bluuebunny/crossref_metadata_2025_split huggingface dataset:
Testing the input‐process‐output model of public participation
Authors: GALAIS CAROL , FERNÁNDEZ‐MARTÍNEZ JOSÉ LUIS , FONT JOAN , SMITH GRAHAM 
Published: November 2020

AbstractThe characteristics of participatory institutions can be articulated in three main dimensions: input, process and output. The common assumption is that a dependency relationship exists, with process serving as a mediator between input and output. This paper puts the model to a rare empirical test drawing on a unique dataset of 70 Spanish advisory councils. Through a combination of exploratory factor and path analyses, we analyse the dimensionality of input, process and output and investigate the direct and indirect impact of inputs on process and outputs. Our analysis provides evidence that input factors have a direct impact on the output factor transparency, but their impact on effects on policy and participant satisfaction is mediated by the process factor deliberation. Further, the capacity of the public administration to steer the advisory council (wardship) mediates negatively the impact of input variables on transparency. The analysis provides a nuanced account of how different input and process design characteristics of participatory institutions have profound direct and indirect effects on their outputs.
What is food‐to‐food fortification? A working definition and framework for evaluation of efficiency and implementation of best practices
Authors: Kruger Johanita , Taylor John R. N. , Ferruzzi Mario G. , Debelo Hawi 
Published: November 2020

AbstractFood‐to‐food fortification (FtFF) is an emerging food‐based strategy that can complement current strategies in the ongoing fight against micronutrient deficiencies, but it has not been defined or characterized. This review has proposed a working definition of FtFF. Comparison with other main food‐based strategies clearly differentiates FtFF as an emerging strategy with the potential to address multiple micronutrient deficiencies simultaneously, with little dietary change required by consumers. A review of literature revealed that despite the limited number of studies (in vitro and in vivo), the diversity of food‐based fortificants investigated and some contradictory data, there are promising fortificants, which have the potential to improve the amount of bioavailable iron, zinc, and provitamin A from starchy staple foods. These fortificants are typically fruits and vegetables, with high mineral as well as ascorbic acid and β‐carotene contents. However, as the observed improvements in micronutrient bioavailability and status are relatively small, measuring the positive outcomes is more likely to be impactful only if the FtFF products are consumed as regular staples. Considering best practices in implementation of FtFF, raw material authentication and ingredient documentation are critical, especially as the contents of target micronutrients and bioavailability modulators as well as the microbiological quality of the plant‐based fortificants can vary substantially. Also, as there are only few developed supply chains for plant‐based fortificants, procurement of consistent materials may be problematic. This, however, provides the opportunity for value chain development, which can contribute towards the economic growth of communities, or hybrid approaches that leverage traditional premixes to standardize product micronutrient content.
Rapid point‐of‐care testing methods/devices for meat species identification: A review
Authors: Kumar Yogesh , Narsaiah Kairam 
Published: November 2020

AbstractThe authentication of animal species is an important issue due to an increasing trend of adulteration and mislabeling of animal species in processed meat products. Polymerase chain reaction is the most sensitive and specific technique for nucleic acid‐based animal species detection. However, it is a time‐consuming technique that requires costly thermocyclers and sophisticated labs. In recent times, there is a need of on‐site detection by point‐of‐care (POC) testing methods and devices under low‐resource settings. These POC devices must be affordable, sensitive, specific, user‐friendly, rapid and robust, equipment free, and delivered to the end users. POC devices should also confirm the concept of micro total analysis system. This review discusses POC testing methods and devices that have been developed for meat species identification. Recent developments in lateral flow assay‐based devices for the identification of animal species in meat products are also reviewed. Advancements in increasing the efficiency of lateral flow detection are also discussed.
Controlling management to deliver diversity and inclusion: Prospects and limits
Authors: Noon Mike , Ogbonna Emmanuel 
Published: November 2020

