Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychometric properties of the Portuguese teacher-version of the Inventory of Callous-Unemotional Traits in Preschoolers

Several authors suggest that Callous-Unemotional (CU) traits may be useful in identifying adolescents who exhibit initial, severe, chronic, and aggressive conduct problems. The Inventory of Callous-Unemotional Traits (ICU) was developed to assess these traits, later associated with psychopathy, in children and adolescents. This study aims to analyze the psychometric properties of the Portuguese version of the teacher-report ICU for preschool-age children. The sample was collected in public schools and comprised the responses of 46 preschool teachers concerning 131 children (62 girls) between 3 and 6 years of age. Results indicate that a two-factor model revealed the best fit to our data, including a Callous and an Uncaring factor, resorting to 12 of the original 24 items. Additionally, the results of this study also provide evidence of measurement invariance across sex for the ICU, allowing us to compare mean levels of CU traits between boys and girls. The results of the present study showed that the Portuguese version of the ICU scale (teacher’s version) seems reliable and valid for evaluating CU traits in preschool children.
An Itemset-Driven Cluster-Oriented Approach to Extract Compact and Meaningful Sets of Association Rules

Extracting association rules from large datasets typically results in a huge amount of rules. An approach to tackle this problem is to filter the resulting rule set, which reduces the rules, at the cost of also eliminating potentially interesting ones. In exploring a new dataset in search of relevant associations, it may be more useful for miners to have an overview of the space of rules obtainable from the dataset, rather than getting an arbitrary set satisfying high values for given interest measures. We describe a rule extraction approach that favors rule diversity, allowing miners to gain an overview of the rule space while reducing semantic redundancy within the rule set. This approach adopts an itemset-driven rule generation coupled with a cluster-based filtering process. The set of rules so obtained provides a starting point for a user-driven exploration of it.
Acute Kidney Injury After Coronary Artery Bypass Grafting and Long-Term Risk of End-Stage Renal Disease

Background— Acute kidney injury (AKI) is a common complication after coronary artery bypass grafting (CABG) and is associated with adverse outcomes. However, the relationship between AKI after CABG and the long-term risk of end-stage renal disease (ESRD) is unknown. Methods and Results— This study included 29 330 patients who underwent primary isolated CABG in Sweden between 2000 and 2008. AKI was classified according to the Acute Kidney Injury Network (AKIN) classification: stage 1, >0.3 mg/dL (>26 &mgr;mol/L) or 50% to 100% increase; stage 2, 100% to 200% increase; and stage 3, >200% increase from the preoperative to postoperative serum creatinine level. Cox proportional hazards regression analysis was used to calculate hazard ratios with 95% confidence intervals for ESRD in AKIN stage 1 and stage 2 to 3. Postoperative AKI occurred in 13% of patients. During a mean follow-up of 4.3±2.4 years, 123 patients (0.4%) developed ESRD, including 50 (1.6%) in AKIN stage 1, 29 (5.2%) in AKIN stage 2 to 3, and 44 (0.2%) without AKI after CABG. After multivariable adjustment, the hazard ratio for ESRD was 2.92 (95% confidence interval, 1.87–4.55) for AKIN stage 1 and 3.81 (95% confidence interval, 2.14–6.79) for AKIN stage 2 to 3. Conclusions— This nationwide study of patients who underwent CABG found that a small increase in the postoperative serum creatinine level was associated with an almost 3-fold increase in the long-term risk of ESRD after adjustment for a number of confounders, including preoperative renal function.
Facial Age Estimation Method Based on Fusion Classification and Regression Model

Due to the large individual differences in the facial features of each person and the fact that the age has a certain time sequence, the age estimation based on face images faces certain difficulties. This article proposes a method based on fusion classification and regression model: A classification model and a regression model are added to the convolutional neural network to train the network under the premise of sharing convolutional layer parameters. The classification of the age of the label is used to code the age distribution, and the age is regressed using the Euclidean distance. The final predicted value of the model is the average of the two. Experiments show that the effect of fusion classification and regression model is better than that of a single model, which improves the accuracy of age estimation.
On the choice of the mesh for the analysis of geostatistical data using R-INLA

