25 papers · ranked by Valyu relevance
Nazmul Shahadat, Anthony S. Maida, Gaochang Wu, Zizhu Fan + 1 more
Advances in visual recognition have relied on increasingly deep and wide convolutional neural networks (CNNs), which often introduce substantial computational and memory costs. This review summarizes recent progress in parameter-efficient CNN design across three directions: hypercomplex representations with…
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…
Wei Li, Yapeng Liu, Xiang Li, Bowen Deng + 4 more
Optimizing drilling parameters is essential for improving drilling efficiency and reducing operational costs in oil and gas engineering. This study presents an intelligent optimization approach for drilling parameters based on a hydraulic-mechanical specific energy (MSE) model. A time-series data fusion framework…
Shiyue Li, Yiling Wang, Zhanpeng Shu, Ramon Grima + 2 more
Biochemical reactions are inherently stochastic, with their kinetics commonly described by chemical master equations (CMEs). However, the discrete nature of molecular states renders likelihood-based parameter inference from CMEs computationally intensive. Here, we introduce an inference method that leverages analytical…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
Philip Sommer, Fleur Zeldenrust, Peter Jedlicka, Alexander D. Bird + 1 more
Neurons exhibit an impressive diversity. Even within the same cell type, firing rates can vary by several orders of magnitude, and key parameters — including resting membrane potential, membrane resistance, and synaptic inputs — differ substantially across neurons. It is presently unclear if this diversity reflects…
Authors not listed
Azo dyes constitute one of the largest and most commercially important classes of synthetic colorants, widely applied in textiles, plastics, inks, and food. However, their manufacture through traditional batch processes is often constrained by safe-ty risks, poor heat and mass transfer, and inconsistent product…
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…
Marco Savioli, Paolo Calligari, Ugo Locatelli, Gianfranco Bocchinfuso
We introduce GROMODEX, a novel tool designed to optimise GROMACS molecular dynamics (MD) simulations using a structured Design of Experiments (DoE) approach. GROMACS, though efficient, requires extensive tuning of parameters to perform optimally on different hardware and molecular systems. Manual tuning is tedious and…
Bolos, V. J., Benitez, R + 2 more
In the framework of data envelopment analysis, we review directional models (Chambers et al., 1996, 1998; Briec, 1997) and show that they are inadequate when inputs and outputs are improved simultaneously under constant returns to scale. Conversely, we introduce a new family of quadratically constrained models with…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Martin-Gutierrez, Samuel, Losada, Juan C. + 2 more
In a social system individual actions have the potential to trigger spontaneous collective reactions. The way and extent to which the activity (number of actions−A) of an individual causes or is connected to the response (number of reactions−R) of the system is still an open question. We measure the relationship…
Authors not listed
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
Xinglin Pan, Shaohuai Shi, Wenxiang Lin, Yuxin Wang + 3 more
The mixture-of-experts (MoE) architecture is commonly employed in contemporary large language models (LLMs) due to its advantage of scaling model size with a sublinear increase in computational demand. Nevertheless, the inference of MoE models demands substantial memory, making it memory-intensive in attention layers…
Shalini Sinha, Mrinal Kanti Rajak, Rajen Pudur
This paper presents a novel Particle Swarm Optimisation (PSO)-based Electronic Load Controller (ELC) with intelligent energy recovery capabilities for Self-Excited Induction Generator (SEIG) systems in off-grid micro-hydro applications. Unlike conventional resistive dump loads, which dissipate excess energy as waste…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
Mahmoud Abdel-Salam, Wael A. Gab-Allah, Eman Mohamed Eldaydamony, Ahmed Atwan
Wireless Sensor Networks (WSNs) play a crucial role in infrastructure monitoring across domains such as smart grids, industrial automation, and environmental sensing. However, energy efficiency remains a key challenge due to the limited battery life of sensor nodes. This work addresses the energy-efficient cluster head…
Niangen Ye, Jiawen Zhu, Baojun Chen, Dong Wang + 3 more
Communication is pivotal in LLM training, and a thorough analysis of the communication efficiency of AI data center (AIDC) network is essential for guiding the design of these capital-intensive clusters. However, conventional metrics are inadequate for such analysis, as they do not directly link network activity to…
Mansour Zoubeirou a Mayaki
Transformer-based models underpin modern natural language processing but incur rapidly growing computational and energy costs. As training scales in both model size and parallelism, accurately predicting energy consumption has become critical for sustainable and cost-aware system design. We present a framework for…
Kayson Fakhar, Danyal Akarca, Andrea I. Luppi, Stuart Oldham + 5 more
Brains are often described as cost-efficient communication networks, optimally balancing the cost of long connections with the benefits of fast communication. Here, inspired by the “use it or lose it” principle, we present a novel game-theoretic model of self-organizing neural units and show that the brain is, in fact…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Binhe Chen, Yaodan Chen, Li Cao, Changzu Chen + 3 more
The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced…
Jacob A. Parker, Alexandre L.S. Filipowicz, Kristen Li, Vijay Balasubramanian + 2 more
Human decision-making behavior varies widely across individuals and task conditions. This variability is often interpreted in terms of different suboptimal decision strategies, but the principles that govern these suboptimalities remain poorly understood. We propose that some of these suboptimalities can be understood…
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A framework for catalysis based on categorical aperture selection rather than temporal acceleration is presented. Traditional catalysis theory describes catalysts as agents that accelerate reactions by lowering activation energies, implicitly treating time as the fundamental variable and reaction rate enhancement as…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…