Miracle-dz/UniFAR-FTUs huggingface dataset:
My Tweets Bring All the Traits to the Yard: Predicting Personality and Relational Traits in Online Social Networks

Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.
KoBE: Knowledge-Based Machine Translation Evaluation

We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source. Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task. On 4 language pairs, we also achieve higher correlation with human judgements than BLEU. To foster further research, we release a dataset containing 1.8 million grounded entity mentions across 18 language pairs from the WMT19 metrics track data.
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog

Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.
Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle

Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Despite significant advances, existing outside-the-vehicle referencing methods consider these modalities separately. Moreover, existing multimodal referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. In this paper, we investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects (e.g. buildings) from the vehicle. We furthermore explore person-specific differences in this interaction by analyzing individuals' performance for pointing and gaze patterns, along with their effect on the driving task. Our statistical analysis shows significant differences in individual behaviour based on object's location (i.e. driver's right side vs. left side), object's surroundings, driving mode (i.e. autonomous vs. normal driving) as well as pointing and gaze duration, laying the foundation for a user-adaptive approach.
Timing analysis of the black hole candidate EXO 1846–031 with Insight-HXMT monitoring

We present the observational results from a detailed timing analysis of the black hole candidate EXO 1846–031 during its outburst in 2019 with the observations of Insight-HXMT, NICER and MAXI. This outburst can be classified roughly into four different states. Type-C quasi-periodic oscillations (QPOs) observed by NICER (about 0.1–6 Hz) and Insight-HXMT (about 0.7–8 Hz) are also reported in this work. Meanwhile, we study various physical quantities related to QPO frequency. The QPO rms–frequency relationship in the energy band 1–10 keV indicates that there is a turning pointing in frequency around 2 Hz, which is similar to that of GRS 1915+105. A possible hypothesis for the relationship above may be related to the inclination of the source, which may require a high inclination to explain it. The relationships between QPO frequency and QPO rms, hardness, total fractional rms and count rate have also been found in other transient sources, which can indicate that the origin of type–C QPOs is non-thermal.
Leveraging Local and Global Descriptors in Parallel to Search Correspondences for Visual Localization

Visual localization to compute 6DoF camera pose from a given image has wide applications such as in robotics, virtual reality, augmented reality, etc. Two kinds of descriptors are important for the visual localization. One is global descriptors that extract the whole feature from each image. The other is local descriptors that extract the local feature from each image patch usually enclosing a key point. More and more methods of the visual localization have two stages: at first to perform image retrieval by global descriptors and then from the retrieval feedback to make 2D-3D point correspondences by local descriptors. The two stages are in serial for most of the methods. This simple combination has not achieved superiority of fusing local and global descriptors. The 3D points obtained from the retrieval feedback are as the nearest neighbor candidates of the 2D image points only by global descriptors. Each of the 2D image points is also called a query local feature when performing the 2D-3D point correspondences. In this paper, we propose a novel parallel search framework, which leverages advantages of both local and global descriptors to get nearest neighbor candidates of a query local feature. Specifically, besides using deep learning based global descriptors, we also utilize local descriptors to construct random tree structures for obtaining nearest neighbor candidates of the query local feature. We propose a new probabilistic model and a new deep learning based local descriptor when constructing the random trees. A weighted Hamming regularization term to keep discriminativeness after binarization is given in the loss function for the proposed local descriptor. The loss function co-trains both real and binary descriptors of which the results are integrated into the random trees.
Early dark energy resolution to the Hubble tension in light of weak lensing surveys and lensing anomalies

