Optimal Kernel Research Articles

Contribution of Rare and Potentially Functionally RelevantSequence Variants in Schizophrenia Risk-Locus Xq28,distal.

Duplications of the Xq28,distal locus have been described in male and female patients with schizophrenia (SCZ) or intellectual disability. The Xq28,distal locus spans eight protein-coding genes (F8, CMC4, MTCP1, BRCC3, VBP1, FUNDC2, CLIC2, and RAB39B) and is flanked by recurrent genomic breakpoints. Thus, the issue of which gene/s at this locus is/are relevant in terms of SCZ pathogenesis remains unclear. The aim of this study was to investigate the contribution of rare and potentially functionally relevant sequence variants within the Xq28,distal locus to SCZ risk using the single-molecule molecular inversion probes (smMIP) method. Targeted sequencing was performed in a cohort of 1935 patients with SCZ and 1905 controls of European ancestry. The consecutive statistical analysis addressed two main areas. On the level of the individual variants, allele counts in the patient and control cohort were systematically compared with a Fisher's exact test: (i) for the entire present study cohort; (ii) for patients and controls separated by sex; and (iii) in combination with data published by the Schizophrenia Exome Meta-Analysis (SCHEMA) consortium. On the gene-wise level, a burden analysis was performed using the X-chromosomal model of the Optimal Unified Sequence Kernel Association Test (SKAT-O), with adjustment for possible sex-specific effects. Targeted sequencing identified a total of 13 rare and potentially functional variants in four patients and 11 controls. However, neither at the level of individual rare and potentially functional variants nor at the level of the eight protein-coding genes at the Xq28,distal locus was a statistically significant enrichment in patients compared to controls observed. Although inconclusive, the present findings represent a step toward improved understanding of the contribution of X-chromosomal risk factors in neuropsychiatric disorder development, which is an underrepresented aspect of genetic studies in this field.

Ultra-High-Resolution Photon-Counting Detector CTA of the Head and Neck: Image Quality Assessment and Vascular Kernel Optimization.

Background: Head and neck CTA requires fine detail evaluation, including characterization of potentially very small vessels and intrastent lumens. Blooming artifacts also hinder evaluation. Objectives: To evaluate image quality of ultra-high-resolution (UHR) photon-counting detector (PCD) CTA of the head and neck and to explore variation of such quality across reconstruction kernels. Methods: This prospective study included patients who underwent clinically indicated head and neck CTA from September 2023 to December 2023. Participants underwent PCD CTA in UHR mode. Reconstructions for each examination included a reference reconstruction (reflecting clinical protocols) using 0.8-mm slice thickness and Bv40 kernel, and six UHR reconstructions using 0.2-mm slice thickness and kernels of varying sharpness (Bv48-Bv80). Quantitative measures were recorded. Two radiologists independently evaluated qualitative measures using Likert scales (1=lowest quality; 5=highest quality). Results: The analysis included 103 participants (mean age, 61.3±13.0 years; 56 male, 48 female). Median vessel sharpness (in HU/mm) was 100.9 for reference reconstruction, and for UHR varied from 110.0 for Bv46 to 121.6 for Bv76 and 134.7 for Bv80. Median right internal carotid artery C2 luminal diameter was 3.8 mm for reference reconstruction, and for UHR increased from 4.1 mm for Bv48 to 4.9 mm for Bv80. For both readers, median overall image quality for reference reconstruction was 3, and for UHR was highest for Bv64 (5); calcified plaque blooming artifact for reference reconstruction was 1, and for UHR was highest for Bv72 (5) and Bv76 (5); stent blooming artifact for reference reconstruction was 1, and for UHR was highest for Bv76 (5) and Bv80 (5); soft-plaque delineation for reference reconstruction was 1, and for UHR was highest for Bv76 (5) or Bv80 (5); small-vessel visualization for reference reconstruction was 1, and for UHR was highest for Bv76 (5) or Bv80 (5). Conclusion: UHR-PCD CTA yielded reduced blooming artifact from calcified plaques or stents, and improved softplaque and small-vessel visualization. These advantages were more pronounced for strongest kernels, although subjective image quality was better for a weaker kernel. Clinical impact: The findings indicate benefits from use of UHR-PCD CTA for head and neck evaluation and may help guide such examinations' kernel selection.

