bluuebunny/crossref_metadata_2025_split huggingface dataset:
Refined Marriage Rates and Coresidential Relationships by Sexual Identity and Age: A Research Note
Authors: Westrick-Payne Krista K. , Manning Wendy D. 
Published: December 2024

Abstract
This research note estimates refined marriage rates and coresidential relationships among individuals identifying as gay, lesbian, and straight in the United States seven years after the legalization of same-sex marriage. Data to estimate refined marriage rates or coresidential relationships by sexual identity are not available in a single national dataset so we leverage data from the U.S. Census Bureau's 2022 American Community Survey and Household Pulse Survey, to estimate refined marriage rates and the share in a coresidential union for straight, lesbian, and gay populations aged 18 to 64, based on the unmarried population and age. Findings highlight the importance of considering age alongside sexual identity when analyzing marital behavior and coresidential relationships. Both refined marriage rates and percentage living in coresidential relationships were lower among gay identifying males across all age groups, whereas rates among lesbian identifying females were comparable to those among straight identifying males and females. This study contributes to research on contemporary relationship dynamics as well as calling for new demographic data and investigations into the complexities of marriage and cohabitation among diverse populations.
Tailored to Women, Provided to Men? Gendered Occupational Inequality in Access to Flexible Working-Time Arrangements
Authors: Jacobi Aljoscha , Hamjediers Maik , Naujoks Tabea 
Published: December 2024

Abstract
Numerous policy-makers and social scientists promote flexible working as a means to reconcile work and family life, often explicitly targeting women. Accordingly, one might expect that flexible working-time arrangements are more prevalent in occupations with a high share of female employees. Yet, previous evidence indicates the opposite. To address this puzzle, we investigate the occupational opportunity structures for flexible working. A devaluation argument proposes that employers perceive female-typed work as being of lower value, thus inhibiting the provision of flexible working-time arrangements in tfemale-dominated occupations. We evaluate this argument against alternative explanations, namely differences in the ability to bargain for flexible working and in structural or practical barriers to flexible working. Descriptive results based on German panel data (GSOEP, 2003–2017) enriched with occupational-level information confirm that female-dominated occupations provide the least access to flexible work. Linear probability models with yearly fixed-effects indicate that power resources and structural barriers account for differences in flexible working between male-dominated and integrated occupations; yet an unexplained disadvantage for female-dominated occupations remains. Moreover, this disadvantage has grown between 2003 and 2017. We interpret this result as support of a cultural devaluation of female-dominated occupations in access to flexible working-time arrangements. Our findings highlight that the empirical reality of gendered occupational opportunity structures contradicts the gendered policy discourse on flexible work.
Teachers’ responses to a case of relational bullying: a typology based on teachers’ strategies and reasoning
Authors: Paljakka Antonia 
Published: December 2024

AbstractAlthough there is a growing body of research on teachers’ responses to bullying, the question of how teachers decide to respond is still under-researched. The present paper investigates participants’ intended responses to a case of relational bullying, the structure of the proposed intervention, the implicit aims of that intervention and the teachers’ reasoning. The study is based on a qualitative vignette study with open-ended questions. The sample comprised 38 secondary school teachers (58% female, 42% male). Qualitative content analysis and type formation were used to analyse the data. Ten different (sub)types of teachers’ responses were constructed based on patterns in the way teachers responded regarding things they would do, things they would avoid, and their reasoning: the relaxed, the cautious, the investigator, the impartial, the enlightened, the situation-oriented, the principle-oriented, the responsible, the experienced and the personally-affected type. The findings are interpreted in the light of previous research, and practical implications as well as implications for teacher education are discussed.
Hong Kong students’ motivational beliefs and emotions in collaborative learning in ESL classrooms: influences of actual and self-perceived English proficiency
Authors: Bai Barry , Zang Xuan , Guo Wenjuan 
Published: December 2024

AbstractAlthough the benefits of collaborative learning across disciplines are well-established, its effectiveness hinges on the quality of execution. Motivational beliefs and emotions are crucial in students' engagement and achievement. Yet, a notable gap exists in examining these variables among students with varying proficiency levels in the context of collaborative learning. To address the gap, the present study investigated 289 fourth- and fifth-grade Hong Kong primary students’ motivational beliefs and emotions in ESL collaborative learning and explored the variation across students’ actual and self-perceived English proficiency. The findings revealed an overall high level of motivational beliefs alongside a medium level of anxiety. Structural equation modelling analyses indicated that self-perceived proficiency positively influenced students' motivational beliefs, whereas actual proficiency did not make a unique contribution. Both self-perceived and actual proficiency, as well as their interaction, reduced anxiety. Actual proficiency had a stronger negative impact on anxiety when self-perceived proficiency was high, and the influence of self-perceived proficiency was significant only when actual proficiency was high. Practical implications are discussed.
A neural network-based adaptive cut-off approach to normality testing for dependent data
Authors: Kim Minwoo , Genton Marc G. , Huser Raphaël , Castruccio Stefano 
Published: December 2024

Abstract
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, however, assessing and testing the marginal behavior is considerably more challenging, as the marginal behavior is impacted by the degree of dependence, which typically leads to an inflation in Type I error rates. We propose a new approach to assess normality for dependent data by non-linearly incorporating existing statistics from normality tests as well as sample moments such as skewness and kurtosis through a neural network with adaptive cut-offs by which the Type I error inflation issue is fixed. We calibrate (deep) neural networks by simulated normal and non-normal data with a wide range of dependence structures and we determine the probability of rejecting the null hypothesis. We compare several approaches for normality tests and demonstrate the superiority of our method in terms of statistical power through an extensive simulation study. A real world application to global temperature data further demonstrates how the degree of spatio-temporal aggregation affects the marginal normality in the data.
Efficient quantum circuit contraction using tensor decision diagrams
Authors: Lopez-Oliva Vicente , Badia Jose M. , Castillo Maribel 
Published: December 2024

AbstractSimulating quantum circuits efficiently on classical computers is crucial given the limitations of current noisy intermediate-scale quantum devices. This paper adapts and extends two methods used to contract tensor networks within the fast tensor decision diagram (FTDD) framework. The methods, called iterative pairing and block contraction, exploit the advantages of tensor decision diagrams to reduce both the temporal and spatial cost of quantum circuit simulations. The iterative pairing method minimizes intermediate diagram sizes, while the block contraction algorithm efficiently handles circuits with repetitive structures, such as those found in quantum walks and Grover’s algorithm. Experimental results demonstrate that, in some cases, these methods significantly outperform traditional contraction orders like sequential and cotengra in terms of both memory usage and execution time. Furthermore, simulation tools based on decision diagrams, such as FTDD, show superior performance to matrix-based simulation tools, such as Google tensor networks, enabling the simulation of larger circuits more efficiently. These findings show the potential of decision diagram-based approaches to improve the simulation of quantum circuits on classical platforms.
The Exportation of Hong Kong’s Quality Granite in the 19th and 20th Centuries: A Historical Re-Evaluation
Authors: Deng Y. Katherine , Poon S.W. , Man K.F. 
Published: December 2024

AbstractSince British colonization in 1841, Hong Kong was described as a “barren and rocky” fishing town in the South China Sea. Archival evidence indicated that local granite blocks had been shipped to mainland China for construction purposes as early as 1844. In 2014, the unearthing of an ancient well made of granite kindled a fresh interest in tracing how these primitive, quality and abundantly available stones had been pursued locally and globally. This paper aims to unearth the ignored facet of Hong Kong in the 19th and 20th centuries by unveiling the exportation history of granite to various destinations. Extensive archival research in Hong Kong, Mainland China and the UK has been conducted, quarry-related historical places visited, and operatives or their descendants in the old quarries interviewed. Both tangible and intangible evidence indicated that, after the initial use of granite blocks as a principal building material in local construction, quality granite was gradually exported to Mainland China and other Asian countries, and further across the Pacific Ocean. With the expanded supplies and the concurrently exported stonemasonry, Hong Kong’s granite played a unique role in shaping the global cityscapes. It is therefore suggested that Hong Kong, at the start of her development, be re-evaluated as a world-class granite producer and exporter.
AI-assisted diagnostic approach for the influenza-like illness in children: decision support system for patients and clinicians
Authors: Lee Youngro , Seo Jongmo , Kim Yun-Kyung 
Published: December 2024

Abstract
Influenza-like illnesses (ILI), such as influenza and RSV, pose significant global health burdens, especially in febrile children under 6 years old. Differentiating these from bacterial infections based solely on clinical symptoms is challenging. While PCR tests are reliable, they are costly and time-consuming. An effective predictive tool would help doctors prioritize tests and guide parents on seeking emergency care for their febrile children. We collected data from 2,559 children who visited the hospital for ILI inspections. We developed XGBoost models, comparing nine different machine learning algorithms. Our AI-assisted diagnostic pipeline consists of two stages: Decision Support System for patients (DSS-P): An in-house model using sex, age, symptoms, and medical history to decide on hospital visits. Decision Support System for clinicians (DSS-C): An in-hospital model incorporating breath sound types and Chest X-ray results to determine the necessity of clinical tests. We tested various experimental settings, including the addition of RAT-tested samples and the combined consideration of influenza and RSV. The performance for influenza achieved an Area Under the Curve of 0.749 and 0.776, while RSV achieved 0.907 and 0.924 in DSS-P and DSS-C, respectively. We identified biomarkers, noting that most biomarkers had opposite effects for influenza and RSV. This study developed predictive models for influenza and RSV and explored their underlying mechanisms. An expectation tool to guide doctors in prioritizing tests or assisting parents in deciding on emergency care for their febrile child would be invaluable. Biomarker analysis performed can provide insight on clinical fields.
Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense
Authors: Huang Jiacheng , Chen Long , Yi Xiaoyin , Yu Ning 
Published: December 2024

Abstract
Deep neural networks have a recognized susceptibility to diverse forms of adversarial attacks in the field of natural language processing and such a security issue poses substantial security risks and erodes trust in artificial intelligence applications among people who use them. Meanwhile, quantum theory-inspired models that represent word composition as a quantum mixture of words have modeled the non-linear semantic interaction. However, modeling without considering the non-linear semantic interaction between sentences in the current literature does not exploit the potential of the quantum probabilistic description for improving the robustness in adversarial settings. In the present study, a novel quantum theory-inspired inter-sentence semantic interaction model is proposed for enhancing adversarial robustness via fusing contextual semantics. More specifically, it is analyzed why humans are able to understand textual adversarial examples, and a crucial point is observed that humans are adept at associating information from the context to comprehend a paragraph. Guided by this insight, the input text is segmented into subsentences, with the model simulating contextual comprehension by representing each subsentence as a particle within a mixture system, utilizing a density matrix to model inter-sentence interactions. A loss function integrating cross-entropy and orthogonality losses is employed to encourage the orthogonality of measurement states. Comprehensive experiments are conducted to validate the efficacy of proposed methodology, and the results underscore its superiority over baseline models even commercial applications based on large language models in terms of accuracy across diverse adversarial attack scenarios, showing the potential of proposed approach in enhancing the robustness of neural networks under adversarial attacks.
Ti3C2Tx Composite Aerogels Enable Pressure Sensors for Dialect Speech Recognition Assisted by Deep Learning
Authors: Xiao Yanan , Li He , Gu Tianyi , Jia Xiaoteng , Sun Shixiang , Liu Yong , Wang Bin , Tian He , Sun Peng , Liu Fangmeng , Lu Geyu 
Published: December 2024

AbstractWearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection. However, current intelligent speech assistants based on pressure sensors can only recognize standard languages, which hampers effective communication for non-standard language people. Here, we prepare an ultralight Ti3C2Tx MXene/chitosan/polyvinylidene difluoride composite aerogel with a detection range of 6.25 Pa-1200 kPa, rapid response/recovery time, and low hysteresis (13.69%). The wearable aerogel pressure sensor can detect speech information through the throat muscle vibrations without any interference, allowing for accurate recognition of six dialects (96.2% accuracy) and seven different words (96.6% accuracy) with the assistance of convolutional neural networks. This work represents a significant step forward in silent speech recognition for human–machine interaction and physiological signal monitoring.
Substance use prediction using artificial intelligence techniques
Authors: Unlu Ali , Subasi Abdulhamit 
Published: December 2024

Abstract
Substance use poses a significant public health challenge worldwide, including in Finland. This study seeks to predict patterns of substance use, aiming to identify the driving factors behind these trends using artificial intelligence techniques. This research utilizes data from the 2022 Finnish National Drug Survey, comprising 3,857 participants, to develop predictive models targeting the use of cannabis, ecstasy, amphetamine, cocaine, and non-prescribed medications. Analysis of 23 questionnaire items yielded 76 features across four substance use dimensions: demographic attributes, experience and preferences of drug use, health-related aspects of drug use, and social attributes of drug use. In addition to traditional machine learning (ML) approaches previously applied in this field, three sophisticated deep learning models—standard LSTM, BiLSTM, and Recursive LSTM—were employed to evaluate their predictive performance. These LSTM models were further augmented with SHAP analysis to identify the primary influences on substance use patterns. While all these artificial intelligence models demonstrated superior predictive performance, our focus was specifically on the outcomes of the LSTM models due to their novel application in this field. The results underscore the exceptional performance of both LSTM and ML models in unraveling complex substance use behaviors, underlining their applicability in diverse public health contexts. This study not only sheds light on the predictors of substance uses but also furthers methodological innovation in drug research, charting new directions for crafting targeted intervention strategies and policies. The observed variability in predictor significance across different substances indicates the necessity for tailored prevention programs catering to particular user groups. Integrating machine learning with social science and public health policy, our research deepens the understanding of the factors influencing substance use and promotes effective strategies for its mitigation. Despite some limitations, this investigation establishes a foundation for future studies and accentuates the critical role of advanced computational techniques in addressing intricate social issues.
Contact angle for theoretical parameterization of immersion freezing rate inferred from the freezing temperature
Authors: Chang Jun-Jie , Chen Jen-Ping , Liu Pei-Hsin 
Published: December 2024

AbstractThis study derived contact angles for fifteen types of pollens, nine types of fungi, ten types of bacteria, one type of diatom, and twelve types of mineral dust for use in the parameterization of immersion freezing based on the classical nucleation theory (CNT). Our approach is to interpret freezing temperature measurement results with the stochastic nucleation concept. In this way, the abundant freezing temperature data available in the literature can be converted to contact angles that needed in the CNT parameterization for a variety of INPs. The derived contact angles compared well with values independently obtained in earlier studies based on a pure-CNT approach using laboratory nucleation rate data. The uncertainties in contact angle calculation associated with the definition of onset nucleation rate, the activation energy, and the ice-nuclei size are estimated to be about ± 1–2°, ± 1–5°, and ± 1–2°, respectively, among different ice-nucleating particles.
Current pathways model for hall thruster plumes in ground-based vacuum test facilities: measurements and observations
Authors: Jovel David R. , Cabrera Janice D. , Walker Mitchell L. R. 
Published: December 2024

AbstractA previous companion paper introduced a current pathways model that represents the electrical coupling between the Hall effect thruster (HET) and the ground-based vacuum test facility operational environment. In this work, we operated a 7-kW class HET at 4.5 kW, 15 A and 6 kW, 20 A on krypton to quantify aspects of the current pathways model to characterize the role metal vacuum chambers play in the thruster’s discharge circuit as a function of discharge current. During HET operation, far-field ion and electron saturation currents at 47 near-facility wall locations were measured using an array of 5.08 cm diameter, stainless-steel planar electrodes. In addition, the plasma properties at three distinct locations within the facility, 25 cm from the facility wall, were obtained using Langmuir probes. Experimental results show that thruster beam ions do not readily neutralize with cathode electrons and instead neutralize with the free electrons provided by the metallic chamber wall. In addition, significant charge-exchange (CEX) ion current was measured in the background plasma environment and constituted about 23% of the total ion current measured by the planar electrode array. Thus, the metallic vacuum chamber surfaces facilitate charge neutralization for both ion populations. Additionally, the plasma environment near the facility walls was characterized to be non-uniform with an estimated plasma sheath capacitance ranging between 0.45 µF and 1.79 µF. Further analysis shows that the plasma sheath at the facility wall behaves like a parallel RC circuit, potentially concealing the thruster’s AC characteristics. Inherent plasma oscillations give rise to inductive effects with inductances that varied between 76.5 nH and 101.4 nH. Hence, the dynamic characteristics of the HET’s discharge are influenced by the capacitive and inductive effects introduced by the vacuum test facility operational environment.
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
Authors: Huang Wei , Zheng Xingyu , Ma Xudong , Qin Haotong , Lv Chengtao , Chen Hong , Luo Jie , Qi Xiaojuan , Liu Xianglong , Magno Michele 
Published: December 2024

AbstractThe LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3’s capabilities when quantized to low bit-width. This exploration can potentially provide new insights and challenges for the low-bit quantization of LLaMA3 and other future LLMs, especially in addressing performance degradation issues that suffer in LLM compression. Specifically, we comprehensively evaluate the 10 existing post-training quantization and LoRA fine-tuning (LoRA-FT) methods of LLaMA3 on 1-8 bits and various datasets to reveal the low-bit quantization performance of LLaMA3. To uncover the capabilities of low-bit quantized MLLM, we assessed the performance of the LLaMA3-based LLaVA-Next-8B model under 2-4 ultra-low bits with post-training quantization methods. Our experimental results indicate that LLaMA3 still suffers from non-negligible degradation in linguistic and visual contexts, particularly under ultra-low bit widths. This highlights the significant performance gap at low bit-width that needs to be addressed in future developments. We expect that this empirical study will prove valuable in advancing future models, driving LLMs and MLLMs to achieve higher accuracy at lower bit to enhance practicality.
Religious Resistance and the Public’s Health
Authors: Sepper Elizabeth , Wiley Lindsay F. 
Published: December 2024

AbstractThe Affordable Care Act’s preventive services mandate requires private insurance plans to serve public health goals. But the employers that facilitate access to insurance for more than half the population hold political views and economic interests that may run counter to public interests. And now, in the name of for-profit employers’ religious rights, the courts are eroding the legal foundations of privately financed public health. Religious objections to the preventive services mandate — of which Braidwood Management, Inc. v. Becerra is just the most recent high-profile example — have become a site of opposition to public health. Courts have radically revised standards for religious exemption, adopting an individualistic frame that discounts population-level effects. Recent decisions could invite free exercise claims that go to the heart of securing population health through the workplace.