Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging

Our main contribution lies in formulation of a generalized, parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far. We use the general model framework for the purpose of formal testing and comparison of alternative specifications, which provides a way to deal with model uncertainty - the main issue of SFA that has not been resolved so far. SFA dates back to Aigner et al. (1977) and Meeusen and van den Broeck (1977), and relies upon the idea of compound error specification with at least two error terms, one representing the observation error and the other interpreted as some form of inefficiency. The models considered here are based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. Hence, it is possible to relax a number of various potentially restrictive assumptions embedded in models used so far. We also develop methods of Bayesian inference that are less restrictive (though more demanding in terms of computation time) compared to the ones used so far and demonstrate inference on the latent variables (i.e., object-specific inefficiency terms, which are important for, e.g., policy-oriented analyses).
Exploring the Absorption Spectrum of Simulated Water from MHz to the Infrared.

Absorption spectra of liquid water at 300 K are calculated from both classical and density functional theory molecular dynamics simulation data, which together span from 1 MHz to hundreds of THz, agreeing well with experimental data qualitatively and quantitavely over the entire range, including the IR modes, the microwave peak, and the intermediate THz bands. The spectra are decomposed into single-molecular and collective components as well as into components due to molecular reorientations and changes in induced molecular dipole moments. These decompositions shed light on the motions underlying the librational and translational (hydrogen-bond stretch) bands at 20 and 5 THz respectively: interactions between donor protons and acceptor lone pair electrons are shown to be important for the line shape in both librational and translational regimes, and in- and out-of-phase librational dimer modes are observed and explored.
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling

Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable success for multiple domains but they suffer from two main limitations: they are either built from several two-domain mappings that are required to be learned independently, or they generate low-diversity results, a problem known as mode collapse. To overcome these limitations, we propose a method named GMM-UNIT, which is based on a content-attribute disentangled representation where the attribute space is fitted with a GMM. Each GMM component represents a domain, and this simple assumption has two prominent advantages. First, it can be easily extended to most multi-domain and multi-modal image-to-image translation tasks. Second, the continuous domain encoding allows for interpolation between domains and for extrapolation to unseen domains and translations. Additionally, we show how GMM-UNIT can be constrained down to different methods in the literature, meaning that GMM-UNIT is a unifying framework for unsupervised image-to-image translation.
EmotiCon: Context-Aware Multimodal Emotion Recognition Using Frege’s Principle

We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities (e.g.faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods.
A Rotation-Invariant Framework for Deep Point Cloud Analysis

Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations. In this article, we introduce a new low-level purely rotation-invariant representation to replace common 3D Cartesian coordinates as the network inputs. Also, we present a network architecture to embed these representations into features, encoding local relations between points and their neighbors, and the global shape structure. To alleviate inevitable global information loss caused by the rotation-invariant representations, we further introduce a region relation convolution to encode local and non-local information. We evaluate our method on multiple point cloud analysis tasks, including (i) shape classification, (ii) part segmentation, and (iii) shape retrieval. Extensive experimental results show that our method achieves consistent, and also the best performance, on inputs at arbitrary orientations, compared with all the state-of-the-art methods.
Renormalized oscillation theory for symplectic eigenvalue problems with nonlinear dependence on the spectral parameter

ABSTRACT In this paper, we establish new renormalized oscillation theorems for discrete symplectic eigenvalue problems with Dirichlet boundary conditions. These theorems present the number of finite eigenvalues of the problem in the arbitrary interval using the number of focal points of a transformed conjoined basis associated with Wronskian of two principal solutions of the symplectic system evaluated at the endpoints a and b. We suppose that the symplectic coefficient matrix of the system depends nonlinearly on the spectral parameter and that it satisfies certain natural monotonicity assumptions. In our treatment, we admit possible oscillations in the coefficients of the symplectic system by incorporating their non-constant rank with respect to the spectral parameter.
Torsion of injective modules and weakly pro-regular sequences

