nils-herrmann/clean_scirepeval huggingface dataset:
Unusual pigments found in a painting by Giotto (c. 1266 1337) reveal diversity of materials used by medieval artists
Venue: Heritage Science

BackgroundThe important trecento Florentine artist Giotto (c. 1266 1337) is renowned for his naturalistic and realistic works in tempera and fresco. His innovative paintng style involved painting expressive, emotive faces and use of pictorial devices for depicting space. This report focuses on the analysis of the materials and methods used in a panel in the collection of the National Gallery of Art, Madonna and Child (1310/1315).ResultsGiotto used inky washes under thin layers of egg tempera paint. Yellow iron earth and lead tin yellow are present in the paint used to depict the lining of the Virgin's mantle. SEM EDX of one of the yellow pigments confirmed it is lead tin yellow type II, PbSn1 xSixO3. The ratio of colorant to the glassy phase indicates this material was produced for use as a pigment rather than as a glass. Ultramarine was not used in this painting, azurite is the blue pigment. The azurite used here does not contain elemental impurities, however malachite and the rare green blue mineral mixite, BiCu6(OH)6(AsO4)3(H2O)3, are found in the blue paint.ConclusionsWe do not know if Giotto intentionally used mixite as a green blue pigment or if it was adventitious with the azurite, though given the rarity of the mineral, the latter is more probabale. Nevertheless, the artist's choice of color was deliberate and the presence of mixite and the clear hued lead tin yellow pigment attest to his coloristic decisions, in this case opting for cool hues rather than the warm blue of ultramarine which we typically associate with paintings of the Madonna from this time period.Graphical abstractA microscopic sample from Giotto's Madonna and Child (c. 1310/15) shows the paint layer structure and range of pigment colors. SEM EDX reveals the chemical diversity of the phases, and BSED proves the presence of the rare mineral mixite, BiCu6(OH)6(AsO4)3H2O
Electrochemical characterization of biodeterioration of paint films containing cadmium yellow pigment
Venue: Journal of Solid State Electrochemistry

The voltammetry of microparticles (VMP) methodology was used to characterize the biological attack of different bacteria and fungi to reconstructed egg tempera and egg linseed oil emulsion paint films containing cadmium yellow (CdS) which mimic historical painting techniques. When these paint films are in contact with aqueous acetate buffer, different cathodic signals are observed. As a result of the crossing of VMP data with attenuated total reflectance Fourier transform infrared spectroscopy (ATR FTIR) scanning electrochemical microscopy (SECM) field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) these voltammetric signals can be associated with the reduction of CdS and different complexes associated to the proteinaceous and fatty acid fractions of the binders. After biological attack with different fungi (Acremonium chrysogenum, Aspergillus niger, Mucor rouxii, Penicillium chrysogenum, and Trichoderma pseudokoningii) and bacteria (Arthrobacter oxydans, Bacillus amyloliquefaciens, and Streptomyces cellulofans) the observed electrochemical signals experience specific modifications depending on the binder and the biological agent, allowing for an electrochemical monitoring of biological attack.
Artificial Intelligence Impact on Cyber Security

Information technology and the world of web are increasing at a very fast pace and so are increasing the crimes related with cyber world. Cyber systems are much prone to various kinds of threats, intrusions and dynamically evolving risks related with them. Software based on conventional security algorithms and mere and human involvement is inadequate for ensuring complete cyber security. Thus, there is an increasing requirement for more powerful and intelligent cyber defense systems to serve the purpose of providing cyber security. The innovative practices of Artificial Intelligence are getting more popular in assisting users to fight crimes and related problems in cyber space. The purpose of this paper is to review cyber security, its associated risks and the advancements made so far by applying Artificial Intelligence methods in cyber defense. The paper also aims in demonstrating he applicability and effectiveness of these AI techniques in present scenario.
A robust quantitative local central limit theorem with applications to enumerative combinatorics and random combinatorial structures

A useful heuristic in the understanding of large random combinatorial structures is the Arratia Tavare principle, which describes an approximation to the joint distribution of component sizes using independent random variables. The principle outlines conditions under which the total variation distance between the true joint distribution and the approximation should be small, and was successfully exploited by Pittel in the cases of integer partitions and set partitions. We provide sufficient conditions for this principle to be true in a general context, valid for certain discrete probability distributions which are \textit{perturbed log concave} via a quantitative local central limit theorem. We then use it to generalize some classical asymptotic statistics in combinatorial theory, as well as assert some new ones.
Sexual Scripts and the Sexual Behavior of Men and Women Who Use Pornography