Abstract Many methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the SPDE approach that requires the creation of a mesh overlying the study area and all the obtained results depend on it. The impact of the mesh on inference and prediction is investigated through simulations. As there is no formal procedure to specify it, we investigate a guideline to create an optimal mesh.
Tracking Unknown Number of Stealth Targets in a Multi-Static Radar with Unknown Receiver Detection Profile Using RCS Model

The reliable detection of geometrically-based stealth targets using a conventional single sensor radar system may be extremely difficult. This is because low Radar Cross Section (RCS) from certain angles results in a low Signal to Noise Ratio (SNR). In the present work, multi-target tracking of stealth targets is investigated in a multi-static radar with passive receivers. The Directions of Arrival (DOA) of targets are estimated by the receivers without knowing the number of targets, and their positions are obtained based on the transmitter beam direction. The B2 bomber aircraft model has been used as a stealth target. The RCS of the model has been simulated for all collection of incident and reflected angles from an oblique impinging plane wave. Probability of Detection (Pd) is modeled using a Toeplitz-based method for different SNRs due to different RCS patterns and is fed to an Iterated Corrected Probability Hypothesis Density (IC-PHD) filter. In spite of considering the transmitter and receivers resolution in our input data generation, the proposed algorithm is able to track the targets individually when they are much close to or even cross each other. Simulation results show the improved performance of the proposed method compared to other existing approaches.
The ALPINE-ALMA [CII] survey

Star formation rate (SFR) measurements at z > 4 have relied mostly on the rest-frame far-ultraviolet (FUV) observations. The corrections for dust attenuation based on the IRX-β relation are highly uncertain and are still debated in the literature. Hence, rest-frame far-infrared (FIR) observations are necessary to constrain the dust-obscured component of the SFR. In this paper, we exploit the rest-frame FIR continuum observations collected by the ALMA Large Program to INvestigate [CII] at Early times (ALPINE) to directly constrain the obscured SFR in galaxies at 4.4 < z < 5.9. We used stacks of continuum images to measure average infrared luminosities taking both detected and undetected sources into account. Based on these measurements, we measured the position of the main sequence of star-forming galaxies and the specific SFR (sSFR) at z ∼ 4.5 and z ∼ 5.5. We find that the main sequence and sSFR do not significantly evolve between z ∼ 4.5 and z ∼ 5.5, as opposed to lower redshifts. We developed a method to derive the obscured SFR density (SFRD) using the stellar masses or FUV-magnitudes as a proxy of FIR fluxes measured on the stacks and combining them with the galaxy stellar mass functions and FUV luminosity functions from the literature. We obtain consistent results independent of the chosen proxy. We find that the obscured fraction of SFRD is decreasing with increasing redshift, but even at z ∼ 5.5 it constitutes around 61% of the total SFRD.
The ALPINE-ALMA [C II] Survey: [C II]158micron Emission Line Luminosity Functions at $z \sim 4-6$

We present the [CII]158μm line luminosity functions (LFs) at z ∼ 4−6 using the ALMA observations of 118 sources, which are selected to have UV luminosity M_(1500A) 10⁹ L_⊙) compared to z∼0. This excess is consistent with a potential population of [C II]-bright UV-faint sources that are largely excluded from the ALPINE target selection. To further constrain such sources, in a companion analysis, Loiacono et al. (2020) estimate the [C II] LF based on serendipitous [C II] sources in the ALPINE maps. Their resulting single data point is consistent with the excess we observe at high-L. Combined, these studies suggest possible redshift evolution in the [C II] LFs at high luminosity between z ∼ 5 and 0. This conclusion is further supported by the inferred [C II] LFs from the far-IR and CO LFs at z ∼ 4−6. Integrating our estimate on the best-fit [C II] LF, we set a constraint on molecular gas mass density at z ∼ 4−6, with ρ_(mol) ∼ (2−7)×10⁷ M_⊙ Mpc⁻³. This is broadly consistent with previous studies, although the uncertainties span more than an order of magnitude. Finally, we find that available model predictions tend to underestimate the number densities of [C II] emitters at z ∼ 5.
XX The Rise of Social

