Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaches to Estimating Marine Protein in Human Collagen for Radiocarbon Date Calibration

Determining the appropriate approach to calibrating radiocarbon dates is challenging when unknown and variable fractions of the carbon sample are derived from terrestrial and marine systems. Uncalibrated dates from a large number of human skeletons from Western Cape and Southern Cape locales, South Africa (n = 187), can be used to explore alternate approaches to the marine carbon correction. The approach that estimates theoretically expected minimum and maximum values for marine carbon (“expected”) is compared to the approach that estimates observed minimum and maximum values (“observed”). Two case studies are explored, wherein skeletons interred together have non-overlapping conventional 14C ages. The case from the Western Cape is explored through carbon isotope values; the case from the Southern Cape uses nitrogen isotope values. In both cases, the approach using observed endpoints yields better date calibration results. Analysis of the large sample shows that mean values for estimated dietary % Marine, as calculated using expected and observed protocols, are significantly different. We conclude that the observed protocol is preferred, and that improved measures of the local marine reservoir (ΔR) are needed for this region.
Tully-Fisher relation

The observed radial velocity of a galaxy consists of two main components: the recession velocity caused by the smooth Hubble expansion and the peculiar velocity resulting from the gravitational attraction of growing structures due to matter density fluctuations. To isolate the recession velocity component and calculate the Hubble constant, accurate measurements of true distances are needed. The Tully-Fisher relation is an empirical correlation between the luminosity and rotational velocity of spiral galaxies that serves as a distance indicator to measure distances independent of redshift. The Tully-Fisher relation has played an important role in Hubble constant measurements since its inception. This chapter delves into the significance of the Tully-Fisher relation in such measurements and explores its implications. We begin by discussing the definition and historical background of the Tully-Fisher relation. We also explore the observational evidence supporting this relation and discuss its advantages and limitations. The chapter then focuses on the methodology of using the Tully-Fisher relation for Hubble constant measurements. This includes detailed explanations of calibration techniques and biases. We emphasize the advantages of utilizing the Tully-Fisher relation, such as its ability to provide accurate distance measurements even at significant redshift where other methods may encounter challenges.
Detecting the phase transition in a strongly interacting Fermi gas by unsupervised machine learning

We study the critical temperature of the superfluid phase transition of strongly-interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and a Bose-Einstein condensate (BEC) of dimers. To this end, we employ the technique of unsupervised machine learning using an autoencoder neural network which we directly apply to time-of-flight images of the fermions. We extract the critical temperature of the phase transition from trend changes in the data distribution revealed in the latent space of the autoencoder bottleneck.
Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators

We study the long-term effects of the 2015 German minimum wage introduction and its subsequent increases on regional employment. Using data from two waves of the Structure of Earnings Survey allows us to estimate models that account for changes in the minimum wage bite over time. While the introduction mainly affected the labour market in East Germany, the raises are also increasingly affecting low-wage regions in West Germany, such that around one third of regions have changed their (binary) treatment status over time. We apply different specifications and extensions of the classic difference-in-differences approach as well as a set of new estimators that enables for unbiased effect estimation with a staggered treatment adoption and heterogeneous treatment effects. Our results indicate a small negative effect on dependent employment of 0.5 percent, no significant effect on employment subject to social security contributions, and a significant negative effect of about 2.4 percent on marginal employment until the first quarter of 2022. The extended specifications suggest additional effects of the minimum wage increases, as well as stronger negative effects on total dependent and marginal employment for those regions that were strongly affected by the minimum wage in 2015 and 2019.
The importance of moisture recycling

In this article, we provide strong evidence for moisture feedback playing an important part in China’s precipitation. Due to the fact that China’s water scarcity has a huge impact on the economy and society, it is not only a problem for the country itself but could become a problem for the whole world in the near future. Most research in the past focused on improving water use efficiency, which are short term solutions. An appropriate use of moisture recycling data could however be a prime tool in solving long term water scarcity problems. Correlation coefficients and the continental precipitation multiplier are methods to quantify the relation between evaporation and precipitation in different areas. These results can be used in a simple forecasting model, enabling better insights in the precipitation. The results show a high correlation coefficient between Eurasia and China with 51% of the precipitation in China being of continental origin.
Interpretable Survival Analysis for Heart Failure Risk Prediction

Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when these assumptions fail. Newer survival models based on machine learning avoid these assumptions and offer improved accuracy, yet sometimes at the expense of model interpretability, which is vital for clinical use. We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions. To evaluate our pipeline, we predict risk of heart failure using a large-scale EHR database. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
Off-Policy Evaluation for Large Action Spaces via Policy Convolution

