Search

This paper examines the impact of higher education on youth unemployment. Following the 2008 financial crisis, youth unemployment returned to the fore as a serious concern among policy makers in Europe. A crucial difference from previous recessions is that this time around supply of higher education opportunities was much higher than in the 1980s, and indeed higher education participation rates grew rapidly in many regions during this period. Drawing on previous work on youth unemployment and the economic impacts of education we identify a variety of channels through which higher education is likely to influence youth unemployment. We examine this issue using a macro-panel of European regions for the period 2002-2012. This decade was characterized by variation in economic activity and higher education rates. Our results suggest that expansion of higher education during this period had a mitigating effect on youth unemployment and not recognizing this external benefit of education risks underestimating the effects of macroeconomic shocks on young people. © 2020, University of Illinois Press. All rights reserved.

Adult content on the Internet may be accessed by children with only a few keystrokes. While separate child-safe accounts may be established, a better approach could be incorporating automatic age estimation capability into the browser. We envision a safer browsing experience by implementing child-safe browsers combined with Internet content rating similar to the film industry. Before such a browser is created it was necessary to test the age estimation module to see whether acceptable error rates are possible. We created an Android application for collecting biometric touch data, specifically tapping data. We arranged with an elementary school, a middle school, a high school, and a university and collected samples from 262 user sessions (ages 5 to 61). From the tapping data, feature vectors were constructed, which were used to train and test 14 regressors and classifiers. Results for regression show the best mean absolute errors of 3.451 and 3.027 years, respectively, for phones and tablets. Results for classification show the best accuracies of 73.63% and 82.28%, respectively, for phones and tablets. These results demonstrate that age estimation, and hence, a child-safe browser, is feasible, and is a worthwhile objective. © 2020 IEEE.

Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust. © 2020 IEEE.

The Southern Connecticut Stellar Interferometer (SCSI) is an intensity interferometer that is designed to use correlated photon arrival times to determine the geometry of stars. Originally a low-cost, two-telescope instrument that used a 1-pixel single-photon avalanche diode (SPAD) detector at the focal plane of each telescope to record photon events, it is now being upgraded to include a third telescope. This will allow for the simultaneous detection of the photon correlation at three baselines, and thus the ability to map out the two-dimensional geometry of the source much more efficiently than with the two-telescope arrangement. Recent papers in the literature suggest that it may be possible to derive phase information in the Fourier domain from such triple correlations for the brightest stars, potentially giving SCSI an imaging capability. Prior to investigating this possibility, steps must be taken to maximize the observing efficiency of the SCSI. We present here our latest efforts in achieving better pointing, tracking, and collimation with our telescopes, and we discuss our first modeling results of the three-telescope situation in order to understand under what conditions the upgraded SCSI could retrieve imaging information. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Based on a pragmatist inspired conception of the social self, the concept of reparations for the harms of genocide is reexamined. Both Raphael Lemkin, the person who invented the term “genocide,” and Claudia Card, a philosopher who examined the evil of genocide, hold similarly expansive notions of the harms inflicted by genocidal violence. Both argued that biological death is not necessarily central to genocide. For Lemkin cultural destruction of the targeted group is just as essential as the actual killing itself. Genocide is a group crime that aims to destroy the group and all the social aspects of group identity. Card similarly sees the target of genocidal violence as the social vitality of the self. This vitality is sustained by group relations. Reparations thus need to be reconceptualize in terms of the restoration of social life of the victim group and not solely on the basis of economic losses. Examples are given for the reparation of the social vitality of communities that have suffered genocide. © 2020 Central European Pragmatist Forum. All rights reserved.

The cyber-behavioral biometric modalities such as keystroke dynamics, mouse dynamics, and touch screen dynamics have come under attacks of different forms in recent days. To address these attacks and other security issues, we present a novel concept of using smartwatch sensor data to continuously verify users in cyberspace and show its potential to be a new standalone cyber-behavioral biometric modality. For our experiments, smartwatch gyroscope and accelerometer data collected from 49 subjects while typing in desktop computer have been considered. We implemented six pattern matching classifiers to compare each verification attempt against the user profile. Experimental results comprising of 282, 240 classification attempts show significantly high True Positive (TP) rates and extremely low False Positive (FP) rates with the highest achieved TP rate of 87.2% and lowest FP rate of 0.2%. With this level of accuracy and natural resiliency to attacks comes with physical biometric property as such in hand movement, we opine that smartwatch movement dynamics, besides being a new biometric trait, can be a solution to the security loopholes in existing cyber-behavioral biometric modalities for continuous verification. © 2020 IEEE.

Promoter regions of long non-coding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than protein-coding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhancer-originated lncRNAs (e-lncRNAs) and promoter-originated lncRNAs (p-lncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p- and e-lncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor p-over e-lncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p-, e-lncRNA promoter recognizer), to classify the promoter of p- and e-lncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoter-proximal transcriptional regulation of p-lncRNAs and e-lncRNAs. © 2019 IEEE.

Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.

Testate amoebae diversity from 28 surface (0-3 cm depth) soil samples found near Cuzco (6 samples), in Machu Piсchu (17 samples), in Aguas Calientes (5 samples), and one bottom sediment sample from the Lake Titicaca near Puno were collected during March of 2016 were analyzed. The 144 testate amoebae species and infra-specific taxa belonging to 27 genera were identified. Nineteen amoebae have not been identified to species level and likely represent new taxa. Species richness varied from one to 54 taxa per sample. The highest diversity was found in rainforests followed by those in meadows and agave habitats. The only bottom sample from Lake Titicaca has yielded two hydrobiont species from the genus Difflugia. In the course of the study, several rare species with limited geographical distribution were observed, namely Centropyxis castaneus, C. compressa, C. deflandriana, C. latideflandriana, C. cf. ohridensis, C. cf. ovoides, C. cf. pannosus, C. stenodeflandriana, Cyclopyxis plana, C. profundistoma, Apodera vas, Argynnia retorta, A. spicata, Certesella certesi, Trachelcorythion pulchellum. Our study fills a geographical gap in the distribution of some flagship species with restricted geographic distribution, e.g. Apodera vas and Certesella certesi in Peru. The results illustrate the continuity of expansion species along the Pacific coast. © 2019 by Revista de Biologia Tropical. All rights reserved.

Many students in higher education have undiagnosed reading disabilities (RDs), but there are few measures to screen for RD in this population. The aim of this study was to determine the ability of tasks that are sensitive to RDs—such as measures of phonemic awareness and working memory—to differentiate university students previously diagnosed with RDs from controls. Participants were university students with an RD (n = 26), a clinical control group diagnosed with attention-deficit/hyperactivity disorder (n = 24), and neurotypical controls (n = 44). Participants completed brief phonological processing and working memory tasks. The RD group scored significantly lower on all tasks than both control groups. The phonological processing tasks alone—without the working memory task—discriminated participants with RDs from controls with excellent sensitivity and specificity. A brief battery of phonemic tasks could be an effective screening instrument for persons with RDs on university campuses. © 2019 John Wiley & Sons, Ltd.

Let D be any of the 10 digraphs obtained by orienting the edges of K4 - e. We establish necessary and sufficient conditions for the existence of a (K∗ n,D)-design for 8 of these digraphs. Partial results as well as some nonexistence results are established for the remaining 2 digraphs. © 2019 Ryan C. Bunge et al., published by Sciendo 2019.

This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior. © 2019 Wael Deabes et al.

ZnO and Fe-doped ZnO nanoparticles were analyzed in ethanol solution and dry powder form using fluorescence spectroscopy. Near-band-edge emission (NBE) and defect emission (DE) peaks were studied. A blue-shift was observed with the NBE emission peak. © OSA 2019. The Author(s).

Carbon nanotubes (CNTs) are a viable product for many different markets. One specific area of promise is the use of CNTs in supercapacitors. The CNT synthesis is a two-step process with the first being the formation of a thin film layer. The thin film substrate is created by means of the Thermal Evaporation Physical Vapor Deposition (TPVD) process. After the thin film substrate is deposited, the CNTs are grown in a Chemical Vapor Deposition (CVD) growth chamber. Analysis of the thin film substrate and CNTs is performed primarily using Scanning Electron Microscopy (SEM). The results show the inclusion of ultrapure water in the CVD process contributed to the success of CNT growth. Multiple prong CNT grow is seen for CVDs trails of silver thin film layer deposited on silicon substrates. The silver nanoparticle had a large diameter with an acute contact angle. The single prong growth indicates the iron nanoparticles formed an obtuse contact angle due to the amorphic surface of alumina substrate. The focus of this project is to explore and investigate different materials and conditions for optimal CNT synthesis in the hopes of creating a uniform forest of vertically aligned carbon nanotubes. © 2019 World Scientific Publishing Company.

Intrinsic fluorescence spectra of fresh normal and cancerous human breast tissues were measured using two selective excitation wavelengths including 290nm and 340nm. Dual-wavelength excitation may reveal more molecular information than single-wavelength excitation. In the meantime, it is significantly faster than the acquisition of excitation-emission (EEM) matrix. Unsupervised machine learning algorithms principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to reduce the dimensionality of the spectral data. The relative concentrations of the basis spectra retrieved by PCA and NMF were considered features of the samples and used to distinguish normal and malignant tissues. The performances of classification using support vector machine (SVM) based on PCA and NMF features were compared. The classification using spectral data with dual-wavelength excitation was compared with single-wavelength excitation. Classification based on NMF-retrieved components from spectral data with dual-wavelength excitation yielded the best performance. © 2019 SPIE.

We report a new measurement of D0-meson production at mid-rapidity (|y|<1) in Au + Au collisions at sNN=200GeV utilizing the heavy flavor tracker, a high resolution silicon detector at the STAR experiment. Invariant yields of D0 mesons with transverse momentum pT9GeV/c are reported in various centrality bins (0-10%, 10-20%, 20-40%, 40-60%, and 60-80%). Blast-wave thermal models are used to fit the D0-meson pT spectra to study D0 hadron kinetic freeze-out properties. The average radial flow velocity extracted from the fit is considerably smaller than that of light hadrons (π,K, and p), but comparable to that of hadrons containing multiple strange quarks (φ,Ξ-), indicating that D0 mesons kinetically decouple from the system earlier than light hadrons. The calculated D0 nuclear modification factors reaffirm that charm quarks suffer a large amount of energy loss in the medium, similar to those of light quarks for pT>4GeV/c in central 0-10% Au + Au collisions. At low pT, the nuclear modification factors show a characteristic structure qualitatively consistent with the expectation from model predictions that charm quarks gain sizable collective motion during the medium evolution. The improved measurements are expected to offer new constraints to model calculations and help gain further insights into the hot and dense medium created in these collisions. © 2019 American Physical Society.