Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitions in asymmetric spike patterns with anomalous diffusion

Complex natural systems at times manifest transitions between disparate diffusive regimes. Efforts to devise measurement techniques capable of identifying the cross-over moments have recently borne fruit, however interpretation of findings remains contentious when the bigger picture is considered. This study uses a phenomenological reaction – diffusion model to obtain a sufficiently general, yet tractable description for the dynamics of a pattern qualitatively redolent of molecular clusters subject to transient anomalous diffusion mechanisms. The resulting system of equations is investigated for substantiation of the reasons difficulties are encountered when attempting to distinguish between various diffusive regimes in experimental settings: a non-monotonic dependence of the pattern’s evolution on parameters defining the diffusion mechanism is a common occurrence, as is a non-injective mapping between a given sequence of diffusion regimes and ensuing drift behaviour. Albeit formally the developed description is limited to piecewise constant anomaly functions, it is useful in computation of approximations to smooth realistic transitions.
Are Citizens and Businesses (Dis)satisfied with the Public Administration in Germany?

Commissioned by the Federal Government, the Federal Statistical Office conducted surveys on satisfaction with government services in Germany. It complements the measurements of administrative burdens and compliance costs employing the Standard Cost Model. In 2015, 5,666 citizens and 1,572 companies rated public agencies in respectively 22 and 10 life events. Both groups were largely satisfied with the public administration. On a scale of –2 (very dissatisfied) to +2 (very satisfied), the aggregate rating is +1.06 and +0.94, respectively. Respondents judged the authorities on 16 factors of satisfaction. Both citizens and businesses were most satisfied with incorruptibility and non‐discrimination, whereas the comprehensibility of forms and laws received low scores. The regression analyses reveal that the different life events were influential drivers of satisfaction. Older or less educated individuals tend to be more satisfied. For companies, the legal form is very influential. Aimed to improve interactions with the public administration, the results of the regression, factor, network and importance‐performance analyses provide fruitful insights to identify potential for amelioration. The Federal Statistical Office evaluates the recent surveys to begin the second wave in 2017.
Fostering Saudi EFL Learners’ Communicative, Collaborative and Critical Thinking Skills Through the Technique of In-Class Debate

Diverse learning styles of the learners require instructors to utilize wide variety of instructional strategies to engage their interest and motivation. In-class debate is a systematic instructional strategy utilized to bring the dynamics of social life into the classroom to cultivate learners’ active-involvement in the content of the subject. It hones learners’ range of skills, such as; oral-communication, social-interaction, critical thinking skills and internalization of the course content. For the purposes of present study, the researcher utilized qualitative cum quantitative research tools to collect the data from the participants. An opinionnaire with 14 items was developed with closed ended questions. The instrument was designed to measure four variables, which are tabulated into four sections (See tables 3-6): (a) in-class debate inculcates learners’ collaborative, communicative and cooperative paradigms;(b) it triggers learners’ complete mastery of the course content, i.e., retention, assimilation and understanding, coupled with boosts stack of skills involved in the process; (c) it fosters EFL learners’ ability to critically evaluate and analyze everything; and (d) finally it focuses on learners’ creative and critical thinking skills and make them active and independent pursuers of knowledge. The survey was administered to 87 female EFL learners of Taif University to identify the impacts of debate on their range of academic skills. The findings of the study indicate that in-class debate effectively generates learners’ several skills since it targets their thinking skill, contemplation, reflection and ultimately stimulates their productive, receptive, analytical and critical thinking skills. Yet, more faculties can be activated and maximum results can be obtained, if this interactive strategy is implemented in a structured format with a properly designed rubric focusing on learners’ specific skills.
The CrowdMapping Mirafiori Sud Experience (Torino, Italy): An Educational Methodology through a Collaborative and Inclusive Process.

The CrowdMapping Mirafiori Sud (CMMS) pilot project, carried out by the Polytechnic of Turin (Italy), involved the academic world (students and professors), the Mirafiori Onlus Foundation, the local administration and the community in a participatory and inclusive process. The district is a large area of Turin characterized by a high average age of its inhabitants and a high percentage of foreigners. The aim of the project is to identify and report, through the use of ICT, the obstacles that prevent residents the most vulnerable categories from using public space. In the start-up phase, it was not born as a PBL approach, but during the application the PBL was considered a development perspective in the educational experience starting from the students' proposals and the exchange of their skills, in the specific declination of the PBL approach known as community engaged learning. For students, this version of PBL has proven to be strategic in the implementation phases of the project, in particular in the mapping of interested parties and the data set and connections between networks of actors.
Leveraging Multisource Information in Matrix Factorization for Social Collaborative Filtering

Collaborative Filtering is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. However, existing works either leave out the user reliability or cannot capture the implicit correlation between two users who are similar but not socially connected. Besides, they also take the trust value between users is either 0 or 1, thus will degenerate the prediction accuracy. In this work, we propose a social collaborative filtering algorithm via leveraging multisource information in matrix factorization, which aims to solve the above problems via employing both rating records and users’ social network information. To this end, we design a novel recommendation framework, which consists of two fundamental components: computation of user reliability and implicit interaction, designing of user-shared feature space. In particular, the latter performs matrix factorization based on user reliability. Finally, a reasonable objective function is designed to synthetically consider the multisource information of users, such that high-quality recommendation can be produced. Experimental results demonstrate that the proposed model achieves better accuracy than other counterpart recommendation techniques.
Asking flexible disaggregated income-gross or net figures-vs single total question: evaluation from Panel on Household Finance

The PHF (Panel on Household Finance) questionnaire offers a couple of flexibility in answering the income information, especially choosing between gross and net values. We have developed a tax microsimulation model for the standardization (converting between gross and net) and imputation. The survey also collects a total monthly household disposable income as many general purpose surveys do. This paper documents our microsimulation model by showing the effectiveness of this innovation in collecting income through surveys and implementation by incorporating the unique tax class choice in German unfront income tax system. We then evaluate the data quality and potential contribution from this innovation by comparing the distributions from ours and a counterfactual asking only gross incomes as commonly done by other surveys. We also compare the difference between the disposable incomes derived by tax microsimulation and aggregation and the single self-reported one. Both comparisons reveal the disagreements appearing to be correlated with the income tax schedule and become significant in most progressive area of the distribution.
Proteomic Analysis of the Nuclear Phosphorylated Proteins in Diary Cow Mammary Epithelial Cells Treated with Methionine

To reveal the major protein related to milk protein synthesis in nucleus of mammary gland epithelial cells and further uncover the mechanism of milk protein synthesis in post-transcription level,two-dimensional gel electrophoresis(2-DE) and matrix-assisted laser desorption/ionization/time of flight mass spectrometry(MALDI-TOF-MS) were used to identify the changes of nuclear phosphorylated proteins in DCMECs treated with methionine,and their expression changes were verified by Western blotting analysis in the current study.5 up-regulated expressed phosphoproteins included staphylococcal nuclease domain-containing protein 1,Septin-6,Glycyl tRNA synthetase,Twinfilin-1 and eukaryotic elongation factor 1-beta.They contributed to multiple functional activities such as DNA transcription,mRNA translation,protein synthesis,cell cycle,cell division and morphological processes.
Education in Africa: What Are We Learning?

Countries across Africa continue to face major challenges in education. In this review, we examine 142 recent empirical studies (from 2014 onward) on how to increase access to and improve the quality of education across the continent, specifically examining how these studies update previous research findings. We find that more than half of the studies evaluate government implemented programs, about one-third include detailed cost analysis, and one quarter evaluate multiple treatment arms. We identify several areas where new studies provide rigorous evidence on topics that do not figure prominently in earlier evidence syntheses. New evidence shows promising impacts of structured pedagogy interventions (which typically provide a variety of inputs, such as lesson plans and training for teachers together with new materials for students) and of mother tongue instruction interventions, as well as from a range of teacher programs, including both remunerative (pay-for-performance of various designs) and non-remunerative (coaching and certain types of training) programs. School feeding delivers gains in both access and learning. New studies also show long-term positive impacts of eliminating school fees for primary school and positive impacts of eliminating fees in secondary school. Education technology interventions have decidedly mixed impacts, as do school grant programs and programs providing individual learning inputs (e.g., uniforms or textbooks). www.cgdev. org David K. Evans and Amina Mendez Acosta
The Expansion of Consumption and the Dynamics of Welfare of the Brazilian Families: An Analysis of the Decomposition of Poverty and Inequality

This article gathers analyses that involve dynamic aspects of welfare, inequality and poverty in Brazil in the periods of 2002-2003 and 2008-2009 from the perspective of the per capita consumption. By means of the data of the Brazilian Family Expenditure Survey (POF), from the construction of the consumption aggregates, the evolution of the consumption structures in the period mentioned above are evaluated, according to the location of families in the Major Brazilian Regions and in the urban and rural areas. For this purpose, the study incorporates the value of services related to existing durable goods in the households in each edition of the survey. The study resorts to graphical and dominance analyses as well as to the calculation of functions that allow measuring and separating the effects of growth and redistribution over social welfare. The role of the consumption structure in changes made to the levels of welfare and poverty is evaluated according to static and dynamic decompositions. The main results indicate that the Durable goods strongly contributed for the growth of consumption and social welfare but they were also a limiting factor for the inequality reduction. In relation to poverty, we observed that in all the geographic areas that were studied poverty in 2008-2009 was lower than in 2002-2003 for the different measures and poverty lines that were used in this study.
PEDA 376K: A Novel Dataset for Deep-Learning Based Porn-Detectors

The rapid expansion of the digital world introduces complex challenges within the forensic and security domains. In particular, the wide availability of online pornographic media is a huge problem for applications that seek to prevent exposure to inappropriate/undesired audiences, or that aim to automate the detection of any illegal behavior. There is a thin veil separating the definition of pornographic and non-pornographic media, making it difficult, even for humans, to agree on a consistent interpretation. Most of the available APIs for detecting NSFW (not-safe-for-work) media are not able to infer clearly whether a file contains pornographic content or not. In general, given an input file, APIs return a set of probability scores, leaving the responsibility of a final binary decision to the users. What is more, NSFW APIs do not publicly share their training datasets.Aiming to mitigate these issues, we introduce a novel dataset of images: Pornographic and Explicit Dataset 376K (PEDA 376K), which was labeled using a well-defined criteria, to aid the development of machine learning for detecting whether an image is pornographic or not. We also trained decision trees for transforming the probabilistic output of standard APIs into binary decisions. We conducted experiments with two datasets, PEDA 376K and RedLight, and found that when APIs are optimized with decision trees, their average accuracies increase. Finally, we propose a deep learning architecture trained directly on the PEDA 376K dataset. When comparing this model against state- of-the-art models and their corresponding optimized outputs, we outperform five existing neural architectures, reaching an overall accuracy of 99.2%.
ERAD 2010-THE SIXTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY Rain heterogeneity studies and specific Z-R relationships determination with x-band and k-band radars to improve rain rate retrieval

During more than 50 years of development, ground-based weather radars have become a research and operational tool well suited for precipitation surveillance and, eventually, quantitative rainfall measurements. The major source of errors lies in the conversion of the radar reflectivity factor Z (mm6 m-3) to rain rate R (mm h-1). These two parameters are related to each other via the raindrop size distribution (DSD) which cannot be inferred by conventional weather radar measurements. Hence, it has been common practice to take a simple power law relationship between Z and R, like the well known Marshall-Palmer relationship. However, DSD is extremely variable in time and space even within a single precipitating event. Thus, many of these relationships have been proposed, nevertheless, in most of the cases, a unique relationship is used for one precipitating event. The aim of this work is to categorize the different rain regimes that might occur even within individual precipitation cells to derive the corresponding specific relationships. Then we will confront the rain estimates with these specific relationships to the classical approach using one single relationship in order to investigate their potential for improve rain estimation. To do so, we analyse the simultaneous measurements of a scanning X-band radar and a vertically pointing K-band radar in their common volume. In section 2, we will describe the radars and data. In section 3, we will present the methods and their performances. Finally, we will outline some conclusions and perspective for future developments.
Missing rating imputation based on product reviews via deep latent variable models

We introduce a deep latent recommender system (deepLTRS) for imputing missing ratings based on the observed ratings and product reviews. Our approach extends a standard variational autoen-coder architecture associated with deep latent variable models in order to account for both the ordinal entries and the text entered by users to score and review products. DeepLTRS assumes a latent representation of both users and products, allowing a natural visualisation of the positioning of users in relation to products. Numerical experiments on simulated and real-world data sets demonstrate that DeepLTRS outperforms the state-of-the-art, in particular in contexts of extreme data sparsity.
Take a Look Around

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.
Cross-domain sentiment classification initiated with Polarity Detection Task

INTRODUCTION: The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. OBJECTIVES: To overcome the dependency of CDSC tasks on manual labeling of the dataset by proposing a polarity detection task. METHODS: We have proposed the CDSC-PDT method that is the polarity Detection Task (PDT) followed by the CDSC task. The proposed PDT task extracts the polarity of reviews from the source domain using the contextual and relevancy information of words in documents and this automatic labeled dataset is further used to train classifiers to make the further classification. RESULTS: Proposed method is comparable to the traditional learning method giving the highest precision 85.7%. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), hence it overcomes the human intervention and is also time saving and cheap process, unlike traditional CDSC tasks.
Toward the use of deep learning and high-resolution digital elevation models for extracting geological landforms: an example for eskers.

Summary Canada Centre for Mapping and Earth Observation (CCMEO) has a long track record in the offer of topographic information of Canada’s territory. In 2015, CCMEO’s team launched the National Elevation Data Strategy, which aims to provide an accurate and up-to-date 3D representation of the territory. As of winter 2019, elevation datasets covering more than 3.2 million km² of the Canadian landmass have been made available freely for Canadians under the product name High-Resolution Digital Elevation Models (HRDEM). This huge amount of new data represents a mine of cartographic and geological information. In order to automate extraction information from that data, CCMEO’s team leveraged the recent developments in deep learning, more specifically Convolutional Neural Networks. A first application using deep learning and high-resolution optical satellite imagery has shown promising results for automatic feature extraction of roads, buildings and waterbodies (Proulx-Bourque and Turgeon-Pelchat, 2018). A similar methodology applied to multiple resolution elevation data was also shown to be promising (Turgeon-Pelchat et al., 2019). In a more geoscience perspective, CCMEO recently began evaluating the synergy of both fully convolutional networks and the HRDEM product for extracting eskers in Northern Canada. It is well-known that machine learning technologies offer immense opportunities in geoscience (Karpatne et al., 2017). The study area for this project consists of 42 500 km² of elevation data spread across Northern Canada. Training and validation data were created in order to train multiple models. Once trained, models were tested on other areas and the performance of the models were compared to ground truth in order to fully analyze the potential and to bring out ideas for future works. Results of this experiment will be available in the course of winter 2019 and will be presented at GeoConvention 2019.