bluuebunny/crossref_metadata_2025_split huggingface dataset:
Knowledge graph embedding methods for entity alignment: experimental review
Authors: Fanourakis Nikolaos , Efthymiou Vasilis , Kotzinos Dimitris , Christophides Vassilis 
Published: June 2023

AbstractIn recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world entity, a task largely known as the Entity Alignment. Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised methods have been proposed that exploit both factual (attribute based) and structural information (relation based) of entities in the KGs. Still, a quantitative assessment of their strengths and weaknesses in real-world KGs according to different performance metrics and KG characteristics is missing from the literature. In this work, we conduct the first meta-level analysis of popular embedding methods for entity alignment, based on a statistically sound methodology. Our analysis reveals statistically significant correlations of different embedding methods with various meta-features extracted by KGs and rank them in a statistically significant way according to their effectiveness across all real-world KGs of our testbed. Finally, we study interesting trade-offs in terms of methods’ effectiveness and efficiency.
When teachers construct tests for assessing students’ competencies: a taxonomy
Authors: Becevic Semir 
Published: June 2023

AbstractLittle is known about how teachers construct tests. For that reason, this study addresses the use of teacher-constructed tests for assessing educational goals, expressed in terms of student mathematical competencies. The focus is on meanings that upper secondary school mathematics teachers assign to their own test construction practices for assessing educational goals, expressed in terms of mathematical competencies in the curriculum. The methodological approach of grounded theory, underlined by symbolic interactionism, is applied to semi-structured interviews with teachers. The core category, the emerging taxonomy, is derived by revealing distinctions in degree of paying attention to competencies: no attention, superficial attention, and qualitative attention, as well as two different phases of the assessment: constructional and marking. Finally, a couple of possible implications for developing and improving test construction are offered. This includes collaborative work, inside and outside of schools, with both prospective and in-service teachers, for improvement of competence implementation in regular teaching and learning in alignment with mathematical content.
Sociobiology on Screen. The Controversy Through the Lens of Sociobiology: Doing What Comes Naturally
Authors: Stuhrmann Cora 
Published: June 2023

AbstractWhen the sociobiology debate erupted in 1975, there were almost too many contributions to the heated exchanges between sociobiologists and their critics to count. In the fall of 1976, a Canadian educational film entitled Sociobiology: Doing What Comes Naturally sparked further controversy due to its graphic visuals and outrageous narration. While critics claimed the film was a promotional tool to further the sociobiological agenda in educational settings, sociobiologists quickly distanced themselves from the film and, in turn, accused the critics of consciously misrepresenting sociobiology by organizing showings of the film. Using audio, video, archival, and published sources, this paper explores the complicated history of Sociobiology: Doing What Comes Naturally and demonstrates how the public debate about the film reflects the positions, polemics, and polarization of the sociobiology debate as a whole.
Effects of dissolved organic carbon gradient on epilimnetic zooplankton communities in lakes
Authors: Estlander Satu , Horppila Jukka 
Published: June 2023

AbstractLake browning is expected to change aquatic ecosystems considerably. The changes that may occur in zooplankton communities with a high concentration of dissolved organic carbon (DOC) are little studied and not yet sufficiently understood. We studied zooplankton communities in Finland in 27 lakes with a wide DOC range. We explored how zooplankton diversity and biomass vary along a DOC gradient and how species belonging to different functional groups respond to varying DOC concentrations. The total biomass of zooplankton was not related to DOC concentration, but diversity decreased and the negative linear trend in diversity coincided with an increase in omnivorous zooplankton taxa, whereas several other herbivorous zooplankton taxa were displaced from the community along the gradient of DOC. The results of our study suggest that some well-adapted taxa, especially omnivorous taxa, can benefit from lake browning, but a larger number of taxa suffer from unfavourable conditions caused by high DOC concentration. DOC-induced changes in predation pressure and changes in phytoplankton community in terms of resource availability for zooplankton should be emphasized in future research to understand the effects of lake browning on zooplankton.
Mapping international research output within ethical, legal, and social implications (ELSI) of assisted reproductive technologies
Authors: Alon Ido , Chebance Zacharie , Massucci Francesco Alessandro , Bounartzi Theofano , Ravitsky Vardit 
Published: June 2023

Abstract
Purpose
Research about ethical, legal, and social implications (ELSI) of assisted reproductive technologies (ART) is influenced by cultural and value-based perspectives. It impacts regulations, funding, and clinical practice, and shapes the perception of ART in society. We analyze trends in the global literature on ELSI of ART between 1999 and 2019. As most output is produced by North America, Western Europe, and Australia, we focus on international research, i.e., academic articles studying a different country than that of the corresponding author.

Methods
The corpus, extracted from PubMed, Web of Science, and Scopus, includes 7714 articles, of which 1260 involved international research. Analysis is based on titles, abstracts and keywords, classification into ART fields and Topic Modeling, the countries of corresponding author, and countries mentioned in abstracts.

Results
An absolute increase in the number of international studies, and their relative proportion. Trends of decentralization are apparent, yet geographic centralization remains, which reflects an unequal distribution of research funds across countries and may result in findings that do not reflect global diversity of norms and values. Preference for studying conceptual challenges through philosophical analysis, and for fields that concern only a portion of ART cycles. Less attention was dedicated to economic analysis and barriers to access, or to knowledge of and attitudes. International studies provide an opportunity to expand and diversify the scope of ELSI research.

Conclusion
We call on the research community to promote international collaborations, focus on less explored regions, and divert more attention to questions of cost, access, knowledge, and attitudes.
SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes
Authors: Biegel Tobias , Helm Patrick , Jourdan Nicolas , Metternich Joachim 
Published: June 2023

AbstractSelf-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning effective representations by solving a supervised pretext task with pseudo-labels generated from unlabeled data provides a promising concept for industrial downstream tasks such as process monitoring. In this paper, we present SSMSPC a novel approach for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general statistical process control (SPC) framework to develop a holistic approach for the detection and localization of anomalous process behavior in discrete manufacturing processes. We propose a pretext task called Location + Transformation prediction, where the objective is to classify both, the type and the location of a randomly applied augmentation on a given time series input. In the downstream task, we follow the one-class classification setting and apply the Hotelling’s $$T^2$$

T
2

statistic on the learned representations. We further propose an extension to the control chart view that combines metadata with the learned representations to visualize the anomalous time steps in the process data which supports a machine operator in the root cause analysis. We evaluate the effectiveness of SSMSPC with two real-world CNC-milling datasets and show that it outperforms state-of-the-art anomaly detection approaches, achieving $$100\%$$

100
%

and $$99.6\%$$

99.6
%

AUROC, respectively. Lastly, we deploy SSMSPC at a CNC-milling machine to demonstrate its practical applicability when used as a process monitoring tool in a running process.
Assessing the performance of QSP models: biology as the driver for validation
Authors: Singh Fulya Akpinar , Afzal Nasrin , Smithline Shepard J. , Thalhauser Craig J. 
Published: June 2023

AbstractValidation of a quantitative model is a critical step in establishing confidence in the model’s suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.
A Practical Approach to SOS Relaxations for Detecting Quantum Entanglement
Authors: Bhardwaj Abhishek 
Published: June 2023

AbstractThis article introduces PnCP, a MATLAB toolbox for constructing positive maps which are not completely positive. We survey optimization and sum of squares relaxation techniques to find the most numerically efficient methods and establish some benchmarks for this construction. We also show how this package can be applied to the problem of classifying entanglement in quantum states.
User experience evaluation of Edurell interface for video augmentation
Authors: Coccoli Mauro , Torre Ilaria , Galluccio Ilenia 
Published: June 2023

AbstractThis paper deals with the use of videos in education and describes an approach to improve the learners’ experience in the first-watch and rewatch learning contexts. Actually, learning through videos raises challenging issues like non-linear navigation and poor structure of the video content. Hence, a novel application is presented, designed to address such issues. To this aim, the proposed Edurell environment enriches standard video lessons with new services based on the contents’ structure of the video, resulting in a new hypervideo educational application. The system extracts the concepts explained in a given video lesson, their prerequisite relations, and the segments of the video where they occur, together with other pieces of knowledge. The resulting knowledge graph allows creating augmented video lessons and enables learners to perform non-linear navigation and in-depth exploration of their learning contents. This idea was implemented in a custom web-based video player with interactive visual tools. Then, a heuristic usability evaluation was performed with HCI experts. Also, a user study with university students was done, aimed to evaluate the perceived usability and the student’s experience in the two mentioned learning contexts. The first results achieved show the effectiveness of the proposed approach.
Derivative-based time-adjusted analysis of diurnal and within-tree variation in the OJIP fluorescence transient of silver birch
Authors: Akinyemi Olusegun Olaitan , Čepl Jaroslav , Keski-Saari Sarita , Tomášková Ivana , Stejskal Jan , Kontunen-Soppela Sari , Keinänen Markku 
Published: June 2023

AbstractThe JIP test, based on fast chlorophyll fluorescence (ChlF) kinetics and derived parameters, is a dependable tool for studying photosynthetic efficiency under varying environmental conditions. We extracted additional information from the whole OJIP and the normalized variable fluorescence (Vt) transient curve using first and second-order derivatives to visualize and localize points of landmark events. To account for light-induced variations in the fluorescence transient, we present a time-adjusted JIP test approach in which the derivatives of the transient curve are used to determine the exact timing of the J and I steps instead of fixed time points. We compared the traditional JIP test method with the time-adjusted method in analyzing fast ChlF measurements of silver birch (Betula pendula) in field conditions studying diurnal and within-crown variation. The time-adjusted JIP test method showed potential for studying ChlF dynamics, as it takes into account potential time shifts in the occurrence of J and I steps. The exact occurrence times of J and I steps and other landmark events coincided with the times of significant differences in fluorescence intensity. Chlorophyll fluorescence parameters were linearly related to photosynthetic photon flux density (PPFD) at different times of day, and the values obtained by the time-adjusted JIP test showed a stronger linear regression than the traditional JIP test. For fluorescence parameters having significant differences among different times of day and crown layers, the time-adjusted JIP test resulted in more clear differences than the traditional JIP test. Diurnal ChlF intensity data indicated that differences between the southern and northern provenance were only evident under low light conditions. Taken together, our results emphasize the potential relevance of considering the time domain in the analysis of the fast ChlF induction.
Generalized multiple change-point detection in the structure of multivariate, possibly high-dimensional, data sequences
Authors: Anastasiou Andreas , Papanastasiou Angelos 
Published: June 2023

AbstractThe extensive emergence of big data techniques has led to an increasing interest in the development of change-point detection algorithms that can perform well in a multivariate, possibly high-dimensional setting. In the current paper, we propose a new method for the consistent estimation of the number and location of multiple generalized change-points in multivariate, possibly high-dimensional, noisy data sequences. The number of change-points is allowed to increase with the sample size and the dimensionality of the given data sequence. Having a number of univariate signals, which constitute the unknown multivariate signal, our algorithm can deal with general structural changes; we focus on changes in the mean vector of a multivariate piecewise-constant signal, as well as changes in the linear trend of any of the univariate component signals. Our proposed algorithm, labeled Multivariate Isolate–Detect (MID) allows for consistent change-point detection in the presence of frequent changes of possibly small magnitudes in a computationally fast way.
Connexive Logic, Probabilistic Default Reasoning, and Compound Conditionals
Authors: Pfeifer Niki , Sanfilippo Giuseppe 
Published: June 2023

AbstractWe present two approaches to investigate the validity of connexive principles and related formulas and properties within coherence-based probability logic. Connexive logic emerged from the intuition that conditionals of the formif not-A,thenA, should not hold, since the conditional’s antecedentnot-Acontradicts its consequentA. Our approaches cover this intuition by observing that the only coherent probability assessment on the conditional event$${A| \overline{A}}$$A|A¯is$${p(A| \overline{A})=0}$$p(A|A¯)=0. In the first approach we investigate connexive principles within coherence-based probabilistic default reasoning, by interpreting defaults and negated defaults in terms of suitable probabilistic constraints on conditional events. In the second approach we study connexivity within the coherence framework of compound conditionals, by interpreting connexive principles in terms of suitable conditional random quantities. After developing notions of validity in each approach, we analyze the following connexive principles:Aristotle’s theses,Aristotle’s Second Thesis,Abelard’s First Principle, andBoethius’ theses. We also deepen and generalize some principles and investigate further properties related to connexive logic (likenon-symmetry). Both approaches satisfyminimalrequirements for a connexive logic. Finally, we compare both approaches conceptually.
Acute myeloid leukemia cells and MSC-derived exosomes inhibiting transformation in myelodysplastic syndrome
Authors: Liu Xiaoli , Ren Fanggang , Li Shuo , Zhang Na , Pu Jeffrey J. , Zhang Hongyu , Xu Zhifang , Tan Yanhong , Chen Xiuhua , Chang Jianmei , Wang Hongwei 
Published: June 2023

Abstract
Aims
To investigate the mechanism of exosomes' role in the transformation of MDS to AML.

Methods
Exosomes in culture supernatants of MDS and AML cell lines, were extracted by ultrafiltration and identified in three ways: morphology, size, and exosome protein surface markers. Exosomes from AML cell lines were then co-cultured with MDS cell lines and their impacts on MDS cell microenvironment, proliferation, differentiation, cell cycle, and apoptosis were analyzed by CCK-8 assay and flow cytometry. Furthermore, exosomes from MSC were extracted for further authentication.

Results
The transmission electron microscopy, nanoparticle tracking analysis, Western blotting, and flow cytometry methods all verify that ultrafiltration is a reliable method to extract exosomes in the culture medium. Exosomes from AML cell lines inhibit the proliferation of MDS cell lines, block cell cycle progression, and promote apoptosis and cell differentiation. It also leads to increased secretion of tumor necrosis factor-α (TNF-α) and reactive oxygen species (ROS) in MDS cell lines. In addition, MSC-derived exosomes were found to inhibit the proliferation of MDS cell lines, arrest cell cycle progression, promote apoptosis, and inhibit differentiation.

Conclusion
Ultrafiltration is a proper methodology in extracting exosomes. The exosomes of AML origin and MSC origin may play a role in MDS leukemia transformation via targeting TNF-α/ROS-Caspase3 pathway.
Few-shot and meta-learning methods for image understanding: a survey
Authors: He Kai , Pu Nan , Lao Mingrui , Lew Michael S. 
Published: June 2023

AbstractState-of-the-art deep learning systems (e.g., ImageNet image classification) typically require very large training sets to achieve high accuracies. Therefore, one of the grand challenges is called few-shot learning where only a few training samples are required for good performance. In this survey, we illuminate one of the key paradigms in few-shot learning called meta-learning. These meta-learning methods, by simulating the tasks which will be presented at inference through episodic training, can effectively employ previous prior knowledge to guide the learning of new tasks. In this paper, we provide a comprehensive overview and key insights into the meta-learning approaches and categorize them into three branches according to their technical characteristics, namely metric-based, model-based and optimization-based meta-learning. Due to the major importance of the evaluation process, we also present an overview of current widely used benchmarks, as well as performances of recent meta-learning methods on these datasets. Based on over 200 papers in this survey, we conclude with the major challenges and future directions of few-shot learning and meta-learning.
Using a Latent Variable Method to Develop a Composite, Multidimensional Measure of Structural Racism at the City Level
Authors: Siegel Michael , Rieders Madeline , Rieders Hannah , Moumneh Jinan , Asfour Julia , Oh Jinseo , Oh Seungjin 
Published: June 2023

Abstract
Introduction
Although structural racism is strongly related to racial health disparities, we are not aware of any composite, multidimensional measure of structural racism at the city level in the United States. However, many of the policies, programs, and institutions that create and maintain structural racism are located at the city level. To expand upon previous research, this paper uses a novel measure to measure structural racism at the city level for the non-Hispanic Black population.

Methods
We used confirmatory factor analysis to model the latent construct of structural racism for 776 U.S. cities. The model included six indicators across five dimensions: racial segregation, incarceration, educational attainment, employment, and economic status. We generated factor scores that weighted the indicators in order to produce the best model fit. The resulting factor scores represented the level of structural racism in each city. We demonstrated the utility of this measure by demonstrating its strong correlation with Black-White disparities in firearm homicide rates.

Results
There were profound differences in the magnitude of structural racism across cities. There were also striking differences in the magnitude of the racial disparity in firearm homicide across cities. Structural racism was a significant predictor of the magnitude of these racial disparities in firearm homicide. Each one standard deviation increase in the structural racism factor score increased the firearm homicide rate ratio by a factor of approximately 1.2 (95% confidence interval, 1.1-1.3).

Conclusions
These new measures can be utilized by researchers to relate structural racism to racial health disparities at the city level.