24 papers · ranked by Valyu relevance
Mourão-Miranda, Janaina, Hussain, Zakria + 6 more
Multiple Kernel Learning (MKL) models combine several kernels in supervised and unsupervised settings to integrate multiple data representations or sources, each represented by a different kernel. MKL seeks an optimal linear combination of base kernels that maximizes a generalized performance measure under a…
Zhou, Zhijian, Tian, Xunye + 10 more
To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-kernel tests. However, we observe a phenomenon that directly maximizing multiple kernel-based statistics may result in highly similar kernels…
Kartik Jhawar, Tapasvi Bhatt, Rohan Sunil, Wang Lipo
Accurately annotating the functions of uncharacterised human proteins remains a major bottleneck in biology. We present MkAtt–SDN2GO, a neural architecture that extends SDN2GO by integrating adaptive multi-kernel convolution and attention mechanisms to predict Gene Ontology terms from protein sequences, domains, and…
Parisa Shahnazari, Kaveh Kavousi, Hamid Reza Khorram Khorshid, Bahram Goliaei + 2 more
Integrating multiple omics modalities is a crucial strategy in cancer research, particularly in metabolomics, enabling early detection and detailed exploration of cancer biomarker signatures. This study evaluates five strategies for integrating metabolomics data from liquid chromatography-mass spectrometry, gas…
Md Ashad Alam
RKUM is an R package developed for implementing robust kernel-based unsupervised methods. It provides functions for estimating the robust kernel covariance operator (CO) and the robust kernel cross-covariance operator (CCO) using generalized loss functions instead of the conventional quadratic loss. These operators…
Shipeng Ren, Guoqing Yang, Deyin Yu, Anqi Wang + 3 more
Discovering meaningful feature (molecule) combinations to define simple, accurate, and easily interpretable decision rules for disease classification and prediction can improve the study of disease diagnosis and prognosis. However, the computational time complexity of constructing feature combinations for each feature…
O. H. L. Preston, T. J. Rogers, K. Worden
This paper presents a detailed analysis of a process for recovering multi-degree-of-freedom Volterra kernels using neural network weights, addressing the fact that such Higher-order Frequency Response Functions (HFRFs) have not previously been directly recovered from data. A novel method is proposed for HFRF recovery…
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…
Aravind R. Krishnan, Thomas Z. Li, Lucas W. Remedios, Michael E. Kim + 11 more
Kernel harmonization is an approach that mitigates systematic differences in quantitative measurements due to the variability of reconstruction kernels. The goal of kernel harmonization is to standardize the pixel noise in images reconstructed with different kernels to a common reference kernel, ensuring comparable and…
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…
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…
Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden + 45 more
This paper presents KernelEvolve – an agentic kernel coding framework – to tackle heterogeneity at–scale for DLRM training and inference. KernelEvolve is designed to take kernel specifications as input and automate the process of kernel generation and optimization for recommendation model across heterogeneous hardware…
Md Shafiqul Islam, Shakti P. Padhy, Douglas Allaire, Raymundo Arróyave
Gaussian Process (GP) regression is a powerful nonparametric Bayesian framework, but its performance depends critically on the choice of covariance kernel. Selecting an appropriate kernel is therefore central to model quality, yet remains one of the most challenging and computationally expensive steps in probabilistic…
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…
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…
Yuhan Lu, Zhuoran Li, Hangze Mao, Qinsiyuan Lyu + 5 more
The most critical attribute of the brain is its ability to coordinate perception, thoughts, and action across different timescales. A prominent theory holds that the cortex is organized along a unidimensional hierarchy, where higher-order regions operate over longer intrinsic timescales than sensory areas, a view…
Álvaro Sánchez-Paniagua Ríos, Juan P. Llerena, Alberto Lastra, Nuria Torrado + 1 more
The performance of Support Vector Machines (SVMs) critically depends on the kernel function choice, which enables implicit mapping of data into high-dimensional feature spaces. While classical kernels like Radial Basis Function (RBF) remain popular, orthogonal polynomial kernels offer mathematically interpretable…
M. L. Kringelbach, G. Deco
Brain dynamics can be described in three different convenient mathematical languages, namely connectome harmonics, turbulence and complex harmonics (CHARM). Here we demonstrate that these theoretical frameworks can be rigorously unified, under the functional calculus, as one self-adjoint operator and its single…
Guiqin Liang, Jian Zhang, Dave Winkler
An accurate classification of material stability often requires fusing multiple features under uncertainty. Dempster-Shafer (D-S) theory is a powerful framework for multi-source information fusion under uncertainty. However, its effectiveness critically depends on the quality of basic probability assignments (BPAs)…
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
We present simple, user-friendly bounds for the expected operator norm of a random kernel matrix under general conditions on the kernel function k(·, ·). Our approach uses decoupling results for U-statistics and the non-commutative Khintchine inequality to obtain upper and lower bounds depending only on scalar…
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…
Marc Sturrock, Vahid Shahrezaei
Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the…
Chendi Han, Zhengshi Yang, Xiaowei Zhuang, Dietmar Cordes
Recent studies have extended nonlinear kernels to Kernel Canonical Correlation Analysis (KCCA), enabling more flexible modeling of complex relationships globally. Building on these developments, we propose three key enhancements to nonlinear KCCA. First, inspired by self-supervised learning in machine learning…
Alexandria McPherson, Sepp Sanchirico, Albert Xu, Eric Larson + 4 more
Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal…