bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plastid Genome Assembly Using Long‐read data
Authors: Zhou Wenbin , Armijos Carolina E. , Lee Chaehee , Lu Ruisen , Wang Jeremy , Ruhlman Tracey A. , Jansen Robert K. , Jones Alan M. , Jones Corbin D. 
Published: March 2023

AbstractAlthough plastid genome (plastome) structure is highly conserved across most seed plants, investigations during the past two decades have revealed several disparately related lineages that experienced substantial rearrangements. Most plastomes contain a large inverted repeat and two single‐copy regions, and a few dispersed repeats; however, the plastomes of some taxa harbour long repeat sequences (>300 bp). These long repeats make it challenging to assemble complete plastomes using short‐read data, leading to misassemblies and consensus sequences with spurious rearrangements. Single‐molecule, long‐read sequencing has the potential to overcome these challenges, yet there is no consensus on the most effective method for accurately assembling plastomes using long‐read data. We generated a pipeline,plastidGenomeAssemblyUsingLong‐read data (ptGAUL), to address the problem of plastome assembly using long‐read data from Oxford Nanopore Technologies (ONT) or Pacific Biosciences platforms. We demonstrated the efficacy of the ptGAUL pipeline using 16 published long‐read data sets. We showed that ptGAUL quickly produces accurate and unbiased assemblies using only ~50× coverage of plastome data. Additionally, we deployed ptGAUL to assemble four newJuncus(Juncaceae) plastomes using ONT long reads. Our results revealed many long repeats and rearrangements inJuncusplastomes compared with basal lineages of Poales. The ptGAUL pipeline is available on GitHub:https://github.com/Bean061/ptgaul.
Manager narcissism, target difficulty, and employee dysfunctional behavior
Authors: Shang Ruidi , Wang Zhichao (Alex) , Zu Yafei 
Published: March 2023

AbstractWe examine whether managers' narcissism explains the difficulty of the performance targets that they set for their subordinate employees and the resulting dysfunctional behaviors of these employees. Utilizing a field‐based data set and a government policy change that imposes higher performance standards, we document both direct and indirect associations between manager narcissism and employee dysfunctional behavior. In particular, we find that managers with a higher degree of narcissism respond to the higher performance standards by setting more difficult targets for their subordinates, which in turn lead to more employee dysfunctional behaviors. Furthermore, after controlling for the effect of target difficulty, we find that manager narcissism also has a direct positive association with employee dysfunctional behavior. Our findings contribute to the management accounting literature by documenting that narcissism, a personality trait that is ubiquitous among managers, plays an important role in affecting managers' control choices and the dysfunctional behaviors of lower‐level employees.
Musical preferences of people with profound intellectual and multiple disabilities: A participatory design with proxies
Authors: Rushton Rosie , Kossyvaki Lila , Terlektsi Emmanouela 
Published: March 2023

AbstractBackgroundInclusive research advocates for the involvement of people with intellectual disabilities to be more than passive participants within the research process.MethodsThis study used a participatory design with proxies to consult with 13 people with profound intellectual and multiple disabilities, on their musical preferences. Four instrumental listening tracks were created for consideration. The expected outcome was that people with profound intellectual and multiple disabilities would contribute to the codesign and development of a Musical Play framework. The study took place in England.FindingsProxy reports suggested that elements of music such as the tempo, tonality and instrumentation influenced the participants' responses. Responses were mixed across all tracks, indicating that the musical preferences of people with profound intellectual and multiple disabilities are variable and individualised.ConclusionFurther exploration of how people with profound intellectual and multiple disabilities can contribute to inclusive research is recommended.
Surface‐Only Dynamic Deformables using a Boundary Element Method
Authors: Sugimoto R. , Batty C. , Hachisuka T. 
Published: March 2023

AbstractWe propose a novel surface‐only method for simulating dynamic deformables without the need for volumetric meshing or volumetric integral evaluations. While based upon a boundary element method (BEM)for linear elastodynamics, our method goes beyond simple adoption of BEM by addressing several of its key limitations. We alleviate large displacement artifacts due to linear elasticity by extending BEM with a moving reference frame and surface‐only fictitious forces, so that it only needs to handle deformations. To reduce memory and computational costs, we present a simple and practical method to compress the series of dense matrices required to simulate propagation of elastic waves over time. Furthermore, we explore a constraint enforcement mechanism and demonstrate the applicability of our method to general computer animation problems, such as frictional contact.
Neural3Points: Learning to Generate Physically Realistic Full‐body Motion for Virtual Reality Users
Authors: Ye Yongjing , Liu Libin , Hu Lei , Xia Shihong 
Published: March 2023