Using data collected from 1,880 heterosexual men and women residing in the United States, this study investigated the associations among gender, pornography consumption, and 20 sexual behaviors observed in popular pornography. Acts were grouped according to whether participants reported engaging or being interested in trying specific sexual behaviors as the (a) aggressor (e.g. hairpulling, spanking, or choking) (b) target (e.g. being spanked or choked) or (c) uncommon and/or degrading sexual activity (e.g. male ejaculation in female partner's mouth, anal sex, double penetration, and ass to mouth) Using sexual script theory, we hypothesized greater use of pornography would be associated with greater likelihood of both having engaged in and interest in engaging in these sexual behaviors. We further hypothesized gender differences consistent with pornographic sexual scripts that frequently portray men as aggressors and women as targets of aggression. Hierarchical multiple regressions revealed significant main effects for gender and pornography use on the three categories of sexual behavior but no significant interactions. Higher pornography use was associated with greater likelihood of both engaging and being interested in trying all categories of sexual behavior. Men were more likely than women to have engaged in aggressive and degrading/uncommon behaviors, and women were more likely than men to have engaged in target behaviors. However, men were more interested than women in trying all three categories of sexual behavior. Results provide partial support for sexual script theory; while higher pornography use increased interest and prior engagement in pornography like sexual behavior, the increases in types of sexual behavior (aggressor, target, or uncommon/degrading) were not moderated by gender.
Optimal Face Iris Multimodal Fusion Scheme
Venue: Symmetry

Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA) a novel population based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.
Abbas E. Kitabchi, PhD, MD: An Exemplary Mentor and Clinical Researcher
Venue: Diabetes Care

The word "mentor" was first used in Homer's epic poem The Odyssey When Odysseus, the king of Ithaca, went to fight the Trojan War, he asked Mentor to serve as a teacher and overseer to his son Telemachus. Mentor failed in his duties, and it was Athena, goddess of war and patroness of the arts and industry, who assumed the form of Mentor and served as Telemachus' wise and trusted adviser and counselor. The first recorded modern usage of the term can be traced to the 18th century book entitled Les Aventures de Telemaque by the French writer Fenelon. Since then, the word "mentor" has evolved to mean trusted adviser, a wise and responsible tutor who shares knowledge with and inspires, challenges, and serves as a role model to a less experienced person. Dr. Abbas E. Kitabchi exemplifies all the attributes of a great mentor, as can be attested by the large number of health care professionals that have benefited from his mentoring during the past four decades. Abbas E. Kitabchi at the Clinical Research Unit, UTHSC, Memphis, TN Dr. Kitabchi, or Abbie as his friends and relatives call him, was born in Teheran, Iran, in 1933. He was the third among seven siblings and the first in his family to receive a high school education. His father, Hossein Eqbal Kitabchi, was a publisher and his mother was a housewife. After completing high school, he immigrated to the U.S. at 17 years of age to attend Cornell College in Mount Vernon, IA, to follow his father's dream for Abbie to become a heart surgeon. As a young immigrant with almost no ability to speak English and only $600 in my pocket, it was indeed uncomfortable and challenging at first. I was scared and lonely living in
Software defined networking for low latency 5G core network
Venue: 2016 International Conference on Military Communications and Information Systems (ICMCIS)

Mobile communications have grown exponentially these last few years leading to an increasing demand. Software Defined Networking appears to be a promising technology in order to provide flexible Quality of Service and very low latency. By modifying the 4G LTE architecture and integrating OpenFlow (SDN protocol) towards the 5G architecture, this new architecture promises to fulfill the requirements and to achieve better performances while participating to the network functions virtualization.
GDTN: Genome Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter UA Collaboration
Venue: PloS one

With a more Internet savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA) The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real time testbed and simulation environment.
Excitonic Effects in Tungsten Disulfide Monolayers on Two Layer Graphene.
Venue: ACS nano

Light emission in atomically thin heterostructures is known to depend on the type of materials and the number and stacking sequence of the constituent layers. Here we show that the thickness of a two dimensional substrate can be crucial in modulating the light emission. We study the layer dependent charge transfer in vertical heterostructures built from monolayer tungsten disulfide (WS2) on one and two layer epitaxial graphene, unravelling the effect that the interlayer electronic coupling has on the excitonic properties of such heterostructures. We bring evidence that the excitonic properties of WS2 can be effectively tuned by the number of supporting graphene layers. Integrating WS2 monolayers with two layer graphene leads to a significant enhancement of the photoluminescence response, up to 1 order of magnitude higher compared to WS2 supported on one layer graphene. Our findings highlight the importance of substrate engineering when constructing atomically thin layered heterostructures.
Doping Induced Tunable Wettability and Adhesion of Graphene.
Venue: Nano letters

We report that substrate doping induced charge carrier density modulation leads to the tunable wettability and adhesion of graphene. Graphene's water contact angle changes by as much as 13deg as a result of a 300 meV change in doping level. Upon either n or p type doping with subsurface polyelectrolytes, graphene exhibits increased hydrophilicity. Adhesion force measurements using a hydrophobic self assembled monolayer coated atomic force microscopy probe reveal enhanced attraction toward undoped graphene, consistent with wettability modulation. This doping induced wettability modulation is also achieved via a lateral metal graphene heterojunction or subsurface metal doping. Combined first principles and atomistic calculations show that doping modulates the binding energy between water and graphene and thus increases its hydrophilicity. Our study suggests that the doping induced modulation of the charge carrier density in graphene influences its wettability and adhesion [corrected] This opens up unique and new opportunities for the tunable wettability and adhesion of graphene for advanced coating materials and transducers.
Controlling Surface Potential of Graphene Using dc Electric Field

In this work, we study surface potential of graphite deposited on SiO 2 /Si substrate using Kelvin Probe Force Microscopy (KPFM) and Electric Force Microscopy (EFM) The amplitude modulated KPFM (AM KPFM) shows that the graphene layer work function is 4.69+ 0.02 eV, whereas frequency modulated KPFM (FM KPFM) revealed 4.50+ 0.02 eV. The work function indifference of 0.19+ 0.02 eV was attributed to the superior resolution of FM KPFM and higher detection sensitivity of AM KPFM. Subsequent EFM mapping suggests that the phase monotonically increases with increasing applied dc bias voltage in the range from 5 V to 5 V. This phase shift is ascribed to the induced charge polarization at tip graphene surface due to interatomic interactions induced by dc field effects.
Device applications of epitaxial graphene on silicon carbide

Graphene has become an extremely hot topic due to its intriguing material properties allowing for ground breaking fundamental research and applications. It is one of the fastest developing materials during the last several years. This progress is also driven by the diversity of fabrication methods for graphene of different specific properties, size, quantity and cost. Graphene grown on SiC is of particular interest due to the possibility to avoid transferring of free standing graphene to a desired substrate while having a large area SiC (semi insulating or conducting) substrate ready for device processing. Here, we present a review of the major current explorations of graphene on SiC in electronic devices, such as field effect transistors (FET) radio frequency (RF) transistors, integrated circuits (IC) and sensors. The successful role of graphene in the metrology sector is also addressed. Typical examples of graphene on SiC implementations are illustrated and the drawbacks and promises are critically analyzed.
Click and Patterned Functionalization of Graphene by Diels Alder Reaction.
Venue: Journal of the American Chemical Society

Chemical functionalization is a promising approach to controllably manipulate the characteristics of graphene. Here, we designed cis dienes, featuring two dihydronaphthalene backbones, to decorate a graphene surface via Diels Alder (DA) click reaction. The installation of a diene moiety into a nonplanar molecular structure to form cis conformation enables a rapid ~5 min) DA reaction between graphene and diene groups. Patterned graphene of sub micrometer resolution can be obtained by easily soaking poly(methyl methacrylate) masked graphene in solution of hydroxyl substituted cis diene at room temperature. The functionalization degree can be further controlled by carrying out the reaction at higher temperature. The present result gives important insight into the effect of molecular conformation on the graphene functionalization process, and provides an effective and facile method for graphene functionalization.
Biodiversity and ecosystem functioning in dynamic landscapes
Venue: Philosophical Transactions of the Royal Society B: Biological Sciences