Pertinence of finite element method for impulse localization in urban environments

Although urban combat is nothing new, recent conflicts have shed light on Western armies' difficulties when operating in such environments. Thus, new surveillance and hazard detection challenges in complex environments are addressed. A new decision aid tool for acoustic sensor positioning has been developed to improve reconnaissance performance. The software's sensor location optimization kernel is based on a genetic algorithm developed by our partners from FKIE. It resolves successive localization problems using pre-computed Times Of Arrival (TOA) between an impulsive acoustic source and various surveillance microphones. The present study discusses the pertinence of the Time-Domain Finite-Element Method (TD-FEM) to simulate wave propagation within an urban area to obtain TOA and resolve localization problems. The approach is compared to experimental measurements in an urban area, and the pros and cons of the TD-FEM for this configuration are discussed.

Optimization of the polynomial fifth-order interpolation 1P kernel in the time domain

Optimization of the polynomial fifth-order interpolation 1P kernel in the time domain

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.

Optimal Kernel Function for High Speed Rail Received Signal Level Dataset

Optimal Kernel Function for High Speed Rail Received Signal Level Dataset

Optimal approximation of a large matrix by a sum of projected linear mappings on prescribed subspaces

We propose and justify a matrix reduction method for calculating the optimal approximation of an observed matrix $A \in \mathbb{C}^{m \times n}$ by a sum $\sum_{i=1}^p \sum_{j=1}^q B_iX_{ij}C_j$ of matrix products where each $B_i \in \mathbb{C}^{m \times g_i}$ and $C_j \in \mathbb{C}^{h_j \times n}$ is known and where the unknown matrix kernels $X_{ij}$ are determined by minimizing the Frobenius norm of the error. The sum can be represented as a bounded linear mapping $BXC$ with unknown kernel $X$ from a prescribed subspace ${\mathcal T} \subseteq \mathbb{C}^n$ onto a prescribed subspace ${\mathcal S} \subseteq \mathbb{C}^m$ defined, respectively, by the collective domains and ranges of the given matrices $C_1,\ldots,C_q$ and $B_1,\ldots,B_p$. We show that the optimal kernel is $X = B^{\dagger}AC^{\dagger}$ and that the optimal approximation $BB^{\dagger}AC^{\dagger}C$ is the projection of the observed mapping $A$ onto a mapping from ${\mathcal T}$ to ${\mathcal S}$. If $A$ is large, $B$ and $C$ may also be large and direct calculation of $B^{\dagger}$ and $C^{\dagger}$ becomes unwieldy and inefficient. The proposed method avoids this difficulty by reducing the solution process to finding the pseudo-inverses of a collection of much smaller matrices. This significantly reduces the computational burden.

Maximum Local Density-Driven Non-overlapping Radial Basis Function Support Kernel Neural Network

The learning and optimization of kernels in the radial basis function neural network (RBFNN) are crucial. However, in existing methods, there are issues of overfitting when learning kernel parameters. The learned kernels are also sensitive to outliers. This paper proposes a general kernel learning strategy for RBFNN called non-overlapping maximum local density support kernel learning (MLD-SKL), which contains two modules, the non-overlapping maximum local density (MLD) kernel learning module and support kernel learning (SKL) module. In the MLD kernel learning stage, the candidate set of kernels is incrementally determined based on the local density of samples. Meanwhile, it is required that the coverage ranges of kernels from different classes do not overlap with each other. This module is effective in reducing the impact of outliers. In the SKL stage, kernel importance indicator is defined to measure the importance of kernels. The learned support kernels are utilized to construct a maximum local density-driven non-overlapping radial basis function support kernel neural network (MLD-RBFSKNN). The RBFNN constructed through MLD-SKL exhibits a more compact structure. The experiments demonstrate that the proposed MLD-RBFSKNN improves accuracy in recognition task. Furthermore, while achieving superior recognition performance, the final constructed network also has the minimum number of kernels.

Benchmarking of quantum fidelity kernels for Gaussian process regression

Abstract Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchmarking of quantum kernels, it is necessary to construct the most optimal functional form of the classical kernels and the most optimal quantum kernels for each given data set. We develop an algorithm that uses an analog of the Bayesian information criterion to optimize the sequence of quantum gates used to estimate quantum kernels for Gaussian process models. The algorithm increases the complexity of the quantum circuits incrementally, while improving the performance of the resulting kernels, and is shown to yield much higher model accuracy with fewer quantum gates than a fixed quantum circuit ansatz. We demonstrate that quantum kernels thus obtained can be used to build accurate models of global potential energy surfaces (PES) for polyatomic molecules. The average interpolation error of the six-dimensional PES obtained with a random distribution of 2000 energy points is 16 cm−1 for H3O+, 15 cm−1 for H2CO and 88 cm−1 for HNO2. We show that a compositional optimization of classical kernels for Gaussian process regression converges to the same errors. This indicates that quantum kernels can achieve the same, though not better, expressivity as classical kernels for regression problems.