The Turing test aimed to recognize the behavior of a human from that of a computer algorithm. Such challenge is more relevant than ever in today’s social media context, where limited attention and technology constrain the expressive power of humans, while incentives abound to develop software agents mimicking humans. These social bots interact, often unnoticed, with real people in social media ecosystems, but their abundance is uncertain. While many bots are benign, one can design harmful bots with the goals of persuading, smearing, or deceiving. Here we discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. We then review current efforts to detect social bots on Twitter. Features related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering.
Impervious surface extraction using coupled spectral–spatial features

Abstract. Accurate extraction of urban impervious surface data from high-resolution imagery remains a challenging task because of the spectral heterogeneity of complex urban land-cover types. Since the high-resolution imagery simultaneously provides plentiful spectral and spatial features, the accurate extraction of impervious surfaces depends on effective extraction and integration of spectral–spatial multifeatures. Different features have different importance for determining a certain class; traditional multifeature fusion methods that treat all features equally during classification cannot utilize the joint effect of multifeatures fully. A fusion method of distance metric learning (DML) and support vector machines is proposed to find the impervious and pervious subclasses from Chinese ZiYuan-3 (ZY-3) imagery. In the procedure of finding appropriate spectral and spatial feature combinations with DML, optimized distance metric was obtained adaptively by learning from the similarity side-information generated from labeled samples. Compared with the traditional vector stacking method that used each feature equally for multifeatures fusion, the approach achieves an overall accuracy of 91.6% (4.1% higher than the prior one) for a suburban dataset, and an accuracy of 92.7% (3.4% higher) for a downtown dataset, indicating the effectiveness of the method for accurately extracting urban impervious surface data from ZY-3 imagery.
Using gravitational force in terrain optimization problems

Optimization problems represent algorithms designed for difficult problems which may require huge amount of space or computational time. Such kind of algorithms bring out solutions which are optimal and at the same time closer to the real life environments where not everything is precise and the possibility of errors is present at every instant. Finding the minimal point in a terrain is a challenge of its own, especially when we are dealing with an unknown area. In order to tackle this problem, we thought of making use of gravitational force, since it is proportionally related to earth center proximity. In an unknown terrain, we spread our agents that are capable to communicate information to one another at randomly generated positions. Later on, each of these agents calculates the gravity variation with altitude at its respective position. Since, we were looking to find the optimal minimum point in the terrain, after the gravity variation with altitude is computed by each agent, the highest gravity is found. This is communicated to the other agents as well and they start moving toward the agent that is currently found at an area with high gravity variation. The agents move toward the high gravity agent with a certain heuristic coefficient. During their path they may encounter other terrain points where the gravitational force is stronger, which would cause a change in the path of other agents making them move toward the newly found position. This is done until an optimal minimum is found by the agents. Our test results so far have been very promising. We aim to develop the algorithm furthermore in order to increase its efficiency and efficacy. We strongly believe that such an algorithm can be used to reach in the unexplored areas of ocean floor, or searching for minerals by minimizing the area of search in an optimal time.
Mechatronic system for measuring hot-forged automotive parts based on image analysis

In-process measurement of parts is a field of constant research. Conventionally, industries have used traditional measuring instruments, such as vernier calipers, or even coordinate measuring machines (CMMs), to perform the control quality. In this paper, a system that is both based on machine vision techniques and integrated with a mechatronic system has been developed with the goal of automating the in-process inspection of hot-forged parts. This system uses six cameras, which are installed around the part to be measured. First, the images obtained from the cameras are processed, and thus the part’s dimensions are estimated. Then a statistical analysis is deployed in order to decide if the part may be approved or not. The experimental results validate the application of the proposed method for measuring parts during their processing in real shop floor conditions.
Dynamic Ordering in a Swarm of Floating Droplets Driven by Solutal Marangoni Effect

