
Scientific Image Restoration Anywhere
The use of deep learning models within scientific experimental facilitie...
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A GradientAware Search Algorithm for Constrained Markov Decision Processes
The canonical solution methodology for finite constrained Markov decisio...
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Fast and accurate learned multiresolution dynamical downscaling for precipitation
This study develops a neural networkbased approach for emulating highr...
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Deep Active Learning for Solvability Prediction in Power Systems
Traditional methods for solvability region analysis can only have inner ...
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Recurrent and Spiking Modeling of Sparse Surgical Kinematics
Robotassisted minimally invasive surgery is improving surgeon performan...
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Transfer Learning with Graph Neural Networks for ShortTerm Highway Traffic Forecasting
Highway traffic modeling and forecasting approaches are critical for int...
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Finding Proofs in Tarskian Geometry
We report on a project to use a theorem prover to find proofs of the the...
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Machine Learning for Antimicrobial Resistance
Biological datasets amenable to applied machine learning are more availa...
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Quantifying mesoscale neuroanatomy using Xray microtomography
Methods for resolving the 3D microstructure of the brain typically start...
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Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization ...
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
We introduce a machine learning model to predict atomization energies of...
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Vanquishing the XCB Question: The Methodology Discovery of the Last Shortest Single Axiom for the Equivalential Calculus
With the inclusion of an effective methodology, this article answers in ...
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A Spectrum of Applications of Automated Reasoning
The likelihood of an automated reasoning program being of substantial as...
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DATeS: A HighlyExtensible Data Assimilation Testing Suite
A flexible and highlyextensible data assimilation testing suite, named ...
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Composing Scalable Nonlinear Algebraic Solvers
Most efficient linear solvers use composable algorithmic components, wit...
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InDepth Exploration of SingleSnapshot Lossy Compression Techniques for NBody Simulations
In situ lossy compression allowing usercontrolled data loss can signifi...
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Doing Moore with Less  Leapfrogging Moore's Law with Inexactness for Supercomputing
Energy and power consumption are major limitations to continued scaling ...
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Extremescale Multigrid Components within PETSc
Elliptic partial differential equations (PDEs) frequently arise in conti...
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Mobile Encryption Gateway (MEG) for Email Encryption
Email cryptography applications often suffer from major problems that pr...
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Global sensitivity analysis for statistical model parameters
Global sensitivity analysis (GSA) is frequently used to analyze the infl...
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GoalOriented Optimal Design of Experiments for LargeScale Bayesian Linear Inverse Problems
We develop a framework for goal oriented optimal design of experiments (...
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Comparative study of finite element methods using the TimeAccuracySize (TAS) spectrum analysis
We present a performance analysis appropriate for comparing algorithms u...
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Improving Performance of Iterative Methods by Lossy Checkponting
Iterative methods are commonly used approaches to solve large, sparse li...
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A Proposal for LoopTransformation Pragmas
Pragmas for loop transformations, such as unrolling, are implemented in ...
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A SoftwareDefined Approach for QoS Control in HighPerformance Computing Storage Systems
Highperformance computing (HPC) storage systems become increasingly cri...
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FixedPSNR Lossy Compression for Scientific Data
Errorcontrolled lossy compression has been studied for years because of...
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A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming
Maximum likelihood estimation of mixture proportions has a long history ...
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Optimizing Lossy Compression RateDistortion from Automatic Online Selection between SZ and ZFP
With everincreasing volumes of scientific data produced by HPC applicat...
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An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters
Covariance inflation and localization are ubiquitously employed to count...
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DATeS: A HighlyExtensible Data Assimilation Testing Suite v1.0
A flexible and highlyextensible data assimilation testing suite, named ...
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Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
We consider the problem of learning the level set for which a noisy blac...
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Scalable Gaussian Process Computations Using Hierarchical Matrices
We present a kernelindependent method that applies hierarchical matrice...
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Statistical Treatment of Inverse Problems Constrained by Differential EquationsBased Models with Stochastic Terms
This paper introduces a statistical treatment of inverse problems constr...
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Loop Optimization Framework
The LLVM compiler framework supports a selection of loop transformations...
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Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained
Software is the key crosscutting technology that enables advances in mat...
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Evaluation of Intel Memory Drive Technology Performance for Scientific Applications
In this paper, we present benchmark data for Intel Memory Drive Technolo...
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DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using ErrorBounded Lossy Compression
DNNs have been quickly and broadly exploited to improve the data analysi...
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Learning to Solve LargeScale SecurityConstrained Unit Commitment Problems
SecurityConstrained Unit Commitment (SCUC) is a fundamental problem in ...
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The KalaiSmorodinski solution for manyobjective Bayesian optimization
An ongoing aim of research in multiobjective Bayesian optimization is to...
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Probabilistic Recalibration of Forecasts
We present a scheme by which a probabilistic forecasting system whose pr...
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A Benchmarking Study to Evaluate Apache Spark on LargeScale Supercomputers
As dataset sizes increase, data analysis tasks in high performance compu...
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Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout
As neural networks have begun performing increasingly critical tasks for...
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Parsl: Pervasive Parallel Programming in Python
Highlevel programming languages such as Python are increasingly used to...
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Scaling Distributed Training of FloodFilling Networks on HPC Infrastructure for Brain Mapping
Mapping all the neurons in the brain requires automatic reconstruction o...
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Neuromorphic Architecture Optimization for TaskSpecific Dynamic Learning
The ability to learn and adapt in real time is a central feature of biol...
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Improving MPI Collective I/O Performance With Intranode Request Aggregation
Twophase I/O is a wellknown strategy for implementing collective MPII...
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Centroidal Voronoi Tessellation Based Methods for Optimal Rain Gauge Location Prediction
With more satellite and model precipitation data becoming available, new...
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Scalable ReinforcementLearningBased Neural Architecture Search for Cancer Deep Learning Research
Cancer is a complex disease, the understanding and treatment of which ar...
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Using recurrent neural networks for nonlinear component computation in advectiondominated reducedorder models
Rapid simulations of advectiondominated problems are vital for multiple...
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MaLTESE: LargeScale SimulationDriven Machine Learning for Transient Driving Cycles
Optimal engine operation during a transient driving cycle is the key to ...
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Argonne National Laboratory
Argonne National Laboratory is a science and engineering research national laboratory operated by the University of Chicago Argonne LLC for the United States Department of Energy located near Lemont, Illinois, outside Chicago.