New SHRIMP Age and Microstructures from a Deformed A-Type Granite, Kanigiri, Southern India: Constraining the Hiatus between Orogenic Closure and Postorogenic Rifting

A new U-Pb zircon SHRIMP age of 1284 Ma from the Kanigiri granite, India, is reported to help constrain the middle to late Mesoproterozoic tectonic evolution of the Nellore schist belt (NSB). The Kanigiri granite has whole-rock chemical characteristics of A-type granites and is marked by light rare earth element enrichment, a strong negative Eu anomaly, and negative Ba, Sr, P, Ti, and Yb anomalies, indicating feldspar, apatite, and ilmenite/magnetite fractionation. Samples show Y/Nb versus Yb/Ta ratios in the range for granites associated with ocean island basalts. This two-mica granite is peraluminous and alkali-calcic to calc-alkalic, and it has high annite to phlogopite proportions (92%–98%). Strong alignment of flattened mafic microgranular enclaves in the granite, together with relatively high- to moderate-temperature crystal plastic deformation fabric in shear zones within the granite, suggest overprinting of subsolidus deformation over a relict magmatic fabric, a feature not very common in true anorogenic granites but reported in late- to postorogenic granites elsewhere. An intrusive relationship with the 1334 MaKanigiri ophiolitic mélange, within the NSB, indicates that there is a ≤50 m.yr. gap between the Mesoproterozoic subduction-accretion, represented by the ophiolite mélange and late- to postorogenic granite emplacement. Although the Kanigiri granite occurs in close proximity to mafic and felsic alkaline plutons belonging to the 1250–1400 Ma Prakasam alkaline province (PAkP) in the northern NSB and there is overlap in age, the A type granitic magma source is apparently unrelated to PAkP alkaline magmatism. Our work further substantiates the observation that A-type granites originate in varied tectonic settings, not necessarily only in a rift-related (intraplate) setting.This work is funded by the Indian Statistical Institut

An Empirical Studies on How the Developers Discussed about Pandas Topics

Pandas is defined as a software library which is used for data analysis in Python programming language. As pandas is a fast, easy and open source data analysis tool, it is rapidly used in different software engineering projects like software development, machine learning, computer vision, natural language processing, robotics, and others. So a huge interests are shown in software developers regarding pandas and a huge number of discussions are now becoming dominant in online developer forums, like Stack Overflow (SO). Such discussions can help to understand the popularity of pandas library and also can help to understand the importance, prevalence, difficulties of pandas topics. The main aim of this research paper is to find the popularity and difficulty of pandas topics. For this regard, SO posts are collected which are related to pandas topic discussions. Topic modeling are done on the textual contents of the posts. We found 26 topics which we further categorized into 5 board categories. We observed that developers discuss variety of pandas topics in SO related to error and excepting handling, visualization, External support, dataframe, and optimization. In addition, a trend chart is generated according to the discussion of topics in a predefined time series. The finding of this paper can provide a path to help the developers, educators and learners. For example, beginner developers can learn most important topics in pandas which are essential for develop any model. Educators can understand the topics which seem hard to learners and can build different tutorials which can make that pandas topic understandable. From this empirical study it is possible to understand the preferences of developers in pandas topic by processing their SO post

Assessing the Severity of Health States based on Social Media Posts

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user's health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health

Assessing the Severity of Health States Based on Social Media Posts

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request healthrelated information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients’ social media posts can help health professionals (HP) in prioritizing the user’s post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user’s health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user’s health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user’s health

A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID 19 epidemic, this misleading information has aggravated the situation by putting peoples mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID 19 from the internet. COVID 19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes

Directional and fluctuating asymmetry in finger and a-b ridge counts in psychosis: a case-control study

BACKGROUND: Several studies have reported alterations in finger and a-b ridge counts, and their derived measures of asymmetry, in schizophrenia compared to controls. Because ridges are fully formed by the end of the second trimester, they may provide clues to disturbed early development. The aim of this study was to assess these measures in a sample of patients with psychosis and normal controls. METHODS: Individuals with psychosis (n = 240), and normal controls (n = 228) were drawn from a catchment-area case-control study. Differences in finger and a-b ridge count and Fluctuating Asymmetry were assessed in three group comparisons (non-affective psychosis versus controls; affective psychosis versus controls; non-affective psychosis versus affective psychosis). The analyses were performed separately for males and females. RESULTS: There were no significant group differences for finger nor a-b ridge counts. While there were no group difference for Directional Asymmetry, for Fluctuating Asymmetry measures men with non-affective psychosis had significantly higher fluctuating asymmetry of the index finger ridge count (a) when compared to controls (FA-correlation score, p = 0.02), and (b) when compared to affective psychosis (adjusted FA-difference score, p = 0.04). CONCLUSION: Overall, measures of finger and a-b ridge counts, and their derived measures of directional and fluctuating asymmetry were not prominent features of psychosis in this sample. While directional asymmetry in cerebral morphology is reduced in schizophrenia, this is not reflected in dermatoglyphic variables

A twelve-image gravitational lens system in the z ~ 0.84 cluster Cl J0152.7-1357

Gravitational lens modeling is presented for the first discovered example of a three-component source for which each component is quadruply imaged. The lens is a massive galaxy member of the cluster Cl J0152.7-1357 at z ~ 0.84. Taking advantage of this exceptional configuration and of the excellent angular resolution of the HST/ACS, we measure the properties of the lens. Several parametric macroscopic models were developed for the lens galaxy, starting from pointlike to extended image models. For a lens model in terms of a singular isothermal sphere with external shear, the Einstein radius is found to be R_{E} = (9.54 +/- 0.15) kpc. The external shear points to the cluster's northern mass peak. The unknown redshift of the source is determined to be higher than 1.9 and lower than 2.9. Our estimate of the lensing projected total mass inside the Einstein radius, M_{len}(R < 9.54 kpc), depends on the source distance and lies between 4.6 and 6.2 x 10^{11} M_{Sun}. This result turns out to be compatible with the dynamical estimate based on an isothermal model. By considering the constraint on the stellar mass-to-light ratio that comes from the evolution of the Fundamental Plane, we can exclude the possibility that at more than 4 sigma level the total mass enclosed inside the Einstein ring is only luminous matter. Moreover, the photometric-stellar mass measurement within the Einstein radius gives a minimum value of 50% (1 sigma) for the dark-to-total matter fraction. The lensing analysis has allowed us to investigate the distribution of mass of the deflector, also providing some interesting indications on scales that are larger and smaller than the Einstein radius of the lens galaxy. The combination of different diagnostics has proved to be essential in determining quantities that otherwise would have not been directly measurable with the current data.Comment: 10 pages, 9 figures, accepted by Astronomy & Astrophysic