Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Different Evaluation Environments for Information Retrieval through Pivot Systems

Classical evaluations of Information Retrieval systems, under the Cranﬁeld Paradigm, compare several systems within one evaluation environment, deﬁned by its settings (document collection, topics, assessments and evaluation measures). In this paper, we pro-pose a framework to handle the comparison of systems across several evaluation environments. To achieve this goal, we investigate the use of pivot systems, allowing an indirect comparison of systems across evaluation environments by computing Result Deltas, i.e. the differences between their evaluation measures values. We detail the proposed pivot-based methodology, deﬁne a pivot characteristics and present experiments to validate our proposal (and in particular the pivot characteristics). We create altered environments that differ from their topic sets using the 2018 and 2020 CLEF eHealth evaluation campaigns (Goeuriot et al. , 2020). We explore the behaviour of the metrics and pivots measuring the correlation between the result deltas, and the ranking of systems through the pivots compared to the ofﬁcial ranking of the systems. Our experiment show that correlations can greatly vary according to the chosen pivot and metric. We show that some pivot/metric pairs achieve high correlation values across the altered environments, with a ranking of systems similar to the ofﬁcial ranking.
S0031182020002176jra 1171..1185

Trypanosoma cruzi has three biochemically and morphologically distinct developmental stages that are programmed to rapidly respond to environmental changes the parasite faces during its life cycle. Unlike other eukaryotes, Trypanosomatid genomes contain protein coding genes that are transcribed into polycistronic pre-mRNAs and have their expression controlled by posttranscriptional mechanisms. Transcriptome analyses comparing three stages of the T. cruzi life cycle revealed changes in gene expression that reflect the parasite adaptation to distinct environments. Several genes encoding RNA binding proteins (RBPs), known to act as key post-transcriptional regulatory factors, were also differentially expressed. We characterized one T. cruzi RBP, named TcZH3H12, which contains a zinc finger domain and is up-regulated in epimastigotes compared to trypomastigotes and amastigotes. TcZC3H12 knockout (KO) epimastigotes showed decreased growth rates and increased capacity to differentiate into metacyclic trypomastigotes. Transcriptome analyses comparing wild type and TcZC3H12 KOs revealed a TcZC3H12-dependent expression of epimastigote-specific genes such as genes encoding amino acid transporters and proteins associated with differentiation (PADs). RNA immunoprecipitation assays showed that transcripts from the PAD family interact with TcZC3H12. Taken together, these findings suggest that TcZC3H12 positively regulates the expression of genes involved in epimastigote proliferation and also acts as a negative regulator of metacyclogenesis.
Structural Differences of the Semantic Network in Adolescents with Intellectual Disability

The semantic network structure is a core aspect of the mental lexicon and is, therefore, a key to understanding language development processes. This study investigated the structure of the semantic network of adolescents with intellectual disability (ID) and children with typical development (TD) using network analysis. The semantic networks of the participants (nID = 66; nTD = 49) were estimated from the semantic verbal fluency task with the pathfinder method. The groups were matched on the number of produced words. The average shortest path length (ASPL), the clustering coefficient (CC), and the network’s modularity (Q) of the two groups were compared. A significantly smaller ASPL and Q and a significantly higher CC were found for the adolescents with ID in comparison with the children with TD. Reasons for this might be differences in the language environment and differences in cognitive skills. The quality and quantity of the language input might differ for adolescents with ID due to differences in school curricula and because persons with ID tend to engage in different out-of-school activities compared to TD peers. Future studies should investigate the influence of different language environments on the language development of persons with ID.
Ret-GAN: Retinal Image Enhancement using Generative Adversarial Networks

With over 200K cases in the U.S. alone, retinal disorders are the most common cause of irreversible blindness. This serves as a primary aim to analyze automated screening tools to detect retinal disorders. We analyze the OCT dataset (84, 484 images) and enhance the images by using Generative Adversarial Networks (GANs). This work specifically focuses on enhancing the quality of source (training) images for better algorithm validatiorr/testing11Authors contributed equally to the work. We synthesize super resolution-based images using generators, discriminators and the adversarial nature of the GANs. The performance of the Ret-GAN is validated by PSNR, SSIM, and loss functions. To test the Ret-GAN generated images, we train a convolutional neural network (CNN) with the original dataset images and super-resolution images. We achieve an accuracy of 0.9825 on Ret-GAN generated image data, and 0.9525 on the original data. We statistically analyze the CNN with a number of evaluation metrics to further validate the results. The proposed scheme is compared to benchmark research findings on the same dataset. Our results are encouraging.
Practical Evaluation of Repeated Recommendations in Personalized Music Discovery