AbstractThis study explores the attempts by executives in an organisation with a strategic business case for diversity and inclusion to close the equality implementation gap—between what is espoused and what is achieved—through greater control over managers in order to direct their actions towards pro‐diversity objectives, with a specific focus on gender and ethnicity in the internal human resource development processes. Drawing on agency theory and exploring data from interviews with executives and senior managers in an exemplar case study (the UK division of a multinational professional services organisation), the study shows how increased control through mandatory diversity training, diversity targets and diversity monitoring can push managers towards making progressive steps but is ultimately constrained by the need for management discretion. The actions and counter narratives by managers reveal the limits of control and expose tensions in an approach that combines attitudinal change with behavioural control. The agency of managers means the equality implementation gap can be reduced but never completely closed.
Risk factors for progression and prognosis of primary Sjögren’s syndrome‐associated interstitial lung disease in a Chinese population
Authors: Xu Yuetong , Zhou Junfei , Dong Xin , Guo Xiaojuan , Lu Yuewu , Zheng Yi 
Published: November 2020

AbstractObjectiveThis study explored differences between primary Sjögren's syndrome‐associated interstitial lung disease (pSS‐ILD) patients with and without ILD progression, and analyzed the factors affecting the progression and prognosis of pSS‐ILD.MethodsThis study is a retrospective cohort study which enrolled 113 pSS‐ILD patients hospitalized between 2011 and 2017.ResultsThe 3‐year survival rate of the pSS‐ILD patients was 91.15%, and the 5‐year survival rate was 84.07%. Univariate analysis showed that Raynaud's syndrome, hypoproteinemia, extensive lung involvement, possible usual interstitial pneumonia pattern were risk factors for the progression of ILD in patients with pSS‐ILD, and cyclophosphamide was a protective factor for the progression of ILD in patients with pSS‐ILD. Multiple logistic regression analysis showed that extensive lung involvement (odds ratio 4.143, 95% CI: 1.203‐14.267, P < .05) was an independent risk factor for the progression of pSS‐ILD. Cox hazard analysis showed that pSS‐ILD with hypoproteinemia (hazard ratio [HR] 17.758, 95% CI: 4.753‐66.340, P <‐ .05) and extensive lung involvement (HR 3.450, 95% CI: 1.419‐8.390, P < .05) were associated with worse survival of patients.ConclusionExtensive lung involvement is an independent risk factor for the progression of ILD in patients with pSS‐ILD. Hypoproteinemia and extensive lung involvement are independent risk factors for mortality in patients with pSS‐ILD, after controlling for potentially influential variables.
Oxytocin at elective caesarean delivery: a dose‐finding study in women with obesity
Authors: Peska E. , Balki M. , Maxwell C. , Ye X. Y. , Downey K. , Carvalho J. C. A. 
Published: November 2020

SummaryProphylactic oxytocin administration at the third stage of labour reduces blood loss and the need for additional uterotonic drugs. Obesity is known to be associated with an increased risk of uterine atony and postpartum haemorrhage. It is unknown whether women with obesity require higher doses of oxytocin in order to achieve adequate uterine tone after delivery. The purpose of this study was to establish the bolus dose of oxytocin required to initiate effective uterine contraction in 90% of women with obesity (the ED90) at elective caesarean delivery. We conducted a double‐blind dose‐finding study using the biased coin up‐down design method. Term pregnant women with a BMI ≥ 40 kg.m−2 undergoing elective caesarean delivery under regional anaesthesia were included. Those with conditions predisposing to postpartum haemorrhage were not included. Oxytocin was administered as an intravenous bolus over 1 minute upon delivery of the fetus. With the first woman receiving 0.5 IU, oxytocin doses were administered according to a sequential allocation scheme. The primary outcome measure was satisfactory uterine tone, as assessed by the operating obstetrician 2 minutes after administration of the oxytocin bolus. Secondary outcomes included the need for rescue uterotonic drugs, adverse effects and estimated blood loss. We studied 30 women with a mean (SD) BMI of 52.3 (7.6) kg.m‐2. The ED90 for oxytocin was 0.75 IU (95%CI 0.5–0.93 IU) by isotonic regression and 0.78 IU (95%CI 0.68–0.88 IU) by the Dixon and Mood method. Our results suggest that women with a BMI ≥ 40 kg.m‐2 require approximately twice as much oxytocin as those with a BMI < 40 kg.m‐2, in whom an ED90 of 0.35 IU (95%CI 0.15–0.52 IU) has previously been demonstrated.
X‐rays had little value in diagnosing children’s abnormal skull shapes, and primary care clinicians should refer concerns to specialist teams
Authors: O’Sullivan Hugh , Bracken Shirley , Doyle Jodie , Twomey Eilish , Murray Dylan J. , Kyne Louise 
Published: November 2020

AbstractAimThis study examined the consensus between the primary care radiological diagnosis and specialist clinical diagnosis of abnormal skull shapes in children.MethodsWe performed a retrospective review of children treated at the National Paediatric Craniofacial Centre at Children's Health Ireland, Dublin, Ireland. Group 1 were referred by primary care colleagues concerned about suspected abnormal skull shapes from 1 January 2015 to 30 May 2017. These included cases where they sought specialist confirmation that the skull shape was normal. Group 2 underwent surgery for craniosynostosis from 1 January 2011 to 25 October 2017. The primary care skull X‐ray reports were examined for both groups to see whether they matched the specialist diagnosis.ResultsGroup 1 comprised 300 children, and 59 (20%) had pre‐referral skull X‐rays. The primary care X‐ray reports and specialist diagnoses agreed in 44 (75%) cases, including 19 (43%) who had a normal skull shape. Group 2 comprised 274 children, and 63 (23%) had pre‐referral skull X‐rays. In this group, there was agreement in 41 (65%) diagnoses; however, the primary care X‐ray reports did not diagnose craniosynostosis for the remaining 22 (35%) children.ConclusionX‐rays were of little value in diagnosing abnormal skull shapes, especially craniosynostosis, and primary care clinicians should refer concerns to specialist teams.
Fracture Patterns Design for Anisotropic Models with the Material Point Method
Authors: Cao Wei , Lyu Luan , Ren Xiaohua , Zhang Bob , Yang Zhixin , Wu Enhua 
Published: November 2020

AbstractPhysically plausible fracture animation is a challenging topic in computer graphics. Most of the existing approaches focus on the fracture of isotropic materials. We proposed a frame‐field method for the design of anisotropic brittle fracture patterns. In this case, the material anisotropy is determined by two parts: anisotropic elastic deformation and anisotropic damage mechanics. For the elastic deformation, we reformulate the constitutive model of hyperelastic materials to achieve anisotropy by adding additional energy density functions in particular directions. For the damage evolution, we propose an improved phase‐field fracture method to simulate the anisotropy by designing a deformation‐aware second‐order structural tensor. These two parts can present elastic anisotropy and fractured anisotropy independently, or they can be well coupled together to exhibit rich crack effects. To ensure the flexibility of simulation, we further introduce a frame‐field concept to assist in setting local anisotropy, similar to the fiber orientation of textiles. For the discretization of the deformable object, we adopt a novel Material Point Method(MPM) according to its fracture‐friendly nature. We also give some design criteria for anisotropic models through comparative analysis. Experiments show that our anisotropic method is able to be well integrated with the MPM scheme for simulating the dynamic fracture behavior of anisotropic materials.
Simulation of Arbitrarily‐shaped Magnetic Objects
Authors: Kim Seung‐wook , Han JungHyun 
Published: November 2020

AbstractWe propose a novel method for simulating rigid magnets in a stable way. It is based on analytic solutions of the magnetic vector potential and flux density, which make the magnetic forces and torques calculated using them seldom diverge. Therefore, our magnet simulations remain stable even though magnets are in close proximity or penetrate each other. Thanks to the stability, our method can simulate magnets of any shapes. Another strength of our method is that the time complexities for computing the magnetic forces and torques are significantly reduced, compared to the previous methods. Our method is easily integrated with classic rigid‐body simulators. The experiment results presented in this paper prove the stability and efficiency of our method.
The Layerizing VoxPoint Annular Convolutional Network for 3D Shape Classification
Authors: Wang Tong , Tao Wenyuan , Own Chung‐Ming , Lou Xiantuo , Zhao Yuehua 
Published: November 2020

