An intercomparison of subtropical cut-off lows in the Southern Hemisphere using recent reanalyses: ERA-Interim, NCEP-CFRS, MERRA-2, JRA-55, and JRA-25

Four recent reanalysis products ERA-Interim, NCEP-CFSR, MERRA-2 and JRA-55 are evaluated and compared to an older reanalysis JRA-25, to quantify their confidence in representing Cut-off lows (COLs) in the Southern Hemisphere. The climatology of COLs based on the minima of 300-hPa vorticity (ξ300) and 300-hPa geopotential (Z300) provides different perspectives of COLs and contributes to the understanding of the discrepancies observed in the literature regarding their numbers and seasonality. The COLs compare better among the newest reanalyses than compared to the older reanalysis JRA-25. The difference in number between the latest reanalyses are generally small for both ξ300 and, with more COLs identified in ξ300 than in Z300 for all reanalyses. The spatial differences observed between the newest reanalyses are mainly due to differences in the track lengths, which is larger in ERA-Interim and JRA-55 than in NCEP-CFSR and MERRA-2, resulting in disparities in the track density. This is likely due to the difference in the assimilation data system used in each reanalysis product. The largest differences in intensities occur in the ξ300, because this field is very sensitive to the reanalysis resolution. The mean separation distance of the COLs that match between the latest reanalyses are generally small, while the older JRA-25 has a broader distribution and larger number of matches with relatively large distances, indicating larger uncertainties in location of COLs. The results show significant improvements for the most recent reanalyses compared to the older JRA-25 reanalysis, indicating a progress in representing the COL properties

Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images

We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environment

Rapidly decaying Fourier-like bases

International audienceIn many applications it is natural to seek to extract a characteristic scale for a function's variations by reference to a frequency spectrum. Although the moments of a spectrum appear to promise simple options to make such a connection, standard Fourier methods fail to yield finite moments when the function's domain is itself finite. We investigate a family of Fourier-like bases with rapidly decaying spectra that yield well-defined moments for such cases. These bases are derived by considering classes of functions for which a normalised mean square derivative is stationary. They are shown to provide precisely the type of spectrum needed to complete a recent investigation of mid-spatial frequency structure on optical surfaces [K. Liang, Opt. Express 27, 3390-3408 (2019)]

Mining Multitemporal In Situ Heterogeneous Monitoring Information for the Assurance of Recorded Land Cover Changes

We present a data mining methodology to filter and validate land cover change detections obtained from multitemporal in situ surveys. As in situ data we use the measurements from the European land use and coverage area frame survey (LUCAS), which provides images with standardized metadata about land cover and land use within the whole territory of the European Union. Multitemporal LUCAS surveys present an anomaly in the amount of land cover changes that disagree with the estimated by experts. Therefore, our methodology analyses the available data in order to explain the existing irregularities in them. The initial step of our methodology is based on database query refinements. The data mining methodology continues with an image analysis process. This analysis calculates similarity measures of the multitemporal images that are used to identify the potential misclassifications. The final step involves a geographic information system based on web technologies. By defining different color codes assigned by the similarity measures, the system represents the examined points on a digital Earth globe. There, a user can easily discriminate potentially misclassified points for subsequent detailed analysis or corrections. The final output of the methodology shows remarkable results for detecting misclassified land cover changes

Caries dental relacionado al PH salival en adolescentes de una institución educativa del distrito de Paijan - Ascope, 2016

Determinar la relación entre la caries dental y pH salival en alumnos de la I.E 80050 José Félix Black del Distrito de Paiján – Ascope. Materiales y Método: Se realizó un estudio prospectivo, transversal, descriptivo y observacional. Se evaluaron a 105 alumnos del 4° y 5° año de secundaria, de las cuales 45 fueron mujeres y 60 varones. Se empleó el índice individual CPOD para evaluar la caries dental y cinta medidora de pH MColorpHast TM para determinar el pH salival. Se empleó la prueba de independencia de criterios Chi Cuadrado ( X 2 ) , considerando un nivel de significancia es de p = 0.05. Resultados: Se encontró que el 61.9% de estudiantes presenta un índice de caries alto, 21.0% presentaron un índice moderado, el 9.5% índice bajo y el 7.6% un índice muy bajo. Además el 81.9% de estudiantes presenta un pH salival ácido, 17.1% pH salival neutro y 1.0% pH salival alcalino. En relación del pH salival con la caries dental, se encontró que los adolescentes con pH salival ácido presentan el 72.1% un índice de caries alto y con un pH salival neutro el 16.7% un índice de caries alto. Conclusiones: Si existe relación entre la caries dental y pH salival en los alumnos de la I.E José Felix Black del Distrito de Paiján – Ascope, existiendo diferencia significativa según el género.To determine the relationship between dental tooth decay and salivary pH in students of the I.E 80050 Jose Felix Black of the District of Paiján - Ascope. MATERIAL AND METHODS: The prospective, transversal, descriptive and observational study, consisting of 105 students from the 4th and 5th year of high school, of which 45 were women and 60 men. The individual OD index was used to evaluate dental tooth decay and MColorpHast TM pH measuring tape to determine the salivary pH. Test of independence of criteria Chi square ( X 2) , considering is a significance level of p = 0.05. RESULTS: We found that the 61.9% of students presents a decay rate high, 21.0% had a moderate rate, 9.5 index % and 7.6% a very low rate. Furthermore 81.9% of students presents a salivary pH acid, 17.1% salivary pH-neutral and 1.0% alkaline salivary pH. Relationship of salivary pH with dental caries was found that teens with acid salivary pH present the 72.1% a rate of caries high and with a salivary pH neutral 16.7% a high decay rate. CONCLUSIONS: If there is a relationship between dental tooth decay and salivary pH in the students of the I.E José Felix Black of the District of Paiján - Ascope, there is a significant difference according to gender