Studies have shown that repeated exposures to novel songs cause an increase in a person’s memory and liking. These studies are commonly verified through self-reporting emotion-based surveys. This paper proposes the “retention rate” as an additional parameter for evaluation, which examines the rate at which the listener revisits the novel items. The authors hypothesize that when a person listens to novel (i.e., both unfamiliar and interesting) pieces of music, the retention rate will be proportional to the number of times the discovery engine suggests the pieces to her, as long as they remain novel. The authors have tested the hypothesis through a six-week human-subject experiment which simulates a real-world listening environment and a follow-up survey. During the experiment period, each subject received, through Discover Weekly in Spotify, suggestions for novel songs up to three times and provided evaluation. One month after the evaluation experiment, the human-subjects answered whether they had revisited the novel songs. Through the analysis of the response and survey data, the researchers conclude that the more times a listener is exposed to a song during the discovery process, the more likely she is to return to the song.
Treatment Recommendations for COVID-19 Patients along with Robust Explanations

The global response to the pandemic introduced by COVID-19 is unprecedented. Scientists develop methods, which analyze data to identify an effective treatment that uncovers possible responses to the SARS-COV-2 virus. However, our global response should be based on knowledge exchange and collaboration among countries. In this paper, we present a recommender system for treatment recommendations, which exploits similar patterns among patients of different clinical studies, and recommends them health interventions (such as to provide oxygen therapy) and drugs (e.g., Remdesivir) based on their symptoms' or diseases' similarity with patients of other similar clinical studies. Our approach can also provide explanations along with recommended treatments to assist doctors in understanding the reasons behind a suggested drug or health intervention. We also perform experiments to identify the effectiveness of our system in terms of recommendation accuracy. Our results demonstrate that our system is able to minimize the false positive and false negative prediction rates. Finally, we provide web links to download both (i) our program's setup file and (ii) our Neo4j database file.
Is it a good time to survey you? Cognitive load classification from blood volume pulse

The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a mobile Coaching System recommending physical and mental health interventions. Patient reported outcomes are important for evaluation of the efficacy of these interventions. Nevertheless a large number of surveys might be overwhelming to patients. To understand the cognitive demand caused by the surveys and to find the adequate time to prompt patients to complete them we carried out a feasibility study. In this study we developed a machine learning cognitive load detector from blood volume pulse (BVP) captured by a photoplethysmography (PPG) signal. PPG sensors are available on consumer-grade smartwatches, which we will use in our Coaching System. We found that personalised 1D convolutional neural networks trained on raw BVP signal performed better in binary high vs low cognitive load classification than the personalised Support Vector Machines trained with heart rate variability and BVP features. We investigated if the further improvements can be obtained by teacher-student semi-supervised model training, nevertheless the performance gains were not notable. In the future we will include additional context information that might aid cognitive load estimation and drive both survey design as well as the timing of the prompts.
WireRoom

We present WireRoom, a computational framework for the intelligent design of abstract 3D wire art to depict a given 3D model. Our algorithm generates a set of 3D wire shapes from the 3D model with informative, visually pleasing, and concise structures. It is achieved by solving a dynamic travelling salesman problem on the surface of the 3D model with a multi-path expansion approach. We introduce a novel explorative computational design procedure by taking the generated wire shapes as candidates, avoiding manual design of the wire shape structure. We compare our algorithm with a baseline method and conduct a user study to investigate the usability of the framework and the quality of the produced wire shapes. The results of the comparison and user study confirm that our framework is effective for producing informative, visually pleasing, and concise wire shapes.
AN ARTIFICIAL NEURAL NETWORK MODELING APPROACH TO PREDICT CRUDE OIL FUTURE

This study is an attempt to predict the crude oil future series by using the Artificial Neural Networks (ANN) models. Twenty various ANN models with four architectures are constructed to predict the series. Forecasting errors when calculated by using two forecasting error measures, it has been observed that the 4-3-1 (α=0.8 and ε=0.8) model has the minimum forecasting error and hence, best suits to predict the estimated series. MSE is found to be best suitable as a forecasting error measure for the crude oil series.
Protons in hydra ted protein powders