We review our recent results on a simple droplet system consisting of droplets of a mixture of two liquids. Although the system is composed of simple chemicals, it is capable of exhibiting complex collective behaviors as a cluster dynamics. We propose a simple inhomogeneous force model to analyze the experimental observations and discuss the mechanisms of the dynamic ordering in our system. The relevance of the observations to hierarchical ordering generally observed in systems of active elements is also discussed.
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)

Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to a surge of interest in explainable systems. In this work, we emphasize the importance of model explanation in various forms such as visual pointing and textual justification. The lack of data with justification annotations is one of the bottlenecks of generating multimodal explanations. Thus, we propose two large-scale datasets with annotations that visually and textually justify a classification decision for various activities, i.e. ACT-X, and for question answering, i.e. VQA-X. We also introduce a multimodal methodology for generating visual and textual explanations simultaneously. We quantitatively show that training with the textual explanations not only yields better textual justification models, but also models that better localize the evidence that support their decision.
Potentially inappropriate medication in older participants of the Berlin Aging Study II (BASE-II) #_#x2013; Sex differences and associations with morbidity and medication use

1 Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Lipid Clinic at the Interdisciplinary Metabolism Center, Berlin, Germany, 2 Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Klinische Pharmakologie und Toxikologie, Berlin, Germany, 3 Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, CharitéUniversitätsmedizin, Berlin, Germany, 4 Berlin Insitute of Health Center for Regenerative Therapies”oder ausführlicher „Charité—Universitätsmedizin Berlin, BCRT—Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
Quantifying Long-Term Scientific Impact

Citation Grabbers Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent “fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127; see the Perspective by Evans) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor. Early citation history can be used to model the total number of citations a paper will receive and to compare journals. [Also see Perspective by Evans] The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
Turkish Validity And Reliability Study of Spirituality Index of Well-Being

Objective: The study's goal was to determine the validity and reliability of the Spirituality Index of Well-Being by adapting it to Turkish in accordance with the methodological research type. Methods: This study was conducted on 107 elderly people who applied to four family health centers in the Kars province's center between May 2017 and January 2018 and met the research criteria. A written permission was obtained from the index's creator, the ethical committee, the institution, and individuals. Tools such as the "Introductory Information Form" and the "Spirituality Index of Well-Being" were used to collect data. Results: For the language validity of the index, expert opinions were taken, and a factor analysis was performed for construct validity. According to explanatory factor analysis, it was found out that the index demonstrates a two-factor construct and factor values are in an appropriate range, and according to confirmatory factor analysis it was determined that the two-factor construct of the index is valid and the fit values are appropriate. It was found out that the Cronbach alpha coefficient is 0.858 and total item score correlation coefficient takes value above 0.44. Conclusion: The Spirituality Index of Well-Being was determined to be a valid and reliable assessment instrument for Turkish society. As a result, it was determined that the scale can be used in clinical practices and research to assess the spiritual well-being of elderly individuals in Turkish society.
Masking Level Difference (MLD): Literature Review

Masking Level Difference composes a tests set for the central auditory processing behavioral evaluation and estimates decoding binaural interaction abilities. MLD test consist of auditory level measurement by emitting a pulsatile clear tone to both ears, at the same moment as a masker rustle is being emitted. This work has the objective of reviewing literature over MLD, providing general information concerning to the test. Binaural Interaction Tests evaluate central auditory nervous system (CANS) on processing dissimilar information, but complementary, emitted to both ears. The rustle has the objective of masking the auditory signal, in a permanent stage. On that test, the individual is submitted simultaneously to an auditory signal and to a rustle. However, emitting a rustle to the other ear becomes the auditory sign more audible. MLD is a psychological/auditorial phenomenon, in which detection or recognizing a binaural or monoaural sign emitted, is improved adjoining a competitive binaural rustle. An individual is normally evaluated at the same stage (speaking, pulsatile tone, masker rustle in both ears) and in a different stage (one of the signs is emitted to 180ºC [356ºF] in a diverse way from the initial one, just to one ear, while the other one is kept in a reverse stage). The test allows identifying individuals with compromised low brainstem.