AbstractAnimating an avatar that reflects a user's action in the VR world enables natural interactions with the virtual environment. It has the potential to allow remote users to communicate and collaborate in a way as if they met in person. However, a typical VR system provides only a very sparse set of up to three positional sensors, including a head‐mounted display (HMD) and Optionally two hand‐held controllers, making the estimation of the user's full‐body movement a difficult problem. In this work, we present a data‐driven physics‐based method for predicting the realistic full‐body movement of the user according to the transformations of these VR trackers and simulating an avatar character to mimic such user actions in the virtual world in realtime. We train our system using reinforcement learning with carefully designed pretraining processes to ensure the success of the training and the quality of the simulation. We demonstrate the effectiveness of the method with an extensive set of examples.
Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding
Authors: Goel Aman , Men Qianhui , Ho Edmond S. L. 
Published: March 2023

AbstractSynthesizing multi‐character interactions is a challenging task due to the complex and varied interactions between the characters. In particular, precise spatiotemporal alignment between characters is required in generating close interactions such as dancing and fighting. Existing work in generating multi‐character interactions focuses on generating a single type of reactive motion for a given sequence which results in a lack of variety of the resultant motions. In this paper, we propose a novel way to create realistic human reactive motions which are not presented in the given dataset by mixing and matching different types of close interactions. We propose a Conditional Hierarchical Generative Adversarial Network with Multi‐Hot Class Embedding to generate the Mix and Match reactive motions of the follower from a given motion sequence of the leader. Experiments are conducted on both noisy (depth‐based) and high‐quality (MoCap‐based) interaction datasets. The quantitative and qualitative results show that our approach outperforms the state‐of‐the‐art methods on the given datasets. We also provide an augmented dataset with realistic reactive motions to stimulate future research in this area.
Out‐of‐core Extraction of Curve Skeletons for Large Volumetric Models
Authors: Chu Yiyao , Wang Wencheng 
Published: March 2023

AbstractExisting methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out‐of‐core models. Although applying out‐of‐core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off‐centered or changing the topological structure. In this paper, we propose an efficient out‐of‐core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out‐of‐core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out‐of‐core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high‐quality curve skeletons from large volumetric models with small memory usage.
StylePortraitVideo: Editing Portrait Videos with Expression Optimization
Authors: Seo Kwanggyoon , Oh Seoung Wug , Lu Jingwan , Lee Joon‐Young , Kim Seonghyeon , Noh Junyong 
Published: March 2023

AbstractHigh‐quality portrait image editing has been made easier by recent advances in GANs (e.g., StyleGAN) and GAN inversion methods that project images onto a pre‐trained GAN's latent space. However, extending the existing image editing methods, it is hard to edit videos to produce temporally coherent and natural‐looking videos. We find challenges in reproducing diverse video frames and preserving the natural motion after editing. In this work, we propose solutions for these challenges. First, we propose a video adaptation method that enables the generator to reconstruct the original input identity, unusual poses, and expressions in the video. Second, we propose an expression dynamics optimization that tweaks the latent codes to maintain the meaningful motion in the original video. Based on these methods, we build a StyleGAN‐based high‐quality portrait video editing system that can edit videos in the wild in a temporally coherent way at up to 4K resolution.
Real‐Time Video Deblurring via Lightweight Motion Compensation
Authors: Son Hyeongseok , Lee Junyong , Cho Sunghyun , Lee Seungyong 
Published: March 2023

AbstractWhile motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real‐time video deblurring framework consisting of a lightweight multi‐task unit that supports both video deblurring and motion compensation in an efficient way. The multi‐task unit is specifically designed to handle large portions of the two tasks using a single shared network and consists of a multi‐task detail network and simple networks for deblurring and motion compensation. The multi‐task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real‐time deblurring. Moreover, by stacking multiple multi‐task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state‐of‐the‐art deblurring quality of our approach, which runs at a much faster speed compared to previous methods and show practical real‐time performance (30.99dB@30fps measured on the DVD dataset).
A Drone Video Clip Dataset and its Applications in Automated Cinematography
Authors: Ashtari Amirsaman , Jung Raehyuk , Li Mingxiao , Noh Junyong 
Published: March 2023

AbstractDrones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely useful information for cinematography and for applications such as camera path planning to capture aesthetically pleasing shots. To design intelligent camera path planners, learning drone camera motions from these edited videos is essential. However, first, this requires to filter drone clips and extract their camera motions out of these edited videos that commonly contain both drone and non‐drone content. Moreover, existing video search engines return the whole edited video as a semantic search result and cannot return only drone clips inside an edited video. To address this problem, we proposed the first approach that can automatically retrieve drone clips from an unlabeled video collection using high‐level search queries, such as “drone clips captured outdoor in daytime from rural places”. The retrieved clips also contain camera motions, camera view, and 3D reconstruction of a scene that can help develop intelligent camera path planners. To train our approach, we needed numerous examples of edited drone videos. To this end, we introduced the first large‐scale dataset composed of edited drone videos. This dataset is also used for training and validating our drone video filtering algorithm. Both quantitative and qualitative evaluations have confirmed the validity of our method.
Classifier Guided Temporal Supersampling for Real‐time Rendering
Authors: Guo Yu‐Xiao , Chen Guojun , Dong Yue , Tong Xin 
Published: March 2023

AbstractWe present a learning based temporal supersampling algorithm for real‐time rendering. Different from existing learning‐based approaches that adopt an end‐to‐end training of a ‘black‐box’ neural network, we design a ‘white‐box’ solution that first classifies the pixels into different categories and then generates the supersampling result based on classification. Our key observation is that the core problem in temporal supersampling for rendering is to distinguish the pixels that consist of occlusion, aliasing, or shading changes. Samples from these pixels exhibit similar temporal radiance change but require different composition strategies to produce the correct supersampling result. Based on this observation, our method first classifies the pixels into several classes. Based on the classification results, our method then blends the current frame with the warped last frame via a learned weight map to get the supersampling results. We design compact neural networks for each step and develop dedicated loss functions for pixels belonging to different classes. Compared to existing learning based methods, our classifier‐based supersampling scheme takes less computational and memory cost for real‐time supersampling and generates visually compelling temporal supersampling results with fewer flickering artifacts. We evaluate the performance and generality of our method on several rendered game sequences and our method can upsample the rendered frames from 1080P to 2160P in just 13.39ms on a single Nvidia 3090GPU.
Real‐Time Rendering of Eclipses without Incorporation of Atmospheric Effects
Authors: Schneegans S. , Gilg J. , Ahlers V. , Gerndt A. 
Published: March 2023

AbstractIn this paper, we present a novel approach for real‐time rendering of soft eclipse shadows cast by spherical, atmosphereless bodies. While this problem may seem simple at first, it is complicated by several factors. First, the extreme scale differences and huge mutual distances of the involved celestial bodies cause rendering artifacts in practice. Second, the surface of the Sun does not emit light evenly in all directions (an effect which is known as limb darkening). This makes it impossible to model the Sun as a uniform spherical light source. Finally, our intended applications include real‐time rendering of solar eclipses in virtual reality, which require very high frame rates. As a solution to these problems, we precompute the amount of shadowing into an eclipse shadow map, which is parametrized so that it is independent of the position and size of the occluder. Hence, a single shadow map can be used for all spherical occluders in the Solar System. We assess the errors introduced by various simplifications and compare multiple approaches in terms of performance and precision. Last but not least, we compare our approaches to the state‐of‐the‐art and to reference images. The implementation has been published under the MIT license.
Learning Multi‐Scale Deep Image Prior for High‐Quality Unsupervised Image Denoising
Authors: Jiang Hao , Zhang Qing , Nie Yongwei , Zhu Lei , Zheng Wei‐Shi 
Published: March 2023

AbstractRecent methods on image denoising have achieved remarkable progress, benefiting mostly from supervised learning on massive noisy/clean image pairs and unsupervised learning on external noisy images. However, due to the domain gap between the training and testing images, these methods typically have limited applicability on unseen images. Although several attempts have been made to avoid the domain gap issue by learning denoising from singe noisy image itself, they are less effective in handling real‐world noise because of assuming the noise corruptions are independent and zero mean. In this paper, we go step further beyond prior work by presenting a novel unsupervised image denoising framework trained from single noisy image without making any explicit assumptions on the noise statistics. Our approach is built upon the deep image prior (DIP), which enables diverse image restoration tasks. However, as is, the denoising performance of DIP will significantly deteriorate on nonzero‐mean noise and is sensitive to the number of iterations. To overcome this problem, we propose to utilize multi‐scale deep image prior by imposing DIP across different image scales under the constraint of a scale consistency. Experiments on synthetic and real datasets demonstrate that our method performs favorably against the state‐of‐the‐art methods for image denoising.
Fine‐Grained Scene Graph Generation with Overlap Region and Geometrical Center
Authors: Zhao Y. Q. , Jin Z. , Zhao H. Y. , Zhang F. , Tao Z. W. , Dou C. F. , Xu X. H. , Liu D. H. 
Published: March 2023