Abstract Let R a commutative ring, an ideal, I an injective R-module and a multiplicatively closed set. When R is Noetherian it is well-known that the -torsion sub-module the factor module and the localization IS are again injective R-modules. We investigate these properties in the case of a commutative ring R by means of a notion of relatively--injective R-modules. In particular we get another characterization of weakly pro-regular sequences in terms of relatively injective modules. Also we present examples of non-Noetherian commutative rings R and injective R-modules for which the previous properties do not hold. Moreover, under some weak pro-regularity conditions we obtain results of Mayer-Vietoris type.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best. A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.
Incidence of postoperative flare-ups after single-visit and multiple-visit endodontic therapy in permanent teeth

Aim: Single- and multiple-visit root canal treatment has been the subject of long-standing debate in the endodontic community, so the purpose of this study was to determine the incidence of postoperative flare-up after single- and multiple-visit endodontic therapy in permanent teeth. Materials and Methods: A total of 65 children aged >10 years who underwent root canal treatment were randomly categorized equally into two treatment groups: the first group underwent single-visit treatment, and the other group underwent multiple-visit therapy. The visual analog scale was employed to evaluate pain preoperatively and postoperatively after obturation. Recall visits were carried out after 1 week, 1 and 3, 6 and 9 months. The Statistical Package for the Social Sciences version 15.0 was employed for statistical analysis. Results: Except at the baseline, at all the other time intervals, the mean pain score in the multiple-visit group was higher as compared to that of the single-visit group; statistically no significant difference was found. Conclusion: The mean pain score in the single-visit group was lower as compared to that of the multiple-visit group; however, the difference between the two groups was statistically significant (P > 0.05).
Comparison of shear bond strength of light cure mineral trioxide aggregate and light cure calcium hydroxide with nanofilled composite: A stereomicroscopic and scanning electron microscope analysis

Objective: The quest for ideal pulp capping materials has given rise to the development of newer materials such as light cure mineral trioxide aggregate (MTA). The bond strength of the pulp capping materials with overlying restoration is one among the several factors that are critical for the success of vital pulp therapy. Hence, we conducted this study to evaluate and compare the shear bond strength (SBS) of light cure MTA and light cure calcium hydroxide with nanofilled composite. Materials and Methods: Thirty acrylic blocks each with a central hole were prepared to uniform dimensions and randomly distributed into two equal groups. In Group I, light cure MTA, and in Group II, light cure calcium hydroxide was used as pulp capping materials. After the application of adhesive system, nanofilled composites were applied onto the pulp capping material using a cylindrical plastic matrix. The SBS was tested on a universal testing machine (Instrom 3366, UK) at a crosshead speed of 0.5 mm/min. The samples were examined under stereomicroscope and scanning electron microscope to analyze different modes of failure. Results: The results were statistically analyzed using independent sample t-test. Light cure MTA attained the mean SBS of 6.54 MPa and light cure calcium hydroxide attained the mean SBS of 6.56 MPa. There was no significant difference statistically in SBS of both the materials (P < 0.05). The modes of failure were predominantly mixed failure followed by cohesive failure within the restorative material in both Group I and II. Conclusion: The results of the study suggest that the SBS of light cure MTA and light cure calcium hydroxide is comparable. The modes of failure analyzed in both light cure MTA and light cure calcium hydroxide are not significantly different statistically. Hence, both materials can be successfully used as pulp capping material with nanofilled composite.
Semi-supervised Disentanglement with Independent Vector Variational Autoencoders

We aim to separate the generative factors of data into two latent vectors in a variational autoencoder. One vector captures class factors relevant to target classification tasks, while the other vector captures style factors relevant to the remaining information. To learn the discrete class features, we introduce supervision using a small amount of labeled data, which can simply yet effectively reduce the effort required for hyperparameter tuning performed in existing unsupervised methods. Furthermore, we introduce a learning objective to encourage statistical independence between the vectors. We show that (i) this vector independence term exists within the result obtained on decomposing the evidence lower bound with multiple latent vectors, and (ii) encouraging such independence along with reducing the total correlation within the vectors enhances disentanglement performance. Experiments conducted on several image datasets demonstrate that the disentanglement achieved via our method can improve classification performance and generation controllability.
Open Adrenalectomy: A 20-Year Review of Our Experience in a Developing Country

