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Computational science

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Displaying 26 - 50 of 159

Towards community-driven metadata standards for light microscopy: tiered guidelines extending the OME model

December 1, 2021
Author(s)
Peter Bajcsy, Mathias Hammer, Maximiliaan Huisman, Alex Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Jaime A. Pimentel, Damir Sudar, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Alison J. North, Renu Gopinathan, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia
While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable

Calculating Voxel-Polyhedron Intersections for Meshing Images

October 9, 2021
Author(s)
Stephen A. Langer, Andrew Reid
Finite element meshes constructed from 3D images are useful in materials science and medical applications when it is necessary to model the actual geometry of a sample, rather than an idealized approximation of it. Constructing the mesh involves computing

Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators

August 6, 2021
Author(s)
Mathieu Riou, Jacob Torrejon, Flavio Abreu Araujo, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Nathan Leroux, Danijela Markovic, Vincent Cros, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier
Present artificial intelligence algorithms require extensive computations to emulate the behavior of large neural networks, operating current computers near their limits, which leads to high energy costs. A possible solution to this problem is the

Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery

June 25, 2021
Author(s)
Peter Bajcsy, Mylene Simon, Sunny Yu, Nick Schaub, Jayapriya Nagarajan, Sudharsan Prativadi, Mohamed Ouladi, Nathan Hotaling
Microscopy image-based measurement variability in high-throughput imaging experiments for biological drug discoveries, such as COVID-19 therapies was addressed in this study. Variability of measurements came from (1) computational approaches (methods), (2)

Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

May 7, 2021
Author(s)
Peter Bajcsy, Michael Majurski
This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing how NN accuracy responds to systematic pruning. This study leverages the NN models generated for the TrojAI challenges. Our pruning-based approach (1) detects any

Optoelectronic Intelligence

May 7, 2021
Author(s)
Jeff Shainline
To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and

Challenge Design and Lessons Learned from the 2018 Differential Privacy Challenges

April 12, 2021
Author(s)
Diane Ridgeway, Mary Theofanos, Terese Manley, Christine Task
The push for open data has made a multitude of datasets available enabling researchers to analyze publicly available information using various statistical and machine learning methods in support of policy development. An area of increasing interest that is

Designing Trojan Detectors in Neural Networks Using Interactive Simulations

February 20, 2021
Author(s)
Peter Bajcsy, Nicholas J. Schaub, Michael P. Majurski
This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the

The membership problem for constant-sized quantum correlations is undecidable

January 26, 2021
Author(s)
Carl A. Miller, Honghao Fu, William Slofstra
When two spatially separated parties make measurements on an unknown entangled quantum state, what correlations can they achieve? How difficult is it to determine whether a given correlation is a quantum correlation? These questions are central to problems

Fast Methods for Finding Multiple Effective Influencers in Real Networks

December 31, 2020
Author(s)
Fern Y. Hunt, Roldan Pozo
We present scalable first hitting time methods for finding a collection of nodes that enables the fastest time for the spread of consensus in a network. That is, given a graph G = (V,E) and a natural number k, these methods find k vertices in G that

Object Measurements from 2D Microscopy Images

December 11, 2020
Author(s)
Peter Bajcsy, Joe Chalfoun, Mylene Simon, Mary C. Brady, Marcin Kociolek
This chapter addresses object measurements from 2D microscopy images. Object measurements (called image features) vary in terms of theoretical formulas for the same image feature, the physical units used to represent pixel-based measurements, the

Reducing the Measurement Time of Exact NOEs by Non-Uniform Sampling

September 3, 2020
Author(s)
Parker J. Nichols, Alexandra Born, Morkos A. Henen, Dean Strotz, David N. Jones, Frank Delaglio, Beat Vogeli
We have previously reported on the measurement of exact NOEs (eNOEs), which yield a wealth of additional information in comparison to conventional NOEs. We have used these eNOEs in a variety of applications, including calculating high-resolution structures

Detection of Dense, Overlapping, Geometric Objects

July 1, 2020
Author(s)
Adele P. Peskin, Boris Wilthan, Michael P. Majurski
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting

Notes on Interrogating Random Quantum Circuits

May 29, 2020
Author(s)
Luis Brandao, Rene C. Peralta
Consider a quantum circuit that, when fed a constant input, produces a fixed-length random bit- string in each execution. Executing it many times yields a sample of many bit-strings that contain fresh randomness inherent to the quantum evaluation. When the

Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete

May 14, 2020
Author(s)
Peter Bajcsy, Steven B. Feldman, Michael P. Majurski, Kenneth A. Snyder, Mary C. Brady
This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica

Summary: Workshop on Machine Learning for Optical Communication Systems

March 26, 2020
Author(s)
Joshua A. Gordon, Abdella Battou, Michael P. Majurski, Dan Kilper, Uiara Celine, Massimo Tonatore, Joao Pedro, Jesse Simsarian, Jim Westdorp, Darko Zibar
Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of

A Science Gateway for Atomic and Molecular Physics

January 7, 2020
Author(s)
Barry I. Schneider, Klaus Bartschat, Oleg Zatsarinny, Igor Bray, Fernando Martin, Armin Scrinzi, Sudhakar Pamidighantam, Jonathan Tennyson, Jimena Gorfinkiel, Markus Klinker
We describe the creation of a new Atomic and Molecular Physics science gateway (AMPGateway). The gateway is designed to bring together a subset of the AMP community to work collectively to make their codes available and easier to use by the partners as
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