AbstractScene graph generation refers to the task of identifying the objects and specifically the relationships between the objects from an image. Existing scene graph generation methods generally use the bounding boxes region features of objects to identify the relationships between objects. However, we feel that the overlap region features of two objects may play an important role in fine‐grained relationship identification. In fact, some fine‐grained relationships can only be obtained from the overlap region features of two objects. Therefore, we propose the Multi‐Branch Feature Combination (MFC) module and Overlap Region Transformer (ORT) module to comprehensively obtain the visual features contained in the overlap regions of two objects. Concretely, the MFC module uses deconvolution and multi‐branch dilation convolution to obtain high‐pixels and multi‐receptive field features in the overlap regions. The ORT module uses the vision transformer to obtain the self‐attention of the overlap regions. The joint use of these two modules achieves the mutual complementation of local connectivity properties of convolution and the global connectivity properties of attention. We also design a Geometrical Center Augmented (GCA) module to obtain the relative position information of the geometric centers between two objects, to prevent the problem that only relying on the scale of the overlap region cannot accurately capture the relationship between two objects. Experiments show that our model ORGC (Overlap Region and Geometrical Center), the combination of the MFC module, the ORT module, and the GCA module, can enhance the performance of fine‐grained relation identification. On the Visual Genome dataset, our model outperforms the current state‐of‐the‐art model by 4.4% on the R@50 evaluation metric, reaching a state‐of‐the‐art result of 33.88.
NSTO: Neural Synthesizing Topology Optimization for Modulated Structure Generation
Authors: Zhong Shengze , Punpongsanon Parinya , Iwai Daisuke , Sato Kosuke 
Published: March 2023

AbstractNature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough boundaries that fail to match the requirements for meeting the growing personal fabrication demands and printing capability. Therefore, we propose the neural synthesizing topology optimization that leverages a self‐supervised coordinate‐based network to optimize structures with significantly shorter computation time, where the network encodes the structural material layout as an implicit function of coordinates. Continuous solution space is further generated from optimization tasks under varying boundary conditions or constraints for users' instant inference of novel solutions. We demonstrate the system's efficacy for a broad usage scenario through numerical experiments and 3D printing.
Digital image enhancement may improve sensitivity of cholesteatoma detection during endoscopic ear surgery
Authors: Ragonesi Talisa , Niederhauser Laura , Fernandez Ignacio Javier , Molinari Giulia , Caversaccio Marco , Presutti Livio , Anschuetz Lukas 
Published: March 2023

AbstractObjectivesThis study investigates the possible benefits and limitations of the digital image enhancement systems provided by Storz Professional Image Enhancement System (SPIES) during endoscopic ear surgery (EES) for cholesteatoma. An increased detection of cholesteatoma residuals during the final steps of endoscopic surgery using DIE technology was hypothesized.DesignCross‐sectional study.SettingTertiary referral hospital.MethodsA total of 10 questionnaires of 18 intraoperative pictures with equal numbers of cholesteatoma and non‐cholesteatoma images, each presented in three different image‐enhancing modalities (Clara, Spectra A, Spectra B), were generated. Fifty‐one experienced ear surgeons participated to the survey and were randomly assigned to a questionnaire and completed it at two time points. The experts were asked to rate for each picture whether cholesteatoma was present or not. The answers were compared with the histopathological reports.ResultsClara showed the highest accuracy in cholesteatoma detection, followed by Spectra A and lastly Spectra B. In contrast, Spectra B showed the highest sensitivity and Clara the highest specificity, while Spectra A was placed in the middle for both values. Using the Spectra B modality, most responses agreed across the two time points. Ear surgeons assessed the usefulness, as well as preference among image modalities for cholesteatoma surgery, in the following order: Clara, Spectra B, Spectra A.ConclusionDigital enhancement technologies are applicable to EES. After complete cholesteatoma removal, Spectra B showed the highest sensitivity in the detection of cholesteatoma residuals as compared with Clara and Spectra A. Thus, Spectra B may be recommended to avoid missing any cholesteatoma residuals during EES.