Selective multiple kernel fuzzy clustering with locality preserved ensemble

Multiple kernel fuzzy clustering (MKFC) has demonstrated promising performance in capturing the non-linear relationships within data. However, its effectiveness relies heavily on the appropriate selection of the fuzzification coefficient and kernel functions. To address this challenge, this paper proposes a novel clustering ensemble approach for improving the robustness and accuracy of the MKFC algorithm. The proposed method employs a multi-objective evolutionary optimization approach to heuristically select the optimal fuzzification and kernel coefficients. By employing the selected coefficients, the MKFC algorithm generates a diverse set of accurate candidate clustering results. Subsequently, a locality preserved ensemble mechanism is introduced to derive the final partition matrix, ensuring that all candidate clusterings within the Pareto non-dominated set contribute to the final consensus matrix. Moreover, this mechanism incorporates the knowledge about the locality of the dataset via a graph regularization term, thereby further enhancing the clustering performance. Comprehensive experiments conducted on widely adopted benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Building detection based on searching of the optimal kernel shapes pruning method on Res2-Unet

<p>In recent years, advances in remote sensing technology have made it feasible to use satellite data for large-scale building detection. Moreover, the building detection from multispectral satellite photography data is necessary; however, it is difficult to recovery the accurate building footprint from the high-resolution pictures. Because the deep learning networks contains high computational cost and over-parameterized. Therefore, network pruning has been used to reduce the storage and computations of convolutional neural network (CNN) models. In this article, we proposed the pruning technique to prune the CNN network from Res2-Unet model for accurately detecting the buildings. Initially, the CNN network is pruned by utilizing the searching of the optimal kernel shapes technique. It is employed to carry out stripe-wise pruning and automatically find the ideal kernel shapes. Then the data quantification is applied to enhance the proposed model and also reduce the complexity. Finally, the enhanced Res2-Unet model is used for the building detection. Moreover, WHU East Asia Satellite and the Massachusetts building dataset are the two available datasets used to access the suggested framework. Compare to the existing models, the proposed model gives better performance.</p>

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Forecast of chlorophyll-a concentration as an indicator of phytoplankton biomass in El Val reservoir by utilizing various machine learning techniques: A case study in Ebro river basin, Spain

The trophic condition of bodies of water, such as oceans, lakes, and reservoirs, can be accurately assessed thanks to the use of chlorophyll-a, or Chl-a, as an indicator of phytoplankton biomass and abundance. In fact, the main molecule in charge of photosynthesis is Chl-a. This work presents a powerful and reliable nonparametric method for predicting the concentration of Chl-a in El Val reservoir using a dataset containing 240,765 samples: the Support Vector Regression (SVR) with different kinds of kernels. This mathematical SVR-relied model was constructed using five years (2018–2022) of water quality variable monitoring (physico-chemical independent variables) in the El Val reservoir (located in the northeast of Spain). For comparison, M5 model trees, a the Multilayer Perceptron (MLP), that is a particular type of artificial neural network (ANN), and multivariate linear regression (MLR) were also used for the same observed data. The Grid Search (GS) algorithm was employed as an optimizer; this approach greatly improves the regression precision by allowing the optimal kernel parameters to be chosen during the SVR training phase. There are two ways to sum up the findings of this investigation. First, it is determined how relevant each input variable is to the Chl-a concentration in the El Val reservoir. Second, this hybrid GS/SVR-relied model with PUK kernel can accurately predict the Chl-a (R2 and r values were 0.8989 and 0.9499, respectively). The model’s agreement with the observed data amply proves the remarkable efficacy of this creative strategy.

A dataset of downscaled monthly precipitation at 1 km resolution in Guangxi, China and ASEAN regions from 2001 to 2020

Located in the southeastern Asia, Guangxi, China and the ASEAN regions have a subtropical monsoon climate with abundant rainfall and frequent floods, which has a great impact on social and economic activities. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, we used the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 as the dependent variable in combination with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and elevation (ELV) from ASTER data as explanatory variables. Then we introduced a geographically weighted regression (GWR) model to construct an annual scale model, depicting the spatial variation of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel functions were adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function was selected based on the correlation coefficient, the root means square error and bias. Thus, we established the dataset of downscaled monthly precipitation in Guangxi, China and ASEAN regions from 2001 to 2020 by extrapolating the GWR model with input variables at 1 km resolution. The dataset of 1-km monthly precipitation from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2,679 stations during 2001–2020 were used for verification. The correlation coefficient, root mean square error and deviation were 0.792, 74.610mm and -0.122%, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations at 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.

Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach

AbstractThis paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval‐valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG‐iMLP for crude oil price forecasting.

Association between rare pathogenic variants in established cancer risk genes and the diagnosis of single and multiple common cancers: A UK Biobank study.