One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers' output for unseen data and evaluate the method's robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.Comment: Submitted preprint to IEE Acces

Cross-Spectral Periocular Recognition with Conditional Adversarial Networks

This work addresses the challenge of comparing periocular images captured in different spectra, which is known to produce significant drops in performance in comparison to operating in the same spectrum. We propose the use of Conditional Generative Adversarial Networks, trained to con-vert periocular images between visible and near-infrared spectra, so that biometric verification is carried out in the same spectrum. The proposed setup allows the use of existing feature methods typically optimized to operate in a single spectrum. Recognition experiments are done using a number of off-the-shelf periocular comparators based both on hand-crafted features and CNN descriptors. Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database (PolyU) as benchmark dataset, our experiments show that cross-spectral performance is substantially improved if both images are converted to the same spectrum, in comparison to matching features extracted from images in different spectra. In addition to this, we fine-tune a CNN based on the ResNet50 architecture, obtaining a cross-spectral periocular performance of EER=1%, and GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU database.Comment: Accepted for publication at 2020 International Joint Conference on Biometrics (IJCB 2020

Sensitivity of identifying cut‑off lows in the Southern Hemisphere using multiple criteria: Implications for numbers, seasonality and intensity

Cut-off Low (COLs) are often associated with heavy precipitation and strong wind events, but there are still uncertainties on how their identification affect the numbers and seasonality. This paper aims to determine the sensitivity of identifying Southern Hemisphere COLs in the ERA-Interim reanalysis to different types of identification criteria. Upper-level cyclones are initially tracked on the 300 hPa level using relative vorticity and geopotential in order to present different perspectives. This reveals significant differences between the numbers and length of the identified tracks for the two fields. To identify the COLs different post-tracking filters are applied which are divided into two steps. Firstly, three filters are considered to separate cut-off lows from open troughs by sampling winds at different offset radial distances from the upper-level cyclonic centres. Secondly, potential vorticity and temperature criteria are imposed to determine how these conditions affect the identified COLs in terms of numbers, seasonality and intensity. It was found that methods based on multiple criteria restrict the COL identification by imposing specific characteristics, while methods based on simpler schemes (e.g. using only winds) can detect larger samples of COLs observed visually in the geopotential maps. Although it is difficult to say which method is more accurate in identifying COLs, because of the subjective aspect of observer’s assessment, a scheme using only winds should be more representative of reality as this simply imposes on the detection system a cyclonic circulation appearance regardless of the physical and dynamical characteristics. Therefore, this type of method could be considered as a standard method for identifying COLs that can be used for either operational or research purposes

Dosimetric Performance and Planning/Delivery Efficiency of a Dual-Layer Stacked and Staggered MLC on Treating Multiple Small Targets: A Planning Study Based on Single-Isocenter Multi-Target Stereotactic Radiosurgery (SRS) to Brain Metastases.

Purpose: To evaluate the dosimetric performance and planning/delivery efficiency of a dual-layer MLC system for treating multiple brain metastases with a single isocenter. Materials and Methods: 10 patients each with 6-10 targets with volumes from 0.11 to 8.57 cc, and prescription doses from 15 to 24 Gy, were retrospectively studied. Halcyon has only coplanar delivery mode. Halcyon V1 MLC modulates only with the lower layer at 1 cm resolution, whereas V2 MLC modulates with both layers at an effective resolution of 0.5 cm. For each patient five plans were compared varying MLC and beam arrangements: the clinical plan using multi-aperture dynamic conformal arc (DCA) and non-coplanar arcs, Halcyon-V1 using coplanar-VMAT, Halcyon-V2 using coplanar-VMAT, HDMLC-0.25 cm using coplanar-VMAT, and HDMLC-0.25 cm using non-coplanar-VMAT. All same-case plans were generated following the same planning protocol and normalization. Conformity index (CI), gradient index (GI), V12Gy, V6Gy, V3Gy, and brain mean dose were compared. Results: All VMAT plans met clinical constraints for critical structures. For targets with diameter \u3c 1 cm, Halcyon plans showed inferior CI among all techniques. For targets with diameter \u3e1 cm, Halcyon VMAT plans had CI similar to non-coplanar VMAT plans, and better than non-coplanar clinical DCA plans. For GI, Halcyon MLC plans performed similarly to coplanar HDMLC plans and inferiorly compared to non-coplanar HDMLC plans. All coplanar VMAT plans (Halcyon MLC and HDMLC) and clinical DCA plans had similar V12Gy, but were inferior compared to non-coplanar VMAT plans. Halcyon plans had slightly reduced V3Gy and mean brain dose compared to HDMLC plans. The difference between Halcyon V1 and V2 is only significant in CI of tumors less than 1cm in diameter. Halcyon plans required longer optimization than Truebeam VMAT plans, but had similar delivery efficiency. Conclusion: For targets with diameter \u3e1 cm, Halcyon\u27s dual-layer stacked and staggered MLC is capable of producing similar dose conformity compared to HDMLC while reducing low dose spill to normal brain tissue. GI and V12Gy of Halcyon MLC plans were, in general, inferior to non-coplanar DCA or VMAT plans using HDMLC, likely due to coplanar geometry and wider MLC leaves. HDMLC maintained its advantage in CI for smaller targets with diameter \u3c1 cm. © 2019 Li, Irmen, Liu, Shi, Alonso-Basanta, Zou, Teo, Metz and Dong