bluuebunny/crossref_metadata_2025 huggingface dataset:
OmniPhotos
Authors: Bertel Tobias , Yuan Mingze , Lindroos Reuben , Richardt Christian
Published: November 2020

Virtual reality headsets are becoming increasingly popular, yet it remains difficult for casual users to capture immersive 360° VR panoramas. State-of-the-art approaches require capture times of usually far more than a minute and are often limited in their supported range of head motion. We introduce OmniPhotos, a novel approach for quickly and casually capturing high-quality 360° panoramas with motion parallax. Our approach requires a single sweep with a consumer 360° video camera as input, which takes less than 3 seconds to capture with a rotating selfie stick or 10 seconds handheld. This is the fastest capture time for any VR photography approach supporting motion parallax by an order of magnitude. We improve the visual rendering quality of our OmniPhotos by alleviating vertical distortion using a novel deformable proxy geometry, which we fit to a sparse 3D reconstruction of captured scenes. In addition, the 360° input views significantly expand the available viewing area, and thus the range of motion, compared to previous approaches. We have captured more than 50 OmniPhotos and show video results for a large variety of scenes. We will make our code available.
Imperceptible manipulation of lateral camera motion for improved virtual reality applications
Authors: Serrano Ana , Martin Daniel , Gutierrez Diego , Myszkowski Karol , Masia Belen
Published: November 2020

Virtual Reality (VR) systems increase immersion by reproducing users' movements in the real world. However, several works have shown that this real-to-virtual mapping does not need to be precise in order to convey a realistic experience. Being able to alter this mapping has many potential applications, since achieving an accurate real-to-virtual mapping is not always possible due to limitations in the capture or display hardware, or in the physical space available. In this work, we measure detection thresholds for lateral translation gains of virtual camera motion in response to the corresponding head motion under natural viewing, and in the absence of locomotion, so that virtual camera movement can be either compressed or expanded while these manipulations remain undetected. Finally, we propose three applications for our method, addressing three key problems in VR: improving 6-DoF viewing for captured 360° footage, overcoming physical constraints, and reducing simulator sickness. We have further validated our thresholds and evaluated our applications by means of additional user studies confirming that our manipulations remain imperceptible, and showing that (i) compressing virtual camera motion reduces visible artifacts in 6-DoF, hence improving perceived quality, (ii) virtual expansion allows for completion of virtual tasks within a reduced physical space, and (iii) simulator sickness may be alleviated in simple scenarios when our compression method is applied.
MakeltTalk
Authors: Zhou Yang , Han Xintong , Shechtman Eli , Echevarria Jose , Kalogerakis Evangelos , Li Dingzeyu
Published: November 2020

We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures. In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods.
Conforming weighted delaunay triangulations
Authors: Alexa Marc
Published: November 2020

Given a set of points together with a set of simplices we show how to compute weights associated with the points such that the weighted Delaunay triangulation of the point set contains the simplices, if possible. For a given triangulated surface, this process provides a tetrahedral mesh conforming to the triangulation, i.e. solves the problem of meshing the triangulated surface without inserting additional vertices. The restriction to weighted Delaunay triangulations ensures that the orthogonal dual mesh is embedded, facilitating common geometry processing tasks.

We show that the existence of a single simplex in a weighted Delaunay triangulation for given vertices amounts to a set of linear inequalities, one for each vertex. This means that the number of inequalities for a given triangle mesh is quadratic in the number of mesh elements, making the naive approach impractical. We devise an algorithm that incrementally selects a small subset of inequalities, repeatedly updating the weights, until the weighted Delaunay triangulation contains all constrained simplices or the problem becomes infeasible. Applying this algorithm to a range of triangle meshes commonly used graphics demonstrates that many of them admit a conforming weighted Delaunay triangulation, in contrast to conforming or constrained Delaunay that require additional vertices to split the input primitives.
A benchmark for rough sketch cleanup
Authors: Yan Chuan , Vanderhaeghe David , Gingold Yotam
Published: November 2020

Sketching is a foundational step in the design process. Decades of sketch processing research have produced algorithms for 3D shape interpretation, beautification, animation generation, colorization, etc. However, there is a mismatch between sketches created in the wild and the clean, sketch-like input required by these algorithms, preventing their adoption in practice. The recent flurry of sketch vectorization, simplification, and cleanup algorithms could be used to bridge this gap. However, they differ wildly in the assumptions they make on the input and output sketches. We present the first benchmark to evaluate and focus sketch cleanup research. Our dataset consists of 281 sketches obtained in the wild and a curated subset of 101 sketches. For this curated subset along with 40 sketches from previous work, we commissioned manual vectorizations and multiple ground truth cleaned versions by professional artists. The sketches span artistic and technical categories and were created by a variety of artists with different styles. Most sketches have Creative Commons licenses; the rest permit academic use. Our benchmark's metrics measure the similarity of automatically cleaned rough sketches to artist-created ground truth; the ambiguity and messiness of rough sketches; and low-level properties of the output parameterized curves. Our evaluation identifies shortcomings among state-of-the-art cleanup algorithms and discusses open problems for future research.
Synthesizing light field from a single image with variable MPI and two network fusion
Authors: Li Qinbo , Kalantari Nima Khademi
Published: November 2020

