From Homma's theorem to Pick's theorem

From Homma’s PL Gauss-Bonnet theorem applied on a PL-complex in a plane, many generalizations of Pick’s theorem on a lattice PL-figure are obtained in a unified geometric way

Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series

Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to the long term unpredictability of chaotic time series. Thus, we have developed a method to select a representative prediction from a set of plausible predictions by means of using LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. Although we have shown the effectiveness of the method, it sometimes fails to select the representative prediction with long predictable horizon. In order to cope with this problem, this paper presents a method to select multiple candidates of representative prediction by means of employing hierarchical K-means clustering with K = 2. From numerical experiments, we show the effectiveness of the method and an analysis of the property of LOOCV predictable horizon.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

The Asexual State Of Chorioactis Geaster, Discovered in Texas, USA, On Buried Log

Fruiting body of C. geaster at Seguin, Texas US

Grading Fruits and Vegetables Using RGB-D Images and Convolutional Neural Network

This paper presents a method for grading fruits and vegetables by means of using RGB-D (RGB and depth) images and convolutional neural network (CNN). Here, we focus on grading according to the size of objects. First, the method transforms positions of pixels in RGB image so that the center of the object in 3D space is placed at the position equidistant from the focal point by means of using the corresponding depth image. Then, with the transformed RGB images involving equidistant objects, the method uses CNN for learning to classify the objects or fruits and vegetables in the images for grading according to the size, where the CNN is structured for achieving both size sensitivity for grading and shift invariance for reducing position error involved in images. By means of numerical experiments, we show the effectiveness and the analysis of the present method.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Aplicación de las TIC en estudiantes de Ciencias de la Actividad Física y del Deporte: plataforma virtual WebCT.

Educational systems have been modified according to the evolution of modern society. In this study, blended learning (combining face-to-face and online learning) has been used for 2 academic years with students of the subject Structure and Organization of Sports Institutions, belonging to the degree of Sports Science, by means of the virtual platform called WebCT. The results indicate that students have had only 5% of incidents with this new teaching model, which leads us to conclude that this model of education is beneficial for students.Los sistemas de enseñanza han sido modificados según la evolución de la sociedad moderna. En este estudio, se ha empleado durante 2 cursos académicos una modalidad de enseñanza mixta (presencial y virtual) con alumnos de la asignatura Estructura y Organización de Instituciones Deportivas, perteneciente a la licenciatura en Ciencias del Deporte, recurriéndose para el aprendizaje virtual a la plataforma WebCT. Los resultados indican que los alumnos han tenido apenas un 5% de incidencias con este nuevo modelo de enseñanza. Como conclusión, este modelo de enseñanza es beneficioso para el alumnado

Synthesis of trans-1,2-dimetalloalkenes through reductive anti-dimagnesiation and dialumination of alkynes

単純分子から有用物質を短工程で製造可能に --炭素と金属を結ぶ新しい方法により合成効率の飛躍的向上へ--. 京都大学プレスリリース. 2023-01-04.Polar reactive organometallic species have been key reagents in synthesis for more than a century. Stereodefined 1, 2-dimetallated alkenes offer promising synthetic utility; however, few methods are available for their preparation due to their relatively low stability. Here we report the reductive anti-1, 2-dimetallation of alkynes to stereoselectively generate trans-1, 2-dimagnesio- and 1, 2-dialuminoalkenes, which are stable and have been demonstrated in organic synthesis. These stereodefined 1, 2-dimetallated alkenes are prepared through the use of a sodium dispersion as a reducing agent, and organomagnesium and organoaluminium halides as reduction-resistant electrophiles. Highly nucleophilic 1, 2-dimagnesioalkenes serve as dual Grignard reagents and have been demonstrated to react with various electrophiles to afford anti-difunctionalized alkenes. The 1, 2-dialuminoalkenes react with paraformaldehyde with dearomatization of the aryl moieties to form the corresponding dearomatized 1, 4-diols, with the overall reaction being regarded as alkynyl-directed dearomatization of arenes. X-ray crystallographic analysis further supports the formation of trans-1, 2-dimagnesio- and 1, 2-dialuminoalkenes, with computational studies providing insight into the mechanism of dearomative difunctionalization

Does the psychological profile influence the position of promising young futsal players?

