Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of menopause on quality of life: A cross sectional study in menopausal females in the north of Jordan

Aims: Menopause can severely effects the somatic, urogenital and psychological aspects of life, our aim is to find out the Health Related Quality of Life in female patients with menopause in a tertiary care hospital. Methods: A cross-sectional, observational study was conducted in the Gynecology out-patient department. All female patients with menopause attending the Gynecology OPD of tertiary care hospital for various complaints were included in the study. After obtaining written informed consent, demographics, relevant medical and surgical history was noted and they were then administered validated questionnaire ‘Health related Quality of Life Questionnaire: Menopause Rating Scale’ (HRQoL). Results: The overall population sample size was 409. The mean age of menopause was 48.91± 4.76 years. Hot flushes and sweating (somatic symptom) were the commonest symptom (86.31%), followed by anxiety (psychological symptom) in 76.53%. Maximum score was attributed to psychological symptoms, while urogenital domain had the minimum score. The association between the duration of menopause (early versus late post-menopause) and severity of symptom complex was found to be significant in psychological symptoms only. Frequency of psychological symptoms decreased as the duration since onset of menopause increased. Occurrence of somatic and urogenital symptoms is not significantly associated with duration of menopause. Conclusion: Menopausal symptoms commonly affect a large number of early and late post-menopausal women and adversely affect health related quality of life. Menopausal symptoms are common in postmenopausal women and except psychological symptoms there is minimal difference in the frequency and severity of other symptoms with duration.
ACUTE KIDNEY INJURY IN OBESE PATIENTS WITHIN THE POSTOPERATIVE PERIOD OF MYOCARDIAL REVASCULARIZATION

Objective: to identify whether there is a relation between obesity and the occurrence of acute kidney injury (AKI) in patients within the post-operative period of myocardial revascularization. Method: quantitative, longitudinal, and prospective study conducted at the general surgical intensive care unit (ICU) in a private tertiary hospital with 57 patients. We adopted a structured questionnaire for data collection. A descriptive and inferential data analysis was performed. Results with p < 0.05 were considered significant. Results: most of the patients were elderly (63±9 years). More than half of the obese patients evolved significantly with kidney injury or renal failure (p = 0.01). The surgery time in obese patients was higher (p = 0.01). The severity index Acute Physiology and Chronic Health Evaluation (APACHE II) was higher in overweight patients when compared to obese patients (p = 0.01). Conclusion: there was a relation between obesity and the occurrence of AKI. Thus, an increased understanding of this relation may guide health professionals, including nurses, in the preparation of preventive measures and, consequently, improve the population’s quality of life, besides reducing costs to the health system. Descriptors: Obesity; Acute Kidney Injury; Thoracic Surgery; Body Mass Index; Intensive Care Units; Disease Prevention.
Effect of Pre-Term Birth on Intellectual Ability: A Community Based Non-Concurrent Cohort study in South India

OBJECTIVES: 1. To estimate the magnitude of intellectual disability in preterm and term born children. 2. To study the effect of preterm birth on intellectual disability. 3. To study the effect of socio-demographic and medical risk factors on intellectual disability. METHODS: The study design was a non-concurrent cohort study. It compared the intellectual ability of Pre-term children born between 2001 to 2005 with their Term counterparts born in the same period. The sample size calculated was 200 preterm born children and 200 term born children. An interview was conducted with the parent or the primary caregiver. Basic socio-demographic data was collected. Antenatal, natal and postnatal details were obtained. Anthropometric measurement was obtained. The screening tool (Brief Intellectual Disability Scale – BIDS) was administered by a single interviewer. BIDS (Brief Intellectual Disability Scale) score >5 is said to be screen positive for intellectual disability with a sensitivity of 71.53% and a specificity of 80.95%. BIDS (Brief Intellectual Disability Scale) score > 11 is said to be diagnostic for intellectual disability with a sensitivity of 54.29% and a specificity of 100.00%. The BIDS questionnaire had a total of ten questions. The responses to the BIDS questionnaire was recorded in the form of Likert scale as “Not true”, “Sometimes true” or “Very true” for the all the children born by preterm and term birth by the parent or the primary caregiver. RESULTS: The incidence of intellectual disability among the preterm children born in Kaniyambadi block was 5.4% compared to 0.49% among the term born children. Preterm born children had 11 times greater risk of developing intellectual disability in comparison to the term born children. 59 (29.2%) of the children born by preterm birth developed probable intellectual disability (BIDS score greater than 5) compared to 11 (5.4%) of their term counterparts. Children born preterm had 6.732 times greater risk of developing probable intellectual disability compared to the children born term. CONCLUSIONS: 1. The incidence of intellectual disability among the preterm children born in Kaniyambadi block was 5.4% compared to 0.49% among the term born children. 2. Preterm born children had 11 times greater risk of developing intellectual disability in comparison to the term born children. 3. 59 (29.2%) of the children born by preterm birth developed probable intellectual disability (BIDS score greater than 5) compared to 11 (5.4%) of their term counterparts. 4. Children born preterm had 6.732 times greater risk of developing probable intellectual disability compared to the children born term.
Clustering Time Series Data through Autoencoder-based Deep Learning Models

Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data mining-based analysis radically into a learning-based model in which existing data sets along with their cluster labels (i.e., train set) are learned to build a supervised learning model and predict the cluster labels of unseen data (i.e., test set). In particular, deep learning techniques are capable of capturing and learning hidden features in a given data sets and thus building a more accurate prediction model for clustering and labeling problem. However, the major problem is that time series data are often unlabeled and thus supervised learning-based deep learning algorithms cannot be directly adapted to solve the clustering problems for these special and complex types of data sets. To address this problem, this paper introduces a two-stage method for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of given time series data in order to create labels and thus be able to transform the problem from unsupervised learning into supervised learning. Second, an autoencoder-based deep learning model is built to learn and model both known and hidden features of time series data along with their created labels to predict the labels of unseen time series data. The paper reports a case study in which financial and stock time series data of selected 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed procedure is capable of achieving 87.5\% accuracy in clustering and predicting the labels for unseen time series data.
Sieve

Current approaches for enforcing Fine Grained Access Control (FGAC) in DBMS do not scale to scenarios when the number of access control policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart spaces wherein systems may be required by legislation, such as Europe's GDPR and California's CCPA, to empower users to specify who may have access to their data and for what purposes. We present Sieve, a layered approach of implementing FGAC in existing DBMSs, that exploits a variety of their features (e.g., UDFs, index usage hints, query explain) to scale to a large number of policies. Given a query, Sieve exploits its context to filter the policies that need to be checked. It also generates guarded expressions that save on evaluation cost by grouping policies and exploit database indices to cut on read cost. Our experimental results demonstrate that existing DBMSs can utilize Sieve to significantly reduce query-time policy evaluation cost. Using Sieve DBMSs can support real-time access control in applications such as emerging smart environments.
The role of formalism in system requirements (full version)

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, good for understandability but lacking in precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as "formal". Many exist, differing in their style, scope and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of 9 complementary criteria, such as level of abstraction, tool availability, traceability support. It classifies the approaches into five categories: general-purpose, natural-language, graph/automata, other mathematical notations, seamless (programming-language-based). It presents approaches in all of these categories, altogether 22 different ones, including for example SysML, Relax, Eiffel, Event-B, Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches. (This is the full version of the survey, including some sections and two appendices which, because of length restrictions, do not appear in the submitted version.)
Crippling and Uncrippling Metabolic Pathways

Ma Krüppel-like factors (KLFs) are deoxyribonucleic acid–binding transcriptional factors that regulate various pathways that control metabolism and other cellular mechanisms. Various KLF isoforms have been associated with cellular, organ, or systemic metabolism. Altered expression or activation of KLFs has been linked to metabolic abnormalities, such as obesity and diabetes, as well as with heart failure. This review article summarizes the metabolic functions of KLFs, as well as the networks of different KLF isoforms that jointly regulate metabolism in health and disease. (J Am Coll Cardiol Basic Trans Science 2018;3:132–56)©2018 TheAuthors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Paraphrase Augmented Task-Oriented Dialog Generation

Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.
Quantum time crystals with programmable disorder in higher dimensions

We present fresh evidence for the presence of discrete quantum time crystals in two spatial dimensions. Discrete time crystals are intricate quantum systems that are concomitant with spontaneous breaking of a discrete time translation symmetry in driven quantum many-body systems undergoing non-equilibrium dynamics, stabilized by many-body localization arising from disorder. We directly target the thermodynamic limit using instances of infinite tensor network states (iPEPS) and implement disorder in a translationally invariant setting by introducing auxiliary systems at each site. We discuss how such disorder can be realized in programmable quantum simulators that are intermediate between analog quantum simulators and quantum computers.
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks

Radiographic images offer an alternative method for the rapid screening and monitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of radiology experts who can provide a timely interpretation of these images. Motivated by this challenge, our paper proposes a novel learning architecture, called Detail-Oriented Capsule Networks (DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT) scans. Our network combines the strength of Capsule Networks with several architecture improvements meant to boost classification accuracies. First, DECAPS uses an Inverted Dynamic Routing mechanism which increases model stability by preventing the passage of information from non-descriptive regions. Second, DECAPS employs a Peekaboo training procedure which uses a two-stage patch crop and drop strategy to encourage the network to generate activation maps for every target concept. The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data. Finally, we use a data augmentation method based on conditional generative adversarial networks to deal with the issue of data scarcity. Our model achieves 84.3% precision, 91.5% recall, and 96.1% area under the ROC curve, significantly outperforming state-of-the-art methods. We compare the performance of the DECAPS model with three experienced, well-trained thoracic radiologists and show that the architecture significantly outperforms them. While further studies on larger datasets are required to confirm this finding, our results imply that architectures like DECAPS can be used to assist radiologists in the CT scan mediated diagnosis of COVID-19.
Serre weights and the Breuil–Mézard conjecture for modular forms

Serre's strong conjecture, now a theorem of Khare and Wintenberger, states that every two-dimensional continuous, odd, irreducible mod $p$ Galois representation $\rho$ arises from a modular form of a specific minimal weight $k(\rho)$, level $N(\rho)$ and character $\epsilon(\rho)$. In this short paper we show that the minimal weight $k(\rho)$ is equal to a notion of minimal weight inspired by the recipe for weights introduced by Buzzard, Diamond and Jarvis. Moreover, using the Breuil-Mezard conjecture we show that both weight recipes are equal to the smallest $k \geq 2$ such that $\rho$ has a crystalline lift of Hodge-Tate type $(0,k-1)$.
X-ray emission from the mixed-morphology supernova remnant HB 9

We present the results of a spectral analysis of the central region of the mixed-morphology supernova remnant HB 9. A prior Ginga observation of this source detected a hard X-ray component above 4 keV, and the origin of this particular X-ray component is still unknown. Our results demonstrate that the extracted X-ray spectra are best represented by a model consisting of a collisional ionization equilibrium plasma with a temperature of ∼0.1–0.2 keV (interstellar matter component) and an ionizing plasma with a temperature of ∼0.6–0.7 keV and an ionization timescale of >1 × 1011 cm−3 s (ejecta component). No significant X-ray emission was found in the central region above 4 keV. The recombining plasma model reported by a previous work does not explain our spectra.
A Hybrid Objective Function for Robustness of Artificial Neural Networks - Estimation of Parameters in a Mechanical System

In several studies, hybrid neural networks have proven to be more robust against noisy input data compared to plain data driven neural networks. We consider the task of estimating parameters of a mechanical vehicle model based on acceleration profiles. We introduce a convolutional neural network architecture that is capable to predict the parameters for a family of vehicle models that differ in the unknown parameters. We introduce a convolutional neural network architecture that given sequential data predicts the parameters of the underlying data's dynamics. This network is trained with two objective functions. The first one constitutes a more naive approach that assumes that the true parameters are known. The second objective incorporates the knowledge of the underlying dynamics and is therefore considered as hybrid approach. We show that in terms of robustness, the latter outperforms the first objective on noisy input data.
A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.
Hawking flux from a black hole with nonlinear supertranslation hair

We study the Hawking flux from a black hole with soft hair by the anomaly cancellation method proposed by Robinson and Wilczek. Unlike the earlier studies considering the black hole with linear supertranslation hair, our study takes into account the supertranslation hair to the quadratic order, which then yields the angular dependent horizon. As a result, highly nontrivial kinetic-mixings appear among the spherical Kaluza-Klein modes of the $(1+1)\mathrm{d}$ near-horizon reduced theory, which obscures the traditional derivation of the Hawking flux. However, after a series of field redefinitions, we can disentangle the mode-mixings into canonical normal modes, but the reduced metrics for these normal modes are mode-dependent. Despite of this, the resultant Hawking flux turns out to be mode-independent and remains the same as the Schwarzschild's one. Thus, one cannot tell the black holes with nonlinear supertranslation hairs from the Schwarzschild's one by examining the Hawking flux, so that the nonlinear soft hairs can be thought as the microstates.
Optimal Regularizing Estimate of Linearized Boltzmann equation without cut-off for Hard Potential

In this article, we present a pseudo-differential calculus method for deriving the regularizing estimate, with respect to velocity v, on the semigroup generated by linearized Boltzmann operator. To this goals, we write the spatially inhomogeneous linearized Boltzmann operator as B = −v · ∇x − A +K and its Fourier transform on x as B̂(y) = Â(y) + K, where −v · ∇x − A, Â(y) are dissipative and hypoelliptic, K is compact. These good properties yields that B generates strongly continuous semigroup. As a consequence, in the case of hard potential, we obtain the optimal regular estimate, with respect to velocity, on the semigroup etB from H(ã−1/2〈y〉l) into H(ã1/2〈y〉l) with a sharp large time decay, building on weighted Sobolev space. Our analysis provides a further study of pseudo-differential calculus as well as semigroup theory to linearized Boltzmann equation.
Do Sequence-to-sequence VAEs Learn Global Features of Sentences?

A longstanding goal in NLP is to compute global sentence representations. Such representations would be useful for sample-efficient semi-supervised learning and controllable text generation. To learn to represent global and local information separately, Bowman & al. (2016) proposed to train a sequence-to-sequence model with the variational auto-encoder (VAE) objective. What precisely is encoded in these latent variables expected to capture global features? We measure which words benefit most from the latent information by decomposing the reconstruction loss per position in the sentence. Using this method, we see that VAEs are prone to memorizing the first words and the sentence length, drastically limiting their usefulness. To alleviate this, we propose variants based on bag-of-words assumptions and language model pretraining. These variants learn latents that are more global: they are more predictive of topic or sentiment labels, and their reconstructions are more faithful to the labels of the original documents.
H- and Dissociation in Ultra-hot Jupiters: A Retrieval Case Study of WASP-18b

Atmospheres of a number of ultra-hot Jupiters (UHJs) with temperatures ≳2000 K have been observed recently. Many of these planets show largely featureless thermal spectra in the near-infrared observed with the HST WFC3 spectrograph (1.1–1.7 μm) even though this spectral range contains strong H2O opacity. Recent works have proposed the possibility of H- opacity masking the H2O feature and/or thermal dissociation of H2O causing its apparent depletion at the high temperatures of UHJs. In this work, we test these hypotheses using observations of the exoplanet WASP-18b as a case study. We report detailed atmospheric retrievals of the planet using the HyDRA retrieval code, extended to include the effects of H- opacity and thermal dissociation. We report constraints on the H2O, CO, and H- abundances as well as the pressure–temperature profile of the dayside atmosphere for retrievals with and without H-/dissociation for each data set. We find that the H2O and H- abundances are relatively unconstrained given the featureless WFC3 spectra. We do not conclusively detect H- in the planet, contrary to previous studies that used equilibrium models to infer its presence. The constraint on the CO abundance depends on the combination of WFC3 and Spitzer data, ranging from solar to super-solar CO values. We additionally see signs of a thermal inversion from two of the data sets. Our study demonstrates the potential of atmospheric retrievals of UHJs, including the effects of H- and thermal dissociation of molecules.