Miracle-dz/UniFAR-FTUs huggingface dataset:
Replication of a British study in Brazil: how do teachers describe mathematical tasks?

Tables 3 and 4: Adjectives with loadings higher than 0.3 on the factor Inquiry on the left and Abstração (Abstraction) on the right. These tables show that, although weak, it seems that British and Brazilian mathematics teachers see a relationship between inquiry and abstract tasks. This relation is not salient in any of the studies if considered in isolation, but the comparison highlights it. Source of the differences The differences discussed before could have arisen from random noise in the data or from confounded variables. However, based on our experience as teachers, teacher educators and researchers in Brazil, we can point out one element that could explain the differences. In Brazil, textbooks are widely adopted by teachers. The federal government provides them for free for every student in the country based on a list of pre-approved textbooks (the list contains about eight textbooks for each educational stage). Bearing that in mind, the presence of the factor Inovação (Innovation) in the Brazilian results could be explained by the fact that the translation we used for the word task (atividade) in the online questionnaire may have influenced teachers to think about something not from textbooks and, consequently, unusual for their daily practice. The absence of Inquiry as a factor in the Brazilian data could also be explained by the lack of open ended, investigative type of tasks in the textbooks. Finally, the occurrence of Abstração (Abstraction) as a factor in the Brazilian data could be due to the presence of proofs and questions such as “Show that. ” in the textbooks, making abstract and formal mathematics readily available in a lesson. Final considerations The similarities suggest that participants in both countries share a common meaning for the adjectives in the lists, but it is still uncertain if this meaning is shared among policy developers and researchers. Actually, the Brazilian data suggested that teachers see problem solving tasks as mostly related to real life and not pure mathematics, Curtis, F. (Ed.) Proceedings of the British Society for Research into Learning Mathematics 37 (2) June 2017 From Conference Proceedings 37-2 (BSRLM) available at bsrlm.org. uk © the author 6 while official documents present problem solving as a broader teaching strategy that should encompass tasks inspired by real life situations as well as by pure mathematical motivations. Moreover, the differences might reflect cultural variations, but they can also result from lack of clarity regarding the meaning of some adjectives, since the list in Portuguese was highly inspired by the list in English. Our next step would be to conduct a confirmatory factor analysis, possibly with a more diverse sample of teachers (considering different regions in Brazil, for instance) and a replication of the second study reported in Foster and Inglis (2017).
Patient-derived xenografts of primary hepatobiliary cancers for disease modeling

Hepatobiliary cancers refer to primary malignancies of liver and biliary tract, which have dismal prognosis. Preclinical models recapitulating biological features of original tumors are valuable in development of therapeutic strategies. Here, we successfully generated patient-derived xenografts (PDXs) from a spectrum of hepatobiliary cancers including hepatocellular carcinoma (HCC, n=2), gallbladder cancer (GBC, n=2), intrahepatic cholangiocarcinoma (IHCC, n=1), and hilar cholangiocarcinoma (n=1). The latency of tumor engraftment ranged from one to four months. Histologically, xenograft models mirrored the parental tumors. HCC xenografts exhibited solid structures with trabecular pattern and were positive for α-fetoprotein. In contrast, tumors derived from cholangiocarcinoma and GBC resembled typical adenocarcinoma with expression of cytokeratin. PDXs also maintained the tumor microenvironment features of corresponding primary tumors including hypervascular status for HCC and abundant stromal component in IHCC. Furthermore, genomic analysis revealed both concordant and unique copy-number alterations between primary tumors and xenografts. In conclusion, PDXs represent a faithful model for disease modeling in distinct types of hepatobiliary cancers and might be helpful for studying tumor biology.
Accuracy of assessing gingival thickness in the esthetic maxillary region by periodontal probing, cone-beam computed tomography and digital scanning

To evaluate the accuracy of four noninvasive methods for measuring gingival thickness (GT) and to examine correlations among the four methods, this study analyzed 180 maxillary anterior teeth from 30 volunteers. The gingival biotype (GB) was assessed by M1-transparency probing (TP). M2-cone-beam computed tomography (CBCT) images with radiopaque material were scanned. M3-intraoral digital scanning (IDS) and M4-extraoral DS (EDS) of a gypsum cast were assessed using a three-dimensional scanner. Measurements of GT at the central incisors (CI), lateral incisors (LI), and canines (CA) were performed at the cementoenamel junction (CEJ) and alveolar ridge crest (ARC) on superimposed DS and CBCT images. The results showed that the GB determined by M1 showed no significant differences in CI, LI, or CA but differed significantly between males and females. At the CEJ position, significant differences were measured by M2, M3, and M4 in most teeth, with the exception of CI in M3 and M4. At the ARC position, the GT of most teeth measured by M2, M3, and M4 showed no significant difference. There were positive correlations among these four methods. It can be concluded that measurements of GT by superimposing IDS or EDS and CBCT images are not reliable.
A Geometric Bisection Algorithm for Automatic Page Layout

Content distribution in a page designed for printing is a difficult task performed by human editors. There are applications and tools to help the editor in this task, but it is sti ll a manual, iteractive process that is made more difficult when content is distributed in columns, as is the case of newspapers. This work describes an algorithm to automatically distribute content in a printable page. Our implementation tries to use all the page, dividing it among the items to be placed according to their area.
Limitations of the Empirical Fisher Approximation

Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order information. Several highly visible works have advocated an approximation known as the empirical Fisher, drawing connections between approximate second-order methods and heuristics like Adam. We dispute this argument by showing that the empirical Fisher---unlike the Fisher---does not generally capture second-order information. We further argue that the conditions under which the empirical Fisher approaches the Fisher (and the Hessian) are unlikely to be met in practice, and that, even on simple optimization problems, the pathologies of the empirical Fisher can have undesirable effects.
Age-Related Changes in Visual Processing Speed: A Pilot Study Using the Motor Free Visual Perception Test- 4

ix Acknowledgements . x Introduction . 1 Literature Review. 2 Older Adults and Driving . 2 Driving Assessment. 4 On-the-road assessments. 5 Pre-driving assessments. 6 Visual Perception . 10 Visual foundation skill. 11 Assessment of Visual Perception . 13 Visual Processing Speed and Response Time . 15 MVPT-3 Response Time Index . 17 Summary and Conclusions . 18 Statement of Purpose . 19 Definitions and Variables . 20 Definitions . 20
The Timing of AnalystsEarnings Forecasts

Most of the literature assumes that the order and timing of analystsearnings forecasts are determined exogenously. This paper analyzes the equilibrium timing strategies for analysts. Consistent with the prior literature I assume that analysts care foremost about the accuracy of their forecasts, but in some cases may have an incentive to bias their forecasts. The paper further assumes that investors reward early forecasting analysts. The main trade-o¤ that an analyst faces in determining the timing of his forecast is between precision and timeliness. The paper introduces a timing game with two analysts, derives its unique pure strategies Subgame Perfect Equilibrium, and provides empirical predictions. The equilibrium has two patterns: either the times of the analysts forecasts cluster, or there is a separation in time of the forecasts. All else equal, an increase in the precision of an analysts private signal induces earlier forecast by this analyst, and increases the likelihood that the analystsforecasts will cluster in time. This prediction may be used in empirical studies to infer the precision of the analystssignals from the observed timing of the forecasts.
Effect of environmental conditions and phenology in the dispersal of secondary Erysiphe necator conidia in a vineyard

An integrated powdery mildew management strategy to identify the principal moments of secondary Erysiphe necator conidia dispersal in a vineyard based on aerobiological, phenological and meteorological data was developed. An adaptation of the physiological P-days model was conducted to obtain a descriptive equation for the prediction of the advantageous meteorological conditions for Erysiphe necator conidia dispersal in the vineyards of the Ribeiro Designation of Origin area. Moreover, a regression model was developed to predict the conidia concentration as a function of the weather and aerobiological variables with the highest influence on airborne E. necator spores. Additionally, phenological observations were conducted during the vegetative cycle of Vitis vinifera, with the aim to identify the most susceptible phenological stages to powdery mildew infection by relating them with the detected atmospheric spore concentrations. The study was carried out from 2008 to 2018 in an experimental vineyard. The E. necator spores were trapped using a Lanzoni VPPS-2000 sampler and the phenological observations were conducted following the BBCH standardized scale. The highest total fungal spore amount per season in the atmosphere of the vineyard was detected in 2013 with 4828 spores m-3, while the lowest amount was recorded in 2009 with 883 spores m-3. In general, the highest daily airborne spore concentrations were detected during the Flowering (stage 6) or in the previous and next stages, whereas the maximum total spore amount by stage was recorded during Development of Fruits (stage 7). The proposed threshold of P-days for potential secondary infections in the Ribeiro D.O. ranges from 120 to 160 P-days. The combination of aerobiological, phenological and meteorological data provides us a useful tool for the knowledge of the Erysiphe necator conidia dispersal behaviour bringing agricultural practices closer to a sustainable system.
The impact of housing markets on banks’ risk-taking behavior: evidence from CESEE

This study empirically evaluates the impact of housing market dynamics and banks’ housing market exposure on banking sector stability in Central, Eastern and Southeastern Europe (CESEE). We investigate whether there are differences between the behavior of banks located in CESEE EU Member States and the behavior of banks located in the Western Balkans. We find evidence that banks’ exposure to the housing market has a significant positive impact on bank stability in both groups of countries. Furthermore, for real estate banks in CESEE EU Member States, we find that house price dynamics are positively correlated with bank stability. This outcome may possibly be related to the fact that real estate banks in these countries have better housing market expertise and, moreover, to the generally more advanced institutional environment. At the same time, we find a negative relationship between house price dynamics and bank stability for real estate banks in the Western Balkans, which might reflect the less advanced stage of institutional development in the region.
BIG DATA – OPPORTUNITIES AND CHALLENGES FOR EDUCATION

The paper reveals the potential of Big Data applied in education. The specifics of Big Data in educational contexts and different sources for their extraction are described. The power of innovative tools for data collection, management, and analysis by which to identify best practices or problems in the educational process is shown. Considering these findings, a conceptual proposal for an example of educational system using Big Data is presented. The possible effects of the implementation of Big Data in education are discussed.
Development of Multilingual Social Media Data Corpus: Development and Evaluation

The purpose of this study is manual annotating, a corpus for Bahasa Indonesia and Bahasa Melayu. Corpus for both languages has been made by many researchers before, but the focus of this research is only on words with the same vocabulary but which have very different meanings. The data were obtained from social media, so informal words were found. As many as 2100 words for each language were identified which were then randomly selected so that 300 words with the same vocabulary but with different meanings were used. The objective of this study was to confirm that this condition can influence the results of polarity sentiment. At the end of this paper, we will show the results of the influence of the conditions of the two languages on the polarity of sentiments. From the manual annotation, an annotation agreement test was made by three Bahasa Indonesia annotators and three Bahasa Melayu annotators. The results of the annotation found that there were 63 out of 300 words that experience different polarity. Results of score agreement among annotations for each language show that there is good agreement among the annotators during annotation process.
Global Warming Primarily Due to Urban Heat Island with Humidity Forcing, 12/18/2019, A. Feinberg, DfRSoft

In this paper we provide a feasibility study to show that >95% of global warming could be due to Urban Heat Islands (UHI) with Humidity Forcing (HF) from Hydro-HotSpots (HHS). We denote hydro-hotspot as precipitation evaporation and bulk heating from low albedo manmade type roads and city surfaces, including cars and engine hoods. This includes both Highly Evaporating Surfaces (HES) and bulk warm waste Rain Water Management (RWM). Such Humidity Forcing (HF) root cause is then albedo forcing due to the creation of HHS. This leads to the conclusion that changing the albedo of cities and roads could be the main solution to global warming.
IIIT-Hyderabad at HASOC 2019: Hate Speech Detection

Automatic identiﬁcation of oﬀensive language in various social media platforms especially Twitter poses a great challenge to the AI community. The repercussions of such writings are hazardous to individuals, communities, organizations and nations. The HASOC shared task attempts for automatic detection of abusive language on Twitter in English, German and Hindi languages. As a part of this task, we (team A3-108) submitted diﬀerent machine learning and neural network based models for all the languages. Our best performing model was an ensemble model of SVM, Random Forest and Adaboost classiﬁers with majority voting.
Hierarchical Complexity: A Formal Theory

Theories of complexity have generally not addressed hierarchical complexity. However, within developmental psychology, notion of hierarchical complexity have come into being in the last twenty years. We outline the formal axioms for a model of hierarchical complexity which assigns an order of hierarchical complexity to every task regardless of domain. The orders correspond to natural numbers, thus ensuring that the orders are separated by gaps of equal size. The model naturally leads to the existence of performance stages, thereby formalizing many implicit properties
GROUND MOTION SIMULATIONS FOR GREAT INTERPLATE EARTHQUAKES AROUND JAPAN: VARIABILITY OF GROUND MOTION RESULTED FROM COMPREXITY IN SOURCE MODELS

We numerically simulate ground motions in sedimentary basins for anticipated great interplate earthquakes along the plate boundaries surrounding Japan. Possible multi-scale heterogeneous rupture scenarios have been modeled combining characteristic properties of the source area and adequate variation in source parameters. Ground motions for anticipated Nankai and Tonankai earthquakes to the Osaka basin, and those for Kanto earthquakes and Off Boso Peninsula earthquakes to the Kanto basin have been simulated and variations been investigated.
Nutrition evaluation and metabolic profiling analysis of liver cirrhosis patients: a cross-sectional study

