Miracle-dz/UniFAR-FTUs huggingface dataset:
P L ] 1 4 O ct 2 02 1 A FUNCTIONAL ABSTRACTION OF TYPED INVOCATION CONTEXTS

In their paper “A Functional Abstraction of Typed Contexts”, Danvy and Filinski show how to derive a type system of the shift and reset operators from a CPS semantics. In this paper, we show how this method scales to Felleisen’s control and prompt operators. Compared to shift and reset , control and prompt exhibit a more dynamic behavior, in that they can manipulate a trail of contexts surrounding the invocation of previously captured continuations. Our key observation is that, by adopting a functional representation of trails in the CPS semantics, we can derive a type system that encodes all and only constraints imposed by the CPS semantics.
The Challenge to Educational Reforms during a Global Emergency: The Case of Progressive Science Education

This article argues that what is most at risk in schooling during a global pandemic, or other similar broad challenges to normal functioning, are those elements that might be considered the less traditional and so the most progressive. After setting out some general background common to the challenge faced by schools and school teachers, this argument is exemplified through the case of school science education. Two particular aspects are considered: one related to pedagogy (responding to learners’ alternative conceptions or ‘misconceptions’) and one related to curriculum (teaching about the nature of science). These are considered ‘progressive’ features in the sense that they have widely been championed as ways of improving and reforming science education across a wide range of national contexts but can be understood to have faced resistance both in the sense of being opposed by ‘reactionary’ stakeholders and in terms of the level of support for teacher adoption. It is argued that at a time when the education system is placed under extreme stress, such progressive elements are at particular risk as teachers and administrators may view them as ‘extras’ rather than ‘core’ features of practice and/or as reflecting more ‘difficult’ educational objectives that may need to be de-prioritised (and so neglected) for the time being. In that sense, they are fragile aspects of practice that lack the resilience of more established, and thus robust, features. It is concluded that where progressive elements are especially valued, they need to become sufficiently embedded in custom and practice to no longer be viewed as luxuries but rather to be recognised as core elements of good teaching to be protected and maintained during a period of emergency.
Evidence from a developing market

Purpose – The purpose of this paper is to examine the effects of working capital management on firm valuation, profitability and risk. Design/methodology/approach – The paper uses a panel data set of 497 firms covering the period 2007 to 2016. The authors test the effects of working capital management on firm valuation, profitability and risk using the panel data methodology that includes firm and year fixed effects regressions. Findings – The authors find a significantly negative relationship between net working capital (NWC) and firm valuation, profitability and risk. The results suggest that, in managing working capital, firm managers must make a trade-off between their objectives for profitability and risk control. Working-capital management is of particular importance in firms with less access to capital; it is also important when firms are expanding their investments during periods of economic recovery. Originality/value – This paper contributes to the literature in several ways. First, to my knowledge, it provides the most comprehensive investigation, to date, on the relationship between working capital management and firm valuation, profitability and risk in an emerging market. Second, this study documents the existence of an optimal level of NWC in an emerging market. Third, firm performance, as measured in both market and accounting value, can be improved with efficient working capital management. Finally, the study includes the impact of the business cycle in an analysis of the effects of working capital management on firm performance.
Neural Networks Oblige Diagnosis of the Ischemic CVA’s by MRI

In present time the technological & computing evolution are promoted for new opportunities that will help to improve the standard of life between the new medical accomplishments, in particular and the standard of diagnostic evaluations. MRI is one of the imaging equipment for the diagnosis which has become more beneficial for technological development, because of this and due to the standard of a diagnosis manufacturer, that is one of the most engaging apparatus in the clinical application. The attentiveness in that pathology & in the general the encephalon picture analysis as the preventive diagnosis. The present research paper suggests the evaluation of the ability of ANN for the automatic identification of an ICVA by tissue images density obtained by MRI. In this examination the diagnosis and their medical reports were used to train the ANN classifier which extracted features from the given images. In this stage the ANN significantly contributes to the ICVA of MRI diagnosis aid, so since the test occurrence automatic identification of ischemic lesions that has been performed with the accuracy results that will be false positive and false negative.
Semantic Segmentation using Deep Learning Approaches - A Study

: Semantic segmentation is an important technology which gets commonly used in medical imaging, autonomous driving vehicles, and also backgrounds for virtual meetings. Classes can be different real-world objects such as roads, cars, bicycles, people, trees, lanes, trucks, buildings etc. Classes can correspond to different anatomical structures and organs when considering medical images. Semantic segmentation is a broadly applicable technology. The techniques discovered to improve current semantic segmentation methods could also lend themselves to improving other dense prediction tasks. These tasks could include optical flow prediction (object motion prediction tasks), image super-resolution such as in remote gaming or in video resolution enhancement, and so on. This paper briefly presents a survey on existing work conducted to achieve semantic segmentation of image problems with the use of deep learning methods as well as image processing approaches. Deep learning provides several methods for semantic segmentation such as 2D convolution networks, 3D convolution networks, etc. This paper discusses the classification, challenges, application, and methods for semantic segmentation.
Engineering cocrystals of Paliperidone with enhanced solubility and dissolution characteristics

In the present study, co-crystals (CCs) of Paliperidone (PPD) with coformers like benzoic acid (BA) and P-amino benzoic acid (PABA) were synthesized and characterized to improve the physicochemical properties and dissolution rate. CCs were prepared by the solvent evaporation (SE) technique and were compared with the products formed by neat grinding (NG) and liquid assisted grinding (LAG) in their enhancement of solubility. The formation of CCs was confirmed by the IR spectroscopy, powder X-ray diffraction and thermal analysis methods. The saturation solubility studies indicate that the aqueous solubility of PPD-BA and PPD-PABA CCs was significantly improved to 1.343±0.162mg/ml and 1.964±0.452mg/ml, respectively, in comparison with the PPD solubility of 0.473mg/ml. This increase in solubility is 2.83-and 3.09-fold, respectively. PPD exhibited a poor dissolution of 37.8% in 60min, while the dissolution of the CCs improved tremendously to 96.07% and 89.65% in 60min. CCs of PPD with BA and PABA present a novel approach to overcome the solubility challenges of poorly water-soluble drug PPD.
PDA-CAD method for the determination of magnesium, pyridoxine and thiamine in a dietary supplement supported by analytical quality by design methodology

In this research, Analytical Quality by Design (AQbD) methodology was used to develop the HILIC-PDA-CAD method for the determination of magnesium, pyridoxine and thiamine content in a dietary supplement. Experiments were conducted on the Dionex Ulitimate 3000 HPLC system with PDA and CAD detectors. Separations were performed on the ZIC-HILIC PEEK column (100 × 4.6 mm, 5 mm) with a mixture of acetonitrile, acetone and ammonium acetate/ammonium formate aqueous solution (48:12:40, v/v/v) as the mobile phase. The detection was performed at 220 nm. The optimal settings of CAD were defined with the one-factor-at-a-time approach: evaporation temperature 50°C, filter constant 10 s, pressure gas 60 psi. The influence of qualitative (salt type) and quantitative factors (pH and salt concentration) on the magnesium retention factor, k Mg and the selectivity factor between pyridoxine and chloride anion, a B6/Cl-, was tested using the I-optimal design. The design space was defined by Monte Carlo simulations and model coefficients' errors were propagated with the aim of identifying the conditions that meet the following criteria: k Mg <4 and a B6/Cl->3.5, with the probability p=95%. From the derived 2D-Design Space graph, 95 mM of ammonium formate pH 4.4 was selected as the optimal composition of the aqueous phase. The method was validated and its reliability in routine application was confirmed.
THE HOMOGENEITY OF SCALES OF INTELLECTUAL ABILITIES: PSYCHOMETRIC ANALYSIS

Relevance. Nowadays the researchers commonly use a limited set of standard procedures and statistical coefficients when develop psychometric instruments and investigate their structure. The routine using of such procedures without taking into account the specific features of the psychometric scales can lead to incomplete or even inadequate results. In this context detailed consideration of the structure of psychometric instruments seems to be important and it may demand various non-standard ways of statistical analysis. Objectives. To conduct detailed analysis of the results of two intelligent subtests at the item level and to assess the sufficiency and adequacy of using standard methods for estimation of reliability and structural validity for these subtests. Methods. We analyze the data collected in intelligence testing of a large sample of respondents (11335 young adults). The respondents passed the KR-3 battery. In this study we examine in detail the structure of the subtests “Syllogisms” and “Analogies”. Specifically, we estimated the reliability of the scales by the Cronbach’s alpha coefficient, and the structure at the item level using the confirmatory factor analysis. Results and conclusions. Estimation of the reliability of the scales by Cronbach’s alpha coefficient showed importance of taking into account the time limitation, which is commonly used in intelligence tests. On the other hand, a detailed analysis of each subtest items made it possible to find out an additional factor which was not originally proposed in the factor structure. This is factor of higher-order abilities of abstract analysis, whilst the subtest originally aimed at estimation of the special abilities. Confirmatory factor analysis showed improvement of fit when this factor was added. The results allow to conclude that the researcher may miss the important properties of scales if not making a detailed analysis of testing procedures and the structure of subtest at the item level, and so may draw incomplete or inadequate conclusions about their psychometric properties
Poverty Dynamics in Turkey: A Multinomial Logit Model

The availability of longitudinal data allows researchers to analyse the dynamics of poverty. By using the Turkish Statistical Institute’s (TurkStat) Income and Living Conditions Survey micro dataset, we analyse the households’ long-term monetary poverty conditions. We categorise poverty as transitory and chronic and employ the multinomial logit method to analyse determinants of each types of poverty. Results indicate that education and household size are the most effective factors for reducing transitory poverty, and for chronic poverty, the most effective factors are having a regular job and having a skilled occupation; insurance, home ownership, and number of children are important determinants for both types of poverty.
Heart rate events classification via explainable fuzzy logic systems

As people, we have no way of knowing whether our heart rate is considered normal or not. The strength and quality of our pulses will deteriorate as we get older. As a result, this may indicate a heart attack or another illness that requires immediate attention. The main goal of this paper is to define the heart rate stage using fuzzy logic systems (FLSs). In practice, however, designing or developing fuzzy logic systems is extremely difficult. To achieve this aim, we suggested a solution that involves: i) classifying the medical expert's criterion for signs of heart rate; ii) developing an explainable fuzzy logic system for heart rate measurement; and iii) evaluating the proposed system with human experts. In addition, the aim of this research was to provide an explainable fuzzy system that people could use to self-monitor heart rate levels and determine their health status. As a result, it is hoped that this research would provide insight into how to improve the development of fuzzy logic systems, especially in the field of medical applications.
SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.
Hierarchical Representation of Complex Intervention Sequences for Automated Subgroup Analysis in Critical Care Settings

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations. Existing work has mainly focused on selecting a few interventions and representing them as binary variables, resulting in oversimplification of intervention representation. The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis. To this end, we have developed Hi-RISE (Hierarchical Representation of Intervention Sequences), an approach that transforms and clusters sequential interventions into a latent space, with the resulting clusters used for heterogenous treatment effect analysis. We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgroup-level effects of sequential interventions and improve targeted intervention planning in critical care settings.
Epithelial-to-mesenchymal transition lowers the cholesterol pathway, wich influences colon tumors differentiation.

Colorectal cancer (CRC) is the second cause of death worldwide. Up to 70% of CRC patients will metastasize at one point. Understanding the chain of events that lead to metastasis occurrence is urgent to identify new biomarkers of progression or new targets to prevent and delay disease evolution. Epithelial to mesenchymal transition (EMT) is a major program engaged during metastasis. EMT is extremely complex to analyze in situ due to the broad involvement of its transcription factors. We hypothesized that a relevant and dynamic in vitro model of pure cancer cells will reveal a combination of new genes that might further identify signs of EMT in cancer tissues. We treated HT-29 cells grown in 3D with an EMT-inducing factor, but also looked at reverse changes after EMT-inducing factor removal. For each condition, pan-transcriptomic analyses were done. Genes that were both induced upon EMT induction and inhibited upon EMT release (mesenchymal to epithelial transition or MET) were selected. Consistent with our hypothesis, we identified new genes for the EMT-MET programs. These genes were used to build a metagene that, when applied to a large database of transcriptomic data from primary colorectal tumors (n= 2,239), had an independent prognosis value. Finally, we submitted this metagene to CMap and identified drugs that might affect EMT-MET programs. Statins, well-known inhibitors of cholesterol synthesis, were among them and effectively delayed MET in vitro. These data show that cholesterol and EMT pathways are opposite regulators and impact differently tumors differentiation and outcome.
Reemergence of Invasive Pneumococcal Disease in Germany During the Spring and Summer of 2021

Abstract Background: The incidence of invasive pneumococcal disease (IPD) decreased worldwide in 2020 and the first quarter of 2021, concurrent with non-pharmaceutical interventions (NPIs) intended to stymie transmission of SARS-CoV-2. In 2021, stringency of these NPI strategies has varied. We investigated age- and serotype-specific variations in IPD case counts in Germany in 2020-2021. Methods: IPD cases through July 31, 2021 were stratified by age group, serotype, or geographic location. IPD surveillance data in 2020-2021 were compared with: 1) IPD surveillance data from 2015-2019, 2) mobility data during 2020 and 2021, and 3) NPI stringency data in 2020 and 2021. Results: IPD began to return towards baseline values among children 0 to 4 years old in April 2021 and exceeded baseline levels by June 2021 (a 9% increase over the average monthly values for 2015-2019). Children 5 to 14, adults aged 15-34 and adults 80 years and older showed increases in IPD cases which went over baseline values starting in July 2021, with increases also starting in Spring 2021. The age distribution and proportion of vaccine serotype IPD remained comparable to previous years despite lower overall case counts in 2020 and 2021. The percent change in IPD incidence compared to the previous five years correlated with changes in mobility and with NPI stringency. Conclusions: IPD levels began to return to and exceed seasonal levels in Spring/Summer 2021 in Germany following sharp declines in 2020 that coincided with NPIs related to the COVID-19 pandemic. Proportions of vaccine serotypes remained largely consistent throughout 2020-2021.
Disrupted reward processing in Parkinson’s disease and its relationship with dopamine state and neuropsychiatric syndromes: a systematic review and meta-analysis

Background Neuropsychiatric symptoms are common in Parkinson’s disease (PD) and predict poorer outcomes. Reward processing dysfunction is a candidate mechanism for the development of psychiatric symptoms including depression and impulse control disorders (ICDs). We aimed to determine whether reward processing is impaired in PD and its relationship with neuropsychiatric syndromes and dopamine replacement therapy. Methods The Ovid MEDLINE/PubMed, Embase and PsycInfo databases were searched for articles published up to 5 November 2020. Studies reporting reward processing task performance by patients with PD and healthy controls were included. Summary statistics comparing reward processing between groups were converted to standardised mean difference (SMD) scores and meta-analysed using a random effects model. Results We identified 55 studies containing 2578 participants (1638 PD and 940 healthy controls). Studies assessing three subcomponent categories of reward processing tasks were included: option valuation (n=12), reinforcement learning (n=37) and reward response vigour (n=6). Across all studies, patients with PD on medication exhibited a small-to-medium impairment versus healthy controls (SMD=0.34; 95% CI 0.14 to 0.53), with greater impairments observed off dopaminergic medication in within-subjects designs (SMD=0.43, 95% CI 0.29 to 0.57). Within-subjects subcomponent analysis revealed impaired processing off medication on option valuation (SMD=0.57, 95% CI 0.39 to 0.75) and reward response vigour (SMD=0.36, 95% CI 0.13 to 0.59) tasks. However, the opposite applied for reinforcement learning, which relative to healthy controls was impaired on-medication (SMD=0.45, 95% CI 0.25 to 0.65) but not off-medication (SMD=0.28, 95% CI −0.03 to 0.59). ICD was the only neuropsychiatric syndrome with sufficient studies (n=13) for meta-analysis, but no significant impairment was identified compared tonon-ICD patients (SMD=−0.02, 95% CI −0.43 to 0.39). Conclusion Reward processing disruption in PD differs according to subcomponent and dopamine medication state, and warrants further study as a potential treatment target and mechanism underlying associated neuropsychiatric syndromes.
ViraPart: A Text Refinement Framework for ASR and NLP Tasks in Persian

—The Persian language is an inﬂectional SOV language. This fact makes Persian a more uncertain language. However, using techniques such as ZWNJ recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text reﬁnement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clariﬁcations. First, used the BERT variant for Persian following by a classiﬁer layer for classiﬁcation procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively. Experimental results show that our proposed approach is very effective in text reﬁnement for the Persian language.
Decorrelating Feature Spaces for Learning General-Purpose Audio Representations

Inspired by the recent progress in self-supervised learning for computer vision, in this paper, through the DeLoRes (Decorrelating latent spaces for Low Resource audio representation learning) framework, we introduce two new general-purpose audio representation learning approaches, the DeLoRes-S and DeLoRes-M. Our main objective is to make our network learn representations in a resource-constrained setting (both data and compute) that can generalize well across a diverse set of downstream tasks. Inspired by the Barlow Twins objective function, we propose learning embeddings invariant to distortions of an input audio sample while ensuring that they contain non-redundant information about the sample. We call this the DeLoRes learning framework, which we employ in different fashions with the DeLoRes-S and DeLoRes-M. In our experiments, we learn audio representations with less than half the number of model parameters and 10% audio samples compared to state-of-the-art algorithms to achieve state-of-the-art results on 7 out of 11 tasks on linear evaluation and 4 out of 11 tasks in the finetuning setup. In addition to being simple and intuitive, our pre-training procedure is amenable to compute through its inherent nature of construction. Furthermore, we conduct extensive ablation studies on our training algorithm, model architecture, and results and make all our code and pre-trained models publicly available1.