A constant early dark energy (EDE) component contributing a fraction ${f}_{\mathrm{EDE}}({z}_{c})\ensuremath{\sim}10%$ of the energy density of the universe around ${z}_{c}\ensuremath{\simeq}3500$ and diluting as or faster than radiation afterwards, can provide a simple resolution to the Hubble tension, the $\ensuremath{\sim}5\ensuremath{\sigma}$ discrepancy---in the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ context---between the ${H}_{0}$ value derived from early- and late-universe observations. However, it has been pointed out that including Large-Scale Structure (LSS) data, which are in $\ensuremath{\sim}3\ensuremath{\sigma}$ tension with $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ and EDE cosmologies, might break some parameter degeneracy and alter these conclusions. We reassess the viability of the EDE against a host of high- and low-redshift measurements, by combining LSS observations from recent weak lensing (WL) surveys with CMB, baryon acoustic oscillation (BAO), growth function (FS) and Supernova Ia (SNIa) data. Introducing a model whose only parameter is ${f}_{\mathrm{EDE}}({z}_{c})$, we report in agreement with past work a $\ensuremath{\sim}2\ensuremath{\sigma}$ preference for nonzero ${f}_{\mathrm{EDE}}({z}_{c})$ from Planck CMB data alone, while the tension with the local ${H}_{0}$ measurement from sh0es is reduced below $2\ensuremath{\sigma}$. Adding BAO, FS and SNIa does not affect this conclusion, while the inclusion of a prior on ${H}_{0}$ from sh0es increase the preference for EDE over $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ to the $\ensuremath{\sim}3.6\ensuremath{\sigma}$ level. After checking the EDE nonlinear matter power spectrum as predicted by standard semi-analytical algorithms via a dedicated set of $N$-body simulations, we test the 1-parameter EDE cosmology against WL data. We find that it does not significantly worsen the fit to the ${S}_{8}$ measurement as compared to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$, and that current WL observations do not exclude the EDE resolution to the Hubble tension. We also caution against the interpretation of constraints obtained from combining statistically inconsistent datasets within the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmology. In light of the CMB lensing anomalies, we show that the lensing-marginalized CMB data also favor nonzero ${f}_{\mathrm{EDE}}({z}_{c})$ at $\ensuremath{\sim}2\ensuremath{\sigma}$, predicts ${H}_{0}$ in $1.4\ensuremath{\sigma}$ agreement with sh0es and ${S}_{8}$ in $1.5\ensuremath{\sigma}$ and $0.8\ensuremath{\sigma}$ agreement with kids-viking and des respectively. There still exists however a $\ensuremath{\sim}2.5\ensuremath{\sigma}$ tension with the joint results from kids-viking and des. With an eye on Occam's razor, we finally discuss promising extensions of the EDE cosmology that could allow us to fully restore cosmological concordance.
The Atacama Cosmology Telescope: A Catalog of >4000 Sunyaev–Zel’dovich Galaxy Clusters

We present a catalog of 4195 optically confirmed Sunyaev–Zel’dovich (SZ) selected galaxy clusters detected with signal-to-noise ratio >4 in 13,211 deg2 of sky surveyed by the Atacama Cosmology Telescope (ACT). Cluster candidates were selected by applying a multifrequency matched filter to 98 and 150 GHz maps constructed from ACT observations obtained from 2008 to 2018 and confirmed using deep, wide-area optical surveys. The clusters span the redshift range 0.04 < z < 1.91 (median z = 0.52). The catalog contains 222 z > 1 clusters, and a total of 868 systems are new discoveries. Assuming an SZ signal versus mass-scaling relation calibrated from X-ray observations, the sample has a 90% completeness mass limit of M 500c > 3.8 × 1014 M ⊙, evaluated at z = 0.5, for clusters detected at signal-to-noise ratio >5 in maps filtered at an angular scale of 2. ′4. The survey has a large overlap with deep optical weak-lensing surveys that are being used to calibrate the SZ signal mass-scaling relation, such as the Dark Energy Survey (4566 deg2), the Hyper Suprime-Cam Subaru Strategic Program (469 deg2), and the Kilo Degree Survey (825 deg2). We highlight some noteworthy objects in the sample, including potentially projected systems, clusters with strong lensing features, clusters with active central galaxies or star formation, and systems of multiple clusters that may be physically associated. The cluster catalog will be a useful resource for future cosmological analyses and studying the evolution of the intracluster medium and galaxies in massive clusters over the past 10 Gyr.
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation

Event-based cameras record an asynchronous stream of per-pixel brightness changes. As such, they have numerous advantages over the standard frame-based cameras, including high temporal resolution, high dynamic range, and no motion blur. Due to the asynchronous nature, efficient learning of compact representation for event data is challenging. While it remains not explored the extent to which the spatial and temporal event “information” is useful for pattern recognition tasks. In this paper, we focus on single-layer architectures. We analyze the performance of two general problem formulations: the direct and the inverse, for unsupervised feature learning from local event data (local volumes of events described in space-time). We identify and show the main advantages of each approach. Theoretically, we analyze guarantees for an optimal solution, possibility for asynchronous, parallel parameter update, and the computational complexity. We present numerical experiments for object recognition. We evaluate the solution under the direct and the inverse problem and give a comparison with the state-of-the-art methods. Our empirical results highlight the advantages of both approaches for representation learning from event data. We show improvements of up to 9% in the recognition accuracy compared to the state-of-the-art methods from the same class of methods.
Age-Net: An MRI-Based Iterative Framework for Biological Age Estimation

The concept of biological age (BA) - although important in clinical practice - is hard to grasp mainly due to lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA $\not \approx$ CA) from the given population. In this manner, we hypothesize that the remaining population should approximate the true BA behaviour. For this initial study, we apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.
The effects of T type baffle on liquid sloshing by MPS method

In this paper, an in-house mesh-free particle solver MLParticle-SJTU, which is developed based on improved moving particle semi-implicit (MPS) method, is employed to numerically simulate the effects of T type baffle on liquid sloshing under surge excitation. At first, a rectangular tank without baffles is modeled to simulate three-dimensional (3-D) liquid sloshing under surge excitation. And the MLParticle-SJTU solver can capture the complex flow phenomena, such as overturning of free surface, breaking wave, impacting the roof of the tank and so on. Secondly, liquid sloshing in same rectangular tank with two kinds of vertical baffles is modeled. The impact pressure and the deformation of free surface can be investigated clearly, indicating that the vertical baffles can reduce the impulse pressure. In order to reduce the pressure amplitude and violent sloshing further, the rectangular tanks with two different T type baffles are simulated numerically. Comparing to the results of the tank with vertical baffles, the T type baffles are more effective on reducing violent liquid sloshing because of the existence of horizontal baffle.
Frequency of microorganisms in vaginal discharges of high-risk pregnant
 women from a hospital in Caruaru, Pernambuco, Brazil

Introduction: During pregnancy, estrogen and progesterone levels may favor vaginal colonization by pathogenic microorganisms that can be associated with obstetric complications and neonatal infections. These pathogens include Candida spp. , Trichomonas vaginalis, Gardnerella vaginalis and Streptococcus agalactiae. Objective: To determine the frequency of such microorganisms amongst high-risk pregnant women receiving prenatal care at a hospital in Caruaru, Pernambuco. Material and method: Analytical study in which samples of vaginal discharge were collected from women experiencing high-risk pregnancies, without restriction of age or gestational period. The samples were submitted to Gram-stained direct smear, as well as to fresh wet-mount examination and to cultures in Sabouraud and blood agar. Results: From May to December 2018, 92 patients were selected for sampling. The frequency of Candida spp. was found to be 31.52%. Concerning Gardnerella vaginalis, the frequency was 1.25%. The rate of colonization by Streptococcus agalactiae was 3.23%. No cases of Trichomonas vaginalis were found in this study. Discussion: The frequency of Candida that was found corroborates the literature, as it is a common infection during pregnancy due to hormonal increase. However, the frequencies of colonization by G. vaginalis, T. vaginalis and S. agalactiae were lower than those found in other studies. Conclusion: Due to the risks that these microorganisms can bring to a pregnant woman and a fetus, health professionals should be alert to signs and symptoms, requesting the screening of these pathogens, as well as treating gestating women when necessary.
A Rare Occurrence of Sinding – Larsen Johansson Syndrome with Osgood – Schlatter Disorder in a Nigerian Child: A Case Report

DOI: 10.21276/sjmcr.2019. 7.1. 2 Abstract: Sinding – Larsen Johansson syndrome (SLJ) and Osgood Schlatter disorder (OSD) are Osteochondroses. Both conditions may coexist, resulting in anterior knee pain in a child. We report a case of its coexistence in a Nigerian child. We reviewed patient medical journal corroborated with clinical examinations and review of relevant literatures. The case was that of an eleven years old active male athlete with anterior knee pain and functional limitation of flexion. Radio-diagnostic imaging confirms the existence of both types of Osteochondroses. We report the coexistence of both conditions in a Nigerian child. Keyword: Sinding-Larsen Johansson Syndrome, Osgood Schlatter disorder, anterior knee pain, Osteochondroses, osteochondritis.
Ultrastructure of Lejeunea spp. leaves surface in a lowland tropical urban forest of Universitas Indonesia Campus, Depok, Indonesia

Putrika A, Nabella DM, Salamah A, Nisyawati, Dwiranti A. 2020. Ultrastructure of Lejeunea spp. leaves surface in a lowland tropical urban forest of Universitas Indonesia Campus, Depok, Indonesia. Biodiversitas 21: 4184-4191. Lejeunea is one of liverworts genera that have a wide distribution in the world. It has many variations of the character that have not been revealed, such as variations of the cell surface. The purpose of this research was to study the ultrastructure of Lejeunea spp. leaves surface in a lowland tropical urban forest of Universitas Indonesia (UI) Campus, Depok, Indonesia. Six species of Lejeunea spp. were studied, i.e., L. anisophylla, L. cocoes, L. exilis, L. papilionacea, L. catanduana, and L. curviloba. The research methods carried out in this study consisted of the sample observation using a light microscope, sample preparation for Scanning Electron Microscope (SEM) observation (fixation, post-fixation, dehydration, drying, mounting), and observations using SEM. The results of observation using a light microscope showed the smooth cell surface of all samples studied. Meanwhile, the differences between six species of Lejeunea in the UI campus could be differentiated under SEM. L. catanduana could be distinguished from other species from its cell wall thickness and texture, ornamentation types, and the number of ornamentations per cell. The texture of the cell wall of L. catanduana was the roughest than the other species due to abundant ornamentation such as papillae on its the cell wall surface. Furthermore, only this species has mamillae on the cell surface. The number of papillae or mamillae was 1-4 per cell. On the other hand, L. cocoes has the thinnest cell wall and slightly rough texture. Only this species has the simple papillae on the cell surface. Thus, the results of this study suggested that the cell surface variations in Lejeunea might be potential to be used as taxonomic characters in grouping species.
Effect of Combined Zinc and Iron Foliar Fertilizer on Physiological and Agronomic Characteristics of Winter Wheat Nongda399

DOI: 10.21276/sjavs.2018. 5.12. 5 Abstract: Wheat is one of most important crop in the world contributing up to 28% of the world edible dry matter and up to 60% of the daily calorie intake in several developing country. Nutritional deficiencies are serious problems of public health in developing countries. To increase the productivity and quality of wheat micronutrient plays an important role. This study was conducted to study the effect of combined Zn and Fe fertilizers as foliar fertilizers on wheat agronomic performance and grain yield quantity. A split-plots in a randomized complete block design with 3 replications was used and PASW Statistics 18 software was used for statistical analysis. Results showed that compared to control (0%Zn+0%Fe), net photosynthetic rate was increased with 100%Zn13, 100%Zn9.5 and 100%Zn5.5 at 45.28, 73.07 and 39.54% respectively while 20%Zn+60%Fe13 increased up to 37.75% at the early filling stage and 100%Zn9.5 by the increase of 29.99% otherwise Zn an Fe had no effect. Flag leaf area increase was observed with 20%Zn+80%Fe13, 20%Zn+80%Fe9.5, 20%Zn+80%Fe5.5 and 100%Fe9.5 at booting stage with 15.84, 15.32, 14.67 and 15.40% increase respectively, at early filling stage with 20%Zn+60%Fe9.5 by 36.25% increase and at late filling stage with 20%Zn+60%Fe13 increased by 25.73%. The stalks dry mass show a more significant effect at booting stage and little effect at early filling stage and no effect at late filling stage. The yield was only affected with 20%Zn+80%Fe13.The inference can be drawn that foliar application of combined Zn and Fe fertilizer can enhance the net photosynthetic rate, SPAD and flag leaf area of Nongda399 winter wheat.