Previous work from this laboratory has shown that hydrated lysozyme powders exhibit a dieleetric behaviour, due to proton eonduetivity, explainable within the frame of percolation theory. Long range proton displaeement appears only above the eritieal hydration for pereolation, when the 2-dimensional motion takes plaee on fluetuating clusters of hydrogen-bonded water moleeules adsorbed on the protein surfaee. The emergenee of biological funetion, enzyme eatalysis, was found to eoineide with the critical hydration for pereolation. More reeentently, we have evaluated the protonic eonduetivity of hydrated lysozyme powders, from room down to liquid N2 temperature. In the high temperature limit a classical isotopie effect can be detected, and the conductivity follows the farniliar Arrhenius law for thermally aetivated hopping. In the low temperature region the eonduetivity shows a temperature dependance in agreement with prediction by the theory of dissipative quantum tunneling. Below room temperature the static dielectric eonstant, and the dieleetrie relaxation time for charge transport showed an increase likely to be identified with the formation of a polaroniesolitonie species as predicted by the theory of proton transport in water ehains, a speeies which displays a larger effective mass and a larger dipole moment than the usual hydrated protonic defects. The purpose of this paper is twofold. In the first seetion we present a tutorial report of some previous experimental results on proton displaeement in slightly hydrated biologieal systems at room temperature, to show that in these systems the emergenee of biologie al funetion eoineides with the onset of pereolative pathways in the water molecules network adsorbed on the
Metal dopants in HfO2-based RRAM: first principle study

Based on density-functional theory (DFT), the effects of metal dopants in HfO2-based RRAM are studied by the Vienna ab initio simulation package (VASP). Metal dopants are classified into two types (interstitial and substitutional) according to the formation energy when they exist in HfO2 cell. Several conductive channels are observed through the isosurface plots of the partial charge density for HfO2 doped with interstitial metals, while this phenomenon cannot be found in HfO2 doped with substitutional metals. The electron density of states (DOS) and the projected electron density of states (PDOS) are calculated and analyzed; it is found that the conduction filament in HfO2 is directly formed by the interstitial metals and further, that the substitutional metals cannot directly generate conduction filament. However, all the metal dopants contribute to the formation of the oxygen vacancy (VO) filament. The formation energy of the VO and the interaction between metal dopants and VO are calculated; it is revealed that the P-type substitutional metal dopants have a strong enhanced effect on the VO filament, the interstitial metal dopants have a minor assistant effect, while Hf-like and N-type substitutional metal dopants have the weakest assistant effect.
The Emerging Phenomenon of Nomophobia in Young Adults: A Systematic Review Study

Background Nomophobia (No MObile PHone PhoBIA) refers to the worry or fear that individuals experience when they are without their mobile phone or they are unable to use it. The term was first coined in 2008 and it is considered a modern type of phobia. The aim of the present study was to retrieve and review the most relevant literature on the prevalence of nomophobia and its relationship with psychosocial and physical health among young adults. Methods A systematic literature review was conducted according the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for original papers on the phenomenon of nomophobia in young adults using the Nomophobia Questionnaire (NMP-Q) as the main measuring instrument. The literature was focused on the 3 main topics of prevalence and level of nomophobia, nomophobia and psychosocial effects, and nomophobia and physical health in young adults. Findings The initial literature corpus consisted of 370 articles of which 40 met the inclusion criteria and were analyzed in detail. Nomophobia was observed among 15.2%-99.7% of the participants. Research findings indicated increased psychological, emotional, social, and physical side effects due to excessive smartphone use. Conclusion The excessive use of the smartphone is an emerging threat for social, mental, and physical health. There is an increased need for further research in this regard as it is evolving into an epidemic outbreak and concerns directly the field of public health.
What Drives Cross-Border Mergers and Acquisitions and Greenfield Foreign Direct Investment Capital Flows? Reconsideration in the Case of the Selected Former Yugoslav Countries

Bearing in mind the different nature and the impact of various types of foreign direct investments (FDI) on the one hand, and the specific macroeconomic environment in the post-socialist countries on the other hand, in this paper we reexamine the selected macroeconomic factors that affect the two types of FDI inflows (cross-border mergers and acquisitions and greenfield FDI) in four countries of the former Socialist Federal Republic of Yugoslavia. The study employs the balanced panel data framework and covers twelve-year period (2006–2017). Having performed the Hausman test, we use the random effect model and provide evidence that: (1) the key FDI macroeconomic determinants in stable business conditions, examined in numerous research studies, can have a different impact on FDI in times characterized by unstability and financial crisis, (2) some determinants of FDI inflows have different importance and direction in the case of cross-border M&A and greenfield FDI. Our findings are relevant for policymakers who should reconsider the key factors that fuel the FDI inflows towards their developing economies.
Learning point embedding for 3D data processing

Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly. By analysis of PointNet, a pioneer in introducing deep learning into point sets, we reveal that current point-based methods are essentially spatial relationship processing networks. In this paper, we take a different approach. Our architecture, named PE-Net, learns the representation of point clouds in high-dimensional space, and encodes the unordered input points to feature vectors, which standard 2D CNNs can be applied to. The recommended network can adapt to changes in the number of input points which is the limit of current methods. Experiments show that in the tasks of classification and part segmentation, PE-Net achieves the state-of-the-art performance in multiple challenging datasets, such as ModelNet and ShapeNetPart.
Dependence of the polycarbonate mechanical performances on boron nitride flakes morphology

