Miracle-dz/UniFAR-FTUs huggingface dataset:
Are California Sea Lions (Zalophus californianus) Sensitive to the Attentional State of their Caretakers

Human-animal relations appear in various contexts (homes, farms, zoos, aquatic parks, etc.) possibly favoring the emergence of the ability to understand heterospecific communication signals in several species. Studies show that dogs (Canis familiaris) have developed the ability to attribute attention to humans, reading their body, head and gaze cues. Horses (Equus caballus) and other species including African gray parrots (Psittacus erithacus) show this ability too. Here, we asked if California sea lions (Zalophus californianus) can discriminate the attentional state of their caretakers. Four sea lions were tested in three increasingly complex experiments requiring them to make a choice between an attentive versus an inattentive caretaker. The first test asked whether sea lions could attribute attention to a human facing them versus facing away. In the second test, the caretaker's head orientation towards the sea lion served as the attentional cue. In the final test, the inattentive caretaker wore dark sunglasses. The results were heterogeneous and showed a higher rate of success than failure in the test 1, but the opposite in test 2. The results in the test 3 were not significant. Furthermore, the latency measures suggested that the subjects did not understand the tasks. It therefore appears that in the situation used here sea lions mainly focused their attention on the experimenter's body orientation; the head did not seem to be a pertinent cue.
Literature review of theoretical studies of phosphorene and graphene as gas sensor for carbon dioxide

Two-dimensional nanomaterials, such as graphene and phosphorene, has been attracting researchers’ interest, due to their possible applications. One of their applications is as gas sensors. Carbon dioxide is a gas, which needs to be monitored because it is one of the responsible for the greenhouse effect and for the changes in temperature. Therefore, the aim of this paper is to organize a literature review of the theoretical articles that use the Density Functional Theory to study graphene and phosphorene as carbon dioxide sensors. Analyzing the adsorption energies of the papers reviewed it is concluded that graphene and phosphorene are more promising sensors for carbon dioxide when this molecule is adsorbed on the graphene edged or when it is adsorbed on doped phosphorene. This literature review opens new perspectives for experimental studies.
Sequential regression techniques with second order methods

SEQUENTIAL REGRESSION TECHNIQUES WITH SECOND ORDER METHODS Burak Cevat Civek M.S. in Electrical and Electronics Engineering Advisor: Süleyman Serdar Kozat July 2017 Sequential regression problem is one of the widely investigated topics in the machine learning and the signal processing literatures. In order to adequately model the underlying structure of the real life data sequences, many regression methods employ nonlinear modeling approaches. In this context, in the first chapter, we introduce highly efficient sequential nonlinear regression algorithms that are suitable for real life applications. We process the data in a truly online manner such that no storage is needed. For nonlinear modeling we use a hierarchical piecewise linear approach based on the notion of decision trees where the space of the regressor vectors is adaptively partitioned. As the first time in the literature, we learn both the piecewise linear partitioning of the regressor space as well as the linear models in each region using highly effective second order methods, i.e., NewtonRaphson Methods. Hence, we avoid the well-known over fitting issues by using piecewise linear models and achieve substantial performance compared to the state of the art. In the second chapter, we investigate the problem of sequential prediction for real life big data applications. The second order Newton-Raphson methods asymptotically achieve the performance of the “best” possible predictor much faster compared to the first order algorithms. However, their usage in real life big data applications is prohibited because of the extremely high computational needs. To this end, in order to enjoy the outstanding performance of the second order methods, we introduce a highly efficient implementation where the computational complexity is reduced from quadratic to linear scale. For both chapters, we demonstrate our gains over the well-known benchmark and real life data sets and provide performance results in an individual sequence manner guaranteed to hold without any statistical assumptions.
Factors influencing European passenger demand for air transport

Passenger air travel demand is influenced by various factors and is crucial to manufacturers, airlines, airports and wider industry. In order to gain detailed insights into drivers of European air transport demand, five factors are analysed, both quantitatively and qualitatively. Focusing on the European air transport market, a detailed description of factors influencing air transport demand serves as input for a statistical analysis. Data from European countries on the gross domestic product per capita (GDP), urbanisation levels, the geographical location of a country, and the degree of education is used for the model. These explanatory variables are tested using a regression analysis in regard to their influence on the passengers’ demand for air transport. Results from the regression analysis reveal that the factors GDP, the geographical location of a country and the level of education are statistically significant, confirming GDP as a demand driver already examined within various studies that also include other factors as explanatory variables. Results also indicate the dynamics between the different factors, such as the positive relationship between income and level of education. Present studies are a good basis to show what drives demand, often focused at a global or country level. This analysis also confirms the essential determinants at a European level.
Continental deformation in Asia from a combined GPS solution

