Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference

This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the finite-population model) or randomly selected from a vaguely defined universe (the super-population model). Both approaches allow for heterogeneity of treatment effects. Appropriate estimation methods and asymptotic moments are discussed for each model using simple differences-in-means estimators and those that include baseline covariates. An empirical application using a large-scale education RCT shows that the choice of the finite- or super-population approach can matter. Thus, the choice of framework and sensitivity analyses should be specified and justified in the analysis protocols.
Role-Perceptions of Dutch Spiritual Caregivers in Implementing Multidisciplinary Spiritual Care: A National Survey

Background: During the course of their disease, patients often not only suffer physical discomfort, but also face psychological, social, and spiritual challenges. However, healthcare professionals often lack the knowledge and skills to address the spiritual dimension and are in need of support for taking this responsibility. Spiritual caregivers are experts in spiritual care, but their contribution to the integration of this care by other healthcare professionals is largely unknown. Objective: The aim of this study was to investigate how Dutch spiritual caregivers view their role in increasing the integration of spiritual care in daily healthcare practice as provided by other healthcare professionals in the Netherlands, and how they address this role. Methods: An online survey was conducted from May until June 2021 among spiritual caregivers working in Dutch healthcare. Data were analysed using descriptive statistics. Results: The majority of the 174 respondents answered that they already fulfil a role in the integration of spiritual care by, for example, providing education, coaching on the job, or participating in multidisciplinary consultation. However, the majority of respondents experienced barriers to their contribution, such as confusion of terminology and use of language while collaborating with other healthcare professionals and reluctance to share information. Conclusions: While spiritual caregivers realise having a role in increasing the integration of spiritual care into daily practice of other healthcare professionals, some practices and perceptions, especially from within their own discipline, may hamper this.
Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets

Although recipe data are very easy to come by nowadays, it is really hard to find a complete recipe dataset – with list of ingredients, nutrient values per ingredients, and per recipe, allergens, etc. Recipe datasets are usually collected from social media websites where users post and publish recipes. Usually written with little to no structure, using both standardized and non-standardized units of measurement. We collect six different recipe datasets, publicly available, in different formats and some including data in different languages. Bringing all of these datasets to the needed format for applying a machine learning (ML) pipeline for nutrient prediction [1], [2], includes data normalization using dictionary based named entity recognition (NER), rule based NER, as well as conversions using external domain specific resources. From the list of ingredients, domain-specific embeddings are created using the same embedding space for all recipes – one ingredient dataset is generated. The result from this normalization process are two corpora – one with predefined ingredient embeddings and one with predefined recipe embeddings. On all six recipe dataset the ML pipeline is evaluated. The results from this use case also confirm that the embeddings merged using the domain heuristic yield better results than the baselines.
Weak Correlation between the Polyanion Environment and Ionic Conductivity in Amorphous Li–P–S Superionic Conductors

: Amorphous Li − P − S materials have been widely used as solid-state electrolytes for all-solid-state batteries because of their high ionic conductivity (10 − 4 to 10 − 3 S cm − 1 ) as well as good synthetic accessibility and processability. Despite the potential of these materials, their amorphous structures have made it challenging to quantify the relation between the structure and conductivity. In this paper, we use ab initio molecular dynamics simulations to investigate the role of the local structure and density in determining the conductivity of amorphous Li − P − S structures with different polyanion units. We observe similar rates for Li-ion hopping regardless of the local P − S polyanion environment in these amorphous materials, indicating that the path connectivity at a larger length scale may be controlling the overall Li conductivity. This finding will serve as an important guideline in the continued development of amorphous solid electrolytes for advanced all-solid-state batteries.
Semantic Similarity Analysis between Future Topics and Their Neighbors in Topic Networks for Network-based Topic Evolution

Topic evolution automatically tracks a set of concepts within a given dataset over time, assisting researchers to overview various research domains. Network-based topic evolution is one of the recent approaches incorporating relational models instead of traditional text-based models for allowing the detection of topic correlation events. Topics are represented with co-occurrence relationships instead of word vectors, connected over time through their positions in a network instead of their semantic similarities. This paper shows that the topics and their network representations share meaningfully similar semantics. The existence of such contextual relationships allows topics to be labeled without having enough direct textual appearances in the document collection. Forty fields-of-study keywords with 64,215 to 5.8 million related articles were selected from the Microsoft Academic Graph dataset containing more than 200 million publications. The semantics of topics within forty topic networks were found using three sets of word embeddings trained from a collection of 10.5 million Medline abstracts from the year 2000 to 2016, and word embeddings of the topics are compared against their network-based representations, which are their neighborhoods in the previous timeslot. Cosine similarities between topics and their neighbors consistently resulted in moderate correlations from the year 2001 to 2015, showing higher values for topics that were already present in the topic networks compared to newly emerging topics. The result suggested that labeling topics based on the network structure are possible without semantic analysis, which is necessary for predicting topic evolutions such as the topic emergence when future documents are unavailable.
Use of Apis mellifera Honey to Predict Heavy Metal Loads in African Fruit Crops? – A Proof of Concept

Introduction The use of insects and their products as indicators of toxic substances is an established approach (Mercuri & Porrini, 1991; Codreanu et al., 2009; Rashed et al., 2009; Skorbiłowicz et al., 2018; Rothman et al., 2019 & 2020; Kaste et al., 2021). In fact, some cases of anthropogenic pollution would not have been successfully traced without the use of insects to identify artificial sources of its origin (Porrini et al., 2003). Furthermore, due to the constant threat of global warming and climate change, there is an ever-increasing demand to employ the services of insects as bioindicators because they are inextricably linked to the ecosystem they belong to and could reveal even the smallest changes occurring around them (Skorbiłowicz et al., 2018). Insects are even well recognized for their roles in showing toxicity due to agricultural usage of pesticides (Kavanagh et al., 2021). The presence or absence of insects on food crops could play key roles in saving lives from acute toxicity; the former indicating safe to eat while the latter, a warning sign of imminent danger due to the absence of insect life on it (Soldano et al., 2016). This indicates that the knowledge of the use of insects as bioindicators has great implications for saving human lives by preventing the consumption of toxic food grains.
High time resolution laser induced fluorescence in pulsed argon plasma

A submillisecond time resolution laser induced fluorescence (LIF) method for obtaining the temporal evolution of the ion velocity distribution function in pulsed argon plasma is presented. A basic LIF system that employs a continuous laser wave pumping and lock-in aided detection of the subsequent fluorescence radiation is modified by addition of a high frequency acousto-optic modulator to provide measurements of the ion flow velocity and ion temperature in a helicon generated pulsed argon plasma with temporal resolutions as high as 30μs.
Nondiscriminating Dropout Prediction beyond Algorithmic Fairness: Ensuring Coverage in Preprocessing Pipelines

The increasing impact of data-based decisions in education has risen concerns about the potential risk of an amplification of educational and societal inequities already implicit in existing data. Nondiscrimination can be characterized in terms of different properties (i.e., fairness, diversity, and coverage). Research mostly focused on fair machine learning, i.e., on the development of algorithms that can detect and, in some cases, correct bias. In this paper, we consider the case of dropout prediction and focus on coverage constraints. Specifically, with reference to this case study, we show why approaches are needed for guaranteeing nondiscrimination during all the steps of the data processing pipeline and discuss how a nondiscriminating pre-processing can be ensured by relying on coverage-based constraints on data transformations.
Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

Accurately predicting Origin-Destination (OD) passenger flow can help metro service quality and efficiency. Existing works have focused on predicting incoming and outgoing flows for individual stations, while little attention was paid to OD prediction in metro systems. The challenges are that OD flows 1) have high temporal dynamics and complex spatial correlations, 2) are affected by external factors, and 3) have sparse and incomplete data slices. In this paper, we propose an Adaptive Feature Fusion Network (AFFN) to a) adaptively fuse spatial dependencies from multiple knowledge-based graphs and even hidden correlations between stations and b) accurately capture the periodic patterns of passenger flows based on the auto-learned impact from external factors. To deal with the incompleteness and sparsity of OD matrices, we extend AFFN to multi-task AFFN to predict the inflow and outflow of each station as a side-task to further improve OD prediction accuracy. We conducted extensive experiments on two real-world metro trip datasets collected in Nanjing and Xi’an, China. Evaluation results show that our AFFN and multi-task AFFN outperform the state-of-the-art baseline techniques and AFFN variants in various accuracy metrics, demonstrating the effectiveness of AFFN and each of its key components in OD prediction.
Erich Regener and the ionisation maximum of the atmosphere

Abstract. In the 1930s the German physicist Erich Regener (1881–1955) did important work on the measurement of the rate of production of ionisation deep under water and in the atmosphere. Along with one of his students, Georg Pfotzer, he discovered the altitude at which the production of ionisation in the atmosphere reaches a maximum, often, but misleadingly, called the Pfotzer maximum. Regener was one of the first to estimate the energy density of cosmic rays, an estimate that was used by Baade and Zwicky to bolster their postulate that supernovae might be their source. Yet Regener's name is less recognised by present-day cosmic ray physicists than it should be, largely because in 1937 he was forced to take early retirement by the National Socialists as his wife had Jewish ancestors. In this paper we briefly review his work on cosmic rays and recommend an alternative naming of the ionisation maximum. The influence that Regener had on the field through his son, his son-in-law, his grandsons and his students, and through his links with Rutherford's group in Cambridge, is discussed in an appendix. Regener was nominated for the Nobel Prize in Physics by Schrodinger in 1938. He died in 1955 at the age of 73.
Thick Data Techniques for Identifying Abnormality in Video Frames for Wireless Capsule Endoscopy

Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn’s disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours for reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning including deep learning has failed to report robust results because of the lack of large samples to train its neural nets. In this paper we are describing a thick data approach that learns from few anchor images provided by sound data sets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies at any CE video and allow these candidate frames to a feature extraction process to provide representative measures on the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach as reported accuracy of detecting Crohn’s Disease based on availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine tune our approach by adding more thick data methods for enhancing the diagnosis accuracy.
A Study on the Heating and Deicing Performance of Microwave-Absorbing Asphalt Mixtures

Road icing in winter brings challenges to traffic safety, and microwave heating and deicing technology is an effective method with the advantages of high efficiency and environmental protection. Magnetite has been widely used as a microwave-absorbing material in pavement. In this paper, magnetite powder formed by crushing natural magnetite and high-purity Fe3O4 powder after purification were mixed to replace mineral powder, and the magnetite aggregate was used to replace the limestone aggregate with the same particle size to enhance the asphalt mixtures’ microwave absorption capacity. The effect of microwave heating time and microwave power on the heating of the asphalt mixtures was studied, and the heating performance of different thicknesses of the asphalt mixtures under microwave radiation was evaluated. The heating performance of the mixtures under different initial temperatures and ice layer thicknesses was also assessed. The results showed that the addition of the magnetite powder–Fe3O4 powder and the magnetite aggregate significantly enhanced the heating performance of the asphalt mixtures by microwave heating. The replacement of the magnetite powder–Fe3O4 powder, the microwave heating time, and the microwave power had positive effects on the heating efficiency of the asphalt mixtures. Moreover, the thinner asphalt mixtures had a better heating performance. The heating and deicing performance of the mixtures decreased with a decline in initial temperature. As the ice thickness increased, the deicing time of the specimen surface to reach 0 °C also increased.
Classification of Univariate Time Series via Temporal Abstraction and Deep Learning

Many time series classification algorithms have been proposed, including deep neural networks based, which so far focused mainly on improving model architectures rather than on data pre-processing. Generalization is crucial in time series classification and it can be achieved by abstracting the data. Data abstraction may also be useful to avoid handling challenges with error measurements, missing values, and irregular sampling. We propose transforming the raw time series into a symbolic time series representation, using a method known as temporal abstraction, before feeding it to the deep neural networks. This transformation can greatly enhance generalization and may potentially improve classification performance. In particular, we investigate the effectiveness of temporal abstraction when combined with convolution-based sequence models or recurrent neural networks. The methods were evaluated on 128 univariate datasets. Our evaluation shows that even when using equal frequency discretization, a relatively simple method, outperforms most state-of-the-art deep neural networks’ performance for univariate time series classification when fed by raw time series.
Abusive Language Detection with Graph based Multi-task Learning

To counter the online abusive language in social media, it is desirable to develop automated detection methods. Previous research has primarily formulated this problem as a sentence-level classification task, ignoring the crucial role of abusive lexicons that can strengthen the model explainability and enable more faithful predictions. Although a few methods have introduced the abusive lexicons for detection, the lexicons they use are either externally provided or labeled by human annotators, suffering from two limitations: (1) lack adaptability to diverse and evolving offensive scenarios; (2) require large human efforts to annotate the words.This paper overcomes the limitations of prior work with a multi-task abusive language detection framework. It combines sentence-level and word-level classification tasks, based on dependency tree based graph attention networks (GAT). With the two tasks, it is encouraged to capture both global and local data properties to produce better sentence representations. It is also advantageous in automatic lexicon construction during the learning process, without human annotations. Extensive experiments on two public datasets exhibit that our proposal can outperform the state-of-the-art baselines. Case studies show that the model explainability can be strengthened with the abusive parts identified by our framework. Our code is released to public. 1
Cross-Lingual Classification of Political Texts Using Multilingual Sentence Embeddings

Abstract Established approaches to analyze multilingual text corpora require either a duplication of analysts’ efforts or high-quality machine translation (MT). In this paper, I argue that multilingual sentence embedding (MSE) is an attractive alternative approach to language-independent text representation. To support this argument, I evaluate MSE for cross-lingual supervised text classification. Specifically, I assess how reliably MSE-based classifiers detect manifesto sentences’ topics and positions compared to classifiers trained using bag-of-words representations of machine-translated texts, and how this depends on the amount of training data. These analyses show that when training data are relatively scarce (e.g., 20K or less-labeled sentences), MSE-based classifiers can be more reliable and are at least no less reliable than their MT-based counterparts. Furthermore, I examine how reliable MSE-based classifiers label sentences written in languages not in the training data, focusing on the task of discriminating sentences that discuss the issue of immigration from those that do not. This analysis shows that compared to the within-language classification benchmark, such “cross-lingual transfer” tends to result in fewer reliability losses when relying on the MSE instead of the MT approach. This study thus presents an important addition to the cross-lingual text analysis toolkit.
Stable and accurate second-order formulation of the shifted wave equation

High order finite difference approximations are derived for a onedimensional model of the shifted wave equation written in second-order form. The domain is discretized using fully compatible summation by parts operators and the boundary conditions are imposed using a penalty method, leading to fully explicit time integration. This discretization yields a strictly stable and efficient scheme. The analysis is verified by numerical simulations in one-dimension. The present study is the first step towards a strictly stable simulation of the second-order formulation of Einstein’s equations in three spatial dimensions. AMS subject classifications: 35L05, 35L20, 65N06, 65N12, 83C05
Chlamydomonas reinhardtii: A Factory of Nutraceutical and Food Supplements for Human Health

Chlamydomonas reinhardtii (C. reinhardtii) is one of the most well-studied microalgae organisms that revealed important information for the photosynthetic and metabolic processes of plants and eukaryotes. Numerous extensive studies have also underpinned its great potential as a biochemical factory, capable of producing various highly desired molecules with a direct impact on human health and longevity. Polysaccharides, lipids, functional proteins, pigments, hormones, vaccines, and antibodies are among the valuable biomolecules that are produced spontaneously or under well-defined conditions by C. reinhardtii and can be directly linked to human nutrition and diet. The aim of this review is to highlight the recent advances in the field focusing on the most relevant applications related to the production of important biomolecules for human health that are also linked with human nutrition and diet. The limitations and challenges are critically discussed along with the potential future applications of C. reinhardtii biomass and processed products in the field of nutraceuticals and food supplements. The increasing need for high-value and low-cost biomolecules produced in an environmentally and economy sustainable manner also underline the important role of C. reinhardtii.
Source Localization and Bayesian leak magnitude inference of sparse wireless sensor data to detect fugitive methane leak

Source localization and emission strength quantification is an ongoing challenge for distributed pollution sources. Here we outline a wireless sensor approach to localize all potential emission sources on an oil and gas well pad under well controlled experimental conditions. Using backtracking algorithms and time synchronized methane and wind measurements, sources are attributed to equipment on the well pad. After localizing the sources, we estimate source magnitude and uncertainty using a Bayesian inference method. The approach outlined in this work can identify and quantify leaks in the close proximity of the sources under dynamic plume dispersion taking into account the site layout, potential source locations and the characteristics of the sensor network. Localization of the system is within a meter from the emission location and the Bayesian approach yields rates that are within a factor 3 of the actual rate. Further, the actual rates are generally within the 95% confidence intervals for the prediction.