Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive sophistication and deliberation times

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide an individually-measurable correlate of cognitive effort. We test a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games.
Nonlinear identification of synchronous generators using a local model approach

A new iterative approach is proposed to model synchronous generators. Different local structures are used for the operating conditions of the synchronous generator with different complexity. Unlike most of the existing methods, which increase the model accuracy based on increasing the number of local model, in the proposed method, there are two choices for increasing model precision at each iteration: (i) increasing the number of local models in one region, or (ii) increasing local model complexity in the same region. The proposed method has been tested on experimental data collected on a 3 kVA micro-machine. Streszczenie. Zaproponowano nową iteracyjną metode modelowania generatorow synchronicznych. W porownaniu do istniejących metod w zaproponowanej metodzie istnieją dwa wybory zwiekszania dokladności iteracji: przez zwiekszanie liczby lokalnych modeli lub przez zwiekszanie zlozoności modelu lokalnego. Metode sprawdzono na przykladzie generatora 3 kVA. (Nieliniowe modelowanie generatorow synchronicznych prze wykorzystaniu modelu lokalnego)
Neural Theorem Prover

In the current project, I build a neural network model to prove theorems in logical forms. Particularly, the model receives a set of axioms (premises) and a theorem to prove (goal). The model needs to select multiple axioms from the axiom list that could prove the goal theorem. I reframe it as a sequence to sequence learning problem, and use double recurrent neural networks to encode the theorem and output a sequence of axioms. I build three models and showed that the model which dynamically encodes the axioms perform much better than the model that does not do so. The current work demonstrates the power of deep neural networks in automated theorem proving and provide a cognitively plausible computational model for logical inference.
Ideal point error for model assessment in data-driven river flow forecasting

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking.
ASSOCIATIVE REGRESSIVE DECISION RULE MINING FOR PREDICTING CUSTOMER SATISFACTORY PATTERNS

Opinion mining also known as sentiment analysis, involves customer satisfactory patterns, sentiments and attitudes toward entities, products, services and their attributes. With the rapid development in the field of Internet, potential customer’s provides a satisfactory level of product/service reviews. The high volume of customer reviews were developed for product/review through taxonomy-aware processing but, it was difficult to identify the best reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is developed to predict the pattern for service provider and to improve customer satisfaction based on the review comments. Associative Regression based Decision Rule Mining performs twosteps for improving the customer satisfactory level. Initially, the Machine Learning Bayes Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After that, Regressive factor of the opinion words and Class labels were checked for Association between the words by using various probabilistic rules. Based on the probabilistic rules, the opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of service preferred by the customers with their review comments. The Associative Regressive Decision Rule helps the service provider to take decision on improving the customer satisfactory level. The experimental results reveal that the Associative Regression Decision Rule Mining (ARDRM) technique improved the performance in terms of true positive rate, Associative Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of similar pattern.
Evaluating an impact origin for Mercury's high‐magnesium region

During its four years in orbit around Mercury, the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft's X‐ray Spectrometer revealed a large geochemical terrane in the northern hemisphere that hosts the highest Mg/Si, S/Si, Ca/Si, and Fe/Si and lowest Al/Si ratios on the planet. Correlations with low topography, thin crust, and a sharp northern topographic boundary led to the proposal that this high‐Mg region is the remnant of an ancient, highly degraded impact basin. Here we use a numerical modeling approach to explore the feasibility of this hypothesis and evaluate the results against multiple mission‐wide data sets and resulting maps from MESSENGER. We find that an ~3000 km diameter impact basin easily exhumes Mg‐rich mantle material but that the amount of subsequent modification required to hide basin structure is incompatible with the strength of the geochemical anomaly, which is also present in maps of Gamma Ray and Neutron Spectrometer data. Consequently, the high‐Mg region is more likely to be the product of high‐temperature volcanism sourced from a chemically heterogeneous mantle than the remains of a large impact event.
Selective Feeding Determines Patterns of Nutrient Release by Stream Invertebrates

Abstract: One common stoichiometric approach to predicting patterns of nutrient release (excretion + egestion) by animals in aquatic ecosystems is to base predictions on elemental mass-balance constrained by homeostatic maintenance. An easily measured resource composite (i.e., seston, epilithon, or leaf litter) often is used to represent ingested stoichiometry, but whether such a composite is a good indicator of food actually ingested is a relatively unexplored assumption. We examined the application of a stoichiometric model to the diets of 4 generalist stream invertebrates. We fed 3 trichopteran and 1 amphipod taxa rations consisting of cultured algae, stream epilithon, and several species of conditioned leaf litter. The rations ranged widely in C:N from 10 to 69 (molar) and in C: P from 165 to 3500. After a 2-d feeding period, we measured NH4+ and PO43- excretion, and C, N, and P egestion rates. The relationships observed between the stoichiometries of release and ration were unexpected. Total N: P release rates conformed to stoichiometric predictions for only 1 taxon. Excretion and egestion rates and ratios were generally similar across diets and rarely varied with ration stoichiometry. These patterns were the result of smaller-than-expected responses to leaf-litter rations, which were the most imbalanced relative to body stoichiometry. Analysis of the C:N stoichiometry of foregut material for 2 taxa showed selective ingestion of an N-rich fraction of leaf litter, in 1 case reducing an apparent 8.4:1 C:N imbalance between diet and body composition to 1.5:1. Our results show that selective feeding can reduce potential stoichiometric imbalances, altering patterns of nutrient release relative to expectations based on bulk-diet stoichiometry. Assuming that stream invertebrates consume materials stoichiometrically similar to a resource composite can obscure understanding of stoichiometric imbalances and the role of invertebrates in nutrient cycles.
Integrating Structured and Unstructured Data Using Text Tagging and Annotation 8

The popular practice of handling structured and unstructured data as distinct information entities often results in decision management failure. Studies show that the majority of data resides in the unstructured format in many organizations, so an infrastructure that meaningfully integrates and manages structured and unstructured data would act as a complete data warehouse (CDW)—the fundamental backbone for true enterprise business intelligence. This approach would also enable data owners to treat data as data rather than segregating it as structured or unstructured. We present the role of text tagging and annotation techniques as a preprocessing step toward the integration of unstructured and structured data. The nature of unstructured data makes it hard to search, retrieve, and analyze; directly integrating it with structured data is non-trivial. However, structure and semantic information can be added to unstructured text content using natural language processing techniques; combining this data with structured data is thus more efficient. Text tagging and annotation are also popular natural language processing techniques. We present a high-level architecture of a generic system that uses text tagging as a preprocessing step to integrate structured and unstructured data. We illustrate the benefits of tagging natural-language text with two realworld applications. Integrating Structured and Unstructured Data Using Text Tagging and Annotation Sreekumar Sukumaran and Ashish Sureka Ashish Sureka is a researcher and practitioner in data mining at Software Engineering and Technology Labs (SETLabs) of Infosys Technologies Limited, India. Ashish_Sureka@infosys.com Sreekumar Sukumaran heads the information management research group at Software Engineering and Technology Labs (SETLabs), Infosys Technologies Limited, India. Sreekumar_S@infosys.com
Datalog and Recursive Query Processing

In recent years, we have witnessed a revival of the use of recursive queries in a variety of emerging application domains such as data integration and exchange, information extraction, networking, and program analysis. A popular language used for expressing these queries is Datalog. This paper surveys for a general audience the Datalog language, recursive query processing, and optimization techniques. This survey differs from prior surveys written in the eighties and nineties in its comprehensiveness of topics, its coverage of recent developments and applications, and its emphasis on features and techniques beyond "classical" Datalog which are vital for practical applications. Specifically, the topics covered include the core Datalog language and various extensions, semantics, query optimizations, magic-sets optimizations, incremental view maintenance, aggregates, negation, and types. We conclude the paper with a survey of recent systems and applications that use Datalog and recursive queries.
Atomic Structure of Graphene and h-BN Layers and Their Interactions with Metals

Since the isolation of graphene and other two-dimensional (2D) materials, such as hexagonal boron nitride (h-BN), these just one atom layer thick materials have spurred a flurry of investigations into their structural properties, morphology and chemistry [1, 2]. Graphene is a 2D crystalline form of carbon and a basic building block for carbon allotropes such as fullerenes, carbon nanotubes and graphite [3]. In graphene, carbon atoms are packed in a planar honeycomb network. The unit cell of single-layer graphene consists of two carbon atoms, separated by 1.42 A, with a lattice constant of 2.46 A. Each atom has s, px and py orbitals and is bonded to three neighbor atoms in the lattice, forming an sp2 atomic network. The pz orbitals overlap between neighboring atoms resulting in so-called filled π and empty π* states, which respectively form the valence and the conduction bands in graphene. However, socalled Bernal (AB-) stacked double layer graphene has four electrons in the unit cell: in this configuration, the individual layers are stacked but shifted with respect to one-another, so that the carbon atoms in one layer sit on top of the empty hexagon centers in the underlying layer. Due to the absence of any measurable bandgap in both single and double layer graphene these materials are often called zero-gap semiconductors. The lack of a bandgap is a serious limitation to the use of graphene in electronics as switching off any graphene-based device would be extremely difficult. Additionally, researchers have shown that the substrate on which graphene layers are placed has dramatic effects on its transport properties. SiO2/Si substrates were used for initial graphene devices [1]. However, due to their surface roughness and to the presence of charged impurities graphene devices built directly on SiO2 substrates could not demonstrate any marked improvement over more traditional designs: they were seemingly never able to utilize the intrinsic properties of graphene [4]. Other substrates (2D materials)
Single nucleotide polymorphisms and mRNA expression of VEGF-A in papillary thyroid carcinoma: Potential markers for aggressive phenotypes Short title: VEGF in papillary thyroid carcinoma

Background and objectives: Polymorphisms of the VEGF gene are known to affect the biological behaviour of cancers but have seldom been studied in thyroid cancer. The aim of the current study is to evaluate the prevalence and relevance of VEGF-A polymorphisms and mRNA expression in papillary thyroid carcinoma (PTC). Materials and methods: Genomic DNA and total RNA were isolated from paraffin embedded tissue from 91 PTC (51 conventional PTC and 40 follicular variant) and 78 control thyroid tissues. Three DNA polymorphisms (+936C>T, +405 C>G and -141 A>C) in the 3' and 5' untranslated region (3'-UTR, 5'-UTR) of VEGFA were studied using PCR and RFLP. Also, the mRNA expression of VEGFA in these tissues was studied by real-time PCR. Results: Distribution of polymorphisms in the 5'-UTR (VEGF-141 A>C and +405 C>G) and 3'-UTR (VEGF +936C>T) were all significantly different in PTC and benign thyroid tissue (p= 0.0001, 0.001 and 0.028 respectively). The VEGF -141 C allele was more common in PTC with lymph node metastases (p= 0.026). VEGF + 405 G alleles and VEGF+936 CC genotype were more common in PTC of advanced pathological staging (p= 0.018 and 0.017 respectively). Also, increased expression of VEGFA mRNA was noted in PTC compared to control (p= 0.009). Within the group of patients with conventional PTC, those with lymph nodal metastases had a higher level of VEGF-A mRNA expression than other patients (p=0.0003). Conclusion: These findings suggest that VEGF polymorphisms and mRNA expression may predict the aggressiveness behaviour of thyroid cancer.
Discrete Quasiconformal Groups I

In this paper we begin an investigation into the geometry of discrete quasiconformal groups. The structure of general topological groups is not as well understood as that of conformal or Mobius groups, a class of groups which have been studied intensively for many years. We hope to gain additional insight into both families by studying groups of homeomorphisms of uniformly bounded dilatation acting on a domain D in ~n, a class which lies between the two kinds mentioned above. One natural way to construct such a group is to conjugate a conformal group by a quasiconformal mapping. Gehring and Palka first asked in [9] if this were the only method, that is, if each quasiconformal group is the quasiconformal conjugate of a conformal group. Sullivan [27] and Tukia [28] answered this question in the affirmative for quasiconformal groups acting on subsets of the extended complex plane ~2; a recent paper by Hinkkanen [11] establishes an analogous result for quasisymmetric or one-dimensional quasiconformal groups acting on ~. Later Tukia [29] constructed for every n > 2 a quasiconformal group which is not isomorphic as a topological group, and hence not quasiconformally conjugate, to a Mobius group. Tukia's examples are not discrete groups and his methods were subsequently modified by Martin [21] to yield discrete groups which are not quasiconformally conjugate to a conformal group. Finally McKemie [22] used Tukia's methods to construct for each K> 1 a Kquasiconformal group not quasiconformally conjugate to a Mobius group. We restrict our attention in this article to discrete quasiconformal groups acting on ~ n and show that these groups resemble in many ways their conformal counterparts. In § 3 we introduce a characteristic convergence property for quasiconformal groups and devote the remainder of Part I of this paper to the study of convergence groups, groups of self homeomorphisms of ~n which have this property. In § 4 we derive several important properties of the limit set L( G) of a discrete convergence group G; for example, L( G) is perfect if it contains more than two points. In § 5 we describe the elementary discrete convergence groups G, that is, those with card(L( G)) ~ 2, and show that we can classify the elements g of a discrete convergence group G as elliptic, parabolic, or loxodromic according to their order and number of fixed points. In § 6 we study the behaviour of iterates of parabolic and loxodromic elements and then use this information to investigate the structure and limit sets of the non-elementary discrete convergence groups G with card(L(G)) > 2. Finally, in § 7 we derive a condition sufficient to imply that a group of homeomorphisms is a convergence
Classification of origin AS behavior based on BGP update streams

This thesis investigates the extent to which BGP updates reveal information about traffic engineering in the Internet. The introduced analysis relies on two weeks of data from multiple observation points. It provides a quantitative analysis of the usage of AS path prepending. It could be observed that AS path prepending is being done by ISPs in the core of the Internet, as well as by ISPs at the edge. It could be shown that BGP updates reveal far more prepended AS paths than static dump files do. The thesis also carries out an in-depth classification of BGP update messages into ”transitions”. Besides the expected traffic engineering, the classification suggests that AS path prepending is also related to backup links. The challenges faced when interpreting the results of the classification indicate that additional analysis of BGP updates is necessary to understand the intentions behind network behaviors.
Exploration and refinement of regression tree models with interactive maps and spatial data transformations

ABSTRACT The problem we address is prediction of expected values of some attribute of spatial objects based on values of other attributes, including the geographic positions. A common approach to obtaining such predictions is regression modelling. It is highly desirable that predictive models are not only accurate but also understandable to the users, which gives preference to simpler models. We propose a set of visualisation techniques and interactive operations that supports exploration, evaluation, refinement and simplification of regression tree models. In particular, the analyst can investigate how the model components and their properties are related to the spatial distribution of the objects, and can make the model better account for the spatial aspect of the data by generating new space-based attributes and supplying them to the model building tool.
Differentiation in the marbled white butterfly species complex driven by multiple evolutionary forces

Genetic and phenotypic data may show convergent or contrasting spatial patterns. Discrepancies between markers may develop in response to different evolutionary forces. In this study we analyse inter‐ and intraspecific differentiation of closely related taxa in the marbled white butterfly species group. Based on genetic and phenotypic characters we test for potential evolutionary drivers and propose a taxonomic revision.
A Current Overview of the Molecular Effects of Nanosilver

Nanosilver plays an important role in nanoscience and nanotechnology, and is becoming increasingly used for applications in nanomedicine. Nanosilver ranges in size from one to one hundred nanometers. Smaller particles more readily enter cells and interact with the cellular components. The exposure dose, particle size, coating, and aggregation state of the nanosilver, as well as the cell type or organism that it is tested on, all have a large determining factor on the effect and potential toxicity of nanosilver. A high exposure dose to nanosilver alters the cellular stress responses and initiates cascades of signaling that can eventually trigger organelle autophagy and apoptosis. This review summarizes the current knowledge of the effects of nanosilver on cellular metabolic function and response to stress. Both the causative effects of nanosilver on oxidative stress, endoplasmic reticulum stress, and hypoxic stress, as well as the effects of nanosilver on the responses to such stresses, are outlined. The interactions and effects of nanosilver on cellular uptake, oxidative stress (reactive oxygen species), inflammation, hypoxic response, mitochondrial function, endoplasmic reticulum function and the unfolded protein response, autophagy and apoptosis, angiogenesis, epigenetics, genotoxicity, and cancer development and tumorigenesis, as well as other pathway alterations are examined in this review.
Reinforcement Systems for Carbon Concrete Composites Based on Low-Cost Carbon Fibers

Carbon concrete polyacrylonitrile (PAN)/lignin-based carbon fiber (CF) composites are a new promising material class for the building industry. The replacement of the traditional heavy and corroding steel reinforcement by carbon fiber (CF)-based reinforcements offers many significant advantages: a higher protection of environmental resources because of lower CO2 consumption during cement production, a longer lifecycle and thus, much less damage to structural components and a higher degree of design freedom because lightweight solutions can be realized. However, due to cost pressure in civil engineering, completely new process chains are required to manufacture CF-based reinforcement structures for concrete. This article describes the necessary process steps in order to develop CF reinforcement: (1) the production of cost-effective CF using novel carbon fiber lines, and (2) the fabrication of CF rebars with different geometry profiles. It was found that PAN/lignin-based CF is currently the promising material with the most promise to meet future market demands. However, significant research needs to be undertaken in order to improve the properties of lignin-based and PAN/lignin-based CF, respectively. The CF can be manufactured to CF-based rebars using different manufacturing technologies which are developed at a prototype level in this study.