EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

EAGLE2006 - an intensive field campaign - was carried out in the Netherlands from the 8th until the 18th of June 2006. Several airborne sensors - an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were used and extensive ground measurements were conducted over one grassland (Cabauw) site and two forest sites (Loobos & Speulderbos) in the central part of the Netherlands, in addition to the acquisition of multi-angle and multi-sensor satellite data. The data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative surface parameter estimation and process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation and originated from the combination of a number of initiatives coming under different funding. The objectives of the EAGLE2006 campaign were closely related to the objectives of other ESA Campaigns (SPARC2004, Sen2Flex2005 and especially AGRISAR2006). However, one important objective of the campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired over vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as Sentinels and for satellite validations for MERIS, MODIS as well as AATSR and ASTER thermal data validation, with activities also related to the ASAR sensor on board ESA’s Envisat platform and those on EPS/MetOp and SMOS. Most of the activities in the campaign are highly relevant for the EU GEMS EAGLE project, but also issues related to retrieval of biophysical parameters from MERIS and MODIS as well as AATSR and ASTER data were of particular relevance to the NWO-SRON EcoRTM project, while scaling issues and complementary between these (covering only local sites) and global sensors such as MERIS/SEVIRI, EPS/MetOP and SMOS were also key elements for the SMOS cal/val project and the ESA-MOST DRAGON programme. This contribution describes the mission objectives and provides an overview of the airborne and field campaigns

Model anisotropic quantum Hall states

Model quantum Hall states including Laughlin, Moore-Read and Read-Rezayi states are generalized into appropriate anisotropic form. The generalized states are exact zero-energy eigenstates of corresponding anisotropic two- or multi-body Hamiltonians, and explicitly illustrate the existence of geometric degrees of in the fractional quantum Hall effect. These generalized model quantum Hall states can provide a good description of the quantum Hall system with anisotropic interactions. Some numeric results of these anisotropic quantum Hall states are also presented.Comment: 10 pages, 5 figure

Cloning and Expression of a Secretory form of Truncated ORF2 (aa 112-607) from Hepatitis E Virus in the pVAX1 Vector

Background: The hepatitis E virus (HEV) accounts for the hepatitis E infection with a high mortality rate in pregnant women. Therefore, the design of the novel and effective vaccines seems essential and the DNA vaccine approach could be useful to achieve this anticipated goal. Objectives: The aim of this study was the cloning of a secretory form of truncated open reading frame 2 (ORF2) of HEV containing amino acids (aa) 112 - 607 into the eukaryotic expression pVAX1 Vector and evaluating of the expression of this recombinant protein in eukaryotic cells. Methods: The truncated ORF2 gene (aa 112 - 607) was cloned in the pVAX1 plasmid by restriction enzyme digest and confirmed by digestion and sequencing. Then, the recombinant plasmid was transfected into eukaryotic cells to express the recombinant protein. The expressed protein in the cell lysate and supernatant was evaluated by immunofluorescence assay (IFA) and western blot assay. Results: Colony polymerase chain reactions (PCR), restriction enzyme digestion, along with DNA sequencing of the recombinant plasmid were performed for confirmation of cloning tPA-PADRE-truncated ORF2 gene (aa 112 - 607) into pVAX1 eukaryotic expression vector. The appearance of the truncated ORF2 protein (56 kDa) in the eukaryotic cells was accepted by the western blot assay, reverse transcription polymerase chain reaction (RT-PCR) method, as well as IFA. Conclusions: All outcomes of the present research showed that pVAX1-tPA-PADRE-truncated ORF2 (aa 112 - 607, 56 kDa) recombinant plasmid was able to express truncated ORF2 from HEV as a potential candidate vaccine

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Eigenvalue variance bounds for Wigner and covariance random matrices

This work is concerned with finite range bounds on the variance of individual eigenvalues of Wigner random matrices, in the bulk and at the edge of the spectrum, as well as for some intermediate eigenvalues. Relying on the GUE example, which needs to be investigated first, the main bounds are extended to families of Hermitian Wigner matrices by means of the Tao and Vu Four Moment Theorem and recent localization results by Erd\"os, Yau and Yin. The case of real Wigner matrices is obtained from interlacing formulas. As an application, bounds on the expected 2-Wasserstein distance between the empirical spectral measure and the semicircle law are derived. Similar results are available for random covariance matrices

Reply to "Comment on 'Fano resonance for Anderson Impurity Systems' "

In a recent Comment, Kolf et al. (cond-mat/0503669) state that our analysis of the Fano resonance for Anderson impurity systems [Luo et al., Phys. Rev. Lett 92, 256602 (2004)] is incorrect. Here we want to point out that their comments are not based on firm physical results and their criticisms are unjustified and invalid.Comment: 1 page, 1 figure, to appear in PR

Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements