Magneto-tunable terahertz absorption in single-layer graphene: A general approach

Terahertz (THz) anisotropic absorption in graphene could be significantly modified upon applying a static magnetic field on its ultra-fast 2D Dirac electrons. In general, by deriving the generalized Fresnel coefficients for monolayer graphene under applied magnetic field, relatively high anisotropic absorption for the incoming linearly polarized light with specific scattering angles could be achieved. We also prove that the light absorption of monolayer graphene corresponds well to its surface optical conductivity in the presence of a static magnetic field. Moreover, the temperature-dependent conductivity of graphene makes it possible to show that a step by step absorption feature would emerge at very low temperatures. We believe that these properties may be considered to be used in novel graphene-based THz application

Bound states for massive Dirac fermions in graphene in a magnetic step field

We calculate the spectrum of massive Dirac fermions in graphene in the presence of an inhomogeneous magnetic field modeled by a step function. We find an analytical universal relation between the bandwidths and the propagating velocities of the modes at the border of the magnetic region, showing how by tuning the mass term one can control the speed of these traveling edge states.Comment: 7 pages, 3 figure

The role of endnote in the process of reference writing

The aim of this study was to investigate the role of Endnote in the process of reference writing for a thesis. Endnote is software that everybody wants to write references in his research or thesis can use it. Since, there are many resources available because of advances in technology; it seems that Endnote will be used in the future by many researchers. Therefore, learning how to use this software can be a great help to all the researchers. But, the most important question is that: is there any significant relationship between using of Endnote and having better references in thesis? Therefore, two groups of Master students were selected as the subjects of the present study as control and experimental groups. They had to do a research before their graduation .First of all, the student as experimental group had instruction in order to get familiar with Endnote software and another group (control group) did not receive any instruction.It must be mentioned that the site of this study was Iran, Bojnord and the samples were selected the master students who were studying in Azad University. And the instruments used in this study for data collection were questionnaire and interview. The result showed that there were not any significant differences between control and experimental groups and consequently, the researcher has concluded that the using of Endnote software cannot be effective enough to make any differences between two groups

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled 'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and was accepted at the ICLR 2016 worksho

Unit and Unitary Cayley Graphs for the Ring of Eisenstein Integers Modulo N

Let En be the ring of Eisenstein integers modulo n. We denote by G(En) and GEn, the unit graph and the unitary Cayley graph of En, respectively. In this paper, we obtain the value of the diameter, the girth, the clique number and the chromatic number of these graphs. We also prove that for each n>1, the graphs G(En) and GEn are Hamiltonian.I would like to thank the referee for the valuable suggestions and comments

Phase-sensitive plasmonic biosensor using a portable and large field-of-view interferometric microarray imager

Nanophotonics, and more specifically plasmonics, provides a rich toolbox for biomolecular sensing, since the engineered metasurfaces can enhance light–matter interactions to unprecedented levels. So far, biosensing associated with high-quality factor plasmonic resonances has almost exclusively relied on detection of spectral shifts and their associated intensity changes. However, the phase response of the plasmonic resonances have rarely been exploited, mainly because this requires a more sophisticated optical arrangement. Here we present a new phase-sensitive platform for high-throughput and label-free biosensing enhanced by plasmonics. It employs specifically designed Au nanohole arrays and a large field-of-view interferometric lens-free imaging reader operating in a collinear optical path configuration. This unique combination allows the detection of atomically thin (angstrom-level) topographical features over large areas, enabling simultaneous reading of thousands of microarray elements. As the plasmonic chips are fabricated using scalable techniques and the imaging reader is built with low-cost off-the-shelf consumer electronic and optical components, the proposed platform is ideal for point-of-care ultrasensitive biomarker detection from small sample volumes. Our research opens new horizons for on-site disease diagnostics and remote health monitoring.Peer ReviewedPostprint (published version

Improving official statistics in emerging markets using machine learning and mobile phone data

Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90%. Mobile phone data, also called CDR, are generated everytime phones are used and recorded by carriers at scale. CDR have generated groundbreaking insights in public health, official statistics, and logistics. However, the fact that most phones in developing countries are prepaid means that the data lacks key information about the user, including gender and other demographic variables. This precludes numerous uses of this data in social science and development economic research. It furthermore severely prevents the development of humanitarian applications such as the use of mobile phone data to target aid towards the most vulnerable groups during crisis. We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates. We here present a systematic cross-country study of the applicability of machine learning for dataset augmentation at low cost. We validate our framework by showing how it can be used to reliably predict gender and other information for more than half a million people in two countries. We show how standard machine learning algorithms trained on only 10,000 users are sufficient to predict individual’s gender with an accuracy ranging from 74.3 to 88.4% in a developed country and from 74.5 to 79.7% in a developing country using only metadata. This is significantly higher than previous approaches and, once calibrated, gives highly accurate estimates of gender balance in groups. Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set. We finally show that our indicators capture a large range of behavioral traits using factor analysis and that the framework can be used to predict other indicators of vulnerability such as age or socio-economic status. Mobile phone data has a great potential for good and our framework allows this data to be augmented with vulnerability and other information at a fraction of the cost

Modeling extra-deep electromagnetic logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.POCTEFA 2014-2020 PIXIL (EFA362/19) MTM2016-76329-

An online experiment during the 2020 US-Iran crisis shows that exposure to common enemies can increase political polarization

A longstanding theory indicates that the threat of a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1670 Republicans and Democrats in the United States were asked to complete an online social learning task with a bot that was labeled as a member of the opposing party. Prior to this task, we exposed respondents to primes about (a) a common enemy (involving Iran and Russia); (b) a patriotic event; or (c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of priming, we found that Republicans-and particularly those with very strong conservative views-were significantly less likely to learn from Democrats when primed about a common enemy. Because our study was in the field during the 2020 Iran Crisis, we were able to further evaluate this finding via a natural experiment-Republicans who participated in our study after the crisis were even less influenced by the beliefs of Democrats than those Republicans who participated before this event. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization in the United States. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science