bluuebunny/crossref_metadata_2025 huggingface dataset:
Complexity Analysis of Time Series Based on Generalized Fractional Order Refined Composite Multiscale Dispersion Entropy
Authors: Wang Yu , Shang Pengjian
Published: November 2020

Based on the dispersion entropy model, combined with multiscale analysis method and fractional order information entropy theory, this paper proposes new models — the generalized fractional order multiscale dispersion entropy (GMDE) and the generalized fractional order refined composite multiscale dispersion entropy (GRCMDE). The new models take the amplitude value information of the sequence itself into consideration, which can make better use of some key information in the sequence and have a higher stability and accuracy. In addition, extending the algorithm to generalized fractional order can make the model better capture the small evolution of the signal data, which is more advantageous for studying the dynamic characteristics of complex systems. This paper verifies the effectiveness of the new models by combining theoretical analysis with empirical research, and further studies the complexity of the financial system and the nature of its multiple time scales. The results show that the proposed GMDE, GRCMDE can better detect the intrinsic nature of financial time series and can distinguish the financial market complexity of different countries.
SARS-CoV-2 and Assisted Reproductive Technology Practice: An Asia Pacific Initiative on Reproduction (ASPIRE) Position Paper
Authors: Wiweko Budi , Ho Tuong M. , Li Rong , Li Tin-Chiu , Tzeng Chii-Ruey , Kovacs Gab , Tanaka Atsushi , Lee Jung Ryeol , Pai Hrishikesh , Khan Haroon Latif ,   on behalf of the Asia Pacific Initiative on Reproduction (ASPIRE)
Published: November 2020

Background: Asia Pacific Initiative on Reproduction (ASPIRE) aims to improve knowledge and awareness of Assisted Reproductive Technology (ART) and infertility-related services, with the aim of improving the quality of patient care.

Methods: A survey was developed and responded by a group of 10 ASPIRE board members to gather in-depth information about current practices, recommendations, and perceptions about SARS-CoV-2 and ART. The collected data were summarized and individual responses pooled among questions requiring voting. The overall rates were computed by response category. The group discussed the summary evidence, until a consensus was reached concerning a series of recommendation on how to make decisions concerning ART service provision during the current (and any future) pandemic. A two-tier recommendation was developed based on agreement rate and implementation priority. Tier 1 comprises recommendations in which the rate of “absolutely agree” responses were [Formula: see text]60%, whereas tier 2 refers to recommendations in which the rate of “absolutely agree” plus “agree” was >60%, but the rate of “absolutely agree” was [Formula: see text]50%.

Results: The survey was responded by all participants between July 24 and July 30, 2020. Nine tier 1 and five tier 2 recommendations are provided concerning prevention, testing, personal protective equipment, informed consent, and quality management. The former indicates the situations in which most individuals should receive the intervention/procedure, whereas the latter relates to those that may be suitable for individual clinics and patients.

Conclusions: This document provides the ASPIRE viewpoint on better managing infertile patients seeking ART during the COVID-19 pandemic. This expert opinion guide aims to help both competent authorities and healthcare providers to deliver quality and safe ART.
Constraining dense hand surface tracking with elasticity
Authors: Smith Breannan , Wu Chenglei , Wen He , Peluse Patrick , Sheikh Yaser , Hodgins Jessica K. , Shiratori Takaaki
Published: November 2020

Many of the actions that we take with our hands involve self-contact and occlusion: shaking hands, making a fist, or interlacing our fingers while thinking. This use of of our hands illustrates the importance of tracking hands through self-contact and occlusion for many applications in computer vision and graphics, but existing methods for tracking hands and faces are not designed to treat the extreme amounts of self-contact and self-occlusion exhibited by common hand gestures. By extending recent advances in vision-based tracking and physically based animation, we present the first algorithm capable of tracking high-fidelity hand deformations through highly self-contacting and self-occluding hand gestures, for both single hands and two hands. By constraining a vision-based tracking algorithm with a physically based deformable model, we obtain an algorithm that is robust to the ubiquitous self-interactions and massive self-occlusions exhibited by common hand gestures, allowing us to track two hand interactions and some of the most difficult possible configurations of a human hand.
Bijective projection in a shell
Authors: Jiang Zhongshi , Schneider Teseo , Zorin Denis , Panozzo Daniele
Published: November 2020

We introduce an algorithm to convert a self-intersection free, orientable, and manifold triangle mesh
T
into a
generalized prismatic shell
equipped with a bijective projection operator to map
T
to a class of discrete surfaces contained within the shell whose normals satisfy a simple local condition. Properties can be robustly and efficiently transferred between these surfaces using the prismatic layer as a common parametrization domain.

The combination of the prismatic shell construction and corresponding projection operator is a robust building block readily usable in many downstream applications, including the solution of PDEs, displacement maps synthesis, Boolean operations, tetrahedral meshing, geometric textures, and nested cages.
iOrthoPredictor
Authors: Yang Lingchen , Shi Zefeng , Wu Yiqian , Li Xiang , Zhou Kun , Fu Hongbo , Zheng Youyi
Published: November 2020

In this paper, we present iOrthoPredictor, a novel system to visually predict teeth alignment in photographs. Our system takes a frontal face image of a patient with visible malpositioned teeth along with a corresponding 3D teeth model as input, and generates a facial image with aligned teeth, simulating a real orthodontic treatment effect. The key enabler of our method is an effective disentanglement of an explicit representation of the teeth geometry from the in-mouth appearance, where the accuracy of teeth geometry transformation is ensured by the 3D teeth model while the in-mouth appearance is modeled as a latent variable. The disentanglement enables us to achieve fine-scale geometry control over the alignment while retaining the original teeth appearance attributes and lighting conditions. The whole pipeline consists of three deep neural networks: a U-Net architecture to explicitly extract the 2D teeth silhouette maps representing the teeth geometry in the input photo, a novel multilayer perceptron (MLP) based network to predict the aligned 3D teeth model, and an encoder-decoder based generative model to synthesize the in-mouth appearance conditional on the original teeth appearance and the aligned teeth geometry. Extensive experimental results and a user study demonstrate that iOrthoPredictor is effective in qualitatively predicting teeth alignment, and applicable to the orthodontic industry.
Opening and closing surfaces
Authors: Sellán Silvia , Kesten Jacob , Sheng Ang Yan , Jacobson Alec
Published: November 2020

We propose a new type of curvature flow for curves in 2D and surfaces in 3D. The flow is inspired by the mathematical morphology
opening
and
closing
operations. These operations are classically defined by composition of dilation and erosion operations. In practice, existing methods implemented this way will result in re-discretizing the entire shape, even if some parts of the surface do not change. Instead, our surface-only curvature-based flow moves the surface selectively in areas that should be repositioned. In our triangle mesh discretization, vertices in regions unaffected by the opening or closing will remain exactly in place and do not affect our method's complexity, which is output-sensitive.
Continuous curve textures
Authors: Tu Peihan , Wei Li-Yi , Yatani Koji , Igarashi Takeo , Zwicker Matthias
Published: November 2020

Repetitive patterns are ubiquitous in natural and human-made objects, and can be created with a variety of tools and methods. Manual authoring provides unmatched degree of freedom and control, but can require significant artistic expertise and manual labor. Computational methods can automate parts of the manual creation process, but are mainly tailored for discrete pixels or elements instead of more general continuous structures. We propose an example-based method to synthesize continuous curve patterns from exemplars. Our main idea is to extend prior sample-based discrete element synthesis methods to consider not only sample positions (geometry) but also their connections (topology). Since continuous structures can exhibit higher complexity than discrete elements, we also propose robust, hierarchical synthesis to enhance output quality. Our algorithm can generate a variety of continuous curve patterns fully automatically. For further quality improvement and customization, we also present an autocomplete user interface to facilitate interactive creation and iterative editing. We evaluate our methods and interface via different patterns, ablation studies, and comparisons with alternative methods.
QuickETC2
Authors: Nah Jae-Ho
Published: November 2020

Compressed textures are indispensable in most 3D graphics applications to reduce memory traffic and increase performance. For higher-quality graphics, the number and size of textures in an application have continuously increased. Additionally, the ETC2 texture format, which is mandatory in OpenGL ES 3.0, OpenGL 4.3, and Android 4.3 (and later versions), requires more complex texture compression than the traditional ETC1 format. As a result, texture compression becomes more and more time-consuming.

To accelerate ETC2 compression, we introduce two new compression techniques, named QuickETC2. The first technique is an early compression-mode decision scheme. Instead of testing all ETC1/2 modes to compress a texel block, we select proper modes for each block by exploiting the luma difference of the block to reduce unnecessary compression overhead. The second technique is a fast luma-based T- and H-mode compression method. When clustering each texel into two groups, we replace the 3D RGB space with the 1D luma space and quickly find the two groups that have the minimum luma differences. We also selectively perform the T- or H-mode and reduce its distance candidates, according to the luma differences of each group. We have implemented both techniques with AVX2 intrinsics to exploit SIMD parallelism. According to our experiments, QuickETC2 can compress more than 2000 1K×1K-sized images per second on an octa-core CPU.
Path tracing estimators for refractive radiative transfer
Authors: Pediredla Adithya , Chalmiani Yasin Karimi , Scopelliti Matteo Giuseppe , Chamanzar Maysamreza , Narasimhan Srinivasa , Gkioulekas Ioannis
Published: November 2020

Rendering radiative transfer through media with a heterogeneous refractive index is challenging because the continuous refractive index variations result in light traveling along curved paths. Existing algorithms are based on photon mapping techniques, and thus are biased and result in strong artifacts. On the other hand, existing unbiased methods such as path tracing and bidirectional path tracing cannot be used in their current form to simulate media with a heterogeneous refractive index. We change this state of affairs by deriving unbiased path tracing estimators for this problem. Starting from the refractive radiative transfer equation (RRTE), we derive a path-integral formulation, which we use to generalize path tracing with next-event estimation and bidirectional path tracing to the heterogeneous refractive index setting. We then develop an optimization approach based on fast analytic derivative computations to produce the point-to-point connections required by these path tracing algorithms. We propose several acceleration techniques to handle complex scenes (surfaces and volumes) that include participating media with heterogeneous refractive fields. We use our algorithms to simulate a variety of scenes combining heterogeneous refraction and scattering, as well as tissue imaging techniques based on ultrasonic virtual waveguides and lenses. Our algorithms and publicly-available implementation can be used to characterize imaging systems such as refractive index microscopy, schlieren imaging, and acousto-optic imaging, and can facilitate the development of inverse rendering techniques for related applications.
Monster mash
Authors: Dvorožňák Marek , Sýkora Daniel , Curtis Cassidy , Curless Brian , Sorkine-Hornung Olga , Salesin David
Published: November 2020

We present a new framework for sketch-based modeling and animation of 3D organic shapes that can work entirely in an intuitive 2D domain, enabling a playful, casual experience. Unlike previous sketch-based tools, our approach does not require a tedious part-based multi-view workflow with the explicit specification of an animation rig. Instead, we combine 3D inflation with a novel rigidity-preserving, layered deformation model, ARAP-L, to produce a smooth 3D mesh that is immediately ready for animation. Moreover, the resulting model can be animated from a single viewpoint --- and without the need to handle unwanted inter-penetrations, as required by previous approaches. We demonstrate the benefit of our approach on a variety of examples produced by inexperienced users as well as professional animators. For less experienced users, our single-view approach offers a simpler modeling and animating experience than working in a 3D environment, while for professionals, it offers a quick and casual workspace for ideation.
MeshWalker
Authors: Lahav Alon , Tal Ayellet
Published: November 2020

Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics---a triangular mesh---and ask how it can be utilized within deep learning. The few attempts to answer this question propose to adapt convolutions & pooling to suit
Convolutional Neural Networks (CNNs).
This paper proposes a very different approach, termed
MeshWalker
to learn the shape directly from a given mesh. The key idea is to represent the mesh by random walks along the surface, which "explore" the mesh's geometry and topology. Each walk is organized as a list of vertices, which in some manner imposes regularity on the mesh. The walk is fed into a
Recurrent Neural Network (RNN)
that "remembers" the history of the walk. We show that our approach achieves state-of-the-art results for two fundamental shape analysis tasks: shape classification and semantic segmentation. Furthermore, even a very small number of examples suffices for learning. This is highly important, since large datasets of meshes are difficult to acquire.
X-Fields
Authors: Bemana Mojtaba , Myszkowski Karol , Seidel Hans-Peter , Ritschel Tobias
Published: November 2020

We suggest to represent an X-Field ---a set of 2D images taken across different view, time or illumination conditions, i.e., video, lightfield, reflectance fields or combinations thereof---by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
Semi-analytic boundary handling below particle resolution for smoothed particle hydrodynamics
Authors: Winchenbach Rene , Akhunov Rustam , Kolb Andreas
Published: November 2020

In this paper, we present a novel semi-analytical boundary handling method for spatially adaptive and divergence-free smoothed particle hydrodynamics (SPH) simulations, including two-way coupling. Our method is consistent under varying particle resolutions and allows for the treatment of boundary features below the particle resolution. We achieve this by first introducing an analytic solution to the interaction of SPH particles with planar boundaries, in 2D and 3D, which we extend to arbitrary boundary geometries using signed distance fields (SDF) to construct locally planar boundaries. Using this boundary-integral-based approach, we can directly evaluate boundary contributions, for any quantity, allowing an easy integration into state of the art simulation methods. Overall, our method improves interactions with small boundary features, readily handles spatially adaptive fluids, preserves particle-boundary interactions across varying resolutions, can directly be implemented in existing SPH methods, and, for non-adaptive simulations, provides a reduction in memory consumption as well as an up to 2× speedup relative to current particle-based boundary handling approaches.
Discovering pattern structure using differentiable compositing
Authors: Reddy Pradyumna , Guerrero Paul , Fisher Matt , Li Wilmot , Mitra Niloy J.
Published: November 2020

Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal. When a pattern is encoded as a flat image without the underlying structure, manually editing the pattern is tedious and challenging as one has to both preserve the individual element shapes and their original relative arrangements. State-of-the-art deep learning frameworks that operate at the pixel level are unsuitable for manipulating such patterns. Specifically, these methods can easily disturb the shapes of the individual elements or their arrangement, and thus fail to preserve the latent structures of the input patterns. We present a novel differentiable
compositing
operator using pattern elements and use it to discover structures, in the form of a layered representation of graphical objects, directly from raw pattern images. This operator allows us to adapt current deep learning based image methods to effectively handle patterns. We evaluate our method on a range of patterns and demonstrate superiority in the context of pattern manipulations when compared against state-of-the-art pixel- or point-based alternatives.
Shape approximation by developable wrapping
Authors: Ion Alexandra , Rabinovich Michael , Herholz Philipp , Sorkine-Hornung Olga
Published: November 2020

We present an automatic tool to approximate curved geometries with piece-wise developable surfaces. At the center of our work is an algorithm that wraps a given 3D input surface with multiple developable patches, each modeled as a discrete orthogonal geodesic net. Our algorithm features a global optimization routine for effectively finding the placement of the developable patches. After wrapping the mesh, we use these patches and a non-linear projection step to generate a surface that approximates the original input, but is also amendable to simple and efficient fabrication techniques thanks to being piecewise developable. Our algorithm allows users to steer the trade-off between approximation power and the number of developable patches used. We demonstrate the effectiveness of our approach on a range of 3D shapes. Compared to previous approaches, our results exhibit a smaller or comparable error with fewer patches to fabricate.
A wave optics based fiber scattering model
Authors: Xia Mengqi (Mandy) , Walter Bruce , Michielssen Eric , Bindel David , Marschner Steve
Published: November 2020

Existing fiber scattering models in rendering are all based on tracing rays through fiber geometry, but for small fibers diffraction and interference are non-negligible, so relying on ray optics can result in appearance errors. This paper presents the first wave optics based fiber scattering model, introducing an azimuthal scattering function that comes from a full wave simulation. Solving Maxwell's equations for a straight fiber of constant cross section illuminated by a plane wave reduces to solving for a 3D electromagnetic field in a 2D domain, and our fiber scattering simulator solves this 2.5D problem efficiently using the boundary element method (BEM). From the resulting fields we compute extinction, absorption, and far-field scattering distributions, which we use to simulate shadowing and scattering by fibers in a path tracer. We validate our path tracer against the wave simulation and the simulation against a measurement of diffraction from a single textile fiber. Our results show that our approach can reproduce a wide range of fibers with different sizes, cross sections, and material properties, including textile fibers, animal fur, and human hair. The renderings include color effects, softening of sharp features, and strong forward scattering that are not predicted by traditional ray-based models, though the two approaches produce similar appearance for complex fiber assemblies under many conditions.
Computational design of cold bent glass façades
Authors: Gavriil Konstantinos , Guseinov Ruslan , Pérez Jesús , Pellis Davide , Henderson Paul , Rist Florian , Pottmann Helmut , Bickel Bernd
Published: November 2020

Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass façades. They are produced by attaching planar glass sheets to curved frames and must keep the occurring stress within safe limits. However, it is very challenging to navigate the design space of cold bent glass panels because of the fragility of the material, which impedes the form finding for practically feasible and aesthetically pleasing cold bent glass façades. We propose an interactive, data-driven approach for designing cold bent glass façades that can be seamlessly integrated into a typical architectural design pipeline. Our method allows non-expert users to interactively edit a parametric surface while providing real-time feedback on the deformed shape and maximum stress of cold bent glass panels. The designs are automatically refined to minimize several fairness criteria, while maximal stresses are kept within glass limits. We achieve interactive frame rates by using a differentiable Mixture Density Network trained from more than a million simulations. Given a curved boundary, our regression model is capable of handling multistable configurations and accurately predicting the equilibrium shape of the panel and its corresponding maximal stress. We show that the predictions are highly accurate and validate our results with a physical realization of a cold bent glass surface.
Freeform quad-based kirigami
Authors: Jiang Caigui , Rist Florian , Pottmann Helmut , Wallner Johannes
Published: November 2020

Kirigami, the traditional Japanese art of paper cutting and folding generalizes origami and has initiated new research in material science as well as graphics. In this paper we use its capabilities to perform geometric modeling with corrugated surface representations possessing an isometric unfolding into a planar domain after appropriate cuts are made. We initialize our box-based kirigami structures from orthogonal networks of curves, compute a first approximation of their unfolding via mappings between meshes, and complete the process by global optimization. Besides the modeling capabilities we also study the interesting geometry of special kirigami structures from the theoretical side. This experimental paper strives to relate unfoldable checkerboard arrangements of boxes to principal meshes, to the transformation theory of discrete differential geometry, and to a version of the Gauss theorema egregium.
Learned hardware-in-the-loop phase retrieval for holographic near-eye displays
Authors: Chakravarthula Praneeth , Tseng Ethan , Srivastava Tarun , Fuchs Henry , Heide Felix
Published: November 2020

Holography is arguably the most promising technology to provide wide field-of-view compact eyeglasses-style near-eye displays for augmented and virtual reality. However, the image quality of existing holographic displays is far from that of current generation conventional displays, effectively making today's holographic display systems impractical. This gap stems predominantly from the severe deviations in the idealized approximations of the "unknown" light transport model in a real holographic display, used for computing holograms.

In this work, we depart from such approximate "ideal" coherent light transport models for computing holograms. Instead, we learn the deviations of the real display from the ideal light transport from the images measured using a display-camera hardware system. After this unknown light propagation is learned, we use it to compensate for severe aberrations in real holographic imagery. The proposed hardware-in-the-loop approach is robust to spatial, temporal and hardware deviations, and improves the image quality of existing methods qualitatively and quantitatively in SNR and perceptual quality. We validate our approach on a holographic display prototype and show that the method can fully compensate unknown aberrations and erroneous and non-linear SLM phase delays, without explicitly modeling them. As a result, the proposed method significantly outperforms existing state-of-the-art methods in simulation and experimentation - just by observing captured holographic images.
Modular primitives for high-performance differentiable rendering
Authors: Laine Samuli , Hellsten Janne , Karras Tero , Seol Yeongho , Lehtinen Jaakko , Aila Timo
Published: November 2020

We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.
A novel discretization and numerical solver for non-fourier diffusion
Authors: Xue Tao , Su Haozhe , Han Chengguizi , Jiang Chenfanfu , Aanjaneya Mridul
Published: November 2020

We introduce the
C-F diffusion model
[Anderson and Tamma 2006; Xue et al. 2018] to computer graphics for diffusion-driven problems that has several attractive properties: (a) it fundamentally explains diffusion from the perspective of the non-equilibrium statistical mechanical Boltzmann Transport Equation, (b) it allows for a finite propagation speed for diffusion, in contrast to the widely employed Fick's/Fourier's law, and (c) it can capture some of the most characteristic visual aspects of diffusion-driven physics, such as hydrogel swelling, limited diffusive domain for smoke flow, snowflake and dendrite formation, that span from Fourier-type to non-Fourier-type diffusive phenomena. We propose a unified convection-diffusion formulation using this model that treats both the diffusive quantity
and
its associated flux as the primary unknowns, and that recovers the traditional Fourier-type diffusion as a limiting case. We design a novel semi-implicit discretization for this formulation on staggered MAC grids and a geometric Multigrid-preconditioned Conjugate Gradients solver for efficient numerical solution. To highlight the efficacy of our method, we demonstrate end-to-end examples of elastic porous media simulated with the Material Point Method (MPM), and diffusion-driven Eulerian incompressible fluids.
Learning to manipulate amorphous materials
Authors: Zhang Yunbo , Yu Wenhao , Liu C. Karen , Kemp Charlie , Turk Greg
Published: November 2020

We present a method of training character manipulation of amorphous materials such as those often used in cooking. Common examples of amorphous materials include granular materials (salt, uncooked rice), fluids (honey), and visco-plastic materials (sticky rice, softened butter). A typical task is to spread a given material out across a flat surface using a tool such as a scraper or knife. We use reinforcement learning to train our controllers to manipulate materials in various ways. The training is performed in a physics simulator that uses position-based dynamics of particles to simulate the materials to be manipulated. The neural network control policy is given observations of the material (e.g. a low-resolution density map), and the policy outputs actions such as rotating and translating the knife. We demonstrate policies that have been successfully trained to carry out the following tasks: spreading, gathering, and flipping. We produce a final animation by using inverse kinematics to guide a character's arm and hand to match the motion of the manipulation tool such as a knife or a frying pan.
PhysCap
Authors: Shimada Soshi , Golyanik Vladislav , Xu Weipeng , Theobalt Christian
Published: November 2020

Marker-less 3D human motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. In consequence, even the most accurate state-of-the-art approaches have significant limitations. Purely kinematic formulations on the basis of individual joints or skeletons, and the frequent frame-wise reconstruction in state-of-the-art methods greatly limit 3D accuracy and temporal stability compared to multi-view or marker-based motion capture. Further, captured 3D poses are often physically incorrect and biomechanically implausible, or exhibit implausible environment interactions (floor penetration, foot skating, unnatural body leaning and strong shifting in depth), which is problematic for any use case in computer graphics.

We, therefore, present
PhysCap
, the first algorithm for physically plausible, real-time and marker-less human 3D motion capture with a single colour camera at 25 fps. Our algorithm first captures 3D human poses purely kinematically. To this end, a CNN infers 2D and 3D joint positions, and subsequently, an inverse kinematics step finds space-time coherent joint angles and global 3D pose. Next, these kinematic reconstructions are used as constraints in a real-time physics-based pose optimiser that accounts for environment constraints (
e.g.
, collision handling and floor placement), gravity, and biophysical plausibility of human postures. Our approach employs a combination of ground reaction force and residual force for plausible root control, and uses a trained neural network to detect foot contact events in images. Our method captures physically plausible and temporally stable global 3D human motion, without physically implausible postures, floor penetrations or foot skating, from video in real time and in general scenes.
PhysCap
achieves state-of-the-art accuracy on established pose benchmarks, and we propose new metrics to demonstrate the improved physical plausibility and temporal stability.
Development of an Alternative Low‐Cost Larval Diet for Mass Rearing of Aedes aegypti Mosquitoes
Authors: Senevirathna Umesha , Udayanga Lahiru , Ganehiarachchi G.A.S.M. , Hapugoda Menaka , Ranathunge Tharaka , Silva Gunawardene Nilmini
Published: November 2020

Background. Aedes aegypti is a major vector of arboviruses that may be controlled on an area‐wide basis, using novel approaches such as Sterile Insect Technique (SIT) and Incompatible Insect Technique (IIT). Larval diet is a critical factor to be considered in mass rearing of Aedes mosquitoes for SIT and IIT programs. Therefore, the current study is aimed at evaluating the effects of two novel diets developed from dry fish powder on the growth and development of immature stages and adult fitness‐related characteristics of Ae. aegypti in Sri Lanka. Method. Three batches of the first instar Ae. aegypti larva, each containing 250 larvae, were exposed to three different larval diets as standard dry fish powder (D1), dry fish powder meal and brewer’s yeast (D2), and International Atomic Energy Agency‐ (IAEA‐) recommended diet (D3), separately. Morphometric and developmental parameters of the 4th instar larvae, pupae, and adult mosquitoes reared under different dietary treatments were measured. The entire experimental setup was replicated thrice. A General Linear Model (GLM) in the form of two‐way ANOVA was used for the statistical analysis. Results. Significant diet‐based variations were observed in the head length, head width, thoracic length, thoracic width, abdominal length, abdominal width, and total length (F2,87 > 4.811; P < 0.05) of Ae. aegypti larvae. The highest pupation success and the larval size were observed from the larvae fed the D2 diet, while the lowest was reported from D1. All adult morphometric parameters of adult male and female Ae. aegypti mosquitoes also denoted significant dietary variations, reporting the best‐sized adults from the D2 diet (F2,87 > 3.54; P < 0.05). Further, significantly higher fecundity and male longevity were also shown by the adult Ae. aegypti (F2,6 > 7.897; P < 0.01) mosquitoes reared under diet D2. Conclusion. Based on all the growth and developmental parameters, the D2 diet tends to perform similar to the IAEA‐recommended diet in mass rearing of Ae. aegypti mosquitoes, while being more inexpensive. Therefore, larval diet D2 could be suggested as the ideal diet for mass rearing of Ae. aegypti for IIT and SIT‐based vector control in Sri Lanka.
Prognostic Significance of COVID‐19 Receptor ACE2 and Recommendation for Antihypertensive Drug in Renal Cell Carcinoma
Authors: Kim Kihun , Ko Yeji , Ko Dai Sik , Kim Yun Hak
Published: November 2020

Purpose. Owing to its worldwide spread, the coronavirus disease (COVID‐19) epidemic was declared a pandemic by the World Health Organization on March 11, 2020. Angiotensin‐converting enzyme 2 (ACE2) is the outer surface protein of the cell membrane that is abundantly distributed in the heart, lungs, and kidneys and plays an important role in molecular docking of the severe acute respiratory syndrome coronavirus 2. In this study, we aimed to analyze the difference in the survival rate according to ACE2 expressions in pan‐cancer. Materials and Methods. We downloaded clinical and genomic data from The Cancer Genome Atlas. We used Kaplan‐Meier with a log‐rank test, and the Cox proportional hazards regression to analyze prognostic significance. Results. In the Kaplan‐Meier curve, clear cell renal cell carcinoma (ccRCC), uveal melanoma, and prostate adenocarcinoma showed statistical significance. In the Cox regression, thyroid carcinoma and glioblastoma multiforme and ccRCC showed significant results. Only ccRCC had statistical significance, and high ACE2 expression is related to good prognosis. It is known that the ACE inhibitor, a primary antihypertensive agent, increases ACE2 expression. Conclusion. Based on these results, we believe that the ACE inhibitor will be important to increase the lifespan of ccRCC patients. This study is the first research to offer a recommendation on the use of anti‐hypertensive drugs to ccRCC patients.
Blood Exosomes Have Neuroprotective Effects in a Mouse Model of Parkinson’s Disease
Authors: Sun Ting , Ding Zhe-Xu , Luo Xin , Liu Qing-Shan , Cheng Yong
Published: November 2020

Parkinson’s disease (PD) is a common and complex neurodegenerative disease; the pathogenesis of which is still uncertain. Exosomes, nanosized extracellular vesicles, have been suggested to participate in the pathogenesis of PD, but their role is unknown. Here, a metabolomic analysis of serum and brain exosomes showed differentially expressed metabolites between 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine hydrochloride- (MPTP-) induced PD mice and control mice, such as oxidized lipids, vitamins, and cholesterol. These metabolites were enriched in coenzyme, nicotinamide, and amino acid pathways related to PD, and they could be served as preclinical biomarkers. We further found that blood-derived exosomes from healthy volunteers alleviated impaired motor coordination in MPTP-treated mice. Results from immunohistochemistry and western blotting indicated that the loss of dopaminergic neurons in substantia nigra and striatum of PD model mice was rescued by the exosome treatment. The exosome treatment also restored the homeostasis of oxidative stress, neuroinflammation, and cell apoptosis in the model mice. These results suggest that exosomes are important mediators for PD pathogenesis, and exosomes are promising targets for the diagnosis and treatment of PD.
Prosthetic Valve Candida Endocarditis: A Case Report with 18F-FDG-PET/CT as Part of the Diagnostic Workup
Authors: Salomäki Soile Pauliina , Saraste Antti , Jalava-Karvinen Päivi , Pirilä Laura , Hohenthal Ulla
Published: November 2020

