Zobrazeno 1 - 10
of 10
pro vyhledávání: '"William Cukierski"'
Autor:
Wei Wu, Drew Abbot, Francisco Zamora-Martínez, Benjamin H. Brinkmann, Phillip Adkins, Quang M. Tieng, Gregory A. Worrell, Min Chen, Joost B. Wagenaar, Juan Pardo, Michael Hills, Edward E. Patterson, Simone C. Bosshard, Jialune He, Charles H. Vite, Iryna Korshunova, F. J. Muñoz-Almaraz, Brian Litt, Paloma Botella-Rocamora, William Cukierski
Publikováno v:
Brain
BRAIN
BRAIN
See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting co
Autor:
Ben Hamner, Swapna Savvana, Martine De Cock, Vani Mandava, William Cukierski, Brian Dalessandro, Claudia Perlich, Senjuti Basu Roy
Publikováno v:
BigData Conference
Microsoft Academic Search is a free search engine specific to scholarly material. It currently covers more than 50 million publications and over 19 million authors across a variety of domains. One of the main challenges in correctly indexing this mat
Autor:
Ken Larrey, Thi Ngoc Tho Nguyen, Joseph Defretin, Jed Irvine, Tapio Manninen, William Cukierski, Chris Hurlburt, Lawrence Neal, Julien Marzat, Tuomas Virtanen, Adam S. Hadley, Xiaoli Z. Fern, Heikki Huttunen, Konstantinos Eftaxias, Hong-Wei Ng, Sarah Frey Hadley, Zhong Lei, Gabor Fodor, Grigorios Tsoumakas, Maxim Milakov, Aleksandr Diment, Yonghong Huang, David R. Callender, Raviv Raich, Anil Thomas, Pekka Ruusuvuori, Matthew G. Betts, Forrest Briggs
Publikováno v:
MLSP
Birds have been widely used as biological indicators for ecological research. They respond quickly to environmental changes and can be used to infer about other organisms (e.g., insects they feed on). Traditional methods for collecting data about bir
Autor:
Martine De Cock, Ben Hamner, William Cukierski, Senjuti Basu Roy, Vani Mandava, Swapna Savanna, Brian Dalessandro, Claudia Perlich
Publikováno v:
KDD Cup
KDD Cup 2013 challenged participants to tackle the problem of author name ambiguity in a digital library of scientific publications. The competition consisted of two tracks, which were based on large-scale datasets from a snapshot of Microsoft Academ
Autor:
Karen J. Meaburn, William Cukierski, Stephen J. Lockett, David J. Foran, Tom Misteli, Kaustav Nandy, Prabhakar R. Gudla
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 13, Iss 1, p 232 (2012)
BMC Bioinformatics, Vol 13, Iss 1, p 232 (2012)
Background Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings oft
Publikováno v:
IJCNN
The growing ubiquity of social networks has spurred research in link prediction, which aims to predict new connections based on existing ones in the network. The 2011 IJCNN Social Network challenge asked participants to separate real edges from fake
Publikováno v:
SPIE Proceedings.
The lack of clear consensus over the utility of multispectral imaging (MSI) for bright-field imaging prompted our team to investigate the benefit of using MSI on breast tissue microarrays (TMA). We have conducted performance studies to compare MSI wi
Publikováno v:
2010 International Conference on Bioinformatics and Biomedical Technology.
We described a combined multiple clustering approach to automatically identify chronic lymphocytic leukemia neoplastic population by flow cytometry immunophenotyping. Flow cytometry data from various specimens were preprocessed by data cross-linking
Publikováno v:
ISBI
A multispectral camera is capable of imaging a histologic slide at narrow bandwidths over the range of the visible spectrum. While several uses for multispectral imaging (MSI) have been demonstrated in pathology [1, 2], there is no unified consensus
Autor:
William Cukierski, David J. Foran
Publikováno v:
ICDM Workshops
High-dimensional data presents a significant challenge to a broad spectrum of pattern recognition and machine-learning applications. Dimensionality reduction (DR) methods serve to remove unwanted variance and make such problems tractable. Several non