bluuebunny/crossref_metadata_2025 huggingface dataset:
The nexus between tourism development, environmental quality and economic growth. Does renewable energy help in achieving carbon neutrality goal?
Authors: Banga Cathrine , Deka Abraham , Ringim Salim Hamza , Mustapha Abubakar Sadiq , Özdeşer Hüseyin , Kilic Hasan
Published: March 2023

Purpose

The current study aims to ascertain the association between tourism development, economic growth and environmental quality by using the short-run and long-run autoregressive distributive lag model.

Design/methodology/approach

Tourism development has a major role to play in improving a nation’s economic growth. However, it is also blamed for exacerbating environmental pollution because of its massive use of energy (non-renewable energy).

Findings

The major findings of this research show that renewable energy (RE) use and gross domestic product (GDP) negatively impact carbon dioxide (CO2) emissions in South Africa. Tourism arrivals and CO2 emissions negatively impact GDP, while capital positively impacts GDP in the long run.

Practical implications

This research recommends the use of RE, since it reduces carbon emissions, and capital, as it remains the major driver of economic growth.

Originality/value

The originality of the current research is that it uses long-period annual time series data from 1971 to 2019 of South Africa, one of the largest tourist nations in Africa. To the best of the authors’ knowledge, no studies have examined South Africa in this context and minimal research has been conducted to ascertain the impact of the tourism industry on the environment, despite the accusations directed toward it.
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

Purpose

This 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/approach

Based 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.

Findings

The 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/value

The 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

Recently, 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

This 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

Numerical 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

In 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

We 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

Facial 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

We 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

Even 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

Point‐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

Determining 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

Graph 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

With 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

High‐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

Semantic 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

We 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.
Efficient Direct Isosurface Rasterization of Scalar Volumes
Authors: Kreskowski A. , Rendle G. , Froehlich B.
Published: March 2023

In this paper we propose a novel and efficient rasterization‐based approach for direct rendering of isosurfaces. Our method exploits the capabilities of task and mesh shader pipelines to identify subvolumes containing potentially visible isosurface geometry, and to efficiently extract primitives which are consumed on the fly by the rasterizer. As a result, our approach requires little preprocessing and negligible additional memory. Direct isosurface rasterization is competitive in terms of rendering performance when compared with ray‐marching‐based approaches, and significantly outperforms them for increasing resolution in most situations. Since our approach is entirely rasterization based, it affords straightforward integration into existing rendering pipelines, while allowing the use of modern graphics hardware features, such as multi‐view stereo for efficient rendering of stereoscopic image pairs for geometry‐bound applications. Direct isosurface rasterization is suitable for applications where isosurface geometry is highly variable, such as interactive analysis scenarios for static and dynamic data sets that require frequent isovalue adjustment.
Fine‐Grained Memory Profiling of GPGPU Kernels
Authors: Buelow Max von , Guthe Stefan , Fellner Dieter W.
Published: March 2023

Memory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine‐grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state‐of‐the‐art memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications.
Multirate Shading with Piecewise Interpolatory Approximation
Authors: Hu Yiwei , Yuan Yazhen , Wang Rui , Yang Zhuo , Bao Hujun
Published: March 2023

Evaluating shading functions on geometry surfaces dominates the rendering computation. A high‐quality but time‐consuming estimate is usually achieved with a dense sampling rate for pixels or sub‐pixels. In this paper, we leverage sparsely sampled points on vertices of dynamically‐generated subdivision surfaces to approximate the ground‐truth shading signal by piecewise linear reconstruction. To control the introduced interpolation error at runtime, we analytically derive an L∞ error bound and compute the optimal subdivision surfaces based on a user‐specified error threshold. We apply our analysis on multiple shading functions including Lambertian, Blinn‐Phong, Microfacet BRDF and also extend it to handle textures, yielding easy‐to‐compute formulas. To validate our derivation, we design a forward multirate shading algorithm powered by hardware tessellator that moves shading computation at pixels to the vertices of subdivision triangles on the fly. We show our approach significantly reduces the sampling rates on various test cases, reaching a speedup ratio of 134% ~ 283% compared to dense per‐pixel shading in current graphics hardware.
Effective Eyebrow Matting with Domain Adaptation
Authors: Wang Luyuan , Zhang Hanyuan , Xiao Qinjie , Xu Hao , Shen Chunhua , Jin Xiaogang
Published: March 2023

We present the first synthetic eyebrow matting datasets and a domain adaptation eyebrow matting network for learning domain‐robust feature representation using synthetic eyebrow matting data and unlabeled in‐the‐wild images with adversarial learning. Different from existing matting methods that may suffer from the lack of ground‐truth matting datasets, which are typically labor‐intensive to annotate or even worse, unable to obtain, we train the matting network in a semi‐supervised manner using synthetic matting datasets instead of ground‐truth matting data while achieving high‐quality results. Specifically, we first generate a large‐scale synthetic eyebrow matting dataset by rendering avatars and collect a real‐world eyebrow image dataset while maximizing the data diversity as much as possible. Then, we use the synthetic eyebrow dataset to train a multi‐task network, which consists of a regression task to estimate the eyebrow alpha mattes and an adversarial task to adapt the learned features from synthetic data to real data. As a result, our method can successfully train an eyebrow matting network using synthetic data without the need to label any real data. Our method can accurately extract eyebrow alpha mattes from in‐the‐wild images without any additional prior and achieves state‐of‐the‐art eyebrow matting performance. Extensive experiments demonstrate the superior performance of our method with both qualitative and quantitative results.
User‐Controllable Latent Transformer for StyleGAN Image Layout Editing
Authors: Endo Y.
Published: March 2023

Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (e.g., translation and rotation), and it is laborious to identify appropriate latent directions and adjust their parameters. In this paper, we tackle the problem of editing the StyleGAN image layout by annotating the image directly. To do so, we propose an interactive framework for manipulating latent codes in accordance with the user inputs. In our framework, the user annotates a StyleGAN image with locations they want to move or not and specifies a movement direction by mouse dragging. From these user inputs and initial latent codes, our latent transformer based on a transformer encoder‐decoder architecture estimates the output latent codes, which are fed to the StyleGAN generator to obtain a result image. To train our latent transformer, we utilize synthetic data and pseudo‐user inputs generated by off‐the‐shelf StyleGAN and optical flow models, without manual supervision. Quantitative and qualitative evaluations demonstrate the effectiveness of our method over existing methods.
Abstract Painting Synthesis via Decremental optimization
Authors: Yan Ming , Pu Yuanyuan , Zhao Pengzheng , Xu Dan , Wu Hao , Yang Qiuxia , Wang Ruxin
Published: March 2023

Existing stroke‐based painting synthesis methods usually fail to achieve good results with limited strokes because these methods use semantically irrelevant metrics to calculate the similarity between the painting and photo domains. Hence, it is hard to see meaningful semantical information from the painting. This paper proposes a painting synthesis method that uses a CLIP (Contrastive‐Language‐Image‐Pretraining) model to build a semantically‐aware metric so that the cross‐domain semantic similarity is explicitly involved. To ensure the convergence of the objective function, we design a new strategy called decremental optimization. Specifically, we define painting as a set of strokes and use a neural renderer to obtain a rasterized painting by optimizing the stroke control parameters through a CLIP‐based loss. The optimization process is initialized with an excessive number of brush strokes, and the number of strokes is then gradually reduced to generate paintings of varying levels of abstraction. Experiments show that our method can obtain vivid paintings, and the results are better than the comparison stroke‐based painting synthesis methods when the number of strokes is limited.
Semi‐MoreGAN: Semi‐supervised Generative Adversarial Network for Mixture of Rain Removal
Authors: Shen Yiyang , Wang Yongzhen , Wei Mingqiang , Chen Honghua , Xie Haoran , Cheng Gary , Wang Fu Lee
Published: March 2023

Real‐world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI‐supervised Mixture Of rain REmoval Generative Adversarial Network (Semi‐MoreGAN). Unlike most of existing methods, Semi‐MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real‐world rainy and clean images to bridge the gap between synthetic and real‐world rain. Extensive experiments show clear improvements of our approach over twenty representative state‐of‐the‐arts on both synthetic and real‐world rainy images. Source code is available at https://github.com/syy-whu/Semi-MoreGAN.
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Authors: Wang Yongzhen , Yan Xuefeng , Zhang Kaiwen , Gong Lina , Xie Haoran , Wang Fu Lee , Wei Mingqiang
Published: March 2023

Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question – if the combination of image restoration and object detection, can boost the performance of cutting‐edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre‐processing step, TogetherNet considers a multi‐task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real‐world datasets demonstrate that our TogetherNet outperforms the state‐of‐the‐art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yz-wang/TogetherNet.
Non‐family shareholder governance and the digital transformation of family firms: Evidence from China
Authors: Du Shanzhong , Ma Lianfu , Li Zhuo , Ma Chengcheng
Published: March 2023

Research Question/Issue

We investigate the influence of non‐family shareholder governance on Chinese family firms' digital transformation strategies.

Research Findings/Insights

We use the socioemotional wealth theory to examine the impact of non‐family shareholder governance on family firms' digital transformation. We find that the influence of non‐family shareholder governance by appointing directors can significantly promote the implementation of digital transformation strategies through their capital and human effects in family firms. In contrast, non‐family shareholders only play a positive role in family firms' digital transformation when they are blockholders. In addition, non‐family shareholder governance plays a significant role in promoting the digital transformation of family firms with entrepreneurship and a high degree of industry competition. Regarding external digital development, the Broadband China policy complements family firms' digital transformation strategies. The characteristics of the appointed directors suggest that the introduction of appointed directors, an excess of appointed directors, and the inclusion of appointed directors with digital expertise can effectively promote the digital transformation of family firms. The characteristics of controlling families show that socioemotional wealth can further optimize the relationship between non‐family shareholder governance and digital transformation.

Theoretical/Academic Implications

We not only verify the rationality and effectiveness of non‐family shareholder governance in combination with digital transformation strategies but also further observe the impact of the heterogeneity of directors appointed by non‐family shareholders on family firms' digital transformation strategies. Thus, our study tests the influence of non‐family shareholder governance on digital transformation and enriches the literature on non‐family shareholder governance and family firms' strategies.

Practitioner/Policy Implications

This study offers insights to family firms regarding how to effectively promote non‐family shareholders' participation in corporate governance and help family firms to achieve the goal of “everlasting foundation.”
Deceased donor vein extension grafts for right living donor kidney transplantation
Authors: Meinders Andrea M. , Knight Richard , Eagar Todd N. , Hobeika Mark , Podder Hemangshu , Gaber A. Osama , Yi Stephanie G.
Published: March 2023

Introduction

In an effort to maximize living donor kidney utilization, we describe the use of deceased donor vein extension grafts for right‐sided living donor kidneys and report our single‐center experience using this technique.

Methods

A retrospective review of kidney transplant recipients (KTR) who received a right living donor kidney with deceased donor vein extension graft. Recipient demographics, postoperative graft function, and surgical complications were reviewed. Living donor nephrectomies were performed laparoscopically. Vein grafts were obtained from recent deceased donor procurements. End‐to‐end anastomosis of the graft to the renal vein was performed prior to implantation.

Results

Thirty‐eight KTR received a right kidney transplant with deceased donor extension grafts. The median recipient age and BMI were 53.0 years and 29.3 kg/m2. Total 71% were male. Ninety‐five percent of grafts displayed immediate graft function, with two recipients requiring temporary dialysis due to anaphylaxis from induction therapy. Median serum creatinine at two weeks was 1.6 mg/dL and at three months was 1.5 mg/dL. There were no graft thromboses.

Conclusion

Utilization of deceased donor extension grafts for short right renal veins is a simple technique that expands the donor pool for living donor renal transplantation. Our experience resulted in no technical complications and excellent early graft function.
Biomarkers of tic severity in children with Tourette syndrome: Motor cortex inhibition measured with transcranial magnetic stimulation
Authors: Batschelett Mitchell A. , Huddleston David A. , Crocetti Deana , Horn Paul S. , Mostofsky Stewart H. , Gilbert Donald L.
Published: March 2023

Aim

To compare transcranial magnetic stimulation (TMS)‐derived measures of primary motor cortex (M1) physiology between children with and without Tourette syndrome, and to dimensionally analyze TMS measures with Tourette syndrome‐related symptom severity.

Method

We used a cross‐sectional experimental design. Sixty 8‐ to 12‐year‐old children participated (30 with Tourette syndrome: three females, mean age 10 years 10 months, standard deviation [SD] 1 year 3 months; 30 typically developing children: seven females, mean age 10 years 7 months, SD 1 year 3 months). In the group with Tourette syndrome, 15 (one female, mean age 10 years 11 months, SD 1 year 3 months) had comorbid attention‐deficit/hyperactivity disorder (ADHD), rated with the Conners, Third Edition and the parent‐reported ADHD rating scales. Tic severity was rated with the Yale Global Tic Severity Scale and urge severity with the Individualized Premonitory Urge for Tics Scale. M1 short‐interval cortical inhibition (SICI) and intracortical facilitation were compared between diagnostic groups and, within the group with Tourette syndrome, correlated with symptom severity using linear mixed‐effects models for repeated measures.

Results

Accounting for ADHD, we found no difference in SICI or intracortical facilitation in those with Tourette syndrome versus typically developing children (p > 0.1). In the group with Tourette syndrome, reduced M1 SICI predicted greater total (p = 0.012) and global (p = 0.002) tic severity. There were no associations with urge severity (p > 0.5).

Interpretation

Reduced M1 SICI is robustly associated with increased tic, but not urge, severity.

What this paper adds

Increased tic severity is associated with reduced motor cortex short‐interval cortical inhibition (SICI).

Children with Tourette syndrome with increased urge severity also show increased tic severity.

However, reduced motor cortex SICI is associated with tic, but not urge, severity.
Inshore migration of Japanese eel Anguilla japonica encouraged by active horizontal swimming during the glass eel stage
Authors: Karaki Tatsuro , Sakamoto Kei , Yamanaka Goro , Kimura Shingo , Kasai Akihide
Published: March 2023

The biomass of the Japanese eel (Anguilla japonica) is generally determined by the recruitment of glass eels into freshwater habitats, but the behavioral biology of their inshore migration remains unknown. With the aid of an ocean prediction system, we elucidated a recruitment migration scenario that can quantitatively reproduce a regional difference in biomass in Japan, which was previously estimated by an environmental DNA sampling method. For their successfully reaching shores, it is necessary to incorporate behavioral changes of glass eels within their migration on the Kuroshio Current, such as shallower depth preferences and horizontal swimming toward lower salinity water. In particular, the latter is essential for encouraging recruitment into both the Seto Inland Sea, with a relatively high ratio (20%–30%) of the total recruitment to Japan and the coasts in the central part of the Pacific side of northern Japan (i.e., the northern limit of the habitable distribution), manifesting that glass eels actively swim toward freshwater near coastal regions. In the subsurface layer, where glass eels mainly conduct diel vertical migration, there is a bifurcation path connecting the Kuroshio Current to the second and third branches of the Tsushima Warm Current, restricting the recruitment of glass eels into the Sea of Japan side of the main inland in Japan. The simulated recruitment validated that the eDNA acts as a proxy indicator for estimating the relative biomass on the regional scale. The simulation supported that the inshore migration of glass eels is determined by active horizontal swimming.
The Reef Environment Centralized InFormation System (RECIFS): An integrated geo‐environmental database for coral reef research and conservation
Authors: Selmoni Oliver , Lecellier Gaël , Berteaux‐Lecellier Véronique , Joost Stéphane
Published: March 2023

Motivation

Host to intricate networks of marine species, coral reefs are among the most biologically diverse ecosystems on Earth. Over the past few decades, major degradations of coral reefs have been observed worldwide, which is largely attributed to the effects of climate change and local stressors related to human activities. Now more than ever, characterizing how the environment shapes the dynamics of the reef ecosystem (e.g., shifts in species abundance, community changes, emergence of locally adapted populations) is key to uncovering the environmental drivers of reef degradation, and developing efficient conservation strategies in response. To achieve these objectives, it is pivotal that environmental data describing the processes driving such ecosystem dynamics, which occur across specific spatial and temporal scales, are easily accessible to coral reef researchers and conservation stakeholders alike.

Main types of variable contained

Multiple environmental variables characterizing various facets of the reef environment, including water chemistry and physics (e.g., temperature, pH, chlorophyll concentration), local anthropogenic pressures (e.g., boat traffic, distance from agricultural or urban areas) and sea currents patterns.

Spatial location and grain

Worldwide reef cells of 5 by 5 km.

Time period and grain

Last 3–4 decades, monthly and yearly resolution.

Major taxa and level of measurement

Environmental data important for coral reefs and associated biodiversity.

Software format

Interactive web application available at https://recifs.epfl.ch.
Modeling Dissolution of Soluble Compounds from Multicomponent NAPL Using a Desorption Approximation
Authors: Gefell Michael J. , Gurung Deviyani
Published: March 2023

Groundwater professionals require methods to estimate the potential time required to achieve remedial goals, including locations within and downgradient of zones containing nonaqueous phase liquids (NAPLs). NAPLs have long been recognized as persistent contaminant sources to groundwater. Dissolution of multicomponent NAPLs is particularly complex, and numerical models that explicitly simulate it are not widely available. This study introduces an equilibrium partitioning approximation to simulate the dissolution of the most soluble chemical components from multicomponent NAPL containing a significant fraction of relatively insoluble mass. The effective distribution coefficient that describes depletion of a specific compound from NAPL is estimated based on the properties of the NAPL and the porous medium. This study also presents numerical modeling results that support the utility of the method, with verification using published empirical data collected during dissolution of residual coal tar in a controlled laboratory sand tank experiment. The numerical modeling method uses equilibrium partitioning as an approximation and matched the concentrations of the two most soluble NAPL components in and downgradient of the NAPL zone with reasonable accuracy. The results suggest that the method should be useful for screening‐level assessments and can be adapted to compare relative groundwater restoration timeframes of select NAPL components for various remedial alternatives.