Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and risk factors of anemia among pregnant women attending antenatal clinic of MOSC medical college hospital, Kolenchery

Introduction: Anemia is one of the most prevalent nutritional deficiency problems affecting pregnant women. Anemia in pregnancy is commonly considered as a risk factor for the poor pregnancy outcome and can result in complications that threaten the life of both mother and fetus. According to WHO, anemia in pregnancy is a condition in which the hemoglobin concentration in the peripheral blood is less than 11gm/100ml. Given the multifactorial nature of this disease, correcting anemia often requires an integrated approach. In order to effectively combat anemia, the contributing factors must be identified and addressed. Aim and objectives: The study was aimed to estimate the prevalence of anemia among pregnant women and to identify the risk-factors of anemia among pregnant women. Materials and Methods: A descriptive cross sectional study was conducted among 398 antenatal women attending antenatal clinic of MOSC Medical College Hospital, Kolenchery from 16/11/2018 to 10/12/2018. Convenience sampling technique was adopted or this study. Questionnaire on risk factors of anemia was used to collect the data. Medical record of all antenatal women who visits the antenatal OPD during the data collection period was reviewed to assess the hemoglobin value. The results were analyzed by using R software and p value <0.05 was considered as significant. Results: The findings of the study revealed that from the total 398 antenatal women, the prevalence of anemia was 42.96%. Among them, about 18.09% were moderately anemic, 24.87% were mildly anemic and no severe anemia was found out. The multiple logistic regression results showed that antenatal bleeding, contraceptive use, iron supplements, irregular menstrual cycle, history of heavy menstruation, loss of appetite, hemorrhoids and bleeding gums were the risk factors of anemia in pregnancy. Conclusion: This study is one of the few studies in Kerala that has focused on exploring and identifying the risk factors of anemia among pregnant women. It is suggested that early detection of risk factors of anemia will help for the effective management of anemia in pregnancy.
Predicting Bond Return Predictability

This paper provides empirical evidence on predictable time variations in out-of-sample bond return predictability. Bond return predictability is associated with periods of high (low) economic activity (uncertainty), which implies that violations of the expectations hypothesis are state dependent and linked to features of the business cycle. These state dependencies in predictability, established by introducing a new multivariate test for equal conditional predictive ability, can be used in real time to improve out-of-sample bond risk premia estimates and investors’ economic utility through a novel dynamic forecast combination scheme that uses predicted forecasting performance to identify the best set of methods to include in the combined forecast. Dynamically combined forecasts exhibit strong countercyclical behavior and peak during recessions. This paper was accepted by Lukas Schmid, finance. Funding: This work was supported by the Danish Council of Independent Research [Grants DFF 7024–00020B and DFF 9033–00003B] and the Danish Finance Institute. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023. 4713 .
Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

The fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.
Histopathology and Ultrastructural Findings of Fatal COVID-19 Infections

Background SARS-CoV-2 is the cause of an ongoing pandemic with a projected 100,000 to 240,000 U.S. deaths. To date, documentation of histopathologic features in fatal cases of COVID-19 has been limited due to small sample size and incomplete organ sampling. Methods Post-mortem examinations were performed on 12 fatal COVID-19 cases in Washington State during February-March 2020. Clinical and laboratory data were reviewed. Tissue examination of all major organs was performed by light microscopy and electron microscopy. The presence of viral RNA in sampled tissues was tested by RT-PCR. Results All 12 patients were older with significant preexisting comorbidities. The major pulmonary finding was diffuse alveolar damage in the acute and/or organizing phases with virus identified in type I and II pneumocytes by electron microscopy. The kidney demonstrated viral particles in the tubular epithelium, endothelium, and podocytes without significant inflammation. Viral particles were also observed in the trachea and large intestines. SARS-CoV-2 RNA was detected in the cardiac tissue of a patient with lymphocytic myocarditis. RT-PCR also detected viral RNA in the subcarinal lymph nodes, liver, spleen, and large intestines. Conclusion SARS-CoV-2 represents the third novel coronavirus to cause widespread human disease since 2002. Similar to SARS and MERS, the primary pathology was diffuse alveolar damage with virus located in the pneumocytes. However, other major organs including the heart and kidneys may be susceptible to viral replication and damage leading to increased mortality in those with disseminated disease. Understanding the pathology of SARS-CoV-2 will be essential to design effective therapies.
Keyphrase Generation with Cross-Document Attention

Keyphrase generation aims to produce a set of phrases summarizing the essentials of a given document. Conventional methods normally apply an encoder-decoder architecture to generate the output keyphrases for an input document, where they are designed to focus on each current document so they inevitably omit crucial corpus-level information carried by other similar documents, i.e., the cross-document dependency and latent topics. In this paper, we propose CDKGen, a Transformer-based keyphrase generator, which expands the Transformer to global attention with cross-document attention networks to incorporate available documents as references so as to generate better keyphrases with the guidance of topic information. On top of the proposed Transformer + cross-document attention architecture, we also adopt a copy mechanism to enhance our model via selecting appropriate words from documents to deal with out-of-vocabulary words in keyphrases. Experiment results on five benchmark datasets illustrate the validity and effectiveness of our model, which achieves the state-of-the-art performance on all datasets. Further analyses confirm that the proposed model is able to generate keyphrases consistent with references while keeping sufficient diversity. The code of CDKGen is available at this https URL.
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation

With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.
Experience Grounds Language

Successful linguistic communication relies on a shared experience of the world, and it is this shared experience that makes utterances meaningful. Despite the incredible effectiveness of language processing models trained on text alone, today's best systems still make mistakes that arise from a failure to relate language to the physical world it describes and to the social interactions it facilitates. Natural Language Processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large text corpora can be deeply enriched from the parallel tradition of research on the contextual and social nature of language. In this article, we consider work on the contextual foundations of language: grounding, embodiment, and social interaction. We describe a brief history and possible progression of how contextual information can factor into our representations, with an eye towards how this integration can move the field forward and where it is currently being pioneered. We believe this framing will serve as a roadmap for truly contextual language understanding.
Privacy in Crisis: A study of self-disclosure during the Coronavirus pandemic

We study observed incidence of self-disclosure in a large dataset of Tweets representing user-led English-language conversation about the Coronavirus pandemic in the month between March 1 and April 3, 2020. Using an unsupervised approach to detection of voluntary disclosure of personal information, we provide early evidence that situational factors surrounding the Coronavirus pandemic may impact individuals' privacy calculus. Text analyses reveal topical shift toward supportiveness and support-seeking in self-disclosing conversation on Twitter. We run a comparable analysis of Tweets from Hurricane Harvey to provide context for observed effects and suggest opportunities for further study.
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of quantization parameters and evaluate their choices on a wide range of neural network models for different application domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are more difficult to quantize, such as MobileNets and BERT-large.
On weak solutions to a fractional Hardy–Hénon equation: Part I: Nonexistence

This paper and [17] treat the existence and nonexistence of stable (resp. outside stable) weak solutions to a fractional Hardy--Henon equation $(-\Delta)^s u = |x|^\ell |u|^{p-1} u$ in $\mathbb{R}^N$ where $0 -2s$, $p>1$, $N \geq 1$ and $N > 2s$. In this paper, the nonexistence part is proved for the Joseph--Lundgren subcritical case.
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.
VV 655 and NGC 4418: Implications of an interaction for the evolution of a LIRG

Context. VV 655, a dwarf irregular galaxy with HI tidal debris, is a companion to the lenticular luminous infrared galaxy (LIRG) NGC 4418. NGC 4418 stands out among nearby LIRGs due to its dense central concentration of molecular gas and the dusty, bi-polar structures along its minor axis suggestive of a wind driven by a central starburst and possible nuclear activity. Aims. We seek to understand the consequences of the ongoing minor interaction between VV 655 and NGC 4418 for the evolution of the LIRG. Specifically, we consider the origin of the gas supply responsible for the unusual nuclear properties of NGC 4418. Methods. We investigate the structural, kinematic, and chemical properties of VV 655 and NGC 4418 by analyzing archival imaging data and optical spectroscopic observations from the SDSS-III and new spectra from SALT-RSS. We characterize their gas-phase metal abundances and spatially resolved, ionized gas kinematics to better understand whether gas transfer between VV 655 and NGC 4418 resulted in the highly obscured nucleus of the LIRG. Results. The gas-phase metallicity in NGC 4418 significantly exceeds that in VV 655. No kinematic disturbances in the ionized gas are observed along the minor axis of NGC 4418, but we see evidence for ionized gas outflows from VV 655 that may increase the cross-section for gas stripping in grazing collisions. A faint, asymmetric outer arm is detected in NGC 4418 of the type normally associated with galaxy-galaxy interactions. Conclusions. The simplest model suggests that the minor interaction between VV 655 and NGC 4418 produced the unusual nuclear properties of the LIRG via tidal torquing of the interstellar medium of NGC 4418 rather than through a significant gas transfer event. In addition to inducing a central concentration of gas in NGC 4418, this interaction also produced an enhanced star formation rate and an outer tidal arm in the LIRG. The VV 655-NGC 4418 system offers an example of the potential for minor collisions to alter the evolutionary pathways of giant galaxies.
A study of profile of contact dermatitis in housewives with reference to vegetables, soaps and detergents

Background: Contact dermatitis in housewives is a common dermatological problem as almost all housewives have to handle vegetables, fruits, soaps and detergents. The purpose of this project was to study etiologic profile of contact dermatitis occurring in housewives.Methods: One hundred housewives, clinically diagnosed as having contact dermatitis, were randomly selected for the study. Each patient was then subjected to patch testing using the Indian standard battery and certain indigenously prepared antigens i.e. vegetables, soaps and detergents. The first reading was taken 60 minutes with second reading at 96 hours. Reading equal to or more than 1+ was considered to be a positive test.Results: Majority patients were in the age group of 20-30 years. Morphological diagnosis was wear and tear dermatitis in 43%, discoid eczema (19%), fingertip eczema (16%), hyperkeratotic (15%) and pompholyx (7%). Nickel sulphate was found to be the commonest allergen (19%) followed by para-phenylenediamine (8%), fragnance mix (7%), Balsam of Peru (6%), parthenium (6%) and cobalt (5%). Amongst vegetables, the commonest culprit was garlic. Allergic reaction to detergents was positive in 13% patients. Forty-four percent of the cases tested positive to the suspected allergen whereas 31% patients tested positive for allergens not initially suspected of causing contact dermatitis in them.Conclusions: Thus patch testing is an important tool in establishing the cause of allergic contact dermatitis of the hands in housewives in whom the hand eczema is multifactorial. This enables the correct etiological diagnosis and proper management of housewives with hand dermatitis.
The Usefulness of the MAYSI-2 and the Mental Health Needs of Youth Offenders in Youth Justice Facilities in New Zealand

Youth offenders in secure care facilities present with high rates of mental health and behavioural difficulties. An accurate assessment of the needs of this vulnerable group is required for referral to services and access to treatment. A common pathway for assessment is through the use of psychometric measures which look at mental health difficulties and alert staff to youth who may need further assessment and possible treatment. This research had two aims: to investigate the usefulness the Massachusetts Youth Screening Instrument-Second Version (MAYSI-2), a commonly used screening tool within the New Zealand (NZ) youth justice system; and to provide a better understanding of the mental health needs of youth offenders in secure care facilities. The sample consisted of 527 youth admitted to one of four secure care facilities within NZ between 2014 and 2016 who completed the MAYSI-2 and a number of other psychometrics. Confirmatory Factor Analysis revealed issues specifically relating to the validity and reliability of the MAYSI-2 within our sample. Mental health needs of the sample were unique. Youth displayed high levels of substance use difficulties, relatively low levels of psychological distress, and similar rates of suicide risk compared with international studies. Findings are discussed with relation to the international literature on both the MAYSI-2 and wider psychopathology of youth offenders in secure care facilities. Clinical implications with respect to the broader use of psychometrics within NZ’s unique youth offender populations are also discussed.
Strong duality for a problem of linear copositive programming

The paper is dedicated to the study of strong duality for a problem of linear copositive programming. Based on the recently introduced concept of the set of normalized immobile indices, an extended dual problem is deduced. The dual problem satisfies the strong duality relations and does not require any additional regularity assumptions such as constraint qualifications. The main difference with the previously obtained results consists in the fact that now the extended dual problem uses neither the immobile indices themselves nor the explicit information about the convex hull of these indices. The strong duality formulations presented in the paper have similar structure and properties as that proposed in the works of M. Ramana, L. Tuncel, and H. Wolkovicz, for semidefinite programming, but are obtained using different techniques.
Predicting nucleation near the spinodal in the Ising model using machine learning

We predict the occurrence of nucleation in the two-dimensional Ising model using the Convolutional Neural Network (CNN) and two logistic regression models. CNN outperforms the latter in systems with different interaction ranges and sizes, especially when the size of the system becomes large. We find that the CNN decreases its prediction power as system gets closer to the spinodal. We give explanation using the ramified droplet structure predicted by spinodal nucleation theory.
PLATON II: New Capabilities and a Comprehensive Retrieval on HD 189733b Transit and Eclipse Data

Recently, we introduced PLanetary Atmospheric Tool for Observer Noobs (PLATON), a Python package that calculates model transmission spectra for exoplanets and retrieves atmospheric characteristics based on observed spectra. We now expand its capabilities to include the ability to compute secondary eclipse depths. We have also added the option to calculate models using the correlated-k method for radiative transfer, which improves accuracy without sacrificing speed. Additionally, we update the opacities in PLATON—many of which were generated using old or proprietary line lists—using the most recent and complete public line lists. These opacities are made available at R = 1000 and R = 10,000 over the 0.3–30 μm range, and at R = 375,000 in select near-IR bands, making it possible to utilize PLATON for ground-based high-resolution cross-correlation studies. To demonstrate PLATON’s new capabilities, we perform a retrieval on published Hubble Space Telescope (HST) and Spitzer transmission and emission spectra of the archetypal hot Jupiter HD 189733b. This is the first joint transit and secondary eclipse retrieval for this planet in the literature, as well as the most comprehensive set of both transit and eclipse data assembled for a retrieval to date. We find that these high signal-to-noise data are well matched by atmosphere models with a C/O ratio of and a metallicity of times solar where the terminator is dominated by extended nanometer-sized haze particles at optical wavelengths. These are among the smallest uncertainties reported to date for an exoplanet, demonstrating both the power and the limitations of HST and Spitzer exoplanet observations.