Miracle-dz/UniFAR-FTUs huggingface dataset:
Cystic fibrosis in adults : short-term and long-term intra-and inter-observer-reproducibility of the Brody score in low-dose multidetector-row-CT lung images

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Predicting admission counseling triumph of colleges using neural networks

Forecasting student admission in colleges and as a result ranking the colleges has intrigued many scholars and academic industry leaders as a difficult and challenging problem. In this study, the use of neural networks in forecasting the performance of technical colleges in the counseling session before it actually takes place is explored. We convert this forecasting problem into a classification one, i.e. , we classify a college based upon its total number of seats filled in one of the five categories, ranging from ‘flop’ to ‘top’. Because our neural network model can predict the success of a college in gaining maximum admissions in an academic year before the actual admissions take place, it can be used as a powerful decision aid by university management, colleges and anticipating students. Since we have used the evaluation version of the software, we are therefore getting the maximum accuracy of 72%. This is mainly due to the reason that the evaluation version does not incorporate all the features required to compute accurate result. Employing the neural network model by using the 10-fold cross validation methodology shows that the neural networks do a much better job of predicting in this setting. Key-Words: Forecasting, neural networks, independent variables, MLP architecture, Placements, Fee – structure, Faculty
A COMBINED METHOD OF AUXILIARY SOURCES-REACTION MATCHING APPROACH FOR ANALYZING MODERATELY LARGE-SCALE ARRAYS OF CYLINDRICAL DIPOLES

In the present paper, a combined method of auxiliary sources (MAS)-reaction matching (RM) approach is presented for the analysis of arrays of arbitrarily located cylindrical dipoles. It is shown that the addition of auxiliary monopole terminal sources to each array element results in a superior solution with regard to the numerical stability of the computed quantities, the behavior of the current distributions of the array elements and the resulting errors of the electric field boundary condition. Numerical results are presented for various representative array configurations, in order to illustrate the features of the proposed method and exhibit its advantages over conventional Method of Moments (MoM) schemes, especially in cases of moderately large-scale arrays. Finally, a few concluding remarks are discussed.
Size Premium Waves

At low frequencies, we document that size and value premia exhibit strong positive co-movement, but are both negatively related to the equity premium. These patterns are explained in an investment-based asset pricing model featuring persistent micro and macro uncertainty. Micro uncertainty generates size and value premia waves, while macroeconomic uncertainty produces equity premium waves. The negative relation between micro and macro uncertainty at low frequencies accounts for the negative relation of the market with size and value premia. The higher sensitivity of size-sorted portfolios to micro uncertainty can explain the observed instability of the size strategy.
RECONTEXTUALISATION, RESEMIOTISATION AND THEIR ANALYSIS IN TERMS OF AN FDG-BASED FRAMEWORK

“Recontextualisation” is the process whereby content that has been given expression in one context (the “source” context) is subsequently reused in a different context (the “destination” context). It is often accompanied by “resemiotisation”, the process whereby content is lifted from one text (the “antecedent” text, situated in the source context) and recast in a modified form during the production of a subsequent text (the “derivative” text, situated in the destination context). The aim of the present paper is to evaluate the adequacy of FDG, incorporating an extended model of context (EMC), as a basis for the analysis of the process of recontextualisation and of the accompanying resemiotisation. The study is based on the analysis of a corpus of texts consisting of one antecedent and six derivative texts, all drawn from the field of science communication. The texts are subjected to a contextual analysis in terms of the categories afforded by an analytical framework based on the EMC. From these contextual analyses, the differences between the source context and each of the six destination contexts are identified. These differences encapsulate the changes that constitute the essence of recontextualisation. In addition, each of the texts is analysed in terms of a three-tier framework based as far as possible on FDG. The resulting analyses are then compared, in order to identify the changes that constitute the essence of resemiotisation. Finally, the relationship between the recontextualisation and resemiotisation is discussed. From the point of view of evaluating the linguistic models employed, it transpires that the categories offered by the EMC appear to be viable as units for the analysis of recontextualisation. As for the analysis of resemiotisation, it turns out that a theoretical foundation for a substantial part of this, though not all, can be supplied by current FDG.
Robust Peer to Peer Mobile Botnet Detection by Using Communication Patterns