A key requirement for the exploitation of two-dimensional (2D)-crystals in the field of composites relies on their large-scale production. In this respect, liquid phase exfoliation of layered-crystals is emerging as one of the most promising approaches for the scalable production of high-quality 2D-crystals. However, the dependence of the 2D crystal flakes morphology, i.e. thickness and lateral size, on the mechanical properties of the polymer composites is not fully understood yet. Herein, we tackle this issue by designing an environmentally friendly approach, based on the exfoliation of bulk hexagonal-boron nitride (h-BN), widely used as filler in polymer composites for its high intrinsic stiffness, i.e. approaching 1 TPa, in a water/surfactant solution with controlled thickness and lateral size by using cascade ultra-centrifugation. Our approach allows us to obtain two populations of flakes with aspect ratio, i.e. lateral size over thickness, equal to 250 and 350, respectively. The h-BN flakes with tuned aspect ratio are subsequently used as filler in a polycarbonate (PC) matrix by exploiting solution blending in 1,3-dioxolane, a solvent with Hansen’s solubility parameters matching the ones of h-BN, thus enhancing the dispersion of the filler inside the matrix, as evaluated by Raman mapping. We tested the composite mechanical properties finding that flakes with higher aspect ratio show superior reinforcements in terms of both ultimate tensile strength and Young’s modulus, compared with their lower aspect ratio counterparts. As example, at 0.1 wt% of loading, the difference in reinforcement in terms of Young’s Modulus is of 56 MPa, being the increment, compared to pristine PC, of ∼22% for composites produced with higher aspect ratio fillers, whereas it is instead of only ∼17% for lower aspect ratio fillers.
Infrared Emission Properties of Active Galactic Nuclei from Swift-BAT Hard X-Ray Survey

The Swift/Burst Alert Telescope (BAT) 105-month survey detected 1632 hard X-ray sources in the 14–195 keV band, which includes 1105 active galactic nuclei (AGNs) that are unbiased against Compton-thin obscuration. We use a series of two-color diagrams to analyze the near, mid (MIR), and far-infrared properties of the Swift/BAT AGNs, based on data from the Two Micron All Sky Survey, the Wide Field Infrared Survey Explorer and the AKARI satellite. The two-color diagrams imply that a Fλ∝λ−α power-law spectrum of α<0 can be used to describe the 1.25–22 μm spectra of most of the Swift/BAT AGNs and that, for some Swift/BAT AGNs, the power-law spectrum can extend to 90 μm. Of the Swift/BAT AGNs, type-1 AGNs and QSOs have similar MIR spectral indexes (α) on average. Type-2 AGNs have redder MIR colors than type-1 sources on average. For 50% of type-2 Swift/BAT AGNs, their near-infrared emissions are dominated by thermal emission of the stars in host galaxies. For 83% of type-1 Swift/BAT AGNs, their near-infrared emissions may be dominated by multi-temperature thermal emissions from an optically thick accretion disk around the central black holes. The spectral index of blazars is smaller than that of other Swift/BAT AGNs, which indicates that blazars/FSRQ have flatter 4–22 μm spectra compared to other Swift/BAT AGNs on average.
Modelling small and large displacements of a sphere on an elastic half-space exposed to a dynamic force

Spheres at medium interfaces are encountered in many applications, including in atomic force microscopy or indentation tests. Although the Hertz theory describes the contact mechanics between an elastic sphere and an elastic half-space for static loading and small deformations very well, there is a need to consider the density of the medium, the mass of the sphere and the radiation damping for dynamic loading to obtain reliable results. In this study, an analytical model for predicting the small and large displacements of a sphere on an elastic half-space exposed to a dynamic force is developed. For this purpose, after summarizing a mathematical model that has recently been proposed for the sphere at a medium interface, a finite element model for the sphere at an elastic interface is developed. Based on the comparison of the mathematical and finite element models, an improved analytical model for the sphere at an elastic interface is developed. In addition to considering the elastic properties of the medium and the size of the sphere, the model developed here takes into account the density of the medium, the mass of the sphere, and the radiation damping, and the model is valid for small and large sphere displacements. The developed model can be used to understand the dynamic responses of spherical objects at medium interfaces in practical applications. Furthermore, the proposed model is a remarkable tool for undergraduate and graduate students and researchers in the fields of engineering, materials science and physics to gain insight into the dynamic responses of spheres at medium interfaces.