Developing accurate off-policy estimators is crucial for both evaluating and optimizing for new policies. The main challenge in off-policy estimation is the distribution shift between the logging policy that generates data and the target policy that we aim to evaluate. Typically, techniques for correcting distribution shift involve some form of importance sampling. This approach results in unbiased value estimation but often comes with the trade-off of high variance, even in the simpler case of one-step contextual bandits. Furthermore, importance sampling relies on the common support assumption, which becomes impractical when the action space is large. To address these challenges, we introduce the Policy Convolution (PC) family of estimators. These methods leverage latent structure within actions -- made available through action embeddings -- to strategically convolve the logging and target policies. This convolution introduces a unique bias-variance trade-off, which can be controlled by adjusting the amount of convolution. Our experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC, especially when either the action space or policy mismatch becomes large, with gains of up to 5 - 6 orders of magnitude over existing estimators.
Multi-Path Bound for DAG Tasks

This paper studies the response time bound of a DAG (directed acyclic graph) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of using a single longest path in previous results, was proposed and leads to the so-called multi-path bound. Multi-path bounds can greatly reduce the response time bound and significantly improve the schedulability of DAG tasks. This paper derives a new multi-path bound and proposes an optimal algorithm to compute this bound. We further present a systematic analysis on the dominance and the sustainability of three existing multi-path bounds and the proposed multi-path bound. Our bound theoretically dominates and empirically outperforms all existing multi-path bounds. What's more, the proposed bound is the only multi-path bound that is proved to be self-sustainable.
Reputation-based synergy and discounting mechanism promotes cooperation

A good group reputation often facilitates more efficient synergistic teamwork in production activities. Here we translate this simple motivation into a reputation-based synergy and discounting mechanism in the public goods game. Specifically, the reputation type of a group, either good or bad determined by a reputation threshold, modifies the nonlinear payoff structure described by a unified reputation impact factor. Results show that this reputation-based incentive mechanism could effectively promote cooperation compared with linear payoffs, despite the coexistence of synergy and discounting effects. Notably, the complicated interactions between reputation impact and reputation threshold result in a sharp phase transition from full cooperation to full defection. We also find that the presence of a few discounting groups could increase the average payoffs of cooperators, leading to an interesting phenomenon that when the reputation threshold is raised, the gap between the average payoffs of cooperators and defectors increases while the overall payoff decreases. We further extend our framework to heterogeneous situations and show how the variability of individuals affect the evolutionary outcomes. Our work provides important insights into facilitating cooperation in social groups.
Predicting Transcription Factor Binding Sites using Transformer based Capsule Network

Prediction of binding sites for transcription factors is important to understand how they regulate gene expression and how this regulation can be modulated for therapeutic purposes. Although in the past few years there are significant works addressing this issue, there is still space for improvement. In this regard, a transformer based capsule network viz. DNABERT-Cap is proposed in this work to predict transcription factor binding sites mining ChIP-seq datasets. DNABERT-Cap is a bidirectional encoder pre-trained with large number of genomic DNA sequences, empowered with a capsule layer responsible for the final prediction. The proposed model builds a predictor for transcription factor binding sites using the joint optimisation of features encompassing both bidirectional encoder and capsule layer, along with convolutional and bidirectional long-short term memory layers. To evaluate the efficiency of the proposed approach, we use a benchmark ChIP-seq datasets of five cell lines viz. A549, GM12878, Hep-G2, H1-hESC and Hela, available in the ENCODE repository. The results show that the average area under the receiver operating characteristic curve score exceeds 0.91 for all such five cell lines. DNABERT-Cap is also compared with existing state-of-the-art deep learning based predictors viz. DeepARC, DeepTF, CNN-Zeng and DeepBind, and is seen to outperform them.
Efeitos do uso de calçado biomimético na marcha de crianças tipicas

Resumo Objetivo: O objetivo deste trabalho foi comparar os parâmetros espaço temporais e cinemáticos da marcha de crianças típicas em três condições: descalças, usando calçados biomiméticos e, calçados de uso diário (não biomiméticos - próprios das crianças). Métodos: Foram analisadas variáveis espaço temporais (velocidade, comprimento e largura da passada), deslocamento vertical do centro de massa (CM), pico de flexão do joelho e altura máxima do pé, coletados via avaliação tridimensional do movimento. Resultados: Comparado com a condição descalça, o uso do calçado biomimético não foi estatisticamente diferente em relação a velocidade da marcha, comprimento da passada e altura do pé. A largura da passada e a altura do pé não foi diferente estatisticamente entre as condições estudadas. O pico de flexão do joelho foi maior nas condições com calçados comparado a condição descalça. Os calçados de uso diário apresentaram maior deslocamento vertical do COM do que nas condições com o calçado biomimético e descalço. Conclusão: Os achados deste trabalho reafirmam que o uso do calçado influencia a marcha de crianças, especificamente na fase de desenvolvimento da marcha mas, que calçados com um design biomimético tem menores impactos no padrão de marcha das crianças. Abstract Objective: To compare the spatial-temporal parameters and walking kinematics of toddlers wearing biomimetic shoes, regular shoes (daily use owned shoes), and barefoot. Methods: Spatial-temporal parameters (speed, step length, and stride width), the mean vertical displacement of the center of mass (COM), knee flexion peak, and maximal foot height were analyzed. Results: Children were not different in biomimetic shoes and barefoot conditions on speed, step length, and COM vertical displacement. There was no difference among conditions on stride width and foot height. The knee flexion peak was greater in shod conditions than barefoot. The regular shoes showed greater COM vertical displacement than biomimetic shoes and barefoot. Conclusion: The findings showed that shoes affected the walking pattern in young children, but a shoe with a biomimetic design had a lesser effect on the walking pattern.
Classification Criteria For Early Knee Osteoarthritis: A Review Article

Abstract Objective The aim of this systematic review (SR) was to define the “state of the art” on classification criteria for early knee osteoarthritis (EKOA). Methods A systematic review was performed using MEDLINE (Pubmed), Web of Science, Scopus, EMBASE, PEDro, CINAHL and Google scholar databases. Two independent reviewers conducted the eligibility review. Any type of study that proposed diagnostic criteria of EKOA was included. Results Seven articles were included according to the inclusion criteria. The evidence presented in this SR shows that there is still no consensus regarding definition and classification of EKOA. At present, there are seven different proposals in the scientific literature, and they only agree on including knee pain and radiographic evaluation in their criteria, but they do not even consider the same situations for including these two factors. Conclusion There is still no consensus regarding definition and classification of EKOA. Knee pain and radiological assessment seem to be the most commonly used criteria, but due to the variability encountered, it is not possible to reach a consensus on a clear definition and diagnosis of EOKA.
Immobilization protects enzymes from plasma-mediated inactivation

Non-thermal plasmas are used in various applications to inactivate biological agents or biomolecules. A complex cocktail of reactive species, (vacuum) UV radiation and in some cases exposure to an electric field together cause the detrimental effects. In contrast to this disruptive property of technical plasmas, we have shown previously that it is possible to use non-thermal plasma-generated species such as H2O2 as cosubstrates in biocatalytic reactions. One of the main limitations in plasma-driven biocatalysis is the relatively short enzyme lifetime under plasma-operating conditions. This challenge could be overcome by immobilizing the enzymes on inert carrier materials. Here, we tested whether immobilization is suited to protect proteins from inactivation by plasma. To this end, using a dielectric barrier discharge device (PlasmaDerm), plasma stability was tested for five enzymes immobilized on ten different carrier materials. A comparative analysis of the treatment times needed to reduce enzyme activity of immobilized and free enzyme by 30% showed a maximum increase by a factor of 44. Covalent immobilization on a partly hydrophobic carrier surface proved most effective. We conclude from the study, that immobilization universally protects enzymes under plasma-operating conditions, paving the way for new emerging applications.
Graph Databases for Diachronic Language Data Modelling

Diachronic analysis, particularly of lexical semantics, is one of the most intriguing and complex tasks in linguistic studies. The integration of lexical semantic information and diachronic language resources plays a critical role in enabling quantitative accounts of language change. Focusing on the case of Latin, a high-resource language among historical languages, we present initial results from integrating Latin corpus data, Latin WordNet
Contextual Profiling of Charged Terms in Historical Newspapers

We extract nouns and corresponding co-occurrent targeted context features from a large corpus of Dutch language newspaper articles, from 1950s through the 1990s. Applying a well-established approach for scoring context feature and centre word associativity, we explore using the scores in the task of identifying key characteristics of known–charged terminology. Then use these features to draw parallels between known–charged and other terms. In the context of the very current de-colonisation efforts amongst museum institutions, such approaches offer an opportunity to condense large quantities of data into the most-significant, salient information for digestion by heritage professionals. The methods were found to indeed yield insights into known and candidate charged terms. Disclaimer: This paper contains derogatory words and phrases. They are provided solely as illustrations of the research results and do not reflect the opinions of the authors or their organisations. In-text examples of derogatory and potentially of-fensive are presented in “quotes, boldfaced and italicised” .
LexExMachinaQA: A framework for the automatic induction ofontology lexica for Question Answering over Linked Data

An open issue for Semantic Question Answering Systems is bridging the so called lexical gap , referring to the fact that the vocabulary used by users in framing a question needs to be interpreted with respect to the logical vocabulary used in the data model of a given knowledge base or knowledge graph. Building on previous work to automatically induce ontology lexica from language corpora by using association rules to identify correspondences between lexical elements on the one hand and ontological vocabulary elements on the other, in this paper we propose LexExMachinaQA, a framework allowing us to evaluate the impact of automatically induced lexicalizations in terms of alleviating the lexical gap in QA systems. Our framework combines the LexEx-Machina approach (Ell et al., 2021) for lexicon induction with the QueGG system proposed by Benz et al. (Benz et al., 2020) that relies on grammars automatically generated from ontology lexica to parse questions into SPARQL. We show that automatically induced lexica yield a decent performance i.t. o. F 1 measure with respect to the QLAD-7 dataset, representing a 34% – 56% performance degradation with respect to a manually created lexicon. While these results show that the fully automatic creation of lexica for QA systems is not yet feasible, the method could certainly be used to bootstrap the creation of a lexicon in a semi-automatic manner, thus having the potential to significantly reduce the human effort involved.
An Empirical Analysis of Task Relations in the Multi-Task Annotation of an Arabizi Corpus

In this study, we deal with the design of computational-linguistic resources and strategies for the analysis of under-resourced languages. In particular, we present empirical analyses aiming at identifying the best path to semi-automatically annotate a dialectal Arabic corpus via a neural multi-task architecture. Such an architecture is used to automatically generate several levels of linguistic annotation which can be evaluated by comparison with the gold annotation. Changing the order in which annotations are produced can have an impact on the quantitative results. Through multiple sets of experiments we show how to get the best performances with this methodology.
Football terminology: compilation and transformation into OntoLex-Lemon resource

The purpose of this article is to present the ongoing project which is the compilation of the first digital Football dictionary in the Ser-bian language, as well as to demonstrate the application of OntoLex and associated modules. The OntoLex-FrAC module for a football-specific dictionary includes information about frequency, attestation, and corpus usage. In this case, a domain-specific corpus was created by the name of SrFudKo, containing news articles about football in Serbian. Multi-word terms were automatically extracted from the Serbian corpus, then manually evaluated and classified as either sport or football-related. An inflection lexicon was produced and transformed into the OntoLex-Lemon format, Frequency information from the extraction phase was assigned to the entries. Finally, a few lexical entries were linked with the attestations from the corpus.
Modeling and Comparison of Narrative Domains with Shallow Ontologies

Ancient myths have fascinated scholars and laymen for centuries. In comparatistic efforts, classical scholars try to detect and interpret variations between versions of the same myth. We present a way to structure the underlying background information in myth variants. The background knowledge of twelve different versions of the popular myth Orpheus and Eurydice has been modeled in individual shallow ontologies that allow inter-and intra-myth comparison.
A First Attempt to Detect Misinformation in Russia-Ukraine War News through Text Similarity

The paper focuses on misinformation detection in established global news outlets’ texts covering significant and well-known events of the Russian-Ukraine war. We created the RUWA dataset and applied unsupervised ML approaches as the first dimension of misinfor-mation detection. We consider several different aspects of semantic similarity identification of the articles from various regions in order to confirm the hypothesis that if the news covering the same event from the outlets of various regions over the world are similar enough it means they reflect each other or, instead, if they are completely divergent it means some of them are likely not trustworthy.
Orbis Annotator: An Open Source Toolkit for the Efficient Annotation and Refinement of Text

Annotated language data plays an important role in training, fine-tuning and evaluating nat-ural language processing components. Never-theless, manually annotating language data is still a cumbersome task. This paper presents the Orbis Annotator framework, a user-friendly, easy to install, web-based software that supports users in efficiently annotating language data. Orbis Annotator supports standard and collaborative workflows, reuse of language resources through corpus versioning, and provides built-in tools for assessing corpus quality. In addition, it offers an API which enables the use of different clients (e.g., web-based, command line, etc.) and the use of third-party tools that accelerate the annotation process by pre-annotating corpora. The paper concludes with an evaluation that compares its features to other open-source annotation frameworks and the description of two use cases that outline its use in more sophisticated settings.