims to map evidence from Randomized Controlled Trials (RCTs) and systematic reviews/Meta-analyses concerning the treatment of Diabetic Nephropathy (DN) with Traditional Chinese Medicine (TCM), understand the distribution of evidence in this field, and summarize the efficacy and existing problems of TCM in treating DN. The intention is to provide evidence-based data for TCM in preventing and treating DN and to offer a reference for defining future research directions.MethodsComprehensive searches of major databases were performed, spanning from January 2016 to May 2023, to include clinical RCTs and systematic reviews/Meta-analyses of TCM in treating DN. The analysis encompasses the publishing trend of clinical studies, the staging of research subjects, TCM syndrome differentiation, study scale, intervention plans, and outcome indicators. Methodological quality of systematic reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist, and evidence distribution characteristics were analyzed using a combination of text and charts.ResultsA total of 1926 RCTs and 110 systematic reviews/Meta-analyses were included. The majority of studies focused on stage III DN, with Qi-Yin deficiency being the predominant syndrome type, and sample sizes most commonly ranging from 60 to 100. The TCM intervention durations were primarily between 12-24 weeks. Therapeutic measures mainly consisted of Chinese herbal decoctions and patented Chinese medicines, with a substantial focus on clinical efficacy rate, TCM symptomatology, and renal function indicators, while attention to quality of life, dosage of Western medicine, and disease progression was inadequate. Systematic reviews mostly scored between 5 and 8 on the AMSTAR scale, and evidence from 94 studies indicated potential positive effects.ConclusionDN represents a significant health challenge, particularly for the elderly, with TCM showing promise in symptom alleviation and renal protection. Yet, the field is marred by research inconsistencies and methodological shortcomings. Future investigations should prioritize the development of standardized outcome sets tailored to DN, carefully select evaluation indicators that reflect TCM’s unique intervention strategies, and aim to improve the robustness of clinical evidence. Emphasizing TCM’s foundational theories while incorporating advanced scientific technologies will be essential for innovating research methodologies and uncovering the mechanisms underlying TCM’s efficacy in DN management

Integrated Profiling of MicroRNAs and mRNAs: MicroRNAs Located on Xq27.3 Associate with Clear Cell Renal Cell Carcinoma

Background: With the advent of second-generation sequencing, the expression of gene transcripts can be digitally measured with high accuracy. The purpose of this study was to systematically profile the expression of both mRNA and miRNA genes in clear cell renal cell carcinoma (ccRCC) using massively parallel sequencing technology. Methodology: The expression of mRNAs and miRNAs were analyzed in tumor tissues and matched normal adjacent tissues obtained from 10 ccRCC patients without distant metastases. In a prevalence screen, some of the most interesting results were validated in a large cohort of ccRCC patients. Principal Findings: A total of 404 miRNAs and 9,799 mRNAs were detected to be differentially expressed in the 10 ccRCC patients. We also identified 56 novel miRNA candidates in at least two samples. In addition to confirming that canonical cancer genes and miRNAs (including VEGFA, DUSP9 and ERBB4; miR-210, miR-184 and miR-206) play pivotal roles in ccRCC development, promising novel candidates (such as PNCK and miR-122) without previous annotation in ccRCC carcinogenesis were also discovered in this study. Pathways controlling cell fates (e. g., cell cycle and apoptosis pathways) and cell communication (e. g., focal adhesion and ECM-receptor interaction) were found to be significantly more likely to be disrupted in ccRCC. Additionally, the results of the prevalence screen revealed that the expression of a miRNA gene cluster located on Xq27.3 was consistently downregulated in at least 76.7% of similar to 50 ccRCC patients. Conclusions: Our study provided a two-dimensional map of the mRNA and miRNA expression profiles of ccRCC using deep sequencing technology. Our results indicate that the phenotypic status of ccRCC is characterized by a loss of normal renal function, downregulation of metabolic genes, and upregulation of many signal transduction genes in key pathways. Furthermore, it can be concluded that downregulation of miRNA genes clustered on Xq27.3 is associated with ccRCC

Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph

The goal of education for sustainable development is to prepare future citizens to make informed decisions and take responsible action to solve problems. The purpose of mathematical literacy is to ensure that all learners develop an understanding of mathematics, and how to relate mathematics to the world and use mathematical knowledge to make valuable decisions in their lives, work, and society. It can be seen that the purpose of mathematical literacy coincides with the goal of education for sustainable development. In addition, math literacy is closely related to self-regulated learning (SRL), which is the key to meaningful learning and sustainable development. In educational research, it is an essential task to cultivate learners’ mathematical literacy and promote their sustainable development. With the rapid growth of emerging technologies, the emergence of big data has brought numerous challenges to various research fields. In the age of big data, educational research that can identify research perspectives and hotspots and summarize research evolution rules from a large body of literature can assist us in deepening subsequent analysis. As a result, in this study, we used CiteSpace and HistCite knowledge map visualization and exploration technology to examine mathematical literacy research trends, major research countries and regions, major research institutions, significant researchers, highly cited papers, research hotspots, and evolution trends on a global scale. Through this study, we found that the earliest literature on mathematical literacy appeared in 1957, and the research on mathematical literacy can be divided into three germination stages (1957–2001), a slow development stage (2001–2011), and a prosperous development stage (2011–2022). Most studies come from developed countries such as the US, the UK, Germany, and Australia. The Universities of Utrecht and Purdue University were the most published institutions, and scholars at Purpura published the most articles. The research object of highly cited literature is mainly children, and the research is primarily carried out through the measurement of students’ mathematical ability and achievement and the analysis of related influencing factors, which provides a direction for how to improve students’ mathematical literacy. The research on mathematical literacy mainly includes four research hotspots: working memory and mathematical literacy; brain science and mathematical literacy; mathematical achievement and mathematical literacy; and the generation strategy of mathematical literacy. The research field of mathematics literacy mainly includes working memory, parietal cortex, math performance, mathematics education, early childhood, parental belief, fractions, cognitive development, and student learning. There are 10 clusters. Different clusters have different evolutionary trends. With the evolution of time, working memory, mathematical education, fractions, and precinct beliefs clustered, gradually expanding from the concentrated research direction to the subdivision field. The clusters of parietal cortex, math performance, early childhood, cognitive development, and students do not show large keyword nodes during the research period. With time, it has gradually expanded from the centralized research direction to the subdivision field. The parietal cortex, math performance, early childhood, cognitive development, and students clusters did not show large keyword nodes during the whole study period

Research Status, Hotspots, and Evolutionary Trends of Global Digital Education via Knowledge Graph Analysis

With the rapid development of emerging technologies such as big data, artificial intelligence, and blockchain and their wide application in education, digital education has received widespread attention in the international education field. The outbreak of COVID-19 in December 2019 further catalyzed the digitalization process in various industries, including education, and forced the education system to carry out digital reform and innovation. Digital education transformation has become a new hotspot of great interest in countries around the world and a major direction for education reform practices. Therefore, to better understand the status of global digital education research, this study uses CiteSpace (6.1.R2) visual analysis software to visualize and quantitatively analyze the literature on digital education research in the social science citation index (SSCI). First, the basic information of digital education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents, and research hotspots of digital education research were analyzed by keyword co-occurrence analysis mapping and keyword time zone mapping. Finally, the research frontiers and development trends of digital education between 2000 and 6 September 2022 were analyzed by cocitation clustering and citations. The results show that, based on the changes in annual publication volume, we can divide the development pulse of the digital education research field into three stages: the budding stage (2000–2006), the slow development stage (2007–2017), and the rapid development stage (6 September 2018–2022); there are 26 core authors in this field of research, among which Selwyn N has the highest number of publications; the USA, England, Spain, Australia, and Germany have the highest number of publications; Open Univ is the institution with the most publications; digital education’s research hotspots are mainly focused on interdisciplinary field practice research and adaptive education research based on big data support. The research frontiers are mainly related to five areas: interdisciplinary development, educational equity, digital education practice, digital education evaluation, and digital education governance. This paper systematically analyzes the latest developments in global digital education research, and objectively predicts that human–computer interdisciplinary teaching models and smart education may become a future development trend of digital education. The findings of this study are useful to readers for understanding the full picture of digital education research so that researchers can conduct more in-depth and targeted research to promote better development of digital education

Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education

With the rapid development of the global digital knowledge economy, educational activities are facing more challenges. Sustainable development education aims to cultivate students’ thinking ability to better integrate with the contemporary world view, so classroom practice should involve innovative teaching and learning. The goal of sustainable development education is to cultivate talents with high-level thinking and sustainable development abilities. The concept of deep learning emphasizes mobilizing students’ internal motivation, focusing on problem-solving ability, improving students’ critical thinking level, and developing students’ lifelong learning ability. The concept of deep learning has evolved with the times. The introduction of the concept of deep learning in teaching can enhance students’ understanding of the nature of knowledge, cultivate students’ high-level thinking, and enable students to achieve better learning results. Integrating the concept of deep learning into teaching has extremely important significance and value for sustainable development education. It has become a hot topic in the world to comprehensively analyze the research status of deep learning and explore how deep learning can help education achieve sustainable development. In this study, CiteSpace (6.1.R2) visualization analysis software was used to visualize and quantitatively analyze the literature on deep learning in the Social Science Citation Index (SSCI). The visualized analysis is conducted on the annual publication amount, authors, institutions, countries, keywords, and high-frequency cited words of deep learning, to obtain the basic information, development status, hot spots, and evolution trends of deep learning research. The results show that the annual publication volume of deep learning is on the rise; deep learning research has entered a rapid growth stage since 2007; the United States has published the most papers and is the center of the global deep learning research collaboration network; the countries involved in the study were often interconnected, but the institutions and authors were relatively dispersed; research in the field of deep learning mainly focuses on concept exploration, influencing factors, implementation strategies and effectiveness of deep learning; learning method, learning strategy, curriculum design, interactive learning environment are the high-frequency keywords of deep learning research. It can be seen that deep learning research has the characteristics of transnationality, multidisciplinary nature and multi-perspective. In addition, this paper systematically analyzes the latest progress in global deep learning research and objectively predicts that using intelligent technology to design appropriate teaching and learning scenarios and evaluation methods may become the future development trend of deep learning. The research results of this paper will help readers to have a comprehensive understanding of deep learning research, provide deeper and more targeted resources for integrating deep learning concepts into teaching, and promote better sustainable development of education