Introduction: The aim of this study was to present our 20-year experience regarding open adrenalectomy (OA) during laparoscopic era in a developing country Turkey. Materials and Methods: A retrospective and descriptive study of patients with adrenal mass undergoing OA in the surgery department of our hospital, between January 1993 and January 2013, was carried out. All operations were performed by two surgeons. Results: Ninety patients who underwent OA in our clinic were reviewed retrospectively. The mean number of adrenal operations per month during this period was 0.38 ± 0.12. The patient included 35 men (38.8%) and 55 women (61.2%), with a mean age of 46.4 ± 17 years. The mean body mass index was 28.4 ± 5.25, and the mean American Society of Anesthesiologists score was 2.6 ± 0.57. The mean operative time was 88 ± 27 min. The mean maximum diameter of all the lesions was 4.8 ± 1.3 cm (range: 1.2–21 cm). The mean blood loss was 118 ± 23 ml during the operations. Postoperative complications were observed in four patients (5.5%). There was no mortality. The length of hospital stay was 6.2 ± 2.1 days. The most frequent type of the histological type was benign adenoma (48.8%). Conclusion: OA in a developing country is a safe method as an alternative for laparoscopic adrenalectomy which has a difficult learning curve.
Ephemeral species in the fossil record? Synchronous coupling of macroevolutionary dynamics in mid-Paleozoic zooplankton

Abstract. We document a positive and strong correlation between speciation and extinction rates in the Paleozoic zooplankton graptoloid clade, between 481 and 419 Ma. This correlation has a magnitude of ∼0.35–0.45 and manifests at a temporal resolution of <50 kyr and, for part of our data set, <25 kyr. It cannot be explained as an artifact of the method used to measure rates, sampling bias, bias resulting from construction of the time series, autocorrelation, underestimation of species durations, or undetected phyletic evolution. Correlations are approximately equal during the Ordovician and Silurian, despite the very different speciation and extinction regimes prevailing during these two periods, and correlation is strongest in the shortest-lived cohorts of species. We infer that this correlation reflects approximately synchronous coupling of speciation and extinction in the graptoloids on timescales of a few tens of thousands of years. Almost half of graptoloid species in our data set have durations of <0.5 Myr, and previous studies have demonstrated that, during times of background extinction, short-lived species were selectively targeted by extinction. These observations may be consistent with the model of ephemeral speciation, whereby new species are inferred to form constantly and at high rate, but most of them disappear rapidly through extinction or reabsorption into the ancestral lineage. Diversity dependence with a lag of ∼1 Myr, also documented elsewhere, may reflect a subsequent and relatively slow, competitive dynamic that governed those species that dispersed beyond their originating water mass and escaped the ephemeral species filter.
Quantification and Comparison of the Impact of the Smoking Status on Oral Polymorphonuclear Leukocyte and Malondialdehyde Levels in Individuals with Chronic Periodontitis: A Double-Blinded Longitudinal Interventional Study

Background: The formation of reactive oxygen species by oral polymorphonuclear leukocytes (oPMNs) is amplified in smokers with chronic periodontitis (CP) causing tissue damage which can be measured by quantifying levels of malondialdehyde (MDA). Objective: To quantify and compare the impact of smoking status on oPMN and MDA in individuals with CP before and after scaling and root planing (SRP). Materials and Methods: Sixty individuals were divided into four groups, namely, periodontally healthy (Group A), current smokers with CP (Group B), former smokers with CP (Group C), and nonsmokers with CP (Group D). Parameters assessed were bleeding on probing (BOP), gingival index (GI), probing pocket depth (PPD), clinical attachment level, gingival recession, periodontal inflamed surface area, salivary MDA, and oPMN at baseline and 6 and 12 weeks after SRP. Results: Increased PPD (P = 0.01) and decreased GI (P = 0.021) was noted in Group B as compared to C and D at baseline. Periodontal intervention caused a greater resolution of inflammation in Groups C and D as compared to B as noted from the GI and BOP. A reduction in MDA (P = 0.074) was noted in Groups C and D as compared to B, and oPMN levels were higher (P = 0.009) in Group C and D as compared to B. Conclusion: Greater periodontal destruction is seen in current smokers than former and nonsmokers with CP. MDA can be considered as a reliable biomarker for oxidative stress as it directly correlates with the oPMN levels.
The effects of split application of enhanced efficiency fertilizers on non-winter nitrous oxide emissions from winter wheat