We propose a learning-based approach to synthesize a light field with a small baseline from a single image. We synthesize the novel view images by first using a convolutional neural network (CNN) to promote the input image into a layered representation of the scene. We extend the multiplane image (MPI) representation by allowing the disparity of the layers to be inferred from the input image. We show that, compared to the original MPI representation, our representation models the scenes more accurately. Moreover, we propose to handle the visible and occluded regions separately through two parallel networks. The synthesized images using these two networks are then combined through a soft visibility mask to generate the final results. To effectively train the networks, we introduce a large-scale light field dataset of over 2,000 unique scenes containing a wide range of objects. We demonstrate that our approach synthesizes high-quality light fields on a variety of scenes, better than the state-of-the-art methods.
Chordal decomposition for spectral coarsening
Authors: Chen Honglin , Liu Hsueh-TI Derek , Jacobson Alec , Levin David I. W.
Published: November 2020

We introduce a novel solver to significantly reduce the size of a geometric operator while preserving its spectral properties at the lowest frequencies. We use chordal decomposition to formulate a convex optimization problem which allows the user to control the operator sparsity pattern. This allows for a trade-off between the spectral accuracy of the operator and the cost of its application. We efficiently minimize the energy with a change of variables and achieve state-of-the-art results on spectral coarsening. Our solver further enables novel applications including volume-to-surface approximation and detaching the operator from the mesh, i.e., one can produce a mesh tailor-made for visualization and optimize an operator separately for computation.
Freely orientable microstructures for designing deformable 3D prints
Authors: Tricard Thibault , Tavernier Vincent , Zanni Cédric , Martínez Jonàs , Hugron Pierre-Alexandre , Neyret Fabrice , Lefebvre Sylvain
Published: November 2020

Nature offers a marvel of astonishing and rich deformation behaviors. Yet, most of the objects we fabricate are comparatively rather inexpressive, either rigid or exhibiting simple homogeneous deformations when interacted with.

We explore the synthesis and fabrication of novel microstructures that mimic the effects of having oriented rigid fibers in an otherwise flexible material: the result is extremely rigid along a transverse direction while being comparatively very flexible in the locally orthogonal plane. By allowing free gradation of the rigidity direction orientation within the object, the microstructures can be designed such that, under deformation, distances along fibers in the volume are preserved while others freely change. Through a simple painting tool, this allows a designer to influence the way the volume reshapes when deformed, and results in a wide range of novel possibilities. Many gradations are possible: local free orientation of the fibers; local control of the overall material rigidity (structure density); local canceling of the effect of the fibers, obtaining a more isotropic material.

Our algorithm to synthesize the structures builds upon procedural texturing. It produces a cellular geometry that can be fabricated reliably despite 3D printing walls at a minimal thickness, allowing prints to be very flexible. The synthesis algorithm is efficient and scales to large volumes.
Offsite aerial path planning for efficient urban scene reconstruction
Authors: Zhou Xiaohui , Xie Ke , Huang Kai , Liu Yilin , Zhou Yang , Gong Minglun , Huang Hui
Published: November 2020

With rapid development in UAV technologies, it is now possible to reconstruct large-scale outdoor scenes using only images captured by low-cost drones. The problem, however, becomes how to plan the aerial path for a drone to capture images so that two conflicting goals are optimized: maximizing the reconstruction quality and minimizing mid-air image acquisition effort. Existing approaches either resort to pre-defined dense and thus inefficient view sampling strategy, or plan the path adaptively but require two onsite flight passes and intensive computation in-between. Hence, using these methods to capture and reconstruct large-scale scenes can be tedious. In this paper, we present an adaptive aerial path planning algorithm that can be done before the site visit. Using only a 2D map and a satellite image of the to-be-reconstructed area, we first compute a coarse 2.5D model for the scene based on the relationship between buildings and their shadows. A novel Max-Min optimization is then proposed to select a minimal set of viewpoints that maximizes the reconstructability under the the same number of viewpoints. Experimental results on benchmark show that our planning approach can effectively reduce the number of viewpoints needed than the previous state-of-the-art method, while maintaining comparable reconstruction quality. Since no field computation or a second visit is needed, and the view number is also minimized, our approach significantly reduces the time required in the field as well as the off-line computation cost for multi-view stereo reconstruction, making it possible to reconstruct a large-scale urban scene in a short time with moderate effort.
Functional optimization of fluidic devices with differentiable stokes flow
Authors: Du Tao , Wu Kui , Spielberg Andrew , Matusik Wojciech , Zhu Bo , Sifakis Eftychios
Published: November 2020

We present a method for performance-driven optimization of fluidic devices. In our approach, engineers provide a high-level specification of a device using parametric surfaces for the fluid-solid boundaries. They also specify desired flow properties for inlets and outlets of the device. Our computational approach optimizes the boundary of the fluidic device such that its steady-state flow matches desired flow at outlets. In order to deal with computational challenges of this task, we propose an efficient, differentiable Stokes flow solver. Our solver provides explicit access to gradients of performance metrics with respect to the parametric boundary representation. This key feature allows us to couple the solver with efficient gradient-based optimization methods. We demonstrate the efficacy of this approach on designs of five complex 3D fluidic systems. Our approach makes an important step towards practical computational design tools for high-performance fluidic devices.
Surface-only ferrofluids
Authors: Huang Libo , Michels Dominik L.
Published: November 2020

We devise a novel surface-only approach for simulating the three dimensional free-surface flow of incompressible, inviscid, and linearly magnetizable ferrofluids. A Lagrangian velocity field is stored on a triangle mesh capturing the fluid's surface. The two key problems associated with the dynamic simulation of the fluid's interesting geometry are the magnetization process transitioning the fluid from a non-magnetic into a magnetic material, and the evaluation of magnetic forces. In this regard, our key observation is that for linearly incompressible ferrofluids, their magnetization and application of magnetic forces only require knowledge about the position of the fluids' boundary. Consequently, our approach employs a boundary element method solving the magnetization problem and evaluating the so-called magnetic pressure required for the force evaluation. The magnetic pressure is added to the Dirichlet boundary condition of a surface-only liquids solver carrying out the dynamical simulation. By only considering the fluid's surface in contrast to its whole volume, we end up with an efficient approach enabling more complex and realistic ferrofluids to be explored in the digital domain without compromising efficiency. Our approach allows for the use of physical parameters leading to accurate simulations as demonstrated in qualitative and quantitative evaluations.
Sketch2CAD
Authors: Li Changjian , Pan Hao , Bousseau Adrien , Mitra Niloy J.
Published: November 2020

We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities between the steps industrial designers follow to draw 3D shapes, and the operations CAD modeling systems offer to create similar shapes. To overcome the strong ambiguity with parsing 2D sketches, we observe that in a sketching sequence, each step makes sense and can be interpreted in the
context
of what has been drawn before. In our system, this context corresponds to a partial CAD model, inferred in the previous steps, which we feed along with the input sketch to a deep neural network in charge of interpreting how the model should be modified by that sketch. Our deep network architecture then recognizes the intended CAD operation and segments the sketch accordingly, such that a subsequent optimization estimates the parameters of the operation that best fit the segmented sketch strokes. Since there exists no datasets of paired sketching and CAD modeling sequences, we train our system by generating synthetic sequences of CAD operations that we render as line drawings. We present a proof of concept realization of our algorithm supporting four frequently used CAD operations. Using our system, participants are able to quickly model a large and diverse set of objects, demonstrating Sketch2CAD to be an alternate way of interacting with current CAD modeling systems.
Rendering near-field speckle statistics in scattering media
Authors: Bar Chen , Gkioulekas Ioannis , Levin Anat
Published: November 2020

We introduce rendering algorithms for the simulation of speckle statistics observed in scattering media under coherent near-field imaging conditions. Our work is motivated by the recent proliferation of techniques that use speckle correlations for tissue imaging applications: The ability to simulate the image measurements used by these speckle imaging techniques in a physically-accurate and computationally-efficient way can facilitate the widespread adoption and improvement of these techniques. To this end, we draw inspiration from recently-introduced Monte Carlo algorithms for rendering speckle statistics under far-field conditions (collimated sensor and illumination). We derive variants of these algorithms that are better suited to the near-field conditions (focused sensor and illumination) required by tissue imaging applications. Our approach is based on using Gaussian apodization to approximate the sensor and illumination aperture, as well as von Mises-Fisher functions to approximate the phase function of the scattering material. We show that these approximations allow us to derive closed-form expressions for the focusing operations involved in simulating near-field speckle patterns. As we demonstrate in our experiments, these approximations accelerate speckle rendering simulations by a few orders of magnitude compared to previous techniques, at the cost of negligible bias. We validate the accuracy of our algorithms by reproducing ground truth speckle statistics simulated using wave-optics solvers, and real-material measurements available in the literature. Finally, we use our algorithms to simulate biomedical imaging techniques for focusing through tissue.
Dynamic facial asset and rig generation from a single scan
Authors: Li Jiaman , Kuang Zhengfei , Zhao Yajie , He Mingming , Bladin Karl , Li Hao
Published: November 2020

The creation of high-fidelity computer-generated (CG) characters for films and games is tied with intensive manual labor, which involves the creation of comprehensive facial assets that are often captured using complex hardware. To simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial models, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic textures, as well as secondary facial components (
e.g.
, teeth and eyeballs). Based on a facial database with over 4, 000 scans with pore-level details, varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of a full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with professional production pipelines for facial animation and rendering. We demonstrate a highly robust and effective framework on a wide range of subjects, and showcase high-fidelity facial animations with automatically generated personalized dynamic textures.
Fast and robust mesh arrangements using floating-point arithmetic
Authors: Cherchi Gianmarco , Livesu Marco , Scateni Riccardo , Attene Marco
Published: November 2020

We introduce a novel algorithm to transform any generic set of triangles in 3D space into a well-formed simplicial complex. Intersecting elements in the input are correctly identified, subdivided, and connected to arrange a valid configuration, leading to a topologically sound partition of the space into piece-wise linear cells. Our approach does not require the exact coordinates of intersection points to calculate the resulting complex. We represent any intersection point as an unevaluated combination of input vertices. We then extend the recently introduced concept of
indirect predicates
[Attene 2020] to define all the necessary geometric tests that, by construction, are both exact and efficient since they fully exploit the floating-point hardware. This design makes our method robust and guaranteed correct, while being virtually as fast as non-robust floating-point based implementations. Compared with existing robust methods, our algorithm offers a number of advantages: it is much faster, has a better memory layout, scales well on extremely challenging models, and allows fully exploiting modern multi-core hardware with a parallel implementation. We thoroughly tested our method on thousands of meshes, concluding that it consistently outperforms prior art. We also demonstrate its usefulness in various applications, such as computing efficient mesh booleans, Minkowski sums, and volume meshes.
Light stage super-resolution
Authors: Sun Tiancheng , Xu Zexiang , Zhang Xiuming , Fanello Sean , Rhemann Christoph , Debevec Paul , Tsai Yun-Ta , Barron Jonathan T. , Ramamoorthi Ravi
Published: November 2020

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite number of lights in the stage, the light transport matrix only represents a sparse sampling on the entire sphere. As a consequence, relighting the subject with a point light or a directional source that does not coincide exactly with one of the lights in the stage requires interpolation and resampling the images corresponding to nearby lights, and this leads to ghosting shadows, aliased specularities, and other artifacts. To ameliorate these artifacts and produce better results under arbitrary high-frequency lighting, this paper proposes a learning-based solution for the "super-resolution" of scans of human faces taken from a light stage. Given an arbitrary "query" light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a "virtual" light source at the query location. This neural network must circumvent the inherent aliasing and regularity of the light stage data that was used for training, which we accomplish through the use of regularized traditional interpolation methods within our network. Our learned model is able to produce renderings for arbitrary light directions that exhibit realistic shadows and specular highlights, and is able to generalize across a wide variety of subjects. Our super-resolution approach enables more accurate renderings of human subjects under detailed environment maps, or the construction of simpler light stages that contain fewer light sources while still yielding comparable quality renderings as light stages with more densely sampled lights.
RoboGrammar
Authors: Zhao Allan , Xu Jie , Konaković-Luković Mina , Hughes Josephine , Spielberg Andrew , Rus Daniela , Matusik Wojciech
Published: November 2020

We present
RoboGrammar
, a fully automated approach for generating optimized robot structures to traverse given terrains. In this framework, we represent each robot design as a graph, and use a graph grammar to express possible arrangements of physical robot assemblies. Each robot design can then be expressed as a sequence of grammar rules. Using only a small set of rules our grammar can describe hundreds of thousands of possible robot designs. The construction of the grammar limits the design space to designs that can be fabricated. For a given input terrain, the design space is searched to find the top performing robots and their corresponding controllers. We introduce Graph Heuristic Search - a novel method for efficient search of combinatorial design spaces. In Graph Heuristic Search, we explore the design space while simultaneously learning a function that maps incomplete designs (e.g., nodes in the combinatorial search tree) to the best performance values that can be achieved by expanding these incomplete designs. Graph Heuristic Search prioritizes exploration of the most promising branches of the design space. To test our method we optimize robots for a number of challenging and varied terrains. We demonstrate that
RoboGrammar
can successfully generate nontrivial robots that are optimized for a single terrain or a combination of terrains.
Reinforced FDM
Authors: Fang Guoxin , Zhang Tianyu , Zhong Sikai , Chen Xiangjia , Zhong Zichun , Wang Charlie C. L.
Published: November 2020

The anisotropy of mechanical strength on a 3D printed model can be controlled in a multi-axis 3D printing system as materials can be accumulated along dynamically varied directions. In this paper, we present a new computational framework to generate specially designed layers and toolpaths of multi-axis 3D printing for strengthening a model by aligning filaments along the directions with large stresses. The major challenge comes from how to effectively decompose a solid into a sequence of strength-aware and collision-free working surfaces. We formulate it as a problem to compute an optimized governing field together with a selected orientation of fabrication setup. Iso-surfaces of the governing field are extracted as working surface layers for filament alignment. Supporting structures in curved layers are constructed by extrapolating the governing field to enable the fabrication of overhangs. Compared with planar-layer based Fused Deposition Modeling (FDM) technology, models fabricated by our method can withstand up to 6
.
35× loads in experimental tests.
An adaptive staggered-tilted grid for incompressible flow simulation
Authors: Xiao Yuwei , Chan Szeyu , Wang Siqi , Zhu Bo , Yang Xubo
Published: November 2020

Enabling adaptivity on a uniform Cartesian grid is challenging due to its highly structured grid cells and axis-aligned grid lines. In this paper, we propose a new grid structure - the adaptive staggered-tilted (AST) grid - to conduct adaptive fluid simulations on a regular discretization. The key mechanics underpinning our new grid structure is to allow the emergence of a new set of tilted grid cells from the nodal positions on a background uniform grid. The original axis-aligned cells, in conjunction with the populated axis-tilted cells, jointly function as the geometric primitives to enable adaptivity on a regular spatial discretization. By controlling the states of the tilted cells both temporally and spatially, we can dynamically evolve the adaptive discretizations on an Eulerian domain. Our grid structure preserves almost all the computational merits of a uniform Cartesian grid, including the cache-coherent data layout, the easiness for parallelization, and the existence of high-performance numerical solvers. Further, our grid structure can be integrated into other adaptive grid structures, such as an Octree or a sparsely populated grid, to accommodate the T-junction-free hierarchy. We demonstrate the efficacy of our AST grid by showing examples of large-scale incompressible flow simulation in domains with irregular boundaries.
Data-driven authoring of large-scale ecosystems
Authors: Kapp Konrad , Gain James , Guérin Eric , Galin Eric , Peytavie Adrien
Published: November 2020

In computer graphics populating a large-scale natural scene with plants in a fashion that both reflects the complex interrelationships and diversity present in real ecosystems and is computationally efficient enough to support iterative authoring remains an open problem. Ecosystem simulations embody many of the botanical influences, such as sunlight, temperature, and moisture, but require hours to complete, while synthesis from statistical distributions tends not to capture fine-scale variety and complexity.

Instead, we leverage real-world data and machine learning to derive a canopy height model (CHM) for unseen terrain provided by the user. Trees in the canopy layer are then fitted to the resulting CHM through a constrained iterative process that optimizes for a given distribution of species, and, finally, an understorey layer is synthesised using distributions derived from biome-specific undergrowth simulations. Such a hybrid data-driven approach has the advantage that it incorporates subtle biotic, abiotic, and disturbance factors implicitly encoded in the source data and evidences accepted biological behaviour, such as self-thinning, climatic adaptation, and gap dynamics.
Nonlinear spectral geometry processing via the TV transform
Authors: Fumero Marco , Möller Michael , Rodolà Emanuele
Published: November 2020

We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such an operator admits a generalized notion of spectral decomposition, yielding a convenient multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques. Our approach entails accurate,
detail-preserving
decomposition and manipulation of 3D shape geometry while taking an especially intuitive form: non-local semantic details are well separated into different bands, which can then be filtered and re-synthesized with a straightforward linear step. Our computational framework is flexible, can be applied to a variety of signals, and is easily adapted to different geometry representations, including triangle meshes and point clouds. We showcase our method through multiple applications in graphics, ranging from surface and signal denoising to enhancement, detail transfer, and cubic stylization.
Towards spatially varying gloss reproduction for 3D printing
Authors: Piovarči Michal , Foshey Michael , Babaei Vahid , Rusinkiewicz Szymon , Matusik Wojciech , Didyk Piotr
Published: November 2020

