Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of optic nerve by histogram analysis on MRI in patients with isolated optic neuritis

Objectives: We aimed to evaluate the Magnetic Resonance Imaging (MRI) histogram texture analyzis of the optic nerve by comparing patients of isolated optic neuritis with a healthy control group and to provide objective information without using contrast in the diagnosis of the disease. Methods: A total of 40 patients, including 20 patients with isolated optic neuritis (13 females, 7 males) and 20 healthy controls (11 females, 9 males), were included in the study. Non-contrast brain MR images of the patient and control groups were analyzed retrospectively. In the coronal T2-weighted MRI sequence of both groups, the Region of Interest (ROI) was placed in the extraocular anterior 1/3 of the optic nerve of both eyes. Numerical data were obtained using histogram analysis and the data were evaluated in the MATLAB program. The data were compared statistically ,in addition, sensitivity and specificity were determined by Receiver Operating Characteristic (ROC) curve analysis. Results: As a result of histogram analysis, a significant difference was found between the mean values in the healthy and affected eye of the patients with isolated optic neuritis and the mean values of the control group (p < 0.05). A significant difference was found in standard deviation, minimum, maximum, median, variance values between both groups. ROC analysis was performed for mean value, AUC = 0.943 and when threshold value was selected as 354.258 Haunsfield Unit, two groups could be differentiated with 84.2% of sensitivity and 92.1% of specificity. We can say that patients with isolated optic neuritis also have histological effects on the clinically asymptomatic eye. Conclusions: Histogram analysis can be used in the diagnosis of the patients with isolated optic neuritis without the need to use contrast in their MRI. In addition, histological effect can be detected in the eye that does not show clinical symptoms with histogram analysis.
Multidimensional Stochastic Processes as Rough Paths: Theory and Applications

Preface Introduction The story in a nutshell Part I. Basics: 1. Continuous paths of bounded variation 2. Riemann-Stieltjes integration 3. Ordinary differential equations (ODEs) 4. ODEs: smoothness 5. Variation and Holder spaces 6. Young integration Part II. Abstract Theory of Rough Paths: 7. Free nilpotent groups 8. Variation and Holder spaces on free groups 9. Geometric rough path spaces 10. Rough differential equations (RDEs) 11. RDEs: smoothness 12. RDEs with drift and other topics Part III. Stochastic Processes Lifted to Rough Paths: 13. Brownian motion 14. Continuous (semi)martingales 15. Gaussian processes 16. Markov processes Part IV. Applications to Stochastic Analysis: 17. Stochastic differential equations and stochastic flows 18. Stochastic Taylor expansions 19. Support theorem and large deviations 20. Malliavin calculus for RDEs Part V. Appendix: A. Sample path regularity and related topics B. Banach calculus C. Large deviations D. Gaussian analysis E. Analysis on local Dirichlet spaces Frequently used notation References Index.
Probabilistic Assessment of the Seismic Peak Ground Acceleration (PGA) at seven Colombian Cities

This paper presents a probabilistic assessment of peak ground acceleration (PGA) observations at seven Colombian cities located in regions with intermediate and high seismic activity. Frequency histograms of the annual exceedance rates were determined using historical data of the annual rate of exceedance for pre-established values of PGA. After determining distribution parameters by means of the method of moments, several probability density functions were examined in order to evaluate the performance of each function. In most sites, the maximum annual PGA appears to be best modeled by a lognormal distribution, which led to its adoption as parent distribution for the entire group of cities. Once the lognormal function is selected as parent distribution, series of PGA values for arbitrary periods of time may be generated through random simulation. Next, series of maximum values in fifty years of observation were simulated for the seven cities, assuming a Lognormal parent distribution. Analytical solutions for extreme values of different sample sizes of lognormal random variables are not known to the authors. However, since extreme values for large samples of lognormal random variables tend asymptotically to a Type I (Gumbel) probability distribution, the latter was adopted as a possible model for the 50 years PGA distributions suggested in the paper for several Colombian cities, as a useful tool for engineering design, whenever the PGA is regarded as an appropriate seismic intensity measure.
Adapting Predictive Models for Cepheid Variable Star Classification Using Linear Regression and Maximum Likelihood

Abstract We describe an approach to automate the classification of Cepheid variable stars into two subtypes according to their pulsation mode. Automating such classification is relevant to obtain a precise determination of distances to nearby galaxies, which in addition helps reduce the uncertainty in the current expansion of the universe. One main difficulty lies in the compatibility of models trained using different galaxy datasets; a model trained using a training dataset may be ineffectual on a testing set. A solution to such difficulty is to adapt predictive models across domains; this is necessary when the training and testing sets do not follow the same distribution. The gist of our methodology is to train a predictive model on a nearby galaxy (e.g., Large Magellanic Cloud), followed by a model-adaptation step to make the model operable on other nearby galaxies. We follow a parametric approach to density estimation by modeling the training data (anchor galaxy) using a mixture of linear models. We then use maximum likelihood to compute the right amount of variable displacement, until the testing data closely overlaps the training data. At that point, the model can be directly used in the testing data (target galaxy).
D’ALEMBERT FORMULA AND THE DIRECT OR INVERSE PROBLEMS FOR WAVE EQUATION

In this paper, a new proof of D’Alembert formula for Cauchy problems of wave equation is proposed. And some D’Alembert-type formulas for Cauchy problem with discontinuous coefficients on semi-infinite initial boundary value problem are proposed. Based on above discussion, the c(? )racteristics methods for the direct and inverse problems of the stress wave equation for uniform layerad elastic medium are investigated. The aim of this paper is to stress the significance and application of the characteristics concept and approach.
A Note on Holography on a Curved Brane

Some recent literature has examined the holographic-induced cosmology of a brane universe in the background of an anti-de Sitter-black hole geometry. In this regard, curved-brane scenarios have begun to receive considerable attention. Our current interest is in a formal discrepancy that exists between two such works by Padilla (hep-th/0111247) and Youm (hep-th/0111276). In particular, these authors have incorporated different values for a conformal factor that is used to relate the thermodynamics of the relevant (AdS bulk and CFT brane) spacetimes. After a more general review, we clarify this issue and discuss the implications on the prior results.
Identificación y tratamiento de la depresión en atención primaria

Se destaca el puesto destacado que la depresión tiene en el quehacer profesional del médico de atención primaria y las líneas generales para sospechar y confirmar la presencia de este trastorno. Se propone la conducta a adoptar frente al enfermo deprimido, tanto en la vertiente de relación médico-enfermo como en la de las recomendaciones sobre el tratamiento farmacológico. Se ofrece una visión general de los fármacos antidepresivos disponibles, así como algunas de sus características farmacocinéticas y farmacodinámicas; se intenta contrastar los medicamentos clásicos, en especial los tricíclicos, con los nuevos antidepresivos, y más particularmente con los inhibidores selectivos de la recaptación de serotonina. Se ofrece también orientación sobre el seguimiento del paciente, el posible régimen profiláctico y la colaboración entre el médico general y el psiquiatra.
Protecting Hadoop with VMware vSphere

VMware vSphere® Fault Tolerance (FT) can be used to protect virtual machines that run vulnerable components of a Hadoop cluster with only a small impact on application performance. A cluster of 24 hosts was used to run three applications characterized by different storage patterns. Various Hadoop configurations were employed to artificially create greater load on the NameNode and JobTracker daemons. With conservative extrapolation, these tests show that uniprocessor virtual machines with FT enabled are sufficient to run the controller daemons for clusters of more than 200 hosts.
Self-consistent description of graphene quantum amplifier

The development of active and passive plasmonic devices is challenging due to the high level of dissipation in normal metals. One possible solution to this problem is using alternative materials. Graphene is a good candidate for plasmonics in the near-infrared region. In this paper, we develop a quantum theory of a graphene plasmon generator. We account for quantum correlations and dissipation effects, thus we are able to describe such regimes of a quantum plasmonic amplifier as a surface plasmon emitting diode and a surface plasmon amplifier using stimulated radiation emission. Switching between these generation types is possible in situ with a variance of the graphene Fermi level. We provide explicit expressions for dissipation and interaction constants through material parameters, and we identify the generation spectrum and the second-order correlation function, which predicts the laser statistics.
The Influences of Government Support in Accepting the Information Technology in Public Organization Culture

Purpose: This study aims to test the success of the technology acceptance model in Yemen culture. This study aims to test the factors influencing the acceptance of technology in Yemen public sector. In addition, the study added two important factors of organization culture and government support to the key factors in the theory of technology acceptance in order to provide better understanding for the factors influencing the acceptance of information technology among the individual perceptions. Methodology: survey questionnaire was distributed to 53 government utilities and 357 cases were used in the analysis. Structural Equition Modeling AMOS 18 was used for the analysis of the proposed model. Findings the study confirmed the theory of TAM and showed its potential capability in the Middle East, particularly in Yemen. the study has provided empirical evidence for the positive effect of subjective norm on the intention behaviour to use towards the actual usage for the technology and positive effect government support on the intention behaviour throughout the positive effect on preceived usefulness and ease of use. the study has provided empirical evidence for the negative effect of organization culture on the intention behaviour to use. Empirical evidence has shown that the employees and managers have the capability to use the technology. Significance : This study has provided empirical evidence for the effects of new technology determinants in the government sector. In particular, it has successfully revealed that organization culture, government support, subjective norm are important determinants in influencing the adoption of technologies.
Applications of the Lefschetz Number to Digital Images