After decades of research on continental tectonics, there is still no consensus on the mode of deformation of continents or on the forces that drive their deformation. In Asia the debate opposes edge‐driven block models, requiring a strong lithosphere with strain localized on faults, to buoyancy‐driven continuous models, requiring a viscous lithosphere with pervasive strain. Discriminating between these models requires continent‐wide estimates of lithospheric strain rates. Previous efforts have relied on the resampling of heterogeneous geodetic and Quaternary faulting data sets using interpolation techniques. We present a new velocity field based on the rigorous combination of geodetic solutions with relatively homogeneous station spacing, avoiding technique‐dependent biases inherent to interpolation methods. We find (1) unresolvable strain rates (<3 × 109/yr) over a large part of Asia, with current motions well‐described by block or microplate rotations, and (2) internal strain, possibly continuous, limited to high‐elevation areas.
Surface heave by steam injection and subsidence by aquifer production- a 2D modeling study with Plaxis

Steam injection is an EOR method, which is mostly used for heavy oil production. This viable method is based on increasing the pore pressure in the reservoir and reducing the viscosity of the heavy oil. In particular cases, the increase in pore pressure and thermal expansion related to the steam injection leads to surface deformation – heave. The results show that the level of heave increases when the reservoir thickness is larger. For a 100 m thick reservoir, with a pressure increase of 5 MPa and temperature increase to 570 K, the heave is 80 mm. Increasing the reservoir thickness by a factor of two (200 m) results in an increase of heave by a factor of 1.6 (130 mm). A similar increase in heave results when we increase the pressure increment in the reservoir. In our experiments, pressure and heave are linearly related. Results depends on rock properties, most noticeably the elastic property, Young’s modulus, and thermal expansion factor. An increase in Young’s modulus leads to decrease in level of heave. An increase of thermal expansion factor leads to increase in heave. When the water required for the steam is extracted from a shallow aquifer, producing this aquifer leads to a reduction of the level of heave that is caused by steam injection. This reduction depends on the pressure reduction in the aquifer and on its thickness. For realistic dimensions and rock properties of the aquifer, this effect appears to be insignificant. Only with very low Young’s modulus (7 GPa) and very thick aquifer, the subsidence due to water depletion can compensate the expansion caused by steam injection.
Dynamic Characteristics of Electrostatically Actuated Microbeams with Slant Crack

An improved model of the slant crack on a microbeam is presented. Based on fracture mechanics, the rotation coefficient for the slant crack is derived as a massless rotational spring accounting for the additional stress intensity factors generated by the orientation of the crack compared to the transverse crack. Comparisons between microbeams with a slant crack of different geometry parameters (slant angle, depth ratio, and crack position) are investigated with regard to the dynamic mechanical behaviors and nonlinear response. By presenting a mathematical modeling, the effects of the slant crack and the electric actuation of an electrostatically actuated fixed-fixed microbeam on the dynamic characteristics are examined in detail. It is shown that the crack position has more significant influence on the pull-in voltage value than the slant angle or the depth ratio. Approaching the slant crack to the fixed end or enlarging the external incentives amplifies the nonlinearity of the microbeam system, while the effects of depth ratio and slant angle are dependent on the crack position. The resonance frequency and the resonance amplitude are affected as well.
Disclosure in Securities Markets and the Firm’s Need for Confidentiality: Theoretical Framework and Regulatory Analysis

This paper explores the relationship between disclosure in securities markets and the firm’s need for confidentiality. Transparency plays a crucial role for both investor protection and the proper functioning of financial markets. Confidentiality protects the value of information which has been costly produced by the company and preserves private incentives for business and technological innovation. The requirement of too detailed or early disclosure in securities markets, even when aimed at greater investor protection and enhanced market efficiency, would be ultimately detrimental since it would hinder the firm’s competitive capacity and weaken the system of private incentives for innovation. EU mandatory disclosure rules protect the firm’s interest in confidentiality and allow public companies to refrain from — or delay — the release of the most sensitive data. The scope of these safe harbours, however, is narrow and always at risk of excessive restriction by the powers of regulatory agencies. Furthermore, when information with competitive value is concurrently significant for investors, refraining from disclosure — no matter if allowed or not by the law — hampers the firm’s capacity to raise finance and makes the process of resource allocation less efficient. Selective disclosure reconciles investors’ demand for information and the firm’s need for confidentiality since it allows market prices to react to the information whilst also bypassing the need for public release of the relevant contents. Reconciling the tension, in turn, constitutes a desirable outcome: the securities market will be more efficient and raising finance will become less costly, especially for the most ‘secrecy sensitive’ projects. At the same time, the system of private incentives for innovation will be fully preserved.
Experimental investigation on performance of short pineapple leaf fiber reinforced tapioca biopolymer composites

The performance of short pineapple leaf fiber (PALF) reinforced tapioca biopolymer (TBP) composites were investigated, specifically the effect of fiber length and fiber composition on mechanical properties (tensile properties, flexural strength, and impact strength). Composite samples with different fiber lengths (< 0.50 mm, 0.51 mm to 1.00 mm, and 1.01 mm to 2.00 mm) and different fiber compositions (10%, 20%, 30%, and 40%) were prepared through crushing, sieving, internal mixing, compression molding, and machining processes. The combination of PALF and TBP enhanced the mechanical properties of composites with 30% as the optimum fiber content. However, the influence of different fiber lengths up to 2.00 mm provided no significant effect on producing maximum tensile properties. Good interfacial adhesion between PALF and TBP was evident from scanning electron microscopy analysis. Therefore, the combination of PALF and TBP has great potential as a renewable and biodegradable polymer. Moreover, PALF-TBP composites are expected to become alternatives to petroleum-based polymers.
Operational detection of contrails from NOAA-AVHRR-data

This paper describes a fully automated scheme that allows for the detection of contrails using the infrared channels of the Advanced Very High Resolution Radiometer (AVHRR). The presented algorithm is adjusted to have a low rate of misdetection in spite of the low contrail detection efficiency resulting from this tuning. A time-series and regional patterns of contrail cloudiness over Central and Western Europe are derived. Daily analysis of noon scenes in 1996 indicates that the average of daytime contrail coverage over Central Europe was 0.5% 0.25 with regional maxima of 1.2%. Comparing noon and midnight scenes shows that night-time contrail coverage, which is especially effective in greenhouse forcing, is approximately one-third of that of daytime.
An Analytical Expression for the Angular Velocity of Rotation of the Earth

In this paper an analytical expression for the angular velocity of the Earth is determined as function of the orbital elements of the Moon and Sun, in case of the triaxial Earth ( A< B< C), a more general expression than in V. P. Dolgachev’s paper Rotation of the Earth in the gravitational field of the Moon and Sun, Astron. Zh. 62, 1003-1007, 1985. The equations are obtained by determining the motion of the axis of figure and the instantaneous axis of rotation of the absolutely rigid Earth.
Genetic Polymorphism Of Cytochrome P450 -2d6*4 In Cannabis Smokers

Genetic variation in genes involved in drug metabolism may account for variations in responses to these drugs and may affect receptor adaptation, toxicity, altered drug effects and cross tolerance. Most drugs are metabolized by cytochrome P450 (CYP 450) system with metabolizing enzyme, CYP 2D6, responsible for the largest contribution. In this work the role of genetic factors in cannabis dependence (hashish smokers) was evaluated by CYP 2D6*4 genotyping (restricted fragment length polymorphism [RFLP]) to investigate whether there is specific genotype associated with cannabis dependency pattern. Results showed that 50% of the cannabis smokers have the heterozygous genotype of cytochrome P450-2D6*4, 27.8% have the homozygous allele and 22.2% have the wild type allele. Also, there is significant correlation between the genetic configuration and the cannabis dependency pattern as presented by the starting age of cannabis use, the regularity of its use and the personal and family norms about its use. It is concluded that there may be some genetic factors that determine the body response to cannabis dependence that need to be more specifically studied. This can be a marker for the risk of becoming dependent and / or the prediction of suitable effective treatment of dependence and also, prevention of relapse after withdrawal.
The Elaboration Likelihood Model (Elm: Replications, Extensions and Some Conflicting Findings