3D printing technology is a powerful tool for manufacturing complex shapes with high-quality textures. Gloss, next to color and shape, is one of the most salient visual aspects of an object. Unfortunately, printing a wide range of spatially-varying gloss properties using state-of-the-art 3D printers is challenging as it relies on geometrical modifications to achieve the desired appearance. A common post-processing step is to apply off-the-shelf varnishes that modify the final gloss. The main difficulty in automating this process lies in the physical properties of the varnishes which owe their appearance to a high concentration of large particles and as such, they cannot be easily deposited with current 3D color printers. As a result, fine-grained control of gloss properties using today's 3D printing technologies is limited in terms of both spatial resolution and the range of achievable gloss. We address the above limitations and propose new printing hardware based on piezo-actuated needle valves capable of jetting highly viscous varnishes. Based on the new hardware setup, we present the complete pipeline for controlling the gloss of a given 2.5 D object, from printer calibration, through material selection, to the manufacturing of models with spatially-varying reflectance. Furthermore, we discuss the potential integration with current 3D printing technology. Apart from being a viable solution for 3D printing, our method offers an additional and essential benefit of separating color and gloss fabrication which makes the process more flexible and enables high-quality color and gloss reproduction.
Lifting freehand concept sketches into 3D
Authors: Gryaditskaya Yulia , Hähnlein Felix , Liu Chenxi , Sheffer Alla , Bousseau Adrien
Published: November 2020

We present the first algorithm capable of automatically lifting real-world, vector-format, industrial design sketches into 3D. Targeting real-world sketches raises numerous challenges due to inaccuracies, use of overdrawn strokes, and construction lines. In particular, while construction lines convey important 3D information, they add significant clutter and introduce multiple accidental 2D intersections. Our algorithm exploits the geometric cues provided by the construction lines and lifts them to 3D by computing their intended 3D intersections and depths. Once lifted to 3D, these lines provide valuable geometric constraints that we leverage to infer the 3D shape of other artist drawn strokes. The core challenge we address is inferring the 3D connectivity of construction and other lines from their 2D projections by separating 2D intersections into 3D intersections and accidental occlusions. We efficiently address this complex combinatorial problem using a dedicated search algorithm that leverages observations about designer drawing pREFERENCES, and uses those to explore only the most likely solutions of the 3D intersection detection problem. We demonstrate that our separator outputs are of comparable quality to human annotations, and that the 3D structures we recover enable a range of design editing and visualization applications, including novel view synthesis and 3D-aware scaling of the depicted shape.
Path differential-informed stratified MCMC and adaptive forward path sampling
Authors: Zirr Tobias , Dachsbacher Carsten
Published: November 2020

Markov Chain Monte Carlo (MCMC) rendering is extensively studied, yet it remains largely unused in practice. We propose solutions to several practicability issues, opening up path space MCMC to become an adaptive sampling framework around established Monte Carlo (MC) techniques. We address non-uniform image quality by deriving an analytic target function for imagespace sample stratification. The function is based on a novel connection between variance and path differentials, allowing analytic variance estimates for MC samples, with potential uses in other adaptive algorithms outside MCMC. We simplify these estimates down to simple expressions using only quantities known in any MC renderer. We also address the issue that most existing MCMC renderers rely on bi-directional path tracing and reciprocal transport, which can be too costly and/or too complex in practice. Instead, we apply our theoretical framework to optimize an adaptive MCMC algorithm that only uses forward path construction. Notably, we construct our algorithm by adapting (with minimal changes) a full-featured path tracer into a single-path state space Markov Chain, bridging another gap between MCMC and existing MC techniques.
Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels
Authors: Ahmed Abdalla G. M. , Wonka Peter
Published: November 2020

We present a novel technique for diffusing Monte Carlo sampling error as a blue noise in screen space. We show that automatic diffusion of sampling error can be achieved by ordering the pixels in a way that preserves locality, such as Morton's Z-ordering, and assigning the samples to the pixels from successive sub-sequences of a single low-discrepancy sequence, thus securing well-distributed samples for each pixel, local neighborhoods, and the whole image. We further show that a blue-noise distribution of the error is attainable by scrambling the Z-ordering to induce isotropy. We present an efficient technique to implement this hierarchical scrambling by defining a context-free grammar that describes infinite self-similar lookup trees. Our concept is scalable to arbitrary image resolutions, sample dimensions, and sample count, and supports progressive and adaptive sampling.
Hydroethanolic Extract from Bridelia atroviridis Müll. Arg. Bark Improves Haematological and Biochemical Parameters in Nicotinamide‐/Streptozotocin‐Induced Diabetic Rats
Authors: Noussi Djouwoug Clarice , Ngueguim Florence Tsofack , Kamkumo Gounoue Raceline , Donfack Gouni Clémence , Kandeda Antoine Kavaye , Philippe Djientcheu Jean , Fifen Rodrigue , Djomeni Dzeufiet Desire Paul , Ngouela Silvère , Sewald Norbert , Lenta Bruno Ndjakou , Dimo Theophile
Published: November 2020

Bridelia atroviridis Müll. Arg. (B. atroviridis) is a plant used in Cameroonian traditional medicine to manage diabetes. The effects of hydroethanolic barks extract from B. atroviridis were evaluated on diabetes disorders including hematology, inflammatory, and oxidative stress parameters. The in vitro antioxidant capacity of the hydroethanolic bark extract (70 : 30) was evaluated. Nicotinamide‐/streptozotocin‐induced diabetic rats were daily treated with the B. atroviridis extract for fifteen days. Glycemia were evaluated every 5 days, insulin sensibility test was performed, and haematological, inflammatory, and oxidative stress parameters were analysed. Histomorphometry of the pancreas was realized. The extract was able to scavenge free radicals in vitro and decrease significantly the blood glucose levels. The treatment resulted in a significant alleviation of insulin resistance, anemia, leukocytopenia, and thrombocytopenia observed in untreated diabetic rats. The extract significantly decreased proinflammatory cytokines TNF‐α, IL‐1β, and IL‐10. The rate of reduced glutathione was increased in the pancreas, whereas the catalase activity and nitrite concentration were decreased. Diabetic control showed a reduced size of Langerhans islet, whereas the size of islets was large in treated groups. The hydroethanolic extract of B. atroviridis was able to improve glycemia and alleviate haematological and inflammatory parameters disorders observed in diabetic conditions, probably due to its antidiabetic, anti‐inflammatory, and antioxidant capacities.
The Development of Three‐DNA Methylation Signature as a Novel Prognostic Biomarker in Patients with Colorectal Cancer
Authors: Gong Shu , Ye Weijian , Liu Tiankai , Jian Shaofen , Liu Wenhua
Published: November 2020

Aims. The prognosis of colorectal cancer (CRC) remains poor. This study aimed to develop and validate DNA methylation‐based signature model to predict overall survival of CRC patients. Methods. The methylation array data of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) database. These patients were divided into training and validation datasets. A risk score model was established based on Kaplan‐Meier and multivariate Cox regression analysis of training cohort and tested in validation cohort. Results. Among total 14,626 DNA methylation candidate markers, we found that a three‐DNA methylation signature (NR1H2, SCRIB, and UACA) was significantly associated with overall survival of CRC patients. Subgroup analysis indicated that this signature could predict overall survival of CRC patients regardless of age and gender. Conclusions. We established a prognostic model consisted of 3‐DNA methylation sites, which could be used as potential biomarker to evaluate the prognosis of CRC patients.
Metformin Prevents Follicular Atresia in Aging Laying Chickens through Activation of PI3K/AKT and Calcium Signaling Pathways
Authors: Yao Jinwei , Ma Yanfen , Zhou Shuo , Bao Tingting , Mi Yuling , Zeng Weidong , Li Jian , Zhang Caiqiao
Published: November 2020

Increased follicular atresia occurs with aging and results in reduced fecundity in laying chickens. Therefore, relieving follicular atresia of aging poultry is a crucial measure to maintain sustained high laying performance. As an antiaging agent, metformin was reported to play important roles in preventing aging in diverse animals. In this study, the physiological state of the prehierarchical follicles in the peak-laying hens (D280) and aged hens (D580) was compared, followed with exploration for the possible capacity of metformin in delaying atresia of the prehierarchical follicles in the aged D580 hens. Results showed that the capacity of yolk deposition within follicles declined with aging, and the point of endoplasmic reticulum- (ER-) mitochondrion contact decreased in the ultrastructure of the follicular cells. Meanwhile, the expression of apoptosis signaling genes was increased in the atretic small white follicles. Subsequently, the H2O2-induced follicular atresia model was established to evaluate the enhancing capacity of metformin on yolk deposition and inhibition of apoptosis in the atretic small white follicles. Metformin inhibited apoptosis through regulating cooperation of the mitochondrion-associated ER membranes and the insulin (PI3K/AKT) signaling pathway. Furthermore, metformin regulated calcium ion homeostasis to relieve ER-stress and inhibited release of mitochondrion apoptosis factors (BAD and caspase). Additionally, metformin activated PI3K/AKT that suppressed activation of BAD (downstream of the insulin signaling pathway) in the atretic follicles. Further, serum estrogen level and liver estrogen receptor-α expression were increased after dietary metformin supplementation in D580 hens. These results indicated that administration of dietary metformin activated the PI3K/AKT and calcium signaling pathway and enhanced yolk deposition to prevent chicken follicular atresia.
Global Estimates of the Prevalence of Depression among Prisoners: A Systematic Review and Meta-analysis
Authors: Bedaso Asres , Ayalew Mohammed , Mekonnen Nibretie , Duko Bereket
Published: November 2020