Diagnosis of Candida spp. infective endocarditis (IE) is challenging, and diagnostic delays are common. We describe two patients with Candida spp. prosthetic valve endocarditis (PVE) and 18fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) as a part of diagnostic workup. We also refer to 5 other cases we found from the published literature. These cases highlight that 18F-FDG-PET/CT can improve diagnostic accuracy in prosthetic valve Candida endocarditis.
The Spread of Information in Virtual Communities
Authors: Zhang Zhen , Du Jin , Meng Qingchun , Rong Xiaoxia , Fan Xiaodan
Published: November 2020

With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.
Experimental Study on Mechanical Properties of Deep Buried Granite under Different Confining Pressures
Authors: Zhao Jun , Zhang Tan
Published: November 2020

Brittle failure of hard rock poses a serious threat to the stability of surrounding rock in deep underground engineering. In order to study the deformation and failure characteristics of deep buried granite under high confining pressure cyclic loading and unloading, MTS815 electro‐hydraulic servo rock test system was used to conduct cyclic loading and unloading tests under confining pressures of 15 MPa, 35 MPa, 45 MPa, and 55 MPa, and the corresponding stress‐strain curves and deformation failure characteristic curves were obtained. The experimental results show the follows: (1) under the same confining pressure, the peak strength, crack initiation stress, crack damage stress, and Poisson’s ratio of the specimens under cyclic loading and unloading are larger than those under conventional triaxial loading and unloading, and the unloading elastic modulus is smaller than that, under conventional triaxial compression; (2) the results show that, under different confining pressures, the granite samples show obvious brittle failure characteristics, the elastic modulus and crack initiation stress increase first and then decrease with the confining pressure, the peak strength and crack damage stress of the samples increase linearly with the confining pressure, and Poisson’s ratio increases first and then remains unchanged with the confining pressure; (3) under the two kinds of stress conditions, the macroscopic failure of the samples is mainly shear failure. The deformation and failure law of granite samples revealed in this study has significant reference value for the selection of rock mass mechanical model of surrounding rock stability of underground engineering, the formulation of surrounding rock support countermeasures, and the evolution law of mechanical parameters with damage variables.
Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network
Authors: Jiang Luxin , Wang Xiaohui
Published: November 2020

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.
Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment
Authors: Chen Jiajia , Zhang Rui , Han Wei , Jiang Wuhua , Hu Jinfang , Lu Xiaoshan , Liu Xingtao , Zhao Pan
Published: November 2020

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.
Failure-Aware and Delay-Predicted Multipath Virtual Queue Scheduling for Multimedia Transmission in Edge IoT
Authors: Qin Jiuren , Wang Zhaoxue , Gao Kai , Zhong Lujie
Published: November 2020

The spread of Edge Internet of Things (IoT) radically changes our lifestyle. However, the multimedia services in edge IoT are still stuck by inefficiency. The dynamic typologies perplex the transmission of massive real-time data. To solve this problem, multipath transmission control protocol (MPTCP) which has a natural advantage in transmission robustness and bandwidth aggregation is becoming a good choice. In this paper, failure-aware and delay-predicted multipath virtual queue scheduling (FD-MVQS) is proposed to optimize the MPTCP performance in edge IoT. FD-MVQS constructs a two-plane cooperative scheduling system. In the control plane, the transmission failure estimation and chaos theory-based arrival delay prediction methods are introduced to provide the foundation for prescheduling. In the data plane, the multipath virtual queue scheduling is designed to allocate segments to different subflows. Simulation results showed that the proposed FD-MVQS performed better than standard and typical multipath transmission solutions in throughput, delay, and segment disorder.