AbstractAnalyzing the geometric and semantic properties of 3D point cloud data via the deep learning networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. In our study, the authors combine the advantage of voxels and point clouds by presenting a new data form of voxel models, called Layer‐Ring data. This data type can retain the fine description of the 3D data, and keep the high efficiency of feature extraction. After that, based on the Layer‐Ring data, a modern network architecture, called VoxPoint Annular Network (VAN), works on the Layer‐Ring data for the feature extraction and object category prediction. The design idea is based on the edge‐extraction and the coordinate representation for each voxel on the separated layer With the flexible design, our proposed VAN can adapt to the layer's geometric variability and scalability Finally, the extensive experiments and comparisons demonstrate that our approach obtained the notable results with the state‐of‐the‐art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40). Moreover, the tests also proved that 3D shape features could learn efficiently and robustly. All relevant codes will be available at https://github.com/helloFionaQ/Vox‐PointNet.
Human Pose Transfer by Adaptive Hierarchical Deformation
Authors: Zhang Jinsong , Liu Xingzi , Li Kun 
Published: November 2020

AbstractHuman pose transfer, as a misaligned image generation task, is very challenging. Existing methods cannot effectively utilize the input information, which often fail to preserve the style and shape of hair and clothes. In this paper, we propose an adaptive human pose transfer network with two hierarchical deformation levels. The first level generates human semantic parsing aligned with the target pose, and the second level generates the final textured person image in the target pose with the semantic guidance. To avoid the drawback of vanilla convolution that treats all the pixels as valid information, we use gated convolution in both two levels to dynamically select the important features and adaptively deform the image layer by layer. Our model has very few parameters and is fast to converge. Experimental results demonstrate that our model achieves better performance with more consistent hair, face and clothes with fewer parameters than state‐of‐the‐art methods. Furthermore, our method can be applied to clothing texture transfer. The code is available for research purposes at https://github.com/Zhangjinso/PINet_PG.
Personalized Hand Modeling from Multiple Postures with Multi‐View Color Images
Authors: Wang Yangang , Rao Ruting , Zou Changqing 
Published: November 2020

AbstractPersonalized hand models can be utilized to synthesize high quality hand datasets, provide more possible training data for deep learning and improve the accuracy of hand pose estimation. In recent years, parameterized hand models, e.g., MANO, are widely used for obtaining personalized hand models. However, due to the low resolution of existing parameterized hand models, it is still hard to obtain high‐fidelity personalized hand models. In this paper, we propose a new method to estimate personalized hand models from multiple hand postures with multi‐view color images. The personalized hand model is represented by a personalized neutral hand, and multiple hand postures. We propose a novel optimization strategy to estimate the neutral hand from multiple hand postures. To demonstrate the performance of our method, we have built a multi‐view system and captured more than 35 people, and each of them has 30 hand postures. We hope the estimated hand models can boost the research of high‐fidelity parameterized hand modeling in the future. All the hand models are publicly available on www.yangangwang.com.
PointSkelCNN: Deep Learning‐Based 3D Human Skeleton Extraction from Point Clouds
Authors: Qin Hongxing , Zhang Songshan , Liu Qihuang , Chen Li , Chen Baoquan 
Published: November 2020

AbstractA 3D human skeleton plays important roles in human shape reconstruction and human animation. Remarkable advances have been achieved recently in 3D human skeleton estimation from color and depth images via a powerful deep convolutional neural network. However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. In this study, we develop a deep learning‐based approach for 3D human skeleton extraction from point clouds. We convert 3D human skeleton extraction into offset vector regression and human body segmentation via deep learning‐based point cloud contraction. Furthermore, a disambiguation strategy is adopted to improve the robustness of joint points regression. Experiments on the public human pose dataset UBC3V and the human point cloud skeleton dataset 3DHumanSkeleton compiled by the authors show that the proposed approach outperforms the state‐of‐the‐art methods.
Deep Separation of Direct and Global Components from a Single Photograph under Structured Lighting
Authors: Duan Z. , Bieron J. , Peers P. 
Published: November 2020

AbstractWe present a deep learning based solution for separating the direct and global light transport components from a single photograph captured under high frequency structured lighting with a co‐axial projector‐camera setup. We employ an architecture with one encoder and two decoders that shares information between the encoder and the decoders, as well as between both decoders to ensure a consistent decomposition between both light transport components. Furthermore, our deep learning separation approach does not require binary structured illumination, allowing us to utilize the full resolution capabilities of the projector. Consequently, our deep separation network is able to achieve high fidelity decompositions for lighting frequency sensitive features such as subsurface scattering and specular reflections. We evaluate and demonstrate our direct and global separation method on a wide variety of synthetic and captured scenes.
Pixel‐wise Dense Detector for Image Inpainting
Authors: Zhang Ruisong , Quan Weize , Wu Baoyuan , Li Zhifeng , Yan Dong‐Ming 
Published: November 2020

AbstractRecent GAN‐based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts. Moreover, the adversarial loss and reconstruction loss (e.g., ℓ1loss) are combined with tradeoff weights, which are also difficult to tune. In this paper, we propose a novel detection‐based generative framework for image inpainting, which adopts the min‐max strategy in an adversarial process. The generator follows an encoder‐decoder architecture to fill the missing regions, and the detector using weakly supervised learning localizes the position of artifacts in a pixel‐wise manner. Such position information makes the generator pay attention to artifacts and further enhance them. More importantly, we explicitly insert the output of the detector into the reconstruction loss with a weighting criterion, which balances the weight of the adversarial loss and reconstruction loss automatically rather than manual operation. Experiments on multiple public datasets show the superior performance of the proposed framework. The source code is available at https://github.com/Evergrow/GDN_Inpainting.
Diversifying Semantic Image Synthesis and Editing via Class‐ and Layer‐wise VAEs
Authors: Endo Y. , Kanamori Y. 
Published: November 2020

AbstractSemantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class‐ and layer‐wise extension to the variational autoencoder (VAE) framework that allows flexible control over each object class at the local to global levels by learning multiple latent spaces. Furthermore, we demonstrate that our method generates images that are both plausible and more diverse compared to state‐of‐the‐art methods via extensive experiments with real and synthetic datasets in three different domains. We also show that our method enables a wide range of applications in image synthesis and editing tasks.
Interactive Design and Preview of Colored Snapshots of Indoor Scenes
Authors: Fu Qiang , Yan Hai , Fu Hongbo , Li Xueming 
Published: November 2020

AbstractThis paper presents an interactive system for quickly designing and previewing colored snapshots of indoor scenes. Different from high‐quality 3D indoor scene rendering, which often takes several minutes to render a moderately complicated scene under a specific color theme with high‐performance computing devices, our system aims at improving the effectiveness of color theme design of indoor scenes and employs an image colorization approach to efficiently obtain high‐resolution snapshots with editable colors. Given several pre‐rendered, multi‐layer, gray images of the same indoor scene snapshot, our system is designed to colorize and merge them into a single colored snapshot. Our system also assists users in assigning colors to certain objects/components and infers more harmonious colors for the unassigned objects based on pre‐collected priors to guide the colorization. The quickly generated snapshots of indoor scenes provide previews of interior design schemes with different color themes, making it easy to determine the personalized design of indoor scenes. To demonstrate the usability and effectiveness of this system, we present a series of experimental results on indoor scenes of different types, and compare our method with a state‐of‐the‐art method for indoor scene material and color suggestion and offline/online rendering software packages.