Botnet on a mobile platform is one of the severe problems for the Internet security. It causes damages to both individual users and the economic system. Botnet detection is required to stop these damages. However, botmasters keep developing their botnets. Peer-to-peer (P2P) connection and encryption are used in the botnet communication to avoid the exposure and takedown. To tackle this problem, we propose the P2P mobile botnet detection by using communication patterns. A graph representation called "graphlet" is used to capture the natural communication patterns of a P2P mobile botnet. The graphlet-based detection does not violate the user privacy, and also effective with encrypted traffic. Furthermore, a machine learning technique with graphlet-based features can detect the P2P mobile botnet even it runs simultaneously with other applications such as Facebook, Line, Skype, YouTube, and Web. Moreover, we employ the Principal Components Analysis (PCA) to analyze graphlet's features to leverage the detection performance when the botnet coexists with dense traffic such as Web traffic. Our work focuses on the real traffic of an advanced P2P mobile botnet named "NotCompatible.C". The detection performance shows high F-measure scores of 0.93, even when sampling only 10% of traffic in a 3-minute duration.
A simultaneous search for High-$z$ LAEs and LBGs in the SHARDS survey

We have undertaken a comprehensive search for both Lyman Alpha Emitters (LAEs) and Lyman Break Galaxies (LBGs) in the SHARDS Survey of the GOODS-N field. SHARDS is a deep imaging survey, made with the 10.4 m Gran Telescopio Canarias (GTC), employing 25 medium band filters in the range from 500 to 941 nm. This is the first time that both LAEs and LBGs are surveyed simultaneously in a systematic way in a large field. We draw a sample of 1558 sources; 528 of them are LAEs. Most of the sources (1434) show rest-frame UV continua. A minority of them (124) are pure LAEs with virtually no continuum detected in SHARDS. We study these sources from $z\sim3.35$ up to $z\sim6.8$, well into the epoch of reionization. Note that surveys done with just one or two narrow band filters lack the possibility to spot the rest-frame UV continuum present in most of our LAEs. We derive redshifts, Star Formation Rates (SFRs), Ly$\alpha$ Equivalent Widths (EWs) and Luminosity Functions (LFs). Grouping within our sample is also studied, finding 92 pairs or small groups of galaxies at the same redshift separated by less than 60 comoving kpc. In addition, we relate 87 and 55 UV-selected objects with two known overdensities at $z=4.05$ and $z=5.198$, respectively. Finally, we show that surveys made with broad band filters are prone to introduce many unwanted sources ($\sim20$% interlopers), which means that previous studies may be overestimating the calculated LFs, specially at the faint end.
Surface Morphology and Wear Investigation of Valves used in LPG- fueled Passenger Car Engines

Liquefied petroleum gas (hereafter LPG), with an Octane Number of ~105, was looked upon and used as a viable vehicle - fuel option as early as 1912. However, its usage gained significant momentum with the advent of dual – fuel technology in the mid 1950s. This technology made it possible for LPG to be used in parallel with another fuel – Gasoline or Diesel. Though the LPG fuel system has become far more sophisticated while moving from 1st generation of technology development to the 5th one, most passenger car engines running on LPG are still not specially designed to operate on LPG. It is a very common practice in many developing countries, including India, to use retrofitted gasoline engines that operate on a dual – fuel mode: LPG and petrol. The extreme temperature conditions in LPG - run retrofitted gasoline engines result in considerable changes in the microstructure and wear growth patterns for both inlet and exhaust valves. This investigation focused on analysis of valve wear using a Pin – on – Disc (POD) wear tester. The characterization of wear was done by wear rate, analyses of worn surfaces, wear debris structure and sub – surfaces. The precipitation characteristics and behavior of secondary particles like carbides, oxides, silicates, etc., was also studied. The microstructure analysis and quantitative metallography of inlet and exhaust valve specimens was carried out by employing Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) techniques. The images thus obtained were utilized for microstructural characterization of the valve specimens. The surface morphology of the valve material was studied and AFM measurements were used for quantitative characterization of the structure as also to gain useful information about crystallographic orientation of individual grains, the formation of cracks, identification of potential crack initiation and fracture sites, etc. A comparative evaluation of microstructure of worn - out valves with new valves was also carried out.
Persistent Activity in Assortative Networks of Integrate and Fire Neurons

A neuronal network can be represented as a directed graph. Each neuron corresponds to a node and each connection between the axon of one neuron and the dendrite of another corresponds to an edge. We investigate the effects of two statistical properties of directed graphs on the capacity of excitatory and inhibitory neuronal networks to exhibit bistability. One measure is node-degree correlation, the propensity of nodes to have similar in-degrees and out-degrees. The other measure is edge-degree correlation, the correlation between the in-degree of one node and the out-degree of a node receiving input from the first. By grouping subpopulations of neurons according to their in and out degrees, we perform simulations testing the effect of these different forms of assortativity on network input/output properties. We show that the node-degree correlation and edge-degree correlation of a neuronal network affect the ranges of synaptic coupling strengths and of external stimulation rates for which there are two steady-state mean firing rates. The existence of bistability and hysteresis is important as the physiological basis of short term memory.
Bayesian Adversarial Learning

