23 papers · ranked by Valyu relevance
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Gang Chen, Daniel S. Pine, Melissa A. Brotman, Ashley R. Smith + 3 more
Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most task-based FMRI designs feature relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision.…
Authors not listed
In this study, an all-perovskite solar cell is proposed consists of MASnI3, CsSnI3 and MAGeI3 proposed to replace conventional expensive HTM, ETM. Several physical parameters of each layer (HTM, ETM and Absorber) of the proposed cell including thickness, doping density, defect density, electron affinity, temperature…
Joseph G. Woods, Yang Ji, Hongwei Li, Aaron Hess + 1 more
To optimize pseudo-continuous arterial spin labeling (PCASL) parameters to maximize SNR efficiency for RF power constrained whole brain perfusion imaging at 7 T. We used Bloch simulations of pulsatile laminar flow to optimize the PCASL parameters for maximum SNR efficiency, balancing labeling efficiency and total RF…
Philipp Wendering, John Ferguson, Rudan Xu, Johannes Kromdijk + 1 more
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic…
François Xavier Machu, Ru Julie Wang, Jean Louis Cheng, Jeremy Cocks + 3 more
'Qiuping Alexandre Wang' 'Stanisław Drożdż' 'Matteo Convertino'] Preferential attachment (PA) is a widely observed behavior in many living systems and has been used in modeling many networks. The aim of this work is to show that the mechanism of PA is a consequence of the fundamental principle of least effort. We…
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer + 1 more
Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to…
Daniel R. Bergman, Trachette Jackson, Harsh Vardhan Jain, Kerri-Ann Norton
Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM…
Igor Lutsenko
Currently, there is quite a strange situation in the knowledge "market". A great number of publications consider efficiency as the "old good friend". At the same time, without the validation of the developed criterion, a reader is invited to use one or the other indicator as efficiency indicator. It resembles the…
Qiuping A. Wang
This paper provides a derivation of Zipf-Pareto laws directly from the principle of least effort. A probabilistic functional of efficiency is introduced as the consequence of an extension of the nonadditivity of the efficiency of thermodynamic engine to a large number of living agents assimilated to engines, all…
Yong-Gang Chen, Yan Cao, Kai Lu, Quanxin Yang + 3 more
Existing algorithms for photovoltaic (PV) parameter extraction struggle to balance accuracy and computational efficiency when handling complex models. To address this gap, a differential evolution with classified mutation (DECM) is proposed, which integrates adaptive mutation strategies and a hierarchical…
Junjie Hu, Xiangyu Li, Dan-Dan Liu, Shiyi Wang + 3 more
The combination of parametric quantum circuits and density matrix coding can significantly reduce the number of parameters in artificial neural networks. The reduction in the number of model parameters helps to improve the commu- nication efficiency when training deep learning models under federated learning…
Jan Kubanek
Understanding how humans and animals can make effective decisions given natural constraints would have a profound impact on economics, psychology, ecology, and related fields. Neoclassical economics provides a formalism for optimal decisions, but the apparatus requires a large number of evaluations of the decision…
Yuxin He, Jin Qin, Jian Hong, Wen-Bo Du
An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them…
Qing Guan, Shuilong Zou, Hong Liu, Qing Chen
This paper proposes a performance evaluation method of public administration departments based on the improved DEA algorithm, which solves the quality problem of performance evaluation of public administration departments and lays a foundation for the long-term development and performance improvement of public…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Mohsen Fathollah Bayati, Seyed Jafar Sadjadi, Xiaosong Hu
In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore…
Donald S. Ene, V.I.E Anireh
- Evaluating how well a whole system or set of subsystems performs is one of the primary objectives of performance testing. We can tell via performance assessment if the architecture implementation meets the design objectives. Performance evaluations of several parallel algorithms are compared in this study. Both…
Guido Schryen
In high performance computing environments, we observe an ongoing increase in the available number of cores. For example, the current TOP500 list reveals that nine clusters have more than 1 million cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the…
Noah J. Miller, Jason S. Bergtold, Allen M. Featherstone, Dao-Zhi Zeng
'Dao-Zhi Zeng'] The use of elasticities of substitution between inputs is a standard method for addressing the effect of a change in the mix of inputs used for production from a technical or cost standpoint. Most estimation methods use parametric production or cost functions or frontiers to estimate these elasticities.…
Nasim Arabjazi, Mohsen Rostamy-Malkhalifeh, Farhad Hosseinzadeh Lotfi, Mohammad Hasan Behzadi + 1 more
'Mohammad Hasan Behzadi' 'Ardashir Mohammadzadeh'] An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel…
Authors not listed
Stochastic Simulation Algorithms (SSA) are a cornerstone in simulating Free Radical Polymerization (FRP) due to their accuracy and reliability. However, computational inefficiency remains a challenge for large-scale and complex polymerization systems. This work introduces a novel stochastic simulation algorithm…