Abstract: This study tested if non-winter cumulative nitrous oxide (N2O) emissions, emission factors, and yield-scaled N2O emissions were affected by split application of enhanced efficiency nitrogen fertilizers in a rain-fed winter wheat crop. Based on initial soil tests, fertilizers were applied at 84 kg N ha-1 in year 1 and 72 kg N ha-1 in year 2. Two trials were completed each year. Trial 1 applied (1) urea, (2) urea with nitrification inhibitor, (3) nitrification and urease inhibitors, and (4) polymer-coated urea as (1) 100% side-banded at planting, 30% side-banded at planting and (2) 70% surface-applied in late fall, or (3) 70% surface-applied in spring at Feekes growth stage 4 (GS4). Trial 2 applied (1) urea–ammonium nitrate (UAN), (2) UAN treated with nitrification inhibitor, (3) urease inhibitor, (4) a combination of both, (5) granular urea, and (6) polymer-coated urea, all applied 50% side-banded at planting and 50% surface-applied at GS4. Cumulative N2O emissions from fertilized soils ranged from 0.101 to 0.433 kg N ha-1. The emission factors for trial 1 were greater in year 1 than year 2 (P ≤ 0.05). There were no treatment differences in cumulative N2O emissions in trial 2. However, cumulative N2O emissions, emission factors, and yield-scaled N2O emissions from trial 1 were higher when fertilizer was split-applied in late fall compared with at GS4 (all P ≤ 0.05). This study demonstrates that under best management practices, it is better to apply the required rate in the form of conventional fertilizer at planting rather than split application.
Factors associated with the presentation of persisting post traumatic stress disorder symptoms in people who have sustained traumatic spinal cord injuries.

Objectives: This study aimed to investigate factors associated with persistent Post-Traumatic Stress Disorder (PTSD) in people with Spinal Cord Injury (SCI). In the context of a cognitive model, it sought to determine how influential cognitive appraisals were in predicting persistent PTSD when compared to other known predictor variables in the literature such as injury severity, gender, previous psychiatric history and social support. Design: A cross-sectional questionnaire survey that examined concurrent predictors of PTSD symptoms and diagnosis primarily using multiple regression analyses. Method: A sample of 50 inpatients receiving rehabilitation for SCI who were 3-24 months post- injury were interviewed using a series of standardised measures of PTSD symptoms and diagnosis, post-traumatic cognitive appraisals, social support, and injury severity. Results: For PTSD symptoms, significant relationships were found for greater injury severity, lower satisfaction with social support and more negative cognitions. PTSD diagnosis was only related to cognitive variables. Negative cognitions were found to predict variance in PTS symptoms over and above the non-cognitive variables although gender and injury severity were also predictors. The only significant predictor of PTSD diagnosis was the cognitive subscale' negative cognitions about the self. Conclusions: Cognitive appraisals were found to be important predictors of persisting PTSD in an SCI population. This supports the cognitive model of PTSD and the development of cognitive therapies for PTSD in this population. Future research directions are discussed.
Exploring veterinary medical students' mindset about intelligence, personality, attitude, and skills and abilities

Background: In recent decades, the topic of mindset has gained considerable attention in the education arena. According to Dweck, people hold beliefs about their personal traits that can be classified into one of the two beliefs: fixed and growth mindsets. Individuals with a fixed mindset believe that their traits cannot be changed. Individuals with a growth mindset believe that their traits can be cultivated over time and enhanced through effort. The purpose of this study was to explore the mindset of veterinary medical students as it pertains to traits such as intelligence, personality, attitude, and skills and abilities. Methods: Using a cross-sectional survey design, 298 veterinary medical students were enrolled in the study. Results: The overwhelming majority of the students believed that skills and abilities could be cultivated and improved over time. However, a considerable proportion of students believed that attitude, intelligence, and personality were largely fixed traits. Conclusions: Implications for mindset have far-reaching effects, including resilience, grit, motivations, and more.