Deep neural networks have been known to be vulnerable to adversarial attacks, raising lots of security concerns in the practical deployment. Popular defensive approaches can be formulated as a (distributionally) robust optimization problem, which minimizes a ``point estimate'' of worst-case loss derived from either per-datum perturbation or adversary data-generating distribution within certain pre-defined constraints. This point estimate ignores potential test adversaries that are beyond the pre-defined constraints. The model robustness might deteriorate sharply in the scenario of stronger test adversarial data. In this work, a novel robust training framework is proposed to alleviate this issue, Bayesian Robust Learning, in which a distribution is put on the adversarial data-generating distribution to account for the uncertainty of the adversarial data-generating process. The uncertainty directly helps to consider the potential adversaries that are stronger than the point estimate in the cases of distributionally robust optimization. The uncertainty of model parameters is also incorporated to accommodate the full Bayesian framework. We design a scalable Markov Chain Monte Carlo sampling strategy to obtain the posterior distribution over model parameters. Various experiments are conducted to verify the superiority of BAL over existing adversarial training methods. The code for BAL is available at \url{https://tinyurl.com/ycxsaewr }.
Detecting the Early Genetic Effects of Habitat Degradation in Small Size Remnant Populations of Machilus thunbergii Sieb. et Zucc. (Lauraceae)

Habitat degradation caused by human activities has reduced the sizes of many plant populations worldwide, generally with negative genetic impacts. However, detecting such impacts in tree species is not easy because trees have long life spans. Machilus thunbergii Sieb. et Zucc. (Lauraceae) is a dominant tree species of broad-leaved evergreen forests distributed primarily along the Japanese coast. Inland habitats for this species have become degraded by human activities. To investigate the effects of habitat degradation on genetic structure, we compared the genetic diversities of mature and juvenile trees of five M. thunbergii populations around Lake Biwa in Japan. Allelic diversity was influenced by past lineage admixture events, but the effects of forest size were not clear. On the other hand, the inbreeding coefficient of the juvenile stage was higher in small populations, whereas large populations maintained panmictic breeding. Also, the extent of genetic differentiation was greater in juveniles than in mature trees. We detected the early genetic effects of habitat degradation in small, isolated M. thunbergii populations, indicating that habitat degradation increases inbreeding and genetic differentiation between populations.
Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01 s and 10.0 s

This paper contains ground-motion prediction equations (GMPEs) for average horizontal-component ground motions as a function of earthquake magnitude, distance from source to site, local average shear-wave velocity, and fault type. Our equations are for peak ground acceleration (PGA), peak ground velocity (PGV), and 5%-damped pseudo-absolute-acceleration spectra (PSA) at periods between 0.01 s and 10 s. They were derived by empirical regression of an extensive strong-motion database compiled by the “PEER NGA” (Pacific Earthquake Engineering Research Center's Next Generation Attenuation) project. For periods less than 1 s, the analysis used 1,574 records from 58 mainshocks in the distance range from 0 km to 400 km (the number of available data decreased as period increased). The primary predictor variables are moment magnitude (M), closest horizontal distance to the surface projection of the fault plane ( RJB ), and the time-averaged shear-wave velocity from the surface to 30 m (VS30). The equations are applicable for M=5–8, RJB <200 km, and VS30=180–1300 m/s.
The VIRMOS deep imaging survey IV: Near-infrared observations

In this paper we present a new deep, wide-field near-infrared imaging survey. Our J- and K-band observations in four separate fields complement optical BVRI, ultraviolet and spectroscopic observations undertaken as part of the VIMOS-VLT deep survey (VVDS). In total, our survey spans ~400arcmis^2. Our catalogues are reliable in all fields to at least Kvega~20.75 and Jvega~21.50 (defined as the magnitude where object contamination is less than 10% and completeness greater than 90%). Taken together these four fields represents a unique combination of depth, wavelength coverage and area. We describe the complete data reduction process and outline a comprehensive series of tests carried out to characterise the reliability of the final catalogues. We compare the statistical properties of our catalogues with literature compilations. We find that our J- and K-selected galaxy counts are in good agreement with previously published works, as are our (J-K) versus K colour-magnitude diagrams. Stellar number counts extracted from our fields are consistent with a synthetic model of our galaxy. Using the location of the stellar locus in colour-magnitude space and the measured field-to-field variation in galaxy number counts we demonstrate that the absolute accuracy of our photometric calibration is at the 5% level or better. Finally, an investigation of the angular clustering of K- selected extended sources in our survey displays the expected scaling behaviour with limiting magnitude, with amplitudes in each magnitude bin in broad agreement with literature values.
Effect of sea salt aerosol on tropospheric bromine chemistry

