1,616 research outputs found
VCC-LF dataset
No full textThis is readme for VCC-LF dataset.
This dataset provides light field mat files that capture by Lytro I.
The light field resolusion is [h,w,u,v,d].
If you use these data or our toolkit code, please cite our paper properly
@inproceedings{ lirsiggraphasia2019,
title={Hierarchical and View-invariant Light Field Segmentation by Maximizing Entropy Rate on 4D Ray Graphs},
author={Li, Rui and Heidrich, Wolfgang},
booktitle={ACM Transactions on Graphics (Proc. SIGGRAPH Asia)},
year={2019},
publisher={ACM}
Primary resistances to EGFR inhibitors (EGFRi) in HPV-positive and -negative oropharyngeal squamous cell carcinoma (OSCC)
No full textHealth care-associated infections (HAIs) in head and neck carcinoma (HNC) patients treated with chemotherapy (CT) and/or radiotherapy (RT)
No full textLF-copying without LF
No full textAbstractA copying approach to ellipsis is presented, whereby the locus of copying is not a level of derived syntactic structure (LF), but rather the derivation itself. The ban on preposition stranding in sprouting follows without further stipulation, and other, seemingly structure sensitive, empirical generalizations about elliptical constructions, including the preposition stranding generalization, follow naturally as well. Destructive operations which ‘repair’ non-identical antecedents are recast in terms of exact identity of derivations with parameters. In the context of a compositional semantic interpretation scheme, the derivational copying approach to ellipsis presented here is revealed to be a particular instance of a proform theory, thus showing that the distinctions between, and arguments about, syntactic and semantic theories of ellipsis need to be revisited
Polynomial Approximation in Ep(D) with 0 < p < 1
No full textAbstractIn this paper, we construct approximants by means of interpolation polynomialsto prove Jackson′s theorem and the Bernstein inequality in Ep(D) with 0 < p < 1
Detecting characteristics of nodal invasion in advanced squamocellular oral cavity cancer through a gene expression profile on primary tumor
No full textTreatment-associated mortality in head and neck cancer receiving chemotherapy and radiation: Meta-analysis of published trials
No full textMean Convergence of Interpolation Polynomials in a Domain with Corners
No full textAbstractIn this paper, we prove mean convergence of interpolation polynomials in a domain with some corners
Machine learning and digital twins: monitoring and control for dynamic security in power systems
No full textThe reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the supervised process of system operation and are therefore able to provide synthetic studied data, where measurement data are scarce. However, for some real-time applications in monitoring and control, such high-fidelity simulation models are not appropriate due to the corresponding computational barrier. There, ML aims to create an application-specific, low-fidelity (lf) approximation of the digital twin. Such trained lf models are used in real-time applications where computational time is scarce and lf information is sufficient. The conceptual intersection of hf and lf models has been little explored and becomes increasingly complex. This chapter aims to provide a conceptual overview of how such hf and lf models can be combined. This chapter is split into two parts where the first part is to introduce ML, lf models, and digital twins, hf models, for power systems analysis, and the second chapter is to use these two types of models to form purpose-driven surrogate lf models, illustrated on the example of dynamic security assessment (DSA). In the first part, the concepts for using DTs as hf models for online power system studies and their corresponding tuning of model parameters are introduced. Subsequently, ML i.e., lf models, are introduced and their corresponding training frameworks. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid
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