11 papers · ranked by Valyu relevance
Rui Cao, John H. Bladon, Stephen J. Charczynski, Michael E. Hasselmo + 1 more
The Weber-Fechner law proposes that our perceived sensory input increases with physical input on a logarithmic scale. Hippocampal “time cells” carry a record of recent experience by firing sequentially during a circumscribed period of time after a triggering stimulus. Different cells have “time fields” at different…
Dale Zhou, Jason Z. Kim, Adam R. Pines, Valerie J. Sydnor + 6 more
Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed…
Andrew A. Chen, Chongliang Luo, Yong Chen, Russell T. Shinohara + 1 more
Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences.…
Senik Matinyan, Jan Pieter Abrahams
High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, we present TERSE, a novel lossless compression algorithm specifically designed for diffraction data. We compare TERSE with the established lossless compression algorithms implemented in gzip, CBF…
Pétur Helgi Einarsson, Páll Melsted
We describe a compression scheme for BUS files and an implementation of the algorithm in the bustools software. Our compression algorithm yields smaller file sizes than gzip, at significantly faster compression and decompression speeds. We evaluated our algorithm on 533 BUS files from scRNA-seq experiments with a total…
Xingyu Liu, Zonglei Zhen, Jia Liu
Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation. However, it remains unclear how objects are represented in DCNNs with an overwhelming number of features and non-linear operations. In…
Herbert J. Bernstein, Alexei S. Soares, Kimberly Horvat, Jean Jakoncic
New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some…
Luke Staniscia, Yun William Yu
Because of the rapid generation of data, the study of compression algorithms to reduce storage and transmission costs is important to bioinformaticians. Much of the focus has been on sequence data, including both genomes and protein amino acid sequences stored in FASTA files. Current standard practice is to use an…
Anas Al-okaily, Abdelghani Tbakhi
Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage has never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated…
J.M. Lázaro-Guevara, K.M. Garrido
Undeveloped countries like Guatemala, where access to high-speed internet connections is limited, downloading and sharing Biological information of thousands of Mega Bits is a huge problem for the beginning and development of Bioinformatics. Based on that information is an urgent necessity to find a better way to share…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…