Abstract. Bromine radicals influence global tropospheric chemistry by depleting ozone and by oxidizing elemental mercury and reduced sulfur species. Observations typically indicate a 50 % depletion of sea salt aerosol (SSA) bromide relative to seawater composition, implying that SSA debromination could be the dominant global source of tropospheric bromine. However, it has been difficult to reconcile this large source with the relatively low bromine monoxide (BrO) mixing ratios observed in the marine boundary layer (MBL). Here we present a new mechanistic description of SSA debromination in the GEOS-Chem global atmospheric chemistry model with a detailed representation of halogen (Cl, Br, and I) chemistry. We show that observed levels of SSA debromination can be reproduced in a manner consistent with observed BrO mixing ratios. Bromine radical sinks from the HOBr + S(IV) heterogeneous reactions and from ocean emission of acetaldehyde are critical in moderating tropospheric BrO levels. The resulting HBr is rapidly taken up by SSA and also deposited. Observations of SSA debromination at southern midlatitudes in summer suggest that model uptake of HBr by SSA may be too fast. The model provides a successful simulation of free-tropospheric BrO in the tropics and midlatitudes in summer, where the bromine radical sink from the HOBr + S(IV) reactions is compensated for by more efficient HOBr-driven recycling in clouds compared to previous GEOS-Chem versions. Simulated BrO in the MBL is generally much higher in winter than in summer due to a combination of greater SSA emission and slower conversion of bromine radicals to HBr. An outstanding issue in the model is the overestimate of free-tropospheric BrO in extratropical winter–spring, possibly reflecting an overestimate of the HOBr∕HBr ratio under these conditions where the dominant HOBr source is hydrolysis of BrNO3.
Method for detecting the notch deviation based on corner point constraint

The notch detection plays a critical role in influencing the quality control of cut-piece. It is difficult to measure the deviation of notch due to the deformation of cut-piece, which in turn affects the subsequent sewing process and the quality of cut-piece. In this paper, a notch detection method based on corner point constraint is proposed to solve this problem. Firstly, all the corner points are extracted from the CAD by using the improved Harris corner detection algorithm. The triangle constraint and the intersection constraint method are used to eliminate extra corner points that are not in the triangle area, and then the notch is obtained. Secondly, the cut-piece and the CAD are superimposed by affine transformation, and the approximate notch area on the cut-piece is obtained in the neighbourhood of the notch on the CAD. Finally, the improved Harris corner detection algorithm is used to extract all the corner points of the notch area on the cut-piece, and then the notch is extracted after removing the extra corner points. The experimental results have shown a good consistency with the expected performance which confirms the effectiveness of the proposed method.
Incremental On-Line Learning of Object Classes using a Combination of Self-Organizing Incremental Neural Networks and Deep Convolutional Neural Networks*

—This paper reports initial results from our ongoing work on Natural Language grounding through situated interaction for robots acting in unstructured environments. We present an approach for incremental on-line learning of object classes based on a novel combination of a Self-Organizing Incremental Neural Network (SOINN) and a deep Convolutional Neural Network (CNN). We evaluate our approach on the RGB-D Object Dataset and on four aspects: 1) object classiﬁcation accuracy, 2) incremental learning in terms of the number of classes, 3) potential for realising active learning, and 4) feasibility to use the system in an on-line setting. Our results show that the approach has an acceptable performance for object classiﬁcation (e.g. over 90% accuracy) and provide some insights into how it could be improved by harnessing interaction with both the environment and a human tutor.
Sleep Deprivation and workplace deviance: a mediation approach

This research paper examines the effect of sleep deprivation on workplace deviance through self- regulatory mechanism. The self- regulatory mechanism includes state hostility and self-control. Sleep deprivation decreases individual's self-control while increasing state hostility leads to increased workplace deviance. The sample has been taken from medical residents from 6 hospitals in Karachi, Pakistan. Findings suggest that self-control does not mediate the relation between sleep deprivation and workplace deviance but state hostility fully mediates the relationship between sleep deprivation and workplace deviance.