Layne T. Watson
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FedQP: Large-Scale Private and Flexible Federated Query Processing Open
State-of-the-art federated learning coordinates stochastic gradient descent across clients to refine shared model parameters while protecting individual datasets. Current methods require a uniform data model and are vulnerable to privacy a…
Remark on Algorithm 1012: Computing Projections with Large Datasets Open
In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least…
Algorithm 1031: MQSI—Monotone Quintic Spline Interpolation Open
MQSI is a Fortran 2003 subroutine for constructing monotone quintic spline interpolants to univariate monotone data. Using sharp theoretical monotonicity constraints, first and second derivative estimates at data provided by a quadratic fa…
View article: Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems Open
VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [ 32 ], which handles two or m…
View article: Design Strategies and Approximation Methods for High-Performance Computing Variability Management
Design Strategies and Approximation Methods for High-Performance Computing Variability Management Open
Performance variability management is an active research area in high-performance computing (HPC). We focus on input/output (I/O) variability. To study the performance variability, computer scientists often use grid-based designs (GBDs) to…
View article: Prediction of high-performance computing input/output variability and its application to optimization for system configurations
Prediction of high-performance computing input/output variability and its application to optimization for system configurations Open
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of input/ou…
On Parallel Real-Time Security Improvement Using Mixed-Integer Programming Open
Network security defenses evolve, responding to real-time attack incidents, modifying the underlying topology, or reallocating defense systems across the network. The present work emphasizes reducing the time to compute new optimal realloc…
View article: Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations
Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations Open
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of input/ou…
View article: Algorithm 1012
Algorithm 1012 Open
DELAUNAYSPARSE contains both serial and parallel codes written in Fortran 2003 (with OpenMP) for performing medium- to high-dimensional interpolation via the Delaunay triangulation. To accommodate the exponential growth in the size of the …
Multidimensional Global Optimization and Robustness Analysis in the Context of Protein–Ligand Binding Open
Accuracy of protein-ligand binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a global multid…
View article: Algorithm 1007
Algorithm 1007 Open
QNSTOP consists of serial and parallel (OpenMP) Fortran 2003 codes for the quasi-Newton stochastic optimization method of Castle and Trosset for stochastic search problems. A complete description of QNSTOP for both local search with stocha…
View article: Reward Shaping for Human Learning via Inverse Reinforcement Learning
Reward Shaping for Human Learning via Inverse Reinforcement Learning Open
Humans are spectacular reinforcement learners, constantly learning from and adjusting to experience and feedback. Unfortunately, this doesn't necessarily mean humans are fast learners. When tasks are challenging, learning can become unacce…
View article: Human Apprenticeship Learning via Kernel-based Inverse Reinforcement Learning.
Human Apprenticeship Learning via Kernel-based Inverse Reinforcement Learning. Open
It has been well demonstrated that inverse reinforcement learning (IRL) is an effective technique for teaching machines to perform tasks at human skill levels given human demonstrations (i.e., human to machine apprenticeship learning). Thi…
A half century of computing Open
The advances in engineering, science, and technology over the past half century have been amazing, but none match the orders of magnitude improvement in computing. The technical achievements are ar...
Personal reflections on 50 years of scientific computing: 1967–2017 Open
Computer hardware, software, numerical algorithms, and science and engineering applications are traced for a half century from the author's perspective.
Robustness of Multidimensional Optimization Outcomes: A General Approach and a Case Study Open
In multidimensional parameter optimization of complex systems, the preferred solution must also be robust to virtually inevitable perturbations and uncertainties. Having a conceptually simple and computationally facile metric that can help…
Managing Computationally Expensive Blackbox Multiobjective Optimization Problems with Libensemble Open
Multiobjective optimization problems (MOPs) are common across many science and engineering fields. A multiobjective optimization algorithm (MOA) seeks to provide an approximation to the tradeoff surface between multiple, possibly conflicti…
JigCell Model Connector: building large molecular network models from components Open
The growing size and complexity of molecular network models makes them increasingly difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hund…