bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Direct Isosurface Rasterization of Scalar Volumes
Authors: Kreskowski A. , Rendle G. , Froehlich B. 
Published: March 2023

AbstractIn 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

AbstractMemory 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

AbstractEvaluating 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

AbstractWe 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

AbstractLatent 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

AbstractExisting 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

AbstractReal‐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

AbstractAdverse 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

AbstractResearch Question/IssueWe investigate the influence of non‐family shareholder governance on Chinese family firms' digital transformation strategies.Research Findings/InsightsWe 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 ImplicationsWe 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 ImplicationsThis 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

AbstractIntroductionIn 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.MethodsA 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.ResultsThirty‐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.ConclusionUtilization 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

AbstractAimTo 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.MethodWe 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.ResultsAccounting 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).InterpretationReduced 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

AbstractThe 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

AbstractMotivationHost 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 containedMultiple 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 grainWorldwide reef cells of 5 by 5 km.Time period and grainLast 3–4 decades, monthly and yearly resolution.Major taxa and level of measurementEnvironmental data important for coral reefs and associated biodiversity.Software formatInteractive 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

AbstractGroundwater 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.