bluuebunny/crossref_metadata_2025_split huggingface dataset:
Achieving organizational environmental citizenship behavior through green transformational leadership: a moderated mediation study
Authors: Priyadarshini Chetna , Chatterjee Namrata , Srivastava Nishit Kumar , Dubey Ritesh Kumar 
Published: March 2023

Purpose
Transformational leadership has caught the significant attention of many academic scholars in the leadership domain. In recent studies, emphasis has been given on green transformational leaders who empower their subordinates, which, in turn, leads to employees’ discretionary behavior toward environmental management of the organization, which has been denoted as organizational environmental citizenship behavior. Organizational citizenship behavior has been strongly advocated as a means to improve the environmental performance of organizations. A green transformational leader encourages his subordinates to undertake activities that are beneficial to the organization’s environmental management, such as thinking about the sustainable development of the organization, solving environmental problems and contributing to the firm’s environmental performance. This study aims to investigate the domain of environmental leadership by examining the mechanism and conditions under which green transformational leaders induce organizational environmental citizenship behavior.

Design/methodology/approach
Information technology (IT) firms across four major cities in India were considered for the survey to examine the proposed hypotheses. Online questionnaires were shared with 1,286 employees working across seven branches of IT companies. A total of 378 respondents completed the survey, but only 293 questionnaires were suitable for further analysis using structural equation modeling.

Findings
The findings of the study show that green transformational leadership promotes green empowerment and organizational citizenship behaviors toward the environment (OCBE) among the employees. It also reveals that environmental passion plays a key role in promoting eco-initiative and eco-helping behavior among the employees when they are empowered for green practices. Also, the influence of resource commitment fosters eco-civic engagement among the employees.

Practical implications
Results hold strong implications for human resource managers on how green transformational leadership approaches can help trigger organizational citizenship behavior among employees. Also, understanding the impact of green empowerment on employees’ involvement in extra-role behavior will help organizations to develop strategies to strengthen their sense of empowerment toward green practices.

Originality/value
This study attempts to investigate the impact of green transformational leadership on employees’ OCBE and the mechanisms through and conditions under which green transformational leadership may impact the indicators of OCBE. The study proposes a mechanism and social and psychological conditions that can potentially explain the linkages between green transformational leadership and OCBE: green empowerment, resource commitment and environmental passion.
A conceptual framework for motivation factors influencing researchers' use of academic web profiles
Authors: Galili Iris Brun , Skov Mette 
Published: March 2023

PurposeThis article provides insight into researchers' use of academic web profiles and an understanding of how the influencing factors highlighted in the literature interact with each other, affecting researchers' motivation to use web profiles.Design/methodology/approachBased on motivation theory and literature related to the use of online profile platforms and academic web profiles, the authors present a conceptual framework for motivation factors influencing researchers' use of academic web profiles. The authors use qualitative interviews with researchers to explore and enrich the conceptual framework.FindingsThe conceptual framework of researchers' motivation space shows the relationships and influences between internal and external motivation in connection to three main categories (Identity and professional goals, Organisation and guidelines, Platforms and technology) and 12 more specific aspects of motivation that all play a role in choices regarding academic online profiles and platforms. Personality also plays an important role in itself – and not always in support of professional goals or workplace guidelines.Originality/valueThe study shows that a holistic perspective is necessary to understand the high degree of complexity in terms of researchers' motivation to use academic online profiles, and the presented conceptual framework can be used to understand and activate motivation factors.
Food protein glycation: A review focusing on stability and in vitro digestive characteristics of oil/water emulsions
Authors: Tang Wei , Zhang Qingchun , Ritzoulis Christos , Walayat Noman , Ding Yuting , Liu Jianhua 
Published: March 2023

AbstractRecently, increasing studies have shown that the functional properties of proteins, including emulsifying properties, antioxidant properties, solubility, and thermal stability, can be improved through glycation reaction under controlled reaction conditions. The use of glycated proteins to stabilize hydrophobic active substances and to explore the gastrointestinal fate of the stabilized hydrophobic substances has also become the hot spot. Therefore, in this review, the effects of glycation on the structure and function of food proteins and the physical stability and oxidative stability of protein‐stabilized oil/water emulsions were comprehensively summarized and discussed. Also, this review sheds lights on the in vitro digestion characteristics and edible safety of emulsion stabilized by glycated protein. It can further serve as a research basis for understanding the role of structural features in the emulsification and stabilization of glycated proteins, as well as their utilization as emulsifiers in the food industry.
Economic freedom and one‐way truck rental prices: An empirical note
Authors: Cardazzi Alexander , Lawson Robert A. 
Published: March 2023

AbstractThis study examines the one‐way truck rental prices for 378 cities. There are large price differentials in one‐way rental prices between city pairs. The pull of people toward higher economic freedom locales and push away from lower economic freedom locales is found to be an important determinant of the city‐pair price differentials.
Fast Numerical Coarsening with Local Factorizations
Authors: He Zhongyun , Pérez Jesús , Otaduy Miguel A. 
Published: March 2023

AbstractNumerical coarsening methods offer an attractive methodology for fast simulation of objects with high‐resolution heterogeneity. However, they rely heavily on preprocessing, and are not suitable when objects undergo dynamic material or topology updates. We present methods that largely accelerate the two main processes of numerical coarsening, namely training data generation and the optimization of coarsening shape functions, and as a result we manage to leverage runtime numerical coarsening under local material updates. To accelerate the generation of training data, we propose a domain‐decomposition solver based on substructuring that leverages local factorizations. To accelerate the computation of coarsening shape functions, we propose a decoupled optimization of smoothness and data fitting. We evaluate quantitatively the accuracy and performance of our proposed methods, and we show that they achieve accuracy comparable to the baseline, albeit with speed‐ups of orders of magnitude. We also demonstrate our methods on example simulations with local material and topology updates.
Stability analysis of explicit MPM
Authors: Bai Song , Schroeder Craig 
Published: March 2023

AbstractIn this paper we analyze the stability of the explicit material point method (MPM). We focus on PIC, APIC, and CPIC transfers using quadratic and cubic splines in two and three dimensions. We perform a fully three‐dimensional Von Neumann stability analysis to study the behavior within the bulk of a material. This reveals the relationship between the sound speed, CFL number, and actual time step restriction and its dependence on discretization options. We note that boundaries are generally less stable than the interior, with stable time steps generally decreasing until the limit when particles become isolated. We then analyze the stability of a single particle to derive a novel time step restriction that stabilizes simulations at their boundaries. Finally, we show that for explicit MPM with APIC or CPIC transfers, there are pathological cases where growth is observed at arbitrarily small time steps sizes. While these cases do not necessarily pose a problem for practical usage, they do suggest that a guarantee of stability may be theoretically impossible and that necessary but not sufficient time step restrictions may be a necessary and practical compromise.
High‐Order Elasticity Interpolants for Microstructure Simulation
Authors: Chan‐Lock Antoine , Pérez Jesús , Otaduy Miguel A. 
Published: March 2023

AbstractWe propose a novel formulation of elastic materials based on high‐order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high‐order interpolants can be regarded as a high‐dimensional extension of radial basis functions, and they allow the interpolation of derivatives of elastic energy, in particular stress and stiffness. Given the proposed parameterization of elasticity models, we devise an algorithm to find optimal model parameters based on training data. We have tested our methodology for the homogenization of 2D microstructures, and we show that it succeeds to match complex behaviors with high accuracy.
Monocular Facial Performance Capture Via Deep Expression Matching
Authors: Bailey Stephen W. , Riviere Jérémy , Mikkelsen Morten , O'Brien James F. 
Published: March 2023

AbstractFacial performance capture is the process of automatically animating a digital face according to a captured performance of an actor. Recent developments in this area have focused on high‐quality results using expensive head‐scanning equipment and camera rigs. These methods produce impressive animations that accurately capture subtle details in an actor's performance. However, these methods are accessible only to content creators with relatively large budgets. Current methods using inexpensive recording equipment generally produce lower quality output that is unsuitable for many applications. In this paper, we present a facial performance capture method that does not require facial scans and instead animates an artist‐created model using standard blendshapes. Furthermore, our method gives artists high‐level control over animations through a workflow similar to existing commercial solutions. Given a recording, our approach matches keyframes of the video with corresponding expressions from an animated library of poses. A Gaussian process model then computes the full animation by interpolating from the set of matched keyframes. Our expression‐matching method computes a low‐dimensional latent code from an image that represents a facial expression while factoring out the facial identity. Images depicting similar facial expressions are identified by their proximity in the latent space. In our results, we demonstrate the fidelity of our expression‐matching method. We also compare animations generated with our approach to animations generated with commercially available software.
Facial Animation with Disentangled Identity and Motion using Transformers
Authors: Chandran Prashanth , Zoss Gaspard , Gross Markus , Gotardo Paulo , Bradley Derek 
Published: March 2023

AbstractWe propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non‐rigid motion during a performance. Our work extends neural 3D morphable models by learning a motion manifold using a transformer architecture. More specifically, we derive a novel transformer‐based autoencoder that can model and synthesize 3D geometry sequences of arbitrary length. This transformer naturally determines frame‐to‐frame correlations required to represent the motion manifold, via the internal self‐attention mechanism. Furthermore, our method disentangles the constant facial identity from the time‐varying facial expressions in a performance, using two separate codes to represent neutral identity and the performance itself within separate latent subspaces. Thus, the model represents identity‐agnostic performances that can be paired with an arbitrary new identity code and fed through our new identity‐modulated performance decoder; the result is a sequence of 3D meshes for the performance with the desired identity and temporal length. We demonstrate how our disentangled motion model has natural applications in performance synthesis, performance retargeting, key‐frame interpolation and completion of missing data, performance denoising and retiming, and other potential applications that include full 3D body modeling.
Detailed Eye Region Capture and Animation
Authors: Kerbiriou Glenn , Avril Quentin , Danieau Fabien , Marchal Maud 
Published: March 2023

AbstractEven if the appearance and geometry of the human eye have been extensively studied during the last decade, the geometrical correlation between gaze direction, eyelids aperture and eyelids shape has not been empirically modeled. In this paper, we propose a data‐driven approach for capturing and modeling the subtle features of the human eye region, such as the inner eye corner and the skin bulging effect due to globe orientation. Our approach consists of an original experimental setup to capture the eye region geometry variations combined with a 3D reconstruction method. Regarding the eye region capture, we scanned 55 participants doing 36 eyes poses. To animate a participant's eye region, we register the different poses to a vertex wise correspondence before blending them in a trilinear fashion. We show that our 3D animation results are visually pleasant and realistic while bringing novel eye features compared to state of the art models.
Point‐augmented bi‐cubic subdivision surfaces
Authors: Karčiauskas K. , Peters J. 
Published: March 2023

AbstractPoint‐Augmented Subdivision (PAS) replaces complex geometry‐dependent guided subdivision, known to yield high‐quality surfaces, by explicit subdivision formulas that yield similarly‐good limit surfaces and are easy to implement using any subdivision infrastructure: map the control net d augmented by a fixed central limit point C, to a finer net (d̃,C) = M(d,C), where the subdivision matrix M is assembled from the provided stencil Tables. Point‐augmented bi‐cubic subdivision improves the state of the art so that bi‐cubic subdivision surfaces can be used in high‐end geometric design: the highlight line distribution for challenging configurations lacks the shape artifacts usually associated with explicit iterative generalized subdivision operators near extraordinary points. Five explicit formulas define Point‐augmented bi‐cubic subdivision in addition to uniform B‐spline knot insertion. Point‐augmented bi‐cubic subdivision comes in two flavors, either generating a sequence of C2‐joined surface rings (PAS2) or C1‐joined rings (PAS1) that have fewer pieces.
SIGDT: 2D Curve Reconstruction
Authors: Marin D. , Ohrhallinger S. , Wimmer M. 
Published: March 2023

AbstractDetermining connectivity between points and reconstructing their shape boundaries are long‐standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to‐date rarely used proximity‐based graph called spheres‐of‐influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece‐wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the Connect2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG‐Connect2D yields the best reconstruction accuracy compared to state‐of‐the‐art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.
MeshFormer: High‐resolution Mesh Segmentation with Graph Transformer
Authors: Li Yuan , He Xiangyang , Jiang Yankai , Liu Huan , Tao Yubo , Hai Lin 
Published: March 2023

AbstractGraph transformer has achieved remarkable success in graph‐based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high‐resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high‐resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary‐preserving simplification is introduced to reduce the data size while maintaining the critical high‐resolution information in segmentation boundaries. Second, a Ricci flow‐based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross‐resolution convolutions, which extracts richer high‐resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real‐world datasets.
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
Authors: Ren Haocheng , Zhang Hao , Zheng Jia , Zheng Jiaxiang , Tang Rui , Huo Yuchi , Bao Hujun , Wang Rui 
Published: March 2023

AbstractWith the rapid development of data‐driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain‐Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine‐grained scene configurations by providing user‐controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.
UTOPIC: Uncertainty‐aware Overlap Prediction Network for Partial Point Cloud Registration
Authors: Chen Zhilei , Chen Honghua , Gong Lina , Yan Xuefeng , Wang Jun , Guo Yanwen , Qin Jing , Wei Mingqiang 
Published: March 2023

AbstractHigh‐confidence overlap prediction and accurate correspondences are critical for cutting‐edge models to align paired point clouds in a partial‐to‐partial manner. However, there inherently exists uncertainty between the overlapping and non‐overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty‐aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation‐invariant geometry‐aware feature representations. With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state‐of‐the‐art methods.
Resolution‐switchable 3D Semantic Scene Completion
Authors: Luo Shoutong , Sun Zhengxing , Sun Yunhan , Wang Yi 
Published: March 2023

AbstractSemantic scene completion (SSC) aims to recover the complete geometric structure as well as the semantic segmentation results from partial observations. Previous works could only perform this task at a fixed resolution. To handle this problem, we propose a new method that can generate results at different resolutions without redesigning and retraining. The basic idea is to decouple the direct connection between resolution and network structure. To achieve this, we convert feature volume generated by SSC encoders into a resolution adaptive feature and decode this feature via point. We also design a resolution‐adapted point sampling strategy for testing and a category‐based point sampling strategy for training to further handle this problem. The encoder of our method can be replaced by existing SSC encoders. We can achieve better results at other resolutions while maintaining the same accuracy as the original resolution results. Code and data are available at https://github.com/lstcutong/ReS-SSC.
USTNet: Unsupervised Shape‐to‐Shape Translation via Disentangled Representations
Authors: Wang Haoran , Li Jiaxin , Telea Alexandru , Kosinka Jiří , Wu Zizhao 
Published: March 2023

AbstractWe propose USTNet, a novel deep learning approach designed for learning shape‐to‐shape translation from unpaired domains in an unsupervised manner. The core of our approach lies in disentangled representation learning that factors out the discriminative features of 3D shapes into content and style codes. Given input shapes from multiple domains, USTNet disentangles their representation into style codes that contain distinctive traits across domains and content codes that contain domain‐invariant traits. By fusing the style and content codes of the target and source shapes, our method enables us to synthesize new shapes that resemble the target style and retain the content features of source shapes. Based on the shared style space, our method facilitates shape interpolation by manipulating the style attributes from different domains. Furthermore, by extending the basic building blocks of our network from two‐class to multi‐class classification, we adapt USTNet to tackle multi‐domain shape‐to‐shape translation. Experimental results show that our approach can generate realistic and natural translated shapes and that our method leads to improved quantitative evaluation metric results compared to 3DSNet. Codes are available at https://Haoran226.github.io/USTNet.