Switching Temporary Teachers for Semi-Supervised Semantic Segmentation

The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on the student's. However, EMA updates raise a problem in that the weights of the teacher and student are getting coupled, causing a potential performance bottleneck. Furthermore, this problem may become more severe when training with more complicated labels such as segmentation masks but with few annotated data. This paper introduces Dual Teacher, a simple yet effective approach that employs dual temporary teachers aiming to alleviate the coupling problem for the student. The temporary teachers work in shifts and are progressively improved, so consistently prevent the teacher and student from becoming excessively close. Specifically, the temporary teachers periodically take turns generating pseudo-labels to train a student model and maintain the distinct characteristics of the student model for each epoch. Consequently, Dual Teacher achieves competitive performance on the PASCAL VOC, Cityscapes, and ADE20K benchmarks with remarkably shorter training times than state-of-the-art methods. Moreover, we demonstrate that our approach is model-agnostic and compatible with both CNN- and Transformer-based models. Code is available at \url{https://github.com/naver-ai/dual-teacher}.Comment: NeurIPS-202

Clinical Characteristics and Genotypes of Rotaviruses in a Neonatal Intensive Care Unit

BackgroundThere are few reports on the symptoms of rotavirus infections in neonates. This study aims to describe clinical signs of rotavirus infections among neonates, with a particular focus on preterm infants, and to show the distribution of genotypes in a neonatal intensive care unit (NICU).MethodsA prospective observational study was conducted at a regional NICU for 1 year. Stool specimens from every infant in the NICU were collected on admission, at weekly intervals, and from infants showing symptoms. Rotavirus antigens were detected by enzyme-linked immunosorbent assay (ELISA), and genotypes were confirmed by Reverse transcription-Polymerase chain reaction (RT-PCR). The infants were divided into three groups: symptomatic preterm infants with and without rotavirus-positive stools [Preterm(rota+) and Preterm(rota–), respectively] and symptomatic full- or near-term infants with rotavirus-positive stools [FT/NT(rota+)]. Demographic and outcome data were compared among these groups.ResultsA total of 702 infants were evaluated for rotaviruses and 131 infants were included in this study. The prevalence of rotavirus infections was 25.2%. Preterm(rota+) differed from Preterm(rota–) and FT/NT(rota+) with respect to frequent feeding difficulty (p = 0.047 and 0.034, respectively) and higher percentage of neutropenia (p = 0.008 and 0.011, respectively). G4P[6] was the exclusive strain in both the Preterm(rota+) (97.7%) and FT/NT(rota+) (90.2%), and it was the same for nosocomial, institutional infections, and infections acquired at home.ConclusionSystemic illness signs such as feeding difficulty and neutropenia are specific for preterm infants with rotavirus infections. G4P[6] was exclusive, regardless of preterm birth or locations of infections. This study might be helpful in developing policies for management and prevention of rotavirus infections in NICUs

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

Point Mutation of Hoxd12 in Mice

Purpose: Genes of the HoxD cluster play a major role in vertebrate limb development, and changes that modify the Hoxd12 locus affect other genes also, suggesting that HoxD function is coordinated by a control mechanism involving multiple genes during limb morphogenesis. In this study, mutant phenotypes were produced by treatment of mice with chemical mutagen, N-ethyl-N-nitrosourea (ENU). We analyzed mutant mice exhibiting the specific microdactyly phenotype and examined the genes affected. Materials and Methods: We focused on phenotype characteristics including size, bone formation, and digit morphology of ENU-induced microdactyly mice. The expressions of several molecules were analyzed by genome-wide screening and quantitative real-time PCR to define the affected genes. Results: We report on limb phenotypes of an ENU-induced A-to-C mutation in the Hoxd12 gene, resulting in alanine-to-serine conversion. Microdactyly mice exhibited growth defects in the zeugopod and autopod, shortening of digits, a missing tip of digit I, limb growth affected, and dramatic increases in the expressions of Fgf4 and Lmx1b. However, the expression level of Shh was not changed Hoxd12 point mutated mice. Conclusion: These results suggest that point mutation rather than the entire deletion of Hoxd12, such as in knockout and transgenic mice, causes the abnormal limb phenotype in microdactyly mice. The precise nature of the spectrum of differences requires further investigation.link_to_subscribed_fulltex

Tau functions as Widom constants

We define a tau function for a generic Riemann-Hilbert problem posed on a union of non-intersecting smooth closed curves with jump matrices analytic in their neighborhood. The tau function depends on parameters of the jumps and is expressed as the Fredholm determinant of an integral operator with block integrable kernel constructed in terms of elementary parametrices. Its logarithmic derivatives with respect to parameters are given by contour integrals involving these parametrices and the solution of the Riemann-Hilbert problem. In the case of one circle, the tau function coincides with Widom's determinant arising in the asymptotics of block Toeplitz matrices. Our construction gives the Jimbo-Miwa-Ueno tau function for Riemann-Hilbert problems of isomonodromic origin (Painlev\'e VI, V, III, Garnier system, etc) and the Sato-Segal-Wilson tau function for integrable hierarchies such as Gelfand-Dickey and Drinfeld-Sokolov.Comment: 26 pages, 6 figure