Search results for "Kernel"
showing 10 items of 357 documents
H-KPP : Hypervisor-Assisted Kernel Patch Protection
2022
We present H-KPP, hypervisor-based protection for kernel code and data structures. H-KPP prevents the execution of unauthorized code in kernel mode. In addition, H-KPP protects certain object fields from malicious modifications. H-KPP can protect modern kernels equipped with BPF facilities and loadable kernel modules. H-KPP does not require modifying or recompiling the kernel. Unlike many other systems, H-KPP is based on a thin hypervisor and includes a novel SLAT switching mechanism, which allows H-KPP to achieve very low (≈6%) performance overhead compared to baseline Linux.
Probabilistic forecast for Northern New Zealand seismic process: a kernel-based approach
2009
Forecast of earthquakes of a given area of Northern New Zealand is provided. It is based on the assumption that future earthquakes activity may be based on the smoothing of past earthquakes. Therefore, seismic activity is described by an intensity function factorized into kernel functions which depend on time longitude and latitude of events.
Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects
2014
Purpose/Objective(s) To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variatio…
Influence of environmental factors on the biosynthesis of type B trichothecenes by isolates of Fusarium spp. from Spanish crops.
2003
Various species of Fusarium can produce trichothecene mycotoxins that contaminate food commodities and can represent a risk for human and animal health. In this paper, a full factorial design was applied to study the influence of incubation temperature, water activity (a(w)) and type of isolate on the production of deoxynivalenol (DON), nivalenol (NIV) and 3-acetyldeoxynivalenol (3-AcDON) in corn kernel cultures by three isolates of Fusarium graminearum and three isolates of Fusarium culmorum from crops grown in Spain. The tested temperatures were 15, 20, 28 and 32 degrees C. The a(w)-values were 0.960, 0.970 and 0.980. Moisture of cultures (within the studied range) did not affect signific…
FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention
2019
International audience; Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel int…
Design of the CGAL 3D Spherical Kernel and application to arrangements of circles on a sphere
2009
AbstractThis paper presents a cgal kernel for algorithms manipulating 3D spheres, circles, and circular arcs. The paper makes three contributions. First, the mathematics underlying two non-trivial predicates are presented. Second, the design of the kernel concept is developed, and the connexion between the mathematics and this design is established. In particular, we show how two different frameworks can be combined: one for the general setting, and one dedicated to the case where all the objects handled lie on a reference sphere. Finally, an assessment about the efficacy of the 3D Spherical Kernel is made through the calculation of the exact arrangement of circles on a sphere. On average w…
Tensor tomography in periodic slabs
2018
Abstract The X-ray transform on the periodic slab [ 0 , 1 ] × T n , n ≥ 0 , has a non-trivial kernel due to the symmetry of the manifold and presence of trapped geodesics. For tensor fields gauge freedom increases the kernel further, and the X-ray transform is not solenoidally injective unless n = 0 . We characterize the kernel of the geodesic X-ray transform for L 2 -regular m -tensors for any m ≥ 0 . The characterization extends to more general manifolds, twisted slabs, including the Mobius strip as the simplest example.
GIS-data related route optimization, hierarchical clustering, location optimization, and kernel density methods are useful for promoting distributed …
2019
Currently, geographic information system (GIS) models are popular for studying location-allocation-related questions concerning bioenergy plants. The aim of this study was to develop a model to investigate optimal locations for two different types of bioenergy plants, for farm and centralized biogas plants, and for wood terminals in rural areas based on minimizing transportation distances. The optimal locations of biogas plants were determined using location optimization tools in R software, and the optimal locations of wood terminals were determined using kernel density tools in ArcGIS. The present case study showed that the utilized GIS tools are useful for bioenergy-related decision-maki…
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
Kernel-Based Inference of Functions Over Graphs
2018
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…