10505 Background: Much of our understanding of cancer risk attributable to rare pathogenic variants (RPV) is derived from family-based and case-only cohort studies, which may be limited by ascertainment bias. We sought to identify associations between RPVs and cancer diagnoses in a large population-based study. Methods: We conducted a case-control study using the UK Biobank. We performed gene-based aggregate testing to examine the relationship between RPVs (defined as pathogenic or likely pathogenic variants) in 96 genes implicated in cancer risk and the diagnosis of 11 common cancers using the combined optimal unified sequence kernel association test (SKAT-O). Odds ratios (OR) and 95% confidence intervals (CI) were then calculated using Firth logistic regression. Results were adjusted for age, sex and genetic ancestry. Bonferroni-adjusted p-values were used to determine statistical significance. Variant pathogenicity was determined by American College of Medical Genetics criteria. Results: We identified 24 genes in which RPVs were statistically significantly associated with at least one of 11 common cancers among 183,626 individuals. The presence of an RPV in one of these 24 genes was associated with increased odds of one cancer (OR 1.8; 95% CI: 1.7-2.0), and multiple cancers (OR 2.5; 95% CI: 2.2-2.9). Conclusions: This large population-based study identified 24 established cancer risk genes in which RPVs were associated with common cancers. We identified both known and novel associations between cancer and RPVs. Some associations—i.e. renal/pancreas and MEN1—may be due to ascertainment and/or misclassification bias. We also show that individuals with RPVs in cancer risk genes not only have higher odds of one cancer diagnosis, but also multiple cancers.[Table: see text]

A Gauss-Newton method for iterative optimization of memory kernels for generalized Langevin thermostats in coarse-grained molecular dynamics simulations.

In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of the dynamics on all time scales generally requires non-Markovian, i.e., non-time-local, thermostats. These thermostats typically take the form of a Generalized Langevin Equation (GLE) determined by a memory kernel. In this work, we use a Markovian embedded formulation of a position-independent GLE thermostat acting independently on each CG degree of freedom. Extracting the memory kernel of this CG model from atomistic reference data requires several approximations. Therefore, this task is best understood as an inverse problem. While our recently proposed approximate Newton scheme allows for the iterative optimization of memory kernels (IOMK), Markovian embedding remained potentially error-prone and computationally expensive. In this work, we present an IOMK-Gauss-Newton scheme (IOMK-GN) based on IOMK that allows for the direct parameterization of a Markovian embedded model.

The Droplet Search Algorithm for Kernel Scheduling

Kernel scheduling is the problem of finding the most efficient implementation for a computational kernel. Identifying this implementation involves experimenting with the parameters of compiler optimizations, such as the size of tiling windows and unrolling factors. This article shows that it is possible to organize these parameters as points in a coordinate space. The function that maps these points to the running time of kernels, in general, will not determine a convex surface. However, this article provides empirical evidence that the origin of this surface (an unoptimized kernel) and its global optimum (the fastest kernel) reside on a convex region. We call this hypothesis the “droplet expectation.” Consequently, a search method based on the Coordinate Descent algorithm tends to find the optimal kernel configuration quickly if the hypothesis holds. This approach—called Droplet Search—has been available in Apache TVM since April of 2023. Experimental results with six large deep learning models on various computing devices (ARM, Intel, AMD, and NVIDIA) indicate that Droplet Search is not only as effective as other AutoTVM search techniques but also 2 to 10 times faster. Moreover, models generated by Droplet Search are competitive with those produced by TVM’s AutoScheduler (Ansor), despite the latter using 4 to 5 times more code transformations than AutoTVM.

Transferable local density-dependent friction in tert-butanol/water mixtures.

Coarse-grained (CG) models informed from molecular dynamics simulations provide a way to represent the structure of an underlying all-atom (AA) model by deriving an effective interaction potential. However, this leads to a speed-up in dynamics due to the lost friction, which is especially pronounced in CG implicit solvent models. Applying a thermostat based on the Langevin equation (LE) provides a way to represent the long-time dynamics of CG particles by reintroducing friction to the system. To improve the representability CG models of heterogeneous molecular mixtures and their transferability over the mixture compositions, we parameterize an LE thermostat in which the friction coefficient depends on the local particle density (LD). The thermostat friction was iteratively optimized with a Markovian variant of the recently introduced Iterative Optimization of Memory Kernels (IOMK) method. We simulated tert-butanol/water mixtures over a range of compositions, which show a distinct clustering behavior. Our model with LD-dependent friction reproduces the AA diffusion coefficients well over the full range of mixtures and is, therefore, transferable with respect to dynamics.

A study of oil–water two-phase flow pattern recognition based on thermal sensors

To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow.