bluuebunny/crossref_metadata_2025_split huggingface dataset:
The aporetic dialogues of Modena on gender differences: Is it all about testosterone? EPISODE I: CRIME
Authors: Brigante Giulia , D'Angelo Giulia , Caccin Vanessa , Coluccia Silvia , Conte Immacolata , Demichelis Veronica Maria , Cecchi Rossana , Simoni Manuela 
Published: November 2024

AbstractThis is the first episode of a series of four discussions on the differences between males and females, in apparently non‐andrological fields. You will read the transcript of discussions that actually took place at the Endocrinology Unit in Modena, Italy, in the form of the aporetic dialogues of ancient Greece. In this episode, the role of testosterone in gender differences in criminal behavior will be explored. The discussants were divided into two groups: group 1, which supports the thesis of a predominant role of testosterone, and group 2, which opposes it. The first group affirmed that both endogenous testosterone and anabolic‐androgenic steroids could trigger aggressive and criminal behavior, regardless of predisposition to psychiatric disease or sociocultural background. The second group asserted the multifactorial genesis of aggressive and criminal behavior, citing other hormonal and non‐hormonal factors, such as neurotransmitters, cortisol, and sociological and psychological aspects. In the end, a forensic physician, acting as a referee, tried to resolve the aporia: are the two theories equivalent or one is superior?
Everyday Bordering and Migrant Schooling Timescapes in Post‐Crisis Athens
Authors: Papoutsi Anna 
Published: November 2024

AbstractThis paper explores the geographies of migrant schooling in Athens following the 2015–2016 crisis, which left thousands of migrants stranded in the country. Drawing on a multi‐sited ethnography of everyday bordering (2017–2018), I examine the school as an ordinary bureaucratic institution and as a physical space in relation to the border. I discuss the processes that rendered the schooling of racialised children a problem to be managed and solved. These migrant schooling timescapes, I argue, were marked by contradictory state logics, and temporalities, and were shaped by context‐specific colonial and racialisation discourses. These tensions shaped the encounters between families on the move and the state throughout the academic year. The paper argues that the school became an everyday space of bordering, controlling membership and reproducing the families’ marginalisation. In this way, it contributes to the literature that highlights the role of ordinary institutions and temporal forms of governance conditioning migrant lives.
Leading while playing: How leader fun pursuit affects leadership perceptions and evaluations
Authors: Zhang Jinghao , Yuan Shuai , Xing Lu (Lucy) , Shao Yan , Deng Yingxin , Li Peikai 
Published: November 2024

AbstractAlthough pursuing fun seems contradictory to work, it may yield beneficial outcomes for not only employees but also leaders in the workplace. The present paper aims to bridge the gap between the workplace fun/play literature and leadership research by introducing the concept of leader fun pursuit and examining its influence on followers' evaluations. Moreover, drawing on the dual perspective model of social evaluation and expectancy violation theory, we examine the effects of leader fun pursuit on followers' perceptions of leader attributes and evaluations of leadership, and posit a gender‐contingent boundary condition. We conducted three studies to develop a scale and examine our hypotheses. Study 1 (four samples, total N = 734) developed a four‐item scale to measure leader fun pursuit and established its reliability and validity. Study 2 (N = 309) used a multi‐wave design to examine the full model. Study 3 (N = 279) used a vignette‐based experiment to strengthen the internal validity of our conceptual model. We found that leader fun pursuit generally enhances followers' evaluations of the leader, and this effect was especially pronounced for female leaders. Overall, we introduce a novel approach to effective leadership and examine the gender differences therein.
The Impact of TMT Stability on Firms' Sustainable Development: ESG Scores as a Constraint or Catalyst for Chinese Heavy‐Polluting Firms?
Authors: Zhou Yan , Xiao Jing , Lan Hailin , Zhou Fuli 
Published: November 2024

ABSTRACTThe impact of environmental, social, and governance (ESG) scores on firm performance (FP) has long been a focus of theoretical and practical debate. We address this controversy by focusing on Chinese heavy‐polluting firms. Drawing on agent theory and threat rigidity theory, we propose that top management team (TMT) stability and external pressures (government environmental punishment and negative media coverage) jointly affect the relationship between ESG scores and FP. Analyzing data from 1392 A‐share listed heavy‐polluting firms in China from 2007 to 2022, our findings reveal that heavy‐polluting firms with higher ESG scores tend to exhibit poorer FP. Notably, this relationship varies based on TMT stability and external pressures. Specifically, TMT stability weakens the negative effect of ESG scores on FP. When external pressures are high, TMT stability negatively impacts the relationship between ESG scores and FP. Conversely, TMT stability positively affects this relationship when external pressures are low. This study identifies the specific conditions in which TMT stability affects from the perspective of agency theory and threat rigidity theory, and provides a new insight for firms and governments in emerging economies to promote ESG practices effectively.
Institutional Imprinting as a Source of Managerial Attention Priority: A Behavioral Theory of Hybrid Organizations
Authors: Zhang Heng , Gong Binglin 
Published: November 2024

ABSTRACTHybrid organizations that pursue both financial and social goals have gained significant attention within the field of corporate social responsibility (CSR). Furthermore, an ongoing concern within hybrid organization research centers on the tensions between multiple goals. We establish a behavioral theory of hybrid organizations based on both the behavioral theory of the firm (BTOF) and imprinting theory to interpret the patterns of simultaneous or sequential pursuit of conflicting goals. Drawing evidence from China's mixed‐ownership enterprises (MOEs), we examine the impact of institutional imprinting on managerial attention priority. Our findings show that originally state‐owned MOEs simultaneously pursue financial and social goals, while originally privately‐owned MOEs sequentially pursue dual goals. Moreover, the persistence and decay of this institutional imprint are affected by internal and external sources of coalition power. By examining how financial performance feedback impacts tensions of dual goals, we contribute to the literature dialogue between hybrid organizations and BTOF.
The Role of Informal Social Networks in Supporting Parents With Learning Disabilities: A Systematic Review
Authors: Threlfall Jennifer , Jobling Hannah , Graham Katherine 
Published: November 2024

ABSTRACTBackgroundParents with learning disabilities are often assessed as lacking the capacity to parent effectively; however, ecological models of parenting indicate that support from informal social networks (ISNs) can help them successfully maintain family life. This systematic review examined evidence regarding (1) parent and child outcomes associated with support from ISNs and (2) perceptions of the roles of ISNs for parents with learning disabilities.MethodsPeer‐reviewed quantitative and qualitative literature was identified through a search of six databases, and a narrative synthesis was subsequently conducted.ResultsTwenty‐six articles were included in the review. Findings of the quantitative synthesis provided preliminary evidence for a positive relationship between support from ISNs and parental mental health, positive parenting practices and child wellbeing. The qualitative synthesis indicated that parents benefited from ISNs through their provision of practical and emotional support and learning skills. Network characteristics that divided supportive from unsupportive members were also identified.ConclusionParents with learning disabilities receive the same positive outcomes from ISNs as other parents, although unique contextual and social challenges may affect their experiences. Future research should explore interventions to build ISNs of parents with learning disabilities that acknowledge their existing strengths as parents and community members.
Genetic Analysis of Heterotaxy in a Consanguineous Cohort
Authors: Al‐Korashy Maarab , Binomar Hadeel , Al‐Mostafa Abeer , Al‐Mogarri Ibrahim , Al‐Oufi Saud , Al‐Admawi Mohamed , Al‐Jufan Mansour , Echahidi Najmeddine , Mokeem Amal , Alfares Ahmed , Ramzan Khushnooda , Tulbah Sahar , Al‐Qahtani Aisha , Takroni Saud , Maddirevula Sateesh , Al‐Hassnan Zuhair 
Published: November 2024

ABSTRACTHeterotaxy (HTX) is a group of clinical conditions with a shared pathology of dislocation of one or more organs along the left–right axis. The etiology of HTX is tremendously heterogeneous spanning environmental factors, chromosomal aberrations, mono/oligogenic variants, and complex inheritance. However, in the vast majority of cases, the etiology of HTX remains elusive. Here, we sought to describe the yield of genetic analysis and spectrum of variants in HTX in our highly consanguineous population. Twenty‐four affected individuals, from 19 unrelated families, were consecutively recruited. Genetic analysis, with exome sequencing, genome sequencing, or multigene panel, detected 9 unique variants, 7 of which were novel, in 8 genes known to be implicated in autosomal recessive form of HTX (C1orf127, CCDC39, CIROP, DNAAF3, DNAH5, DNAH9, MMP21, and MNS1) providing a yield of 42.1%. Of note, 7 of the 9 variants were homozygous, while 2 were inherited in compound heterozygosity, including a heterozygous CNV deletion. A search for candidate genes in negative cases did not reveal a plausible variant. Our work demonstrates a relatively high yield of genetic testing in HTX in a consanguineous population with an enrichment of homozygous variants. The significant genetic heterogeneity observed herewith underscores the complex developmental mechanisms implicated in the pathogenesis of HTX and supports adopting a genome‐wide analysis in the diagnostic evaluation of HTX.
Cinematic Gaussians: Real‐Time HDR Radiance Fields with Depth of Field
Authors: Wang Chao , Wolski Krzysztof , Kerbl Bernhard , Serrano Ana , Bemana Mojtaba , Seidel Hans‐Peter , Myszkowski Karol , Leimkühler Thomas 
Published: November 2024

AbstractRadiance field methods represent the state of the art in reconstructing complex scenes from multi‐view photos. However, these reconstructions often suffer from one or both of the following limitations: First, they typically represent scenes in low dynamic range (LDR), which restricts their use to evenly lit environments and hinders immersive viewing experiences. Secondly, their reliance on a pinhole camera model, assuming all scene elements are in focus in the input images, presents practical challenges and complicates refocusing during novel‐view synthesis. Addressing these limitations, we present a lightweight method based on 3D Gaussian Splatting that utilizes multi‐view LDR images of a scene with varying exposure times, apertures, and focus distances as input to reconstruct a high‐dynamic‐range (HDR) radiance field. By incorporating analytical convolutions of Gaussians based on a thin‐lens camera model as well as a tonemapping module, our reconstructions enable the rendering of HDR content with flexible refocusing capabilities. We demonstrate that our combined treatment of HDR and depth of field facilitates real‐time cinematic rendering, outperforming the state of the art.
LightUrban: Similarity Based Fine‐grained Instancing for Lightweighting Complex Urban Point Clouds
Authors: Lu Z.A. , Xiong W.D. , Ren P. , Jia J.Y. 
Published: November 2024

AbstractLarge‐scale urban point clouds play a vital role in various applications, while rendering and transmitting such data remains challenging due to its large volume, complicated structures, and significant redundancy. In this paper, we present LightUrban, the first point cloud instancing framework for efficient rendering and transmission of fine‐grained complex urban scenes. We first introduce a segmentation method to organize the point clouds into individual buildings and vegetation instances from coarse to fine. Next, we propose an unsupervised similarity detection approach to accurately group instances with similar shapes. Furthermore, a fast pose and size estimation component is applied to calculate the transformations between the representative instance and the corresponding similar instances in each group. By replacing individual instances with their group's representative instances, the data volume and redundancy can be dramatically reduced. Experimental results on large‐scale urban scenes demonstrate the effectiveness of our algorithm. To sum up, our method not only structures the urban point clouds but also significantly reduces data volume and redundancy, filling the gap in lightweighting urban landscapes through instancing.
Inverse Garment and Pattern Modeling with a Differentiable Simulator
Authors: Yu Boyang , Cordier Frederic , Seo Hyewon 
Published: November 2024

AbstractThe capability to generate simulation‐ready garment models from 3D shapes of clothed people will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the digital world. This will have notable impact on fields like shape capture in social VR, and virtual try‐on in the fashion industry. To align with the garment modeling process standardized by the fashion industry and cloth simulation software, it is required to recover 2D patterns, which are then placed around the wearer's body model and seamed prior to the draping simulation. This involves an inverse garment design problem, which is the focus of our work here: Starting with an arbitrary target garment geometry, our system estimates its animatable replica along with its corresponding 2D pattern. Built upon a differentiable cloth simulator, it runs an optimization process that is directed towards minimizing the deviation of the simulated garment shape from the target geometry, while maintaining desirable properties such as left‐to‐right symmetry. Experimental results on various real‐world and synthetic data show that our method outperforms state‐of‐the‐art methods in producing both high‐quality garment models and accurate 2D patterns.
Color‐Accurate Camera Capture with Multispectral Illumination and Multiple Exposures
Authors: Gao H. , Mantiuk R. K. , Finlayson G. D. 
Published: November 2024

AbstractCameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction.
SCARF: Scalable Continual Learning Framework for Memory‐efficient Multiple Neural Radiance Fields
Authors: Wang Yuze , Wang Junyi , Wang Chen , Duan Wantong , Bao Yongtang , Qi Yue 
Published: November 2024

AbstractThis paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi‐layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo‐realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross‐scene weight matrix and a set of scene‐specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo‐realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state‐of‐the‐art rendering quality of continual learning NeRF on NeRF‐Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.
GauLoc: 3D Gaussian Splatting‐based Camera Relocalization
Authors: Xin Zhe , Dai Chengkai , Li Ying , Wu Chenming 
Published: November 2024

Abstract3D Gaussian Splatting (3DGS) has emerged as a promising representation for scene reconstruction and novel view synthesis for its explicit representation and real‐time capabilities. This technique thus holds immense potential for use in mapping applications. Consequently, there is a growing need for an efficient and effective camera relocalization method to complement the advantages of 3DGS. This paper presents a camera relocalization method, namely GauLoc, in a scene represented by 3DGS. Unlike previous methods that rely on pose regression or photometric alignment, our proposed method leverages the differential rendering capability provided by 3DGS. The key insight of our work is the proposed implicit featuremetric alignment, which effectively optimizes the alignment between rendered keyframes and the query frames, and leverages the epipolar geometry to facilitate the convergence of camera poses conditioned explicit 3DGS representation. The proposed method significantly improves the relocalization accuracy even in complex scenarios with large initial camera rotation and translation deviations. Extensive experiments validate the effectiveness of our proposed method, showcasing its potential to be applied in many real‐world applications. Source code will be released at https://github.com/xinzhe11/GauLoc.
Robust Diffusion‐based Motion In‐betweening
Authors: Qin Jia , Yan Peng , An Bo 
Published: November 2024

AbstractThe emergence of learning‐based motion in‐betweening techniques offers animators a more efficient way to animate characters. However, existing non‐generative methods either struggle to support long transition generation or produce results that lack diversity. Meanwhile, diffusion models have shown promising results in synthesizing diverse and high‐quality motions driven by text and keyframes. However, in these methods, keyframes often serve as a guide rather than a strict constraint and can sometimes be ignored when keyframes are sparse. To address these issues, we propose a lightweight yet effective diffusion‐based motion in‐betweening framework that generates animations conforming to keyframe constraints. We incorporate keyframe constraints into the training phase to enhance robustness in handling various constraint densities. Moreover, we employ relative positional encoding to improve the model's generalization on long range in‐betweening tasks. This approach enables the model to learn from short animations while generating realistic in‐betweening motions spanning thousands of frames. We conduct extensive experiments to validate our framework using the newly proposed metrics K‐FID, K‐Diversity, and K‐Error, designed to evaluate generative in‐betweening methods. Results demonstrate that our method outperforms existing diffusion‐based methods across various lengths and keyframe densities. We also show that our method can be applied to text‐driven motion synthesis, offering fine‐grained control over the generated results.
Improving obesity management: Insights from the ACTION Switzerland survey of people with obesity, physicians and dietitians
Authors: Durrer Dominique , Pasi Patrick , Peterli Ralph , Fischer‐Taeschler Doris , Fontana Gabriela , Cavadini Gionata , Gerber Philipp A. 
Published: November 2024

SummaryACTION Switzerland (NCT05232786) examined obesity‐related perceptions, attitudes, behaviours and potential barriers to treatment among people with obesity (PwO) and healthcare professionals (HCPs). In March/April 2022, adult PwO (body mass index ≥30 kg/m2, per self‐reported height/weight) and physicians/certified dietitians who manage PwO in Switzerland completed online surveys in a cross‐sectional design. Overall, 1002 PwO, 125 physicians and 25 dietitians completed the survey. Most physicians (97%) and dietitians (100%), but only 57% of PwO, recognized obesity as a chronic disease. Only 42% of PwO considered themselves to have obesity/extreme obesity, while 61% who had discussed weight with an HCP reported receiving an obesity diagnosis. Many PwO (76%) believed weight loss was entirely their responsibility; physicians were less likely than dietitians to agree it was completely their patients' responsibility (28% vs. 68%). Physicians and dietitians report primarily initiating conversations about weight when patients have obesity‐related comorbidities (85% and 64%); their top reasons for not discussing obesity were patients' perceived lack of motivation (76% and 60%) or interest (72% and 64%) in losing weight. In conclusion, some PwO are not aware that obesity is a chronic disease and incorrectly assume complete responsibility for weight loss. Improved communication between PwO and HCPs is required.