Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Jiachen Yang, Zhiyong Ding, Fei Guo, Huogen Wang + 1 more
In this paper, we investigate the problem of optimization multivariate performance measures, and propose a novel algorithm for it. Different from traditional machine learning methods which optimize simple loss functions to learn prediction function, the problem studied in this paper is how to learn effective…
Samuel Kienzle, Lisa Junghans, Anja Wittmann, Stefan Wieschalka + 4 more
Applying a single parameter set to describe complex mammalian kinetics often is too simplistic, as it fails to capture sensitive cell-to-environment interactions that may be exploited to optimize production performance. To resolve this time dependency, intra-experimental parameter shifts as part of design of dynamic…
Jim Jing-Yan Wang
—In this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. This problem has two features: the data points are presented by multiple views, and the target of learning is to optimize complex multivariate performanc e measures. We…
Asier Salicio-Paz, Ixone Ugarte, Jordi Sort, Eva Pellicer + 3 more
'Eva García-Lecina' 'Eugenijus Norkus' 'Loreta Tamasauskaite-Tamasiunaite'] Univariate and multivariate optimizations of a novel electroless nickel formulation have been carried out by means of the Taguchi method. From the compositional point of view, adjustment of the complexing agent concentration in solution is…
Jingbin Wang, Haoxiang Wang, Yi‐Hua Zhou, Nancy McDonald
—In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear…
Héctor Fernández, María Alicia Zon, Sabrina Antonella Maccio, Rubén Darío Alaníz + 7 more
'Rubén Darío Alaníz' 'Aylen Di Tocco' 'Roodney Alberto Carrillo Palomino' 'Jose Alberto Cabas Rodríguez' 'Adrian Marcelo Granero' 'Fernando J. Arévalo' 'Sebastian Noel Robledo' 'Gastón Darío Pierini'] We summarize the application of multivariate optimization for the construction of electrochemical biosensors. The…
Semyon Khokhriakov, Ravi Reddy Manumachu, Alexey Lastovetsky
In this work, we show that energy proportionality does not hold true for multicore CPUs. This finding creates the opportunity for bi-objective optimization of applications for performance and energy. We propose and study the first application-level method for bi-objective optimization of multithreaded data-parallel…
Jie Feng, Mingdong He, Lei Jin, Hui Dou + 1 more
Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Xuelei Wang, Jana Zweerings, Michael Lührs, Fengyu Cong + 5 more
Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be…
Carlos Llopis-Albert, Francisco Rubio, Carlos Devece, Shouzhen Zeng
Vehicle handling and stability performance and ride comfort is normally assessed through standard field test procedures, which are time consuming and expensive. However, the rapid development of digital technologies in the automotive industry have enabled to properly model and simulate the full-vehicle dynamics, thus…
Dorian Pustina, Brian Avants, Olufunsho Faseyitan, John Medaglia + 1 more
Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Mohammad Dehghani, Pavel Trojovský, Mikołaj Leszczuk, Szymon Łukasik + 1 more
'Szymon Szott'] With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable…
Zoe Kutulakos, Patrick Slade
Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Jannis Born, Babu Ram Naidu Ramachandran, Sandra Alejandra Romero Pinto, Stefan Winkler + 1 more
The effect of task load on performance is investigated by simultaneously collecting multi-modal physiological data and participant response data. Periodic response to a questionnaire is also obtained. The goal is to determine combinations of modalities that best serve as predictors of task performance. A group of…
Tao Chen, Miqing Li
Automatically tuning software configuration for optimizing a single performance attribute (e.g., minimizing latency) is not trivial, due to the nature of the configuration systems (e.g., complex landscape and expensive measurement). To deal with the problem, existing work has been focusing on developing various…
Adithya Sagar, Rachel LeCover, Christine Shoemaker, Jeffrey Varner
Mathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Ming Zhang, Naijie Gu, Kaixin Ren
This paper proposes a performance model for general matrix multiplication (GEMM) on decoupled access/execute (DAE) architecture platforms, in order to guide improvements of the GEMM performance in the Godson-3B1500. This model focuses on the features of access processors (APs) and execute processors (EPs). To reduce…
Qingrui Li, Yongquan Zhou, Qifang Luo, Dragan Pamucar
Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Authors not listed
This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and…