Stress control as well as other psychological characteristics influence sports performance (SP) and could be relevant according to the playing position in team sports, such as the futsal where players have different specific functions within the team. The aim of this study was to analyze the psychological characteristics and profile related to SP of top-level young futsal players, according to the offensive or defensive role. A total of one hundred sixtyseven young promises futsal players participated in this study (84 U16 and 83 U19) and have been chosen to play Championship of Spain Selections. The Psychological Characteristics related to SP for soccer players Questionnaire was used, and one-way ANOVA test was performed based on the playing position (goalkeeper, defender and defender-wing, wing and wing-defender, pivot and wing-pivot, and universal). Results showed that goalkeepers had the best psychological profile and characteristics related to SP. Pivots and wing-pivots had less self-confidence, and universals players, less stress control in relation to the rest of the playing positions (p < 0.05). The main findings revealed that the psychological characteristics and profile related to SP in young promises futsal players are different according to the playing position, and this study suggest the inclusion of psychological-training programs in order to improve the psychological abilities of players, especially for players with offensive role who seek to score goals

Chromium carbides and cyclopropenylidenes

Carbon tetrabromide can be reduced with CrBr2 in THF to form a dinuclear carbido complex, [CrBr2(thf)(2))][CrBr2(thf)(3)](mu-C), along with formation of [CrBr3(thf)(3)]. An X-ray diffraction (XRD) study of the pyridine adduct displayed a dinuclear structure bridged by a carbido ligand between 5- and 6-coordinate chromium centers. The carbido complex reacted with two equivalents of aldehydes to form alpha,beta-unsaturated ketones. Treatment of the carbido complex with alkenes resulted in a formal double-cyclopropanation of alkenes by the carbido moiety to afford spiropentanes. Isotope labeling studies using a C-13-enriched carbido complex, [CrBr2(thf)(2))][CrBr2(thf)(3)](mu-C-13), identified that the quaternary carbon in the spiropentane framework was delivered by carbide transfer from the carbido complex. Terminal and internal alkynes also reacted with the carbido complex to form cyclopropenylidene complexes. A solid-state structure of the diethylcyclopropenylidene complex, prepared from 3-hexyne, showed a mononuclear cyclopropenylidene chromium(iii) structure

Probabilistic Prediction of Chaotic Time Series Using Similarity of Attractors and LOOCV Predictable Horizons for Obtaining Plausible Predictions

This paper presents a method for probabilistic prediction of chaotic time series. So far, we have developed several model selection methods for chaotic time series prediction, but the methods cannot estimate the predictable horizon of predicted time series. Instead of using model selection methods employing the estimation of mean square prediction error (MSE), we present a method to obtain a probabilistic prediction which provides a prediction of time series and the estimation of predictable horizon. The method obtains a set of plausible predictions by means of using the similarity of attractors of training time series and the time series predicted by a number of learning machines with different parameter values, and then obtains a smaller set of more plausible predictions with longer predictable horizons estimated by LOOCV (leave-one-out cross-validation) method. The effectiveness and the properties of the present method are shown by means of analyzing the result of numerical experiments.22nd International Conference, ICONIP 2015, November 9-12, 2015, Istanbul, Turke

Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Recently, we have presented a method of probabilistic prediction of chaotic time series. The method employs learning machines involving strong learners capable of making predictions with desirably long predictable horizons, where, however, usual ensemble mean for making representative prediction is not effective when there are predictions with shorter predictable horizons. Thus, the method selects a representative prediction from the predictions generated by a number of learning machines involving strong learners as follows: first, it obtains plausible predictions holding large similarity of attractors with the training time series and then selects the representative prediction with the largest predictable horizon estimated via LOOCV (leave-one-out cross-validation). The method is also capable of providing average and/or safe estimation of predictable horizon of the representative prediction. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in our previous study, and this paper employs bagging (bootstrap aggregating) to improve the performance, which enables us to analyze the validity and the effectiveness of the method