24 papers · ranked by Valyu relevance
Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto, Chak Shing Lee + 1 more
Kolmogorov–Arnold Networks (KANs) have shown strong potential for efficiently approximating complex nonlinear functions. However, the original KAN formulation relies on B-spline basis functions, which incur substantial computational overhead due to De Boor's algorithm. To address this limitation, recent work has…
Tizian Wenzel, Gabriele Santin
The search for the optimal shape parameter for Radial Basis Function (RBF) kernel approximation has been an outstanding research problem for decades. In this work, we establish a theoretical framework for this problem by leveraging a recently established theory on sharp direct, inverse and saturation statements for…
Sergio A. Alvarez
We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the large bandwidth limit. This strongly suggests that kernel-based constructions reliant on metric properties of the RKHS will yield results for Gaussian RBF kernels that…
Anant Agnihotri, Michael Krebsbach, Florentin Reiter, Thomas Wellens
Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines suffers from exponential concentration as system size increases, preventing an efficient scaling beyond fewqubit systems. We introduce the…
Yi Ding, Ying Zhao, Yan Pei, Antonio Falcó
Kernel methods are widely applied across various domains; however, structural limitations of reproducing kernels in Hilbert spaces pose significant challenges. Many challenges inherent to Hilbert spaces can be effectively addressed within the framework of Banach spaces. In this work, we define the semi-inner product…
Satya Pratik Srivastava, Rohan Gorantla, Sharath Krishna Chundru, Claire J.R. Winkelman + 2 more
Active learning (AL) prioritises which compounds to measure next for protein–ligand affinity when assay or simulation budgets are limited. We present an explainable AL framework built on Gaussian process regression and assess how molecular representations, covariance kernels, and acquisition policies affect enrichment…
Edmilson Roberto Braga, Roberto Márcio Braga Júnior, Mauro Sérgio Braga, Janete Maria Cerutti + 2 more
This study reports the development of a portable multispectral optoelectronic system for automated thyroid cancer detection in immunohistochemically stained histological slides. The platform integrates a 14-band AS7343 multispectral sensor, a dual-fiber optical setup operating in transreflectance geometry, and a…
Authors not listed
Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride based systems is strongly…
Ishna Satyarth, Eric C. Larson, Devin A. Matthews
Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Mohamed Abdellatief, Ahmed M. Saqr
The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such…
Tanner, John, Davies, Nicholas + 12 more
1 Centre for Quantum Information, Simulation and Algorithms, The University of Western Australia, 35 Stirling Hwy, Crawley WA, 6009, Australia 2 Pawsey Supercomputing Centre, 1 Bryce Avenue, Kensington WA, 6151, Australia 3 School of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling…
Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki
Machine Learning Modeling of Atmospheric Molecular Clusters Authors: Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modeling: the formation of new aerosol particles. While…
Souvik Seal, Anirban Chakraborty, Chloe Mattila, Mark Rubinstein + 4 more
High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of…
Canhua Lai, Peng Zhang, Xiaobing Dai, Yuanxun Zheng + 1 more
The oceanic wet-thermal and chloride salt environment creates extremely harsh service conditions for marine infrastructures. As a green construction material, geopolymer concrete has a promising application prospect in marine engineering due to its excellent durability. The impact resistance of geopolymer concrete…
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Yixi Chen, Weixuan Liang, Tianrui Liu, Junjie Huang + 3 more
Kernel power k-means (KPKM) leverages a family of means to mitigate local minima issues in kernel k-means. However, KPKM faces two key limitations: (1) the computational burden of the full kernel matrix restricts its use on extensive data, and (2) the lack of authentic centroid-sample assignment learning reduces its…
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Yaoduo Zhang, Jian Zhang, Yunliang Zang
Detecting novel stimuli is a fundamental neural function, yet its machine learning counterpart—out-of-distribution (OOD) detection—remains challenging, with models often making overconfident predictions on unseen inputs. Inspired by the strong pattern-separation capabilities of cerebellum-like circuits, we introduce a…
Mustafa Coşkun, Filipa Blasco Lopes, Pınar Kubilay Tolunay, Mark R. Chance + 1 more
Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type…
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…
Yi Peng, Haiquan Zhao, Jinhui Hu
Most existing nonlinear adaptive filtering algorithms only account for output noise, neglecting the fact that input noise is also prevalent in practice. Although the recently proposed bias-compensated kernel least mean square (BCKLMS) algorithm addresses input noise in the nonlinear errors-in-variables (EIV) model, it…
Authors not listed
The reliability of the random phase approximation (RPA) and σ-functional methods in conjunction with the mixed Gaussian and plane wave (GPW) basis set scheme as implemented in the CP2K package is investigated. First, based on results for thermochemical properties of molecules and structural properties of crystalline…