The relationship between biodiversity and ecosystem functioning (BEF) and its consequence for ecosystem services has predominantly been studied by controlled, short term and small scale experiments under standardized environmental conditions and constant community compositions. However, changes in biodiversity occur in real world ecosystems with varying environments and a dynamic community composition. In this theme issue, we present novel research on BEF in such dynamic communities. The contributions are organized in three sections on BEF relationships in (i) multi trophic diversity, (ii) non equilibrium biodiversity under disturbance and varying environmental conditions, and (iii) large spatial and long temporal scales. The first section shows that multi trophic BEF relationships often appear idiosyncratic, while accounting for species traits enables a predictive understanding. Future BEF research on complex communities needs to include ecological theory that is based on first principles of species averaged body masses, stoichiometry and effects of environmental conditions such as temperature. The second section illustrates that disturbance and varying environments have direct as well as indirect (via changes in species richness, community composition and species' traits) effects on BEF relationships. Fluctuations in biodiversity (species richness, community composition and also trait dominance within species) can severely modify BEF relationships. The third section demonstrates that BEF at larger spatial scales is driven by different variables. While species richness per se and community biomass are most important, species identity effects and community composition are less important than at small scales. Across long temporal scales, mass extinctions represent severe changes in biodiversity with mixed effects on ecosystem functions. Together, the contributions of this theme issue identify new research frontiers and answer some open questions on BEF relationships in dynamic communities of real world landscapes.
Relaxed Earth Mover's Distances for Chain and Tree connected Spaces and their use as a Loss Function in Deep Learning
Venue: ArXiv

The Earth Mover's Distance (EMD) computes the optimal cost of transforming one distribution into another, given a known transport metric between them. In deep learning, the EMD loss allows us to embed information during training about the output space structure like hierarchical or semantic relations. This helps in achieving better output smoothness and generalization. However EMD is computationally expensive.Moreover, solving EMD optimization problems usually require complex techniques like lasso. These properties limit the applicability of EMD based approaches in large scale machine learning. We address in this work the difficulties facing incorporation of EMD based loss in deep learning frameworks. Additionally, we provide insight and novel solutions on how to integrate such loss function in training deep neural networks. Specifically, we make three main contributions: (i) we provide an in depth analysis of the fastest state of the art EMD algorithm (Sinkhorn Distance) and discuss its limitations in deep learning scenarios. (ii) we derive fast and numerically stable closed form solutions for the EMD gradient in output spaces with chain and tree connectivity; and (iii) we propose a relaxed form of the EMD gradient with equivalent computational complexity but faster convergence rate. We support our claims with experiments on real datasets. In a restricted data setting on the ImageNet dataset, we train a model to classify 1000 categories using 50K images, and demonstrate that our relaxed EMD loss achieves better Top 1 accuracy than the cross entropy loss. Overall, we show that our relaxed EMD loss criterion is a powerful asset for deep learning in the small data regime.
Document fraud detection by ink analysis using texture features and histogram matching
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

This paper proposes an efficient method to detect any fraud document. It considers texture features, such as Local binary pattern and Gabor filters and performs a histogram matching to analyze the document. Texture features and RGB color information of each word in the document are extracted. Normalized histograms of two different images of a document are compared to generate a matching score for decision making. The advantage of the proposed method is that it is fully unsupervised and hence, it does not require any prior knowledge and is less time consuming. The method is found to be very efficient.
Machine learning algorithms for document clustering and fraud detection
Venue: 2016 International Conference on Data Science and Engineering (ICDSE)

Machine Learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze the data to understand the patterns in it. The unstructured data gets transformed into semi structured data and structured data in stages by using text mining and clustering (k means) techniques. Classification is another machine learning technique that can be implemented for use cases like "fraud detection and cross sell up sell opportunity identification" in banking, financial services and insurance industry. This paper focuses on the implementation of both document clustering algorithm and a set of classification algorithms (Decision Tree, Random Forest and Naive Bayes) along with appropriate industry use cases. Also, the performance of three classification algorithms will be compared by calculation of "Confusion Matrix" which in turn helps us to calculate performance measures such as, "accuracy" "precision" and "recall"