Hasil untuk "Hungary"

L. Popescu

P. Anversa, Boston, USA J.J.M. Bergeron, Montreal, Canada M. Bhatia, Christchurch, New Zealand O. Binah, Haifa, Israel H.E. Blum, Freiburg, Germany T.B. Bolton, London, UK C. Bona, New York, USA M.D. Bootman, Cambridge, UK N. Bornstein, Tel Aviv, Israel G. Bussolati, Turin, Italy K. Camphausen, Bethesda, USA M. Caraglia, Naples, Italy Y.H. Chen, Shanghai, China C.-M. Cheng, Hsinchu, Taiwan S.N. Constantinescu, Brussels, Belgium P. Doevendans, Utrecht, The Netherlands B. Eyden, Manchester, UK M.S. Faussone-Pellegrini, Florence, Italy W. Franke, Heidelberg, Germany L. Frati, Rome, Italy T. Fujimoto, Nagoya, Japan P.M. Glazer, New Haven, USA J. Gooch, Atlanta, USA H. zur Hausen, Heidelberg, Germany A.M. Hofer, Boston, USA R.E. Horch, Erlangen, Germany O. Hovatta, Huddinge, Sweden S.S. Hu, Beijing, China J.D. Huizinga, Hamilton, Canada M. Ivan, Indianapolis, USA B. Jena, Detroit, USA Y.T. Konttinen, Helsinki, Finland T. Kornberg, San Francisco, USA S. Kostin, Bad Nauheim, Germany R.C. Kukreja, Richmond, VA, USA R. Langer, Cambridge, USA J.W. Lawler, Boston, USA F. Lupu, Oklahoma City, USA J. Meldolesi, Milan, Italy M. Mercola, San Diego, USA V.M. Miller, Rochester, USA K. Morgan, Boston, USA J.F. Morris, Oxford, UK C. Mummery, Utrecht, The Netherlands F. Murad, Houston, USA D.F. Mureşanu, Cluj-Napoca, Romania H. O’Neil, Canberra, Australia K.P. Nephew, Bloomington, USA M. Pesce, Milan, Italy O.H. Petersen, Liverpool, UK N.C. Popescu, Bethesda, USA D. Pozo, Sevilla, Spain M.Z. Ratajczak, Louisville, USA U. Ripamonti, Johannesburg, South Africa J. Rubin, Bethesda, USA A. Samali, Galway, Ireland P.R. Sanberg, Tampa, USA R.C. dos Santos Goldenberg Rio de Janeiro, Brasil M. Simionescu, Bucharest, Romania G.W. Sledge, Indiana, USA R.V. Stan, Hanover, USA G. Steinhoff, Rostock, Germany M. Stürzl, Erlangen, Germany M. Taggart, Newcastle upon Tyne, UK A. Tosaki, Debrecen, Hungary N.A. Turner, Leeds, UK C.A. Vacanti, Boston, USA A. Vaheri, Helsinki, Finland L. Vécsei, Szeged, Hungary V. Velculescu, Baltimore, USA C. Wang, Beijing, China R.A. Wang, Xi’an, China B. Winblad, Stockholm, Sweden C. Zhang, Newark, USA

A. Gretton, Karsten M. Borgwardt, M. Rasch et al.

L. Halpern, Miklós Koren, Adam Szeidl

E. Shorter

H. Kuhn

A. Gretton, Karsten M. Borgwardt, M. Rasch et al.

We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be computed in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

Peter J. Lane, J. Salk, Marjorie A. Lyles

R. Dekeyser

K. Kiss

Marjorie A. Lyles, J. Salk

Craige Roberts

M. Elgart

B. Woodcock, J. Bullock, R. Shore et al.

M. Ashton, Kibeom Lee, M. Perugini et al.

J. Ziegler, Daisy Bertrand, Dénes Tóth et al.

C. Gamba, E. Jones, M. Teasdale et al.

The Great Hungarian Plain was a crossroads of cultural transformations that have shaped European prehistory. Here we analyse a 5,000-year transect of human genomes, sampled from petrous bones giving consistently excellent endogenous DNA yields, from 13 Hungarian Neolithic, Copper, Bronze and Iron Age burials including two to high (~22 × ) and seven to ~1 × coverage, to investigate the impact of these on Europe’s genetic landscape. These data suggest genomic shifts with the advent of the Neolithic, Bronze and Iron Ages, with interleaved periods of genome stability. The earliest Neolithic context genome shows a European hunter-gatherer genetic signature and a restricted ancestral population size, suggesting direct contact between cultures after the arrival of the first farmers into Europe. The latest, Iron Age, sample reveals an eastern genomic influence concordant with introduced Steppe burial rites. We observe transition towards lighter pigmentation and surprisingly, no Neolithic presence of lactase persistence. Recent advances in high-throughput sequencing techniques have enabled the analysis of ancient human genomes. Here the authors sequence ancient human genomes that span a period of 5,000 years, to understand the ancestral influence on Europe's genetic landscape.

F. Bray, R. Sankila, J. Ferlay et al.

Anna Karen Gárate-Escamila, A. Hassani, Emmanuel Andres

Abstract The prediction of cardiac disease helps practitioners make more accurate decisions regarding patients' health. Therefore, the use of machine learning (ML) is a solution to reduce and understand the symptoms related to heart disease. The aim of this work is the proposal of a dimensionality reduction method and finding features of heart disease by applying a feature selection technique. The information used for this analysis was obtained from the UCI Machine Learning Repository called Heart Disease. The dataset contains 74 features and a label that we validated by six ML classifiers. Chi-square and principal component analysis (CHI-PCA) with random forests (RF) had the highest accuracy, with 98.7% for Cleveland, 99.0% for Hungarian, and 99.4% for Cleveland-Hungarian (CH) datasets. From the analysis, ChiSqSelector derived features of anatomical and physiological relevance, such as cholesterol, highest heart rate, chest pain, features related to ST depression, and heart vessels. The experimental results proved that the combination of chi-square with PCA obtains greater performance in most classifiers. The usage of PCA directly from the raw data computed lower results and would require greater dimensionality to improve the results.

G. Lukács, Anna Bostock

Yihong Xu, Aljosa Osep, Yutong Ban et al.

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multi-object tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers. As a key ingredient, we propose a Deep Hungarian Net (DHN) module that approximates the Hungarian matching algorithm. DHN allows estimating the correspondence between object tracks and ground truth objects to compute differentiable proxies of MOTA and MOTP, which are in turn used to optimize deep trackers directly. We experimentally demonstrate that the proposed differentiable framework improves the performance of existing multi-object trackers, and we establish a new state of the art on the MOTChallenge benchmark. Our code is publicly available from https://github.com/yihongXU/deepMOT.