bi this paper we report on three studies designed to replicate and extend the Elaboradon likelihood Model (ELM) presented in Petty. Cacioppo and Schumann's (1983) Joumal of Consumer Research article. The reported studies were conducted with undergraduate students from two major universides using methodologies almost idendcal to diose of Petty et. al. However, the results do not corroborate previous work on the ELM. We offer several possible explanadons for the model's failure to generalize across products, across source manipuladons and across arguments.
Evaluation of Gluten-Free Amaranth and Navy Bean Flour Blends on Quality of Sugar Cookies

Gluten -free sugar cookies were made from amaranth ( Amaranthus caudatus ) and navy bean flours of different ratios. The physical properties of flour blends, dough, and cookies were evaluated. This study found that navy bean and its blends with amaranth had greater water holding capacity (WHC) than that of wheat flour. The increased WHC was observed as the amount of navy bean flours increased in blends. The amaranth flour had the highest water soluble index (WSI) and pasting viscosities. The WSI, pasting viscosities and rheological elastic properties of composites were improved by amaranth flour. Differences were also found in geometrical and textural properties of the doughs and cookies. The cookies made from flour blends have lower width and spread factors; however, they had higher thickness and volume resulting in higher yield compared to wheat flour. Overall, the cookies made by amaranth, navy bean, and their blends were rated “acceptable” in color, flavor, texture, and overall acceptability in the sensory study. The flavorings of vanilla, cinnamon, and almond extracts improved sensory scores of cookies made from blends, making them indistinguishable from cookies using wheat flour. This study suggested that the amaranth-navy bean blends could be good gluten-free candidates for health-promoting food products.
Automatic Pain Detection in Video Sequences for Neuro-Rehabilitation

Adaptive and interactive mental engagement combined with positive emotional state are requirements for an optimal outcome of the neuro-rehabilitation process for patients with brain damage usually caused by TBI (traumatic brain injury), stroke or brain disease such as cancer, epilepsy, and Alzheimer's disease. We propose a method for automatic pain recognition in video sequences using the landmarks data from Supervised Descent Method and applying Support Vector Machine (SVM) for data classification. This method is suitable for being part of assistive medical system for neuro-rehabilitation of patients with TBI. The experiments with a video dataset with patients with shoulder pain show very good recognition rate (95,7%) for recognizing the painful facial states of the subjects.
Clustering Non-Stationary Data Streams with Online Deep Learning

With more devices connected, sensor data logged and people active in social networks, the trend towards working with dynamic data is clear. The number of applications where it becomes essential to perform real time analysis on data streams grows accordingly, each with its own challenges. From this area of data stream analysis we benchmark the performance of current state of the art clustering algorithms: CluStream, DenStream and ClusTree. We also adapt a Variational Autoencoder to perform in the context of non-stationary data streams and assess its generative capabilities for dimensionality reduction. From this limited lab experiment we show that while there is a significant improvement in the clustering accuracy of high dimensional datasets after a dimensionality reduction with a Variational Autoencoder, not all clustering algorithms benefit in the same way from it. Additionally we show that regardless of the clustering algorithm, no relevant improvement in the purity of the clusters could be obtained after the dimensionality reduction.
Enhancing the Accuracy of Microwave Element Models by Artificial Neural Networks

In the recent PSpice programs, five types of the GaAs FET model have been implemented. However, some of them are too sophisticated and therefore very difficult to measure and identify afterwards, especially the realistic model of Parker and Skellern. In the paper, simple enhance- ments of one of the classical models are proposed first. The resulting modification is usable for the accurate modeling of both GaAs FETs and pHEMTs. Moreover, its updated ca- pacitance function can serve as an accurate representation of microwave varactors, which is also important. The preci- sion of the updated models can be strongly enhanced using the artificial neural networks. In the paper, both using an ex- clusive neural network without an analytic model and coope- rating a corrective neural network with the updated analytic model will be discussed. The accuracy of the analytic mo- dels, the models based on the exclusive neural network, and the models created as a combination of the updated analytic model and the corrective neural network will be compared.
Evaluation of Methods for Gene Selection in Melanoma Cell Lines

A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome ( G 2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis.