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European first-year university students accept evolution but lack substantial knowledge about it: a standardized European cross-country assessment

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Evolution: Education and Outreach 2021年第1期14卷 1-22页

作者： Kuschmierz, Paul Beniermann, Anna Bergmann, Alexander Pinxten, Rianne Aivelo, Tuomas Berniak-Woźny, Justyna Bohlin, Gustav Bugallo-Rodriguez, Anxela Cardia, Pedro Cavadas, Bento Filipe Barreiras Pinto Cebesoy, Umran Betul Cvetković, Dragana D. Demarsy, Emilie Đorđević, Mirko S. Drobniak, Szymon M. Dubchak, Liudmyla Dvořáková, Radka M. Fančovičová, Jana Fortin, Corinne Futo, Momir Geamănă, Nicoleta Adriana Gericke, Niklas Grasso, Donato A. Lendvai, Ádám Z. Mavrikaki, Evangelia Meneganzin, Andra Mogias, Athanasios Möller, Andrea Mota, Paulo G. Naciri, Yamama Németh, Zoltán Ożańska-Ponikwia, Katarzyna Paolucci, Silvia Pap, Péter László Petersson, Maria Pietrzak, Barbara Pievani, Telmo Pobric, Alma Porozovs, Juris Realdon, Giulia Sá-Pinto, Xana Savković, Uroš B. Sicard, Mathieu Sofonea, Mircea T. Sorgo, Andrej Stermin, Alexandru N. Tăușan, Ioan Torkar, Gregor Türkmen, Lütfullah Tutnjević, Slavica Uitto, Anna E. Varga, Mirna Vazquez-Ben, Lucia Venetis, Constantinos Viguera, Enrique Virtbauer, Lisa Christine Vutsova, Albena Yruela, Inmaculada Zandveld, Jelle Graf, Dittmar Institute for Didactics of Biology Justus-Liebig-University Giessen Giessen Germany Institute for Biology Teaching and Learning Research in Biology Education Humboldt-Universität Zu Berlin Berlin Germany Faculty of Life Science Department of Biology University of Leipzig Leipzig Germany Didactica Research Unit Faculty of Social Sciences & Behavioural Ecology & Ecophysiology Group Department of Biology University of Antwerp Antwerp Belgium Organismal and Evolutionary Biology Research Program University of Helsinki Helsinki Finland University of Information Technology and Management in Rzeszów Rzeszów Poland Public & Science (VA) Stockholm Sweden Department of Pedagogy and Didactics University of A Coruña A Coruña Spain Escola Superior de Educação Politécnico do Porto Porto Portugal Department of Science and Mathematics Polytechnic Institute of Santarém/School of Education Santarém Portugal Faculty of Education/Department of Mathematics and Science Education Usak University Uşak Turkey Chair of Genetics and Evolution Faculty of Biology University of Belgrade Belgrade Serbia Department of Botany and Plant Biology University of Geneva Geneva Switzerland Department of Evolutionary Biology Institute for Biological Research “Siniša Stanković” - National Institute of Republic of Serbia University of Belgrade Belgrade Serbia Institute of Environmental Sciences Jagiellonian University Kraków Poland Department of Parliamentarism and Political Management National Academy for Public Administration Under the President of Ukraine Kyiv Ukraine Department of Teaching and Didactics of Biology Charles University Praha Czech Republic Department of Biology Faculty of Education Trnava University Trnava Slovakia Laboratory of Didactic André Revuz University of Paris Paris France Institut Ruđer Bošković/Division of Molecular Biology Zagreb Croatia Department/Research Center in Systems Ecology and Sustainability University of Bucharest Bucharest Romania Department of Envir

Background: Investigations of evolution knowledge and acceptance and their relation are central to evolution education research. Ambiguous results in this field of study demonstrate a variety of measuring issues, for instance differently theorized constructs, or a lack of standardized methods, especially for cross-country comparisons. In particular, meaningful comparisons across European countries, with their varying cultural backgrounds and education systems, are rare, often include only few countries, and lack standardization. To address these deficits, we conducted a standardized European survey, on 9200 first-year university students in 26 European countries utilizing a validated, comprehensive questionnaire, the “Evolution Education Questionnaire”, to assess evolution acceptance and knowledge, as well as influencing factors on evolution acceptance. Results: We found that, despite European countries’ different cultural backgrounds and education systems, European first-year university students generally accept evolution. At the same time, they lack substantial knowledge about it, even if they are enrolled in a biology-related study program. Additionally, we developed a multilevel-model that determines religious faith as the main influencing factor in accepting evolution. According to our model, knowledge about evolution and interest in biological topics also increase acceptance of evolution, but to a much lesser extent than religious faith. The effect of age and sex, as well as the country’s affiliation, students’ denomination, and whether or not a student is enrolled in a biology-related university program, is negligible. Conclusions: Our findings indicate that, despite all their differences, most of the European education systems for upper secondary education lead to acceptance of evolution at least in university students. It appears that, at least in this sample, the differences in knowledge between countries reflect neither the extent to which school curricul

关键词： Acceptance Assessment Attitude Europe Evolution Higher education Knowledge Multilevel modeling Religious faith Socioscientific issues

Learning binary latent variable models: A tensor eigenpair approach

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

Model reduction with memory and the machine learning of dynamical systems

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arXiv 2018年

作者： Ma, Chao Wang, Jianchun Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Beijing Institute of Big Data Research Beijing100871 China

The well-known Mori-Zwanzig theory tells us that model reduction leads to memory effect. For a long time, modeling the memory effect accurately and efficiently has been an important but nearly impossible task in developing a good reduced model. In this work, we explore a natural analogy between recurrent neural networks and the Mori-Zwanzig formalism to establish a systematic approach for developing reduced models with memory. Two training models-a direct training model and a dynamically coupled training model-are proposed and compared. We apply these methods to the Kuramoto-Sivashinsky equation and the Navier-Stokes equation. Numerical experiments show that the proposed method can produce reduced model with good performance on both short-term prediction and long-term statistical properties. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

Hyperuniformity of generalized random organization models

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arXiv 2018年

作者： Ma, Zheng Torquato, Salvatore Department of Physics Princeton University and Princeton New Jersey08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Studies of random organization models of monodisperse (i.e., identical) spherical particles have shown that a hyperuniform state is achievable when the system goes through an absorbing phase transition to a critical state. Here we investigate to what extent hyperuniformity is preserved when the model is generalized to particles with a size distribution and/or nonspherical shapes. We begin by examining binary disks in two dimensions and demonstrate that their critical states are hyperuniform as two-phase media, but not hyperuniform nor multihyperuniform as point patterns formed by the particle centroids. We further confirm the generality of our findings by studying particles with a continuous size distribution. Finally, to study the effect of rotational degrees of freedom, we extend our model to noncircular particles, namely, hard rectangles with various aspect ratios, including the hard-needle limit. Although these systems exhibit only short-range orientational order, hyperuniformity is still preserved. Our analysis reveals that the redistribution of the "mass" of the particles rather than the particle centroids is central to this dynamical process. The consideration of the "active volume fraction" of generalized random organization models may help to resolve which universality class they belong to and hence may lead to a deeper theoretical understanding of absorbing-state models. Our results suggest that general particle systems subject to random organization can be a robust way to fabricate a wide class of hyperuniform states of matter by tuning the structures via different particle-size and -shape distributions. This in turn potentially enables the creation of multifunctional hyperuniform materials with desirable optical, transport and mechanical properties. Copyright © 2018, The Authors. All rights reserved.

关键词： Critical current density (superconductivity)

Inference and Sampling of K33-free Ising Models

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arXiv 2018年

作者： Likhosherstov, Valerii Maximov, Yury Chertkov, Michael Skolkovo Institute of Science and Technology Moscow Russia Theoretical Division Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM United States Graduate Program in Applied Mathematics University of Arizona TucsonAZ United States

We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time. The tractability also implies an ability to sample configurations of this model in polynomial time. The notion of tractability extends the basic case of planar zero-field Ising models. Our starting point is to describe algorithms for the basic case, computing partition function and sampling efficiently. Then, we extend our tractable inference and sampling algorithms to models whose triconnected components are either planar or graphs of O(1) size. In particular, it results in a polynomial-time inference and sampling algorithms for K33 (minor)-free topologies of zero-field Ising models-a generalization of planar graphs with a potentially unbounded genus. Copyright © 2018, The Authors. All rights reserved.

关键词： Ising model

The Weyl-Heisenberg ensemble: Statistical mechanics meets time-frequency analysis

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The Weyl-Heisenberg ensemble: Statistical mechanics meets ti...

International Conference on Sampling Theory and Applications

作者： Luis Daniel Abreu Joao Pereira Jose Luis Romero Salvatore Torquato Austrian Academy of Sciences Acoustics Research Institute Program in Applied and Computational Mathematics Princeton University Department of Chemistry Princeton Materials Institute and Program in Applied and Computational Mathematics Princeton University

ISBN: (纸本)9781538615669

We present our recent work on the Weyl-Heisenberg ensemble and its statistical properties [4]. The WH ensemble is a class of determinantal point processes associated with the Schrodinger representation of the Heisenberg group. As a special example, WH ensembles include a multi-layer extension of the Ginibre ensemble modeling the distribution of electrons in higher Landau levels. We describe the hyperuniformity of WH ensembles, which characterizes a state of matter for which (scaled) density fluctuations diminish towards zero at the largest length scales. Our approach is based on methods from time-frequency analysis. We introduce the main results from [4] highlighting time-frequency techniques and connections to the theory of polyanalytic functions, and also present some small extensions.

关键词： Weyl-Heisenberg ensemble Hyperuniformity Higher Landau levels Disorder Fluctuations

Evaluating Large Language Models for Enhancing Radiology Specialty Examination: A Comparative Study with Human Performance

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Academic Radiology 2025年

作者： Liu, Hao-Yun Chen, Shyh-Jye Wang, Weichung Lee, Chung-Hsi Hsu, Hsian-He Shen, Shu-Huei Chiou, Hong-Jen Lee, Wen-Jeng Department of Medical Imaging National Taiwan University Hospital Hsin-Chu Branch Br. No.25 Lane 442 Sec.1 Jingguo Rd Hsinchu City 300 Taiwan National Taiwan University College of Medicine No.1 Jen Ai Road Section 1 Taipei 100 Taiwan Department of Medical Imaging National Taiwan University Hospital No. 7 Zhongshan S. Rd. Zhongzheng Dist. Taipei City 100229 Taiwan Institute of Applied Mathematical Sciences Department of Mathematics and Data Science Degree Program National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 106319 Taiwan Graduate Institute of Health and Biotechnology Law Taipei Medical University 250 Wuxing Street Taipei City Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center No.325 Sec.2 Chenggong Rd. Neihu District Taipei City Taiwan Department of Radiology Taipei Veterans General Hospital No.201 Sec. 2 Shipai Rd. Beitou District Taipei City Taiwan National Yang Ming Chiao Tung University School of Medicine No. 118 Sec. 1 Zhongxiao West Road Taipei City 100 Taiwan

Rationale and Objectives: The radiology specialty examination assesses clinical decision-making, image interpretation, and diagnostic reasoning. With the expansion of medical knowledge, traditional test design faces challenges in maintaining accuracy and relevance. Large language models (LLMs) demonstrate potential in medical education. This study evaluates LLM performance in radiology specialty exams, explores their role in assessing question difficulty, and investigates their reasoning processes, aiming to develop a more objective and efficient framework for exam design. Materials and Methods: This study compared the performance of LLMs and human examinees in a radiology specialty examination. Three LLMs (GPT-4o, o1-preview, and GPT-3.5-turbo-1106) were evaluated under zero-shot conditions. Exam accuracy, examinee accuracy, discrimination index, and point-biserial correlation were used to assess LLMs’ ability to predict question difficulty and reasoning processes. The data provided by the Taiwan Radiological Society ensures comparability between AI and human performance. Results: As for accuracy, GPT-4o (88.0%) and o1-preview (90.9%) outperformed human examinees (76.3%), whereas GPT-3.5-turbo-1106 showed significantly lower accuracy (50.2%). Question difficulty analysis revealed that newer LLMs excel in solving complex questions, while GPT-3.5-turbo-1106 exhibited greater performance variability. Discrimination index and point-biserial Correlation analyses demonstrated that GPT-4o and o1-preview accurately identified key differentiating questions, closely mirroring human reasoning patterns. These findings suggest that advanced LLMs can assess medical examination difficulty, offering potential applications in exam standardization and question evaluation. Conclusion: This study evaluated the problem-solving capabilities of GPT-3.5-turbo-1106, GPT-4o, and o1-preview in a radiology specialty examination. LLMs should be utilized as tools for assessing exam question dif

关键词： Accuracy Evaluation GPT LLMs Radiology specialty examination

A bicriteria perspective on l-penalty approaches a corrigendum to siddiqui and gabriel's L-Penalty Approach for Solving MPECs

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arXiv 2018年

作者： Dächert, D. Siddiqui, Kerstin Saez-Gallego, Sauleh Gabriel, Javier Morales, Steven A. Miguel, Juan University of Duisburg-Essen Faculty of Economics and Business Administration Department for Management Science and Energy Economics Berliner Platz 6-8 Essen45127 Germany Departments of Civil Engineering and Applied Mathematics Statisitics Johns Hopkins University 3400 N Charles Street BaltimoreMD21218 United States Siemens Wind Power A/S Borupvang 9 Ballerup2750 Denmark Department of Mechanical Engineering Applied Mathematics Statistics and Scientific Computation Program University of Maryland College ParkMD20742-3021 United States University of Málaga Department of Applied Mathematics School of Industrial Engineers Málagaroom 2.556L-B

This paper presents a corrigendumto Theorems 2 and 3 in Siddiqui and Gabriel (2013). In brief, we revise the claim that their L-penalty approach yields a so-lution satisfying complementarity for any positive value of L, in general. This becomes evident when interpreting the L-penalty method as a weighted-sum scalarization of a bicriteria optimization problem. We also elaborate further assumptions under which the L-penalty approach yields a solution satisfying complementarity. Copyright © 2018, The Authors. All rights reserved.

关键词： Constrained optimization

Time-frequency analysis and determinantal point processes 13

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Time-frequency analysis and determinantal point processes

13th International Conference on Theoretical and Computational Acoustics, ICTCA 2017

作者： Abreu, Luís Daniel Gröchenig, Karlheinz Pereira, João M. Romero, José L. Torquato, Salvatore Acoustics Research Institute Austrian Academy of Sciences Vienna Austria NuHAG Faculty of Mathematics University of Vienna Vienna Austria Program in Applied and Computational Mathematics Princeton University New Jersey United States Princeton Materials Institute Department of Chemistry Princeton University New Jersey United States