Malnutrition is the most common complication found in cirrhosis patients. It has been associated with development of severe complications and poor prognosis. However, the nutrition status of cirrhosis patients is often underestimated. Analyzing nutrition risk, malnutrition, body composition, and metabolic profiling of liver cirrhosis patients, the current study examined the mutual correlation of these indexes. The current study enrolled 120 liver cirrhosis patients and 20 concurrent healthy controls. Nutrition risk screening was conducted with NRS-2002. Nutrition status was evaluated by subjective global assessment (SGA). Body mass index (BMI) and body composition levels were measured by multi-frequency bioelectrical impedance analysis. Metabolic profiling was detected by tandem mass spectrometry. This study further explored the mutual correlation between these indexes. There were no significant differences in prevalence of nutritional risk and malnutrition between patients with different causes of liver cirrhosis (X2=0.845, P=0.358; X2=1.386, P=0.239; X2=0.702, P=0.402). There were significant differences in malnutrition, nutrition risk, and body composition of different CTP score liver cirrhosis patients (X2=15.609, P<0.001; X2=27.799, P=0.037; X2=6.599, P<0.001; T=34.179, P<0.001). Homocysteine and phenylalanine in the nutrition risk group was significant higher than that in the non-nutrition risk group (T=2.106, P=0.037; T=3.019, P=0.004), while alanine and citrulline/arginine ratios were significantly lower than those in the non-nutrition risk group (T=-4.508, P<0.001; T=-3.552, P=0.001). Nutrition risk and malnutrition commonly exist in liver cirrhosis patients. It presents with body composition alterations. Amino acids and other substances lead to metabolic disorders, further leading poor prognosis. These may be improved and prevented through active nutrition support treatment.
Two Generator Subgroups of SL(2,C)

For the purposes of constructing explicit solutions to second-order linear homogeneous differential equations on the Riemann sphere the Kovacic algorithm partitions the subgroups of SL(2,C) into four classes and initially determines which class contains the differential Galois group of the input equation. We prove in the case of the hypergeometric and Riemann equations that the relevant class can be determined directly from the coefficients by elementary calculation. We also treat the (non-algebraic form of the) Lamé equation, to which the Kovacic algorithm is not directly applicable. In that instance we combine the Kovacic results with ours to produce an algorithm for determining the class of the associated group. From the group-theoretic viewpoint the problem solved herein is the following: given arbitrary S, T ∈ SL(2,C), determine which class contains the group 〈S, T 〉 generated by S and T . c © 1999 Academic Press
ENZYME BREAKER FOR GALACTOMANNAN BASED FRACTURNG FLUID BACKGROUND OF THE INVENTION

A method of fracturing a subterranean formation in a well bore is shown in which a gellable fracturing fluid is first formed by blending together an aqueous fluid, a hydratable polymer, a suitable cross-linking agent for cross-linking the hydratable polymer to form a polymer gel and an enzyme breaker. The cross-linked polymer gel is pumped into the well bore under sufficient pres sure to fracture the surrounding formation. The enzyme breaker is allowed to degrade the cross-linked polymer with time to reduce the viscosity of the fluid so that the fluid can be pumped from the formation back to the well surface. The particular enzyme breaker utilized has an activity in the range from about 2.0 to 11.0 and is effective to attack only specific linkages in the cross linked polymer gel.
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks

Perceived personality traits attributed to an individual do not have to correspond to their actual personality traits and may be determined in part by the context in which one encounters a person. These apparent traits determine, to a large extent, how other people will behave towards them. Deep neural networks are increasingly being used to perform automated personality attribution (e.g., job interviews). It is important that we understand the driving factors behind the predictions, in humans and in deep neural networks. This paper explicitly studies the effect of the image background on apparent personality prediction while addressing two important confounds present in existing literature; overlapping data splits and including facial information in the background. Surprisingly, we found no evidence that background information improves model predictions for apparent personality traits. In fact, when background is explicitly added to the input, a decrease in performance was measured across all models.
Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments

To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e.g., Backblaze SMART logs). However, in real-world production environments, the data quality is imperfect (e.g., inaccurate labeling, missing data samples, and complex failure types), thereby degrading the prediction accuracy. We present RODMAN, a robust data preprocessing pipeline that refines data samples before feeding them into ML models. We start with a large-scale trace-driven study of over three million disks from Alibaba Cloud's data centers, and motivate the practical challenges in ML-based disk failure prediction. We then design RODMAN with three data preprocessing echniques, namely failure-type filtering, spline-based data filling, and automated pre-failure backtracking, that are applicable for general ML models. Evaluation on both the Alibaba and Backblaze datasets shows that RODMAN improves the prediction accuracy compared to without data preprocessing under various settings.
MLRG Deep Curvature

We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape. Despite of providing rich information into properties of neural network and useful for a various designed tasks, curvature information is still not made sufficient use for various reasons, and our method aims to bridge this gap. We present a primer, including its main practical desiderata and common misconceptions, of \textit{Lanczos algorithm}, the theoretical backbone of our package, and present a series of examples based on synthetic toy examples and realistic modern neural networks tested on CIFAR datasets, and show the superiority of our package against existing competing approaches for the similar purposes.