Miracle-dz/UniFAR-FTUs huggingface dataset:
Classroom Peer Effects and Student Achievement Classroom Peer Effects and Student Achievement

In this paper, we analyze the impact of classroom peers on individual student performance with a unique longitudinal data set covering all Florida public school students in grades 3–10 over a five-year period. Unlike many previous data sets used to study peer effects in education, our data allow us to identify each member of a given student's classroom peer group in elementary, middle and high school as well as the classroom teacher responsible for instruction. As a result, we can control for individual student fixed effects simultaneously with individual teacher fixed effects, thereby alleviating biases due to endogenous assignment of both peers and teachers, including some dynamic aspects of such assignments. Our estimation strategy, which focuses on the influence of peers' fixed (observed and unobserved) characteristics on individual test score gains, also alleviates potential biases due to measurement error of peer quality, simultaneity of peer outcomes, and mean reversion. Under linear-in-means specifications, estimated peer effects are small to nonexistent, but we find some sizable and significant peer effects within nonlinear models. For example, we find that peer effects depend on an individual student's own ability and on the ability level of the peers under consideration, results that suggest Pareto-improving redistributions of students across classrooms and/or schools. Estimated peer effects tend to be smaller when teacher fixed effects are included than when they are omitted, a result that suggests co-movement of peer and teacher quality within a student over time. We also find that peer effects tend to be stronger at the classroom level than the grade level. ___________________________________ We wish to thank the staff of the Florida Department of Education's K–20 Education Data Warehouse for their assistance in obtaining and interpreting the data used in this study. The views expressed is this paper are solely our own and do not necessarily reflect the opinions of the Florida Department of Education.
Effect of Host Resistance on Foliar Late Blight Severity, Disease Development and Progress on Selected Irish Potato Varieties in Kenya

Background: Late Blight Disease, caused by the fungal pathogen, Phytophthora infestans, is a major constraint of Irish potato production in Kenya. The disease can destroy a crop, causing up to 100% yield loss. Small scale holder farmers in Kisii County continuously grow Irish potato that is susceptible to P. infestans, which require a number of fungicide sprays. This study was formulated out of the realization that Irish potato plays a major role in food security and contributes to poverty alleviation. Also, the commonly used protectant fungicides for late blight disease are expensive, hazardous and are not effective against P. infestans . Aim: To evaluate the effect of host resistance to foliar late blight severity, disease development and progress on selected potato varieties under field and glasshouse conditions in Kisii County, Kenya. Study Design: Nine potato varieties (Tigoni, Meru red, Kenya mpya, Sherekea, Shangi, Purple gold, Asante, Mang’ere and Nyayo) were grown in the field and glasshouse following a randomized
Uncovering the big five model personality traits and organization identification

Personality differences affect employees’ tendencies to act, react and identify with the organization. This research study aims at exploring and analyzing the effect of personality traits using the Big five model on organization identification. Review and analysis of extant literature was conducted to identify the research gap and problem. The research hypothesis was developed through qualitative approach (focus groups), and a pilot study. It was assumed that personality traits of the Big five model will have an effect on organization identification. A number of 900 questionnaires were distributed through a specialized organization in data collection among multi-sectorial domains (industrial, services, governmental, educational and healthcare). Sound and reliable published research measures were adopted considering common method bias. Statistical analysis and results revealed an effect between both constructs. Discussion and research recommendations were proposed .
Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Manual sleep stage annotation is a time-consuming but often essential step in the analysis of sleep data. To address this bottleneck several algorithms have been proposed that automate this process, reporting performance levels that are on par with manual annotation according to measures of inter-rater agreement. Here we first demonstrate that inter-rater agreement can provide a biased and imprecise measure of annotation quality. We therefore develop a principled framework for assessing performance against a consensus annotation derived from multiple experienced sleep researchers. We then construct a new sleep stage classifier that combines automated feature extraction using linear discriminant analysis with inference based on vigilance state-dependent contextual information using a hidden Markov model. This produces automated annotation accuracies that exceed expert performance on rodent electrophysiological data. Furthermore, our classifier is shown to be robust to errors in the training data, robust to experimental manipulations, and compatible with different recording configurations. Finally, we demonstrate that the classifier identifies both successful and failed attempts to transition between vigilance states, which may offer new insights into the occurrence of short awake periods between REM and NREM sleep. We call our classifier ‘Somnotate’ and make an implementation available to the neuroscience community.
Explaining Query Answers under Inconsistency-Tolerant Semantics over Description Logic Knowledge Bases

Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. This paper addresses the problem of explaining why a tuple is a (non-)answer to a query under such semantics. We define explanations for positive and negative answers under the brave, AR and IAR semantics. We then study the computational properties of explanations in the lightweight description logic DL-LiteR. For each type of explanation, we analyze the data complexity of recognizing (preferred) explanations and deciding if a given assertion is relevant or necessary. We establish tight connections between intractable explanation problems and variants of propositional satisfiability (SAT), enabling us to compute explanations by exploiting solvers for Boolean satisfaction and optimization problems. We implemented the proposed algorithms and empirically showed that the method works.
Repository of the Max Delbrück Center for Molecular Medicine (MDC) in the

: Higher body fatness is not only associated with a higher risk of hypertension, type 2 diabetes, and coronary heart disease but also with certain types of cancer. The scope of this review is to summarize the epidemiological evidence for an association between body fatness and specific types of cancer and to outline the mediating role of obesity-related biomarkers in this context. Epidemiological studies have gathered convincing evidence that greater body fatness is associated with a higher risk of colorectal cancer, postmenopausal breast cancer, endometrial cancer, esophageal adenocarcinoma, renal cell carcinoma, and pancreatic cancer. Further, evidence for an association between higher body fatness and higher risk of ovarian cancer, advanced prostate cancer, and hepatocellular carcinoma is growing. Abdominal obesity is an independent risk factor for colorectal cancer beyond general obesity, whereas an independent role is less clear for other obesity-related cancer types. Epidemiological biomarker studies have shown that the positive association between body fatness and risk of cancer may be partly explained by hyperinsulinemia and altered concentrations in adi-pokines and sex-steroid hormones. In addition, obe-sity-associated low-grade inflammation plays a role in colorectal carcinogenesis. While epidemiology has contributed substantially to the understanding of the role of higher body fatness and related metabolic alterations in the development of cancer, further epidemiological biomarker studies are necessary to elucidate the complex interrelations between mediating pathways as well as to study novel pathways. Knowledge resulting from this research may help identify an obesity phenotype that is particularly strongly associated with cancer risk and thus pave the way for targeted prevention of cancer morbidity and mortality.
Racial/Ethnic Identities and Related Attributed Experiences of Multiracial

Surveys from 50 multiracial Japanese European Americans supported the endorsement of multiple simulianeous racial/ethnic identities and a differentiated multiracial identity. Experiences associated with being multiracial included feeling different, sensitivity to cultural cues, appreciation of different viewpoints, acceptance of difference, and disliking exclusion. Implications for research and therapy are discussed. Los estudios de 50 Americanos Japoneses Europeos multiraciales sostuvieron la aprobacl6n de multiples identidades racial/^tnicos simultdneas y una identidad multiracial diferenciada. Las experiencias relacionadas con ser multiracial, sentirse diferente, la sensibilidad de indicaciones culturales, reconocimiento de puntos de vista diferentes, aceptacion de diferencia, y la aversi6n de exclusi6n. Las implicaciones para la investigaci6n y terapia se discuten.
Tissue and Isoform-selective Activation of Protein Kinase C in Insulin-resistant Obese Zucker Rats – Effects of Feeding

The mechanisms of insulin resistance in the obese Zucker rat have not been clearly established but increased diacylglycerol-protein kinase C (DAG-PKC) signalling has been associated with decreased glucose utilisation in states of insulin resistance and non-insulin-dependent diabetes mellitus. The purpose of this study was to characterise tissue-and isoform-selective differences in DAG-PKC signalling in insulin-sensitive tissues from obese Zucker rats, and to assess the effects of feeding on DAG-PKC pathways. Groups of male obese (fa/fa, n=24) and lean (fa/-, n=24) Zucker rats were studied after baseline measurements of fasting serum glucose, triglycer-ides, insulin and oral glucose tolerance tests. Liver, epidi-dymal fat and soleus muscle samples were obtained from fed and overnight-fasted rats for measurements of DAG, PKC activity and individual PKC isoforms in cytosol and membrane fractions. Obese rats were heavier (4887 vs 3159 g) with fasting hyperglycaemia (10·50·8 vs 7·70·1 mM) and hyperinsulinaemia (7167363 vs 25162 pM) relative to lean controls. In fasted rats, PKC activity in the membrane fraction of liver was significantly higher in the obese group (17416 vs 10812 pmol/ min/mg protein, P<0·05) but there were no differences in muscle and fat. The fed state was associated with increased DAG levels and threefold higher PKC activity in muscle tissue of obese rats, and increased expression of the major muscle isoforms, PKC-and PKC-: e.g. PKC activity in the membrane fraction of muscle from obese animals was 28342 (fed) vs 10720 pmol/min/mg protein (fasting) compared with 19727 (fed) and 15421 pmol/ min/mg protein (fasting) in lean rats. In conclusion, hepatic PKC activity is higher in obese rats under basal fasting conditions and feeding-induced activation of DAG-PKC signalling occurs selectively in muscle of obese (fa/fa) rats due to increased DAG-mediated activation and/or synthesis of PKC-and PKC-. These changes in PKC are likely to exacerbate the hyperglycaemia and hypertriglyceridaemia associated with obesity-induced diabetes.
Resting-state functional MRI in an intraoperative MRI setting: proof of feasibility and correlation to clinical outcome of patients

obJective The authors’ aim in this paper is to prove the feasibility of resting-state (RS) functional MRI (fMRI) in an intraoperative setting (iRS-fMRI) and to correlate findings with the clinical condition of patients pre-and postoperatively. methods Twelve patients underwent intraoperative MRI-guided resection of lesions in or directly adjacent to the central region and/or pyramidal tract. Intraoperative RS (iRS)–fMRI was performed pre-and intraoperatively and was correlated with patients’ postoperative clinical condition, as well as with intraoperative monitoring results. Independent component analysis (ICA) was used to postprocess the RS-fMRI data concerning the sensorimotor networks, and the mean z-scores were statistically analyzed. results iRS-fMRI in anesthetized patients proved to be feasible and analysis revealed no significant differences in preoperative z-scores between the sensorimotor areas ipsi-and contralateral to the tumor. A significant decrease in z-score (p < 0.01) was seen in patients with new neurological deficits postoperatively. The intraoperative z-score in the hemisphere ipsilateral to the tumor had a significant negative correlation with the degree of paresis immediately after the operation (r = - 0.67, p < 0.001) and on the day of discharge from the hospital (r = - 0.65, p < 0.001). Receiver operating characteristic curve analysis demonstrated moderate prognostic value of the intraoperative z-score (area under the curve 0.84) for the paresis score at patient discharge.
AN ENSEMBLE STACKING MODEL FOR RUMOR DETECTION BASED ON ARABIC TWEETS

Effective rumor detection within Arabic-language social networks is crucial for mitigating misinformation's impact on users. Despite extensive research on rumor detection in English, investigations into Arabic rumors are limited, despite Arabic's significance for 25 nations. This study proposes a unique hybrid ensemble model for detecting Arabic rumor tweets, addressing both Ensemble stacking-based Machine Learning and Deep Learning dimensions. The initial phase of the proposed model involves the strategic implementation of Machine Learning stacking, wherein standalone classifiers, including the Decision Tree, Random Forest, and Gaussian Naive Bayesian models, are thoughtfully employed. Impressively, the Random Forest and Gaussian Naive Bayesian models exhibit commendable accuracies, both attaining 86%, while the Decision Tree model registers a respectable accuracy of 84%. To further amplify accuracy, a subsequent stage incorporates logistic regression, culminating in an overall accuracy of 87%. In the pursuit of advancing the bounds of accuracy and performance, the study extends its exploration to the realm of deep learning stacking. This facet is manifested through the construction of four distinct neural network models, the collective accuracy of which varies between the noteworthy ranges of 77% to 89%. By judiciously integrating these neural network models with logistic regression in the ensuing stage, the accuracy remarkably ascends to an impressive 90%. This enhancement, amounting to a 3% increase over the machine learning stacking approach, substantially augments vital performance metrics encompassing precision, accuracy, recall, and F1-Score, thus significantly refining the landscape of rumor detection.
Long short-term prediction guides human metacognitive reinforcement learning

23 Long-term prediction ensures stable but slow learning. Short-term pre-24 diction is fast but deemed to be risky in context-changing environments. 25 To understand how humans resolve this conﬂict, we ﬁtted nine biolog-26 ically plausible reinforcement learning models to 82 human behavior 27 data using a human-model policy matching method. One meta-RL 28 model replicates subjects’ context-dependent behavior, passing underﬁt-29 ting and overﬁtting tests. The same model survived a generalizability 30 test simulating various Markov decision tasks and an
HOW MARKETERS CAN INCREASE THE RELEVANCE OF EMAIL MARKETING CAMPAIGNS: DATA ANALYSIS WITH MACHINE LEARNING METHODS

Despite the emergence of many digital marketing strategies, email remains the most effective strategy of all. It generates a high return on investment (ROI) and offers a fast way of retention of customers. Is still the preferred channel, not only for businesses, but also for clients, Convinced of its great capacity for influence, more and more companies are looking for a relevant marketing strategy. Convincing the customer to open the email is the first step to an effective campaign. Therefore, it is important to understand how marketers can improve the open rate of a marketing campaign. Data Driven Marketing encompasses the many techniques and applications that affect digital marketing directly related to the use of customer data. Machine learning can predict user needs based on customer data and past behaviors. Those predictions can then be used to suggest offers that are based on the individual. The objective of the work is to analyze the main factors that increase the opening rate of marketing campaigns. To this end, we are developing classification algorithms to predict whether a campaign will be classified as relevant or irrelevant. To achieve this, we used and evaluated three different classiﬁers. Our results showed that it is possible to predict the performance of a campaign with an accuracy of about 82%, using Adaptive Boosting algorithm and the redundant filter selection technique.
to the rescue: Discerning Content and Services in a Tangled Web

A careful perusal of the Internet evolution reveals two major trends - explosion of cloud-based services and video streaming applications. In both of the above cases, the owner (e.g., CNN, YouTube, or Zynga) of the content and the organization serving it (e.g., Akamai, Limelight, or Amazon EC2) are decoupled, thus making it harder to understand the association between the content, owner, and the host where the content resides. This has created a tangled world wide web that is very hard to unwind, impairing ISPs’ and network ad-ministrators’ capabilities to control the trafﬁc ﬂowing on the network. In this paper, we present DN-Hunter, a system that leverages the information provided by DNS trafﬁc to discern the tangle. Parsing through DNS queries, DN-Hunter tags trafﬁc ﬂows with the associated domain name. This association has several applications and reveals a large amount of useful information: ( i ) Provides a ﬁne-grained trafﬁc visibility even when the trafﬁc is encrypted (i.e., TLS/SSL ﬂows), thus enabling more effective policy controls, ( ii ) Identiﬁes ﬂows even before the ﬂows begin, thus providing superior network management capabilities to administrators, ( iii ) Un-derstand and track (over time) different CDNs and cloud providers that host content for a particular resource, ( iv ) Discern all the services/content hosted by a given CDN or cloud provider in a particular geography and time, and ( v ) Provides insights into all applications/services running on any given layer-4 port number. We conduct extensive experimental analysis and show that the results from real trafﬁc traces, ranging from FTTH to 4G ISPs, that support our hypothesis. Simply put, the information provided by DNS trafﬁc is one of the key components required to unveil the tangled web, and bring the capabilities of controlling the trafﬁc back to the network carriers.
International Journal of Drug Policy

Background: Crack use constitutes a major problem in cities across Brazil. While existing data suggest that crack use is generally concentrated among disenfranchised young people with extensive health problems and crime involvement, extensive data gaps exist. To address this issue, this study aimed to assess key characteristics of young crack users in two Brazilian cities. Methods: N = 160 regular and young adult (ages 18–24) crack users were recruited by community-based methods in the cities of Rio de Janeiro (Southeast) and Salvador (Northeast). Assessments included an interviewer-administered questionnaire on key social, drug use, health and service use characteristics, as well as serological testing of HBV, HCV and HIV status, and were conducted anonymously between November 2010 and June 2011. Participants provided informed consent and received transportation vouchers following assessment completion. The study was approved by institutional ethics review boards. Results: The majority of participants were: male, with less than high school education, unstably housed (Rio only); gained income from legal or illegal work; arrested by police in past year (Salvador only); had numerous daily crack use episodes and shared paraphernalia (Salvador only); co-used alcohol, tobacco, cannabis and cocaine; had no injection history; rated physical and mental health as ‘fair’ or lower (Sal-vador only); had unprotected sex; were never HIV tested; were not HIV, HBV or HCV positive; and did not use existing social or health services, but desired access to crack user speciﬁc services. Conclusion: Crack users in the two Brazilian sites featured extensive socio-economic marginalization, crack and poly-drug use as well as sexual risk behaviours, and compromised health status. Social and health service utilization are low, yet needs are high. There is an urgent need for further research and for targeted interventions for crack use in Brazil. © 2013 Elsevier B.V. All rights reserved.
A SURVEY: DEEP LEARNING CLASSIFIERS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Hyperspectral imaging (HSI) is a popular subject in remote sensing data processing because of the huge quantity of data included in these images, which enables for improved description and utilization of the surface of the earth by integrating abundant spectral and spatial data. However, due to the high dimensionality of HSI data and the limited number of labelled examples available, conducting HSI image classification poses significant technical and pragmatic hurdles. Approaches to HSI classification with deep learning have gained major successes in recent years as new deep learning algorithms emerge, giving unique prospects for hyperspectral image classification research and development. Initially, a quick introduction to standard deep learning (DL) models is provided, followed by a comparison of the performance of common DL based HSI approaches. Finally, the difficulties and future research prospects are explored.
Detection of Omicron Virus Based on Convolutional Neural Network

COVID-19, an upper respiratory viral infection, is spreading too quickly and is now found almost everywhere on the planet. A chest x-ray and RT-PCR are used to confirm Covid-19. However, resource constraints in developing countries make this difficult. This paper aims to monitor infection in Egyptian's lungs using chest X-rays and convolutional neural networks in the early-stage detection. The omicron chest X-ray dataset is gathered from public records along with hospital and doctor arrangements that are approved by their patients to achieve our goal. The classification mission's backbone is a classic neural network (CNN), which benefited from its speed while deep networks are being trained and reduced the impact of vanishing gradient problems. Images are resized and pre-processed to improve sharpness and contrast before being used to validate the proposed system. Meanwhile, by feeding images into a deep neural network, infectiousness can be predicted. The deep learning measurement's receiver operating characteristics (ROC) region under the curve is 0.9888, with 96.2 percent sensitivity, 98 percent accuracy, and 100 percent precision, according to the findings. It has been demonstrated that the proposed system can be easily modified to increase efficiency by adding additional images (normal and infected). As a quick alternative to the current PCR-based method, the proposed system offers a significant advantage to all nations in terms of screening and diagnosing OMICRON. © 2022 Little Lion Scientific