Background. Prison populations tend to be marginalized and disadvantaged of the rights and freedoms that other people in the community benefit from. Their separation from families, a narrow room and lack of privacy in the prison, violence between prisoners, and the uncertainty about the future result in psychological distress, for example, depression. The review has synthesized previous studies conducted on the topic and summarized to formulate recommendations for future prison health care services. Methods. We systematically searched the databases: PubMed, Psych Info, and SCOPUS, as well as manual Google Scholar searches, were conducted to retrieve published literature globally. We have included observational studies, written in English language. Estimates were pooled using a random-effects model. The study protocol was registered in PROSPERO with protocol number CRD42020156108. Subgroup and sensitivity analysis was conducted, and heterogeneity across the studies was evaluated using Q and the

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-test. Publication bias was assessed by inspection of the funnel plot and Egger’s regression test. Result. A total of 1313 studies were initially identified through the electronic database; among these, a total of 73 full-text articles were retrieved for further appraisal. Further, 32 full-text articles were included in the final systematic review and meta-analysis. In this meta-analysis, the pooled prevalence of depression among prisoners was 36.9% (95% CI; 27.3-47.6). The pooled prevalence of depression among prisoners in the developing and developed countries was 39.2% and 33.1%, respectively. Moreover, the prevalence of depression was 19.1% and 54% for the studies that used diagnostic and screen tools to diagnose or screen depression, respectively. A leave-one-out analysis revealed that the pooled prevalence of depression among prisoners was not dependent on a single study removal or addition. Thus, the pooled prevalence of depression ranges between 35.3 and 38.0%. Conclusion. The prevalence of depression among prisoners was high. Therefore, regular and continuous screening of depressive symptoms for prisoners along with its appropriate management is highly recommended.
Mechanism by which TRAF6 Participates in the Immune Regulation of Autoimmune Diseases and Cancer
Authors: Wang Jingjing , Wu Xinjie , Jiang Mengyu , Tai Guixiang
Published: November 2020

Tumor necrosis factor (TNF) receptor‐associated factor 6 (TRAF6), an E3 ubiquitin ligase, is a signal transduction molecule shared by the interleukin‐1 receptor (IL‐1R)/Toll‐like receptor (TLR) family and the TNFR superfamily. TRAF6 has a unique TRAF domain and RING finger domain that mediate intracellular signaling events. In the immune system, TRAF6‐mediated signaling has been shown to be critical for the development, homeostasis, and activation of a variety of immune cells, including B cells, T cells, dendritic cells, and macrophages. Although the pathogenesis and etiology of autoimmune diseases and cancer are not fully understood, it is worth noting that existing studies have shown that TRAF6 is involved in the pathogenesis and development of a variety of these diseases. Herein, we reviewed the role of TRAF6 in certain immune cells, as well as the function and potential effect of TRAF6 in autoimmune diseases and cancer. Our review indicates that TRAF6 may be a novel target for autoimmune diseases and cancer.
Prevalence and Antifungal Susceptibility Profile of Clinically Relevant Candida Species in Postmenopausal Women with Diabetes
Authors: Al Halteet Sarah , Abdel-Hadi Ahmed , Hassan Mohamed , Awad Mohamed
Published: November 2020

The incidence of diabetes mellitus has increased in Saudi Arabia, which has raised the risk of vulvovaginal candidiasis (VVC). This study highlights the prevalence and antifungal susceptibility of Candida species among postmenopausal women with diabetes with symptoms of VVC in Taif, a city in Saudi Arabia. Several diagnostic tools were used to differentiate the yeast isolates, including microscopic examination, culture morphology on CHROM agar, further confirmation with the VITEK 2 system, and ITS1 and ITS4 region sequencing. Antifungal susceptibility of the selected Candida species was determined using the VITEK 2 system (bioMérieux Inc., USA). Out of the 550 high vaginal swabs investigated, 86 specimens were Candida species positive (15.6%) with a significant difference according to age; the positivity in the 45–50 years’ age group (12%) was higher than that in the 51–55 years’ age group (3.6%). Candida albicans was the most common causative agent in 51 samples (59.3%), followed by C. glabrata in 21 samples (24.41%) and C. krusei in 14 samples (16.27%), with no significant differences between the age groups. Three isolates, including two C. albicans and one C. krusei, exhibited resistance against all the tested antifungal agents. CHROM agar and VITEK 2 were accurate phenotypic tools to identify Candida species with 100% sensitivity and specificity and were consistent with the phylogenetic characterization. The data emphasized the importance of identifying Candida species and their antifungal susceptibility among postmenopausal women with diabetes, highlighting the potential risk posed by diabetes in this age group.