The goal of this paper is to develop some applications of the Lefschetz xed point theorem for digital images. We also deal with relative and reduced Lefschetz xed point theorem for digital complexes. We give some examples related to the topic. We calculate degree of the antipodal map for sphere-like digital images using xed point properties.
Fuels and Consumption

Wildland fuels, defined as the combustible biomass of live and dead vegetation, are foundational to fire behavior, ecological effects, and smoke modeling. Along with weather and topography, the composition, structure and condition of wildland fuels drive fire spread, consumption, heat release, plume production and smoke dispersion. To refine inputs to existing and next-generation smoke modeling tools, improved characterization of the spatial and temporal dynamics of wildland fuels is necessary. Computational fluid dynamics (CFD) models that resolve fire– atmosphere interactions offer a promising new approach to smoke prediction. CFD models rely on three-dimensional (3D) characterization of wildland fuelbeds (trees, S. J. Prichard (B) School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA e-mail: sprich@uw.edu E. M. Rowell Tall Timbers Research Station, Tallahassee, FL, USA e-mail: erowell@talltimbers.org A. T. Hudak U.S. Forest Service, Rocky Mountain Research Station, Moscow, ID, USA e-mail: andrew.hudak@usda.gov R. E. Keane U.S. Forest Service, Rocky Mountain Research Station, Missoula, MT, USA e-mail: robert.keane@usda.gov E. L. Loudermilk · B. S. Hornsby U.S. Forest Service, Southern Research Station, Athens, GA, USA e-mail: elloudermilk@fs.fed. us B. S. Hornsby e-mail: benjamin.hornsby@usda.gov D. C. Lutes U.S. Forest Service, Rocky Mountain Research Station, Missoula, MT, USA e-mail: duncan.lutes@usda.gov R. D. Ottmar U.S. Forest Service, Pacific Northwest Research Station, Seattle, WA, USA e-mail: roger.ottmar@usda.gov This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022 D. L. Peterson et al. (eds.), Wildland Fire Smoke in the United States, https://doi.org/10.1007/978-3-030-87045-4_2 11 12 S. J. Prichard et al. shrubs, herbs, downedwood and forest floor fuels). Advances in remote sensing technologies are leading to novel ways to measure wildland fuels and map them at submeter to multi-kilometer scales as inputs to next-generation fire and smoke models. In this chapter, we review traditional methods to characterize fuel, describe recent advances in the fields of fuel and consumption science to inform smoke science, and discuss emerging issues and challenges.
Detection and Genome Sequence Analysis of Avian Metapneumovirus Subtype A Viruses Circulating in Commercial Chicken Flocks in Mexico

Simple Summary The use of next-generation sequencing to identify pathogens in clinical samples is continually improving, providing genomic data that can be used to: (1) identify what viruses are present in particular samples, (2) determine the viral genotype, pathotype, or lineage, (3) predict which viruses are likely to cause severe disease, and (4) help guide what vaccines to use for control efforts. As part of a project to improve NGS for diagnostics, samples were collected from commercial chicken farms in Mexico and transported on FTA cards. One of the commonly identified viruses was avian metapneumovirus, an important upper respiratory avian pathogen consisting of subtypes A-D. Complete genomes of seven subtype-A isolates were identified, which are distinct from other previously reported subtype-A strains. Although subtype-A viruses were previously reported in Mexico, this is the first comprehensive complete genome sequence analysis of Mexican subtype-A viruses. These data provide a much clearer picture of viruses circulating in Mexico and demonstrate the value of next-generation sequencing to identify pathogens where surveillance is not routinely performed. Because subtype-A is not found in the U.S., a modified real-time reverse-transcriptase polymerase chain reaction test was evaluated that can quickly identify the virus in poultry samples. Abstract Avian metapneumoviruses (aMPV subtypes A-D) are respiratory and reproductive pathogens of poultry. Since aMPV-A was initially reported in Mexico in 2014, there have been no additional reports of its detection in the country. Using nontargeted next-generation sequencing (NGS) of FTA card-spotted respiratory samples from commercial chickens in Mexico, seven full genome sequences of aMPV-A (lengths of 13,288–13,381 nucleotides) were de novo assembled. Additionally, complete coding sequences of genes N (n = 2), P and M (n = 7 each), F and L (n = 1 each), M2 (n = 6), SH (n = 5) and G (n = 2) were reference-based assembled from another seven samples. The Mexican isolates phylogenetically group with, but in a distinct clade separate from, other aMPV-A strains. The genome and G-gene nt sequences of the Mexican aMPVs are closest to strain UK/8544/06 (97.22–97.47% and 95.07–95.83%, respectively). Various amino acid variations distinguish the Mexican isolates from each other, and other aMPV-A strains, most of which are in the G (n = 38), F (n = 12), and L (n = 19) proteins. Using our sequence data and publicly available aMPV-A data, we revised a previously published rRT-PCR test, which resulted in different cycling and amplification conditions for aMPV-A to make it more compatible with other commonly used rRT-PCR diagnostic cycling conditions. This is the first comprehensive sequence analysis of aMPVs in Mexico and demonstrates the value of nontargeted NGS to identify pathogens where targeted virus surveillance is likely not routinely performed.
W AVELET F EATURE M APS C OMPRESSION FOR L OW B ANDWIDTH C ONVOLUTIONAL N EURAL N ETWORKS

Quantization is one of the most effective techniques for compressing Convolutional Neural Networks (CNNs), which are known for requiring extensive computational resources. However, aggressive quantization may cause severe degradation in the prediction accuracy of such networks, especially in image-to-image tasks such as semantic segmentation and depth prediction. In this paper, we propose Wavelet Compressed Convolution (WCC)—a novel approach for activation maps compression for 1 × 1 convolutions (the workhorse of modern CNNs). WCC achieves compression ratios and computational savings that are equivalent to low bit quantization rates at a relatively minimal loss of accuracy. To this end, we use a hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and deﬁne the convolution on the compressed activation map. WCC can be utilized with any 1 × 1 convolution in an existing network architecture. By combining WCC with light quantization, we show that we achieve compression rates equal to 2-bit and 1-bit with minimal degradation in image-to-image tasks.
Do we still need gold standard for evaluation ?

The availability of a huge mass of textual data in electronic format has increased the need for fast and accurate techniques for textual data processing. Machine learning and statistical approaches have been increasingly used in NLP since the 1990s, mainly because they are quick, versatile and efﬁcient. However, despite this evolution of the ﬁeld, evaluation still rely (most of the time) on a comparison between the output of a probabilistic or statistical system on the one hand, and a non-statistic, most of the time hand-crafted, gold standard on the other hand. In order to be able to compare these two sets of data, which are inherently of a different nature, it is ﬁrst necessary to modify the statistical data so that they ﬁt with the hand-crafted reference. For example, a statistical parser, instead of producing a score of grammaticality, will have to produce a binary value for each sentence (grammatical vs ungrammatical) or a tree similar to the one stored in the treebank used as a reference. In this paper, we take the example of the acquisition of subcategorization frames from corpora as a practical example. Our study is motivated by the fact that, even if a gold standard is an invaluable resource for evaluation, a gold standard is always partial and does not really show how accurate and useful results are. We describe the task (SCF acquisition) and show how it is a typical NLP task. We then very brieﬂy describe our SCF acquisition system before discussing different issues related to the evaluation using a gold standard. Lastly, we adopt the classical distinction between intrinsic and extrinsic evaluation and show why this framework is relevant for SCF acquisition. We show that, even if intrinsic evaluation correlates with extrinsic evaluation, these two evaluation frameworks give a complementary insight on the results. In the conclusion, we quickly discuss the case of other NLP tasks.
Modern services led growth and development in a structuralist dual economy: Long‐run implications of skilled labor constraint

Motivated by the South Asian experience, this paper examines the role of expansion of skilled labor force for modern services led growth and development in economies with low average educational attainment. The model economy consists of two capital‐using sectors—a service sector that employs only skilled labor and an industry sector that does not require skilled labor. The service sector represents modern skilled‐labor intensive services whereas the industry sector represents a typical South Asian industry. Supply of unskilled labor is unlimited but skilled labor is relatively scarce and grows at a finite rate. Increase in the skill premium only partially explains growth of skilled‐labor supply while the rest depends on autonomous factors, which may be influenced by education policies of the government. The main result specifies a set of necessary and sufficient conditions for existence of a unique steady state characterized by balanced sectoral growth at a rate determined by the autonomous part of skilled labor growth. A low wage share and stagnant investment conditions in the industry sector are conducive for both existence and local stability of this steady state. The model shows that supply‐side factors may completely determine steady state growth rates in structuralist models despite presence of unemployed resources.