bluuebunny/crossref_metadata_2025 huggingface dataset:
Lightweight structure design under force location uncertainty
Authors: Ulu Erva , Mccann James , Kara Levent Burak
Published: July 2017

We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests.
Variational stokes
Authors: Larionov Egor , Batty Christopher , Bridson Robert
Published: July 2017

We propose a novel unsteady Stokes solver for coupled viscous and pressure forces in grid-based liquid animation which yields greater accuracy and visual realism than previously achieved. Modern fluid simulators treat viscosity and pressure in separate solver stages, which reduces accuracy and yields incorrect free surface behavior. Our proposed implicit variational formulation of the Stokes problem leads to a symmetric positive definite linear system that gives properly coupled forces, provides unconditional stability, and treats difficult boundary conditions naturally through simple volume weights. Surface tension and moving solid boundaries are also easily incorporated. Qualitatively, we show that our method recovers the characteristic rope coiling instability of viscous liquids and preserves fine surface details, while previous grid-based schemes do not. Quantitatively, we demonstrate that our method is convergent through grid refinement studies on analytical problems in two dimensions. We conclude by offering practical guidelines for choosing an appropriate viscous solver, based on the scenario to be animated and the computational costs of different methods.
A multi-scale model for simulating liquid-hair interactions
Authors: Fei Yun (Raymond) , Maia Henrique Teles , Batty Christopher , Zheng Changxi , Grinspun Eitan
Published: July 2017

The diverse interactions between hair and liquid are complex and span multiple length scales, yet are central to the appearance of humans and animals in many situations. We therefore propose a novel multi-component simulation framework that treats many of the key physical mechanisms governing the dynamics of wet hair. The foundations of our approach are a discrete rod model for hair and a particle-in-cell model for fluids. To treat the thin layer of liquid that clings to the hair, we augment each hair strand with a height field representation. Our contribution is to develop the necessary physical and numerical models to evolve this new system and the interactions among its components. We develop a new reduced-dimensional liquid model to solve the motion of the liquid along the length of each hair, while accounting for its moving reference frame and influence on the hair dynamics. We derive a faithful model for surface tension-induced cohesion effects between adjacent hairs, based on the geometry of the liquid bridges that connect them. We adopt an empirically-validated drag model to treat the effects of coarse-scale interactions between hair and surrounding fluid, and propose new volume-conserving dripping and absorption strategies to transfer liquid between the reduced and particle-in-cell liquid representations. The synthesis of these techniques yields an effective wet hair simulator, which we use to animate hair flipping, an animal shaking itself dry, a spinning car wash roller brush dunked in liquid, and intricate hair coalescence effects, among several additional scenarios.
Perceptual evaluation of liquid simulation methods
Authors: Um Kiwon , Hu Xiangyu , Thuerey Nils
Published: July 2017

This paper proposes a novel framework to evaluate fluid simulation methods based on crowd-sourced user studies in order to robustly gather large numbers of opinions. The key idea for a robust and reliable evaluation is to use a reference video from a carefully selected real-world setup in the user study. By conducting a series of controlled user studies and comparing their evaluation results, we observe various factors that affect the perceptual evaluation. Our data show that the availability of a reference video makes the evaluation consistent. We introduce this approach for computing scores of simulation methods as
visual accuracy
metric. As an application of the proposed framework, a variety of popular simulation methods are evaluated.
Orthotropic
k
-nearest foams for additive manufacturing
Authors: Martínez Jonàs , Song Haichuan , Dumas Jérémie , Lefebvre Sylvain
Published: July 2017

Additive manufacturing enables the fabrication of objects embedding
meta-materials.
By creating fine-scale structures, the object's physical properties can be graded (e.g. elasticity, porosity), even though a single base material is used for fabrication. Designing the fine and detailed geometry of a metamaterial while attempting to achieve specific properties is difficult. In addition, the structures are intended to fill comparatively large volumes, which quickly leads to large data structures and intractable simulation costs. Thus, most metamaterials are defined as periodic structures repeated in regular lattices. The periodicity simplifies modeling, simulation, and reduces memory costs - however it limits the possibility to smoothly grade properties along free directions.

In this work, we propose a novel metamaterial with controllable,
freely
orientable,
orthotropic
elastic behavior - orthotropy means that elasticity is controlled independently along three orthogonal axes, which leads to materials that better adapt to uneven, directional load scenarios, and offer a more versatile material design primitive. The fine-scale structures are generated procedurally by a stochastic process, and resemble a foam. The absence of global organization and periodicity allows the free gradation of density, orientation, and stretch, leading to the controllable orthotropic behavior. The procedural nature of the synthesis process allows it to scale to arbitrarily large volumes at low memory costs.

We detail the foam structure synthesis, analyze and discuss its properties through numerical and experimental verifications, and finally demonstrate the use of orthotropic materials for the design of 3D printed objects.
FlowRep
Authors: Gori Giorgio , Sheffer Alla , Vining Nicholas , Rosales Enrique , Carr Nathan , Ju Tao
Published: July 2017

We present
FlowRep
, an algorithm for extracting descriptive compact 3D curve networks from meshes of free-form man-made shapes. We infer the desired compact curve network from complex 3D geometries by using a series of insights derived from perception, computer graphics, and design literature. These sources suggest that visually descriptive networks are
cycle-descriptive
, i.e their cycles unambiguously describe the geometry of the surface patches they surround. They also indicate that such networks are designed to be
projectable
, or easy to envision when observed from a static general viewpoint; in other words, 2D projections of the network should be strongly indicative of its 3D geometry. Research suggests that both properties are best achieved by using networks dominated by
flowlines
, surface curves aligned with principal curvature directions across anisotropic regions and strategically extended across sharp-features and isotropic areas. Our algorithm leverages these observation in the construction of a compact descriptive curve network. Starting with a curvature aligned quad dominant mesh we first extract sequences of mesh edges that form long, well-shaped and reliable flowlines by leveraging directional similarity between nearby meaningful flowline directions We then use a compact subset of the extracted flowlines and the model's sharp-feature, or trim, curves to form a sparse, projectable network which describes the underlying surface. We validate our method by demonstrating a range of networks computed from diverse inputs, using them for surface reconstruction, and showing extensive comparisons with prior work and artist generated networks.
Modeling surface appearance from a single photograph using self-augmented convolutional neural networks
Authors: Li Xiao , Dong Yue , Peers Pieter , Tong Xin
Published: July 2017

We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary
labeled
training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.
Data-driven synthesis of smoke flows with CNN-based feature descriptors
Authors: Chu Mengyu , Thuerey Nils
Published: July 2017

We present a novel data-driven algorithm to synthesize high resolution flow simulations with reusable repositories of space-time flow data. In our work, we employ a descriptor learning approach to encode the similarity between fluid regions with differences in resolution and numerical viscosity. We use convolutional neural networks to generate the descriptors from fluid data such as smoke density and flow velocity. At the same time, we present a deformation limiting patch advection method which allows us to robustly track deformable fluid regions. With the help of this patch advection, we generate stable space-time data sets from detailed fluids for our repositories. We can then use our learned descriptors to quickly localize a suitable data set when running a new simulation. This makes our approach very efficient, and resolution independent. We will demonstrate with several examples that our method yields volumes with very high effective resolutions, and non-dissipative small scale details that naturally integrate into the motions of the underlying flow.
Harmonic global parametrization with rational holonomy
Authors: Bright Alon , Chien Edward , Weber Ofir
Published: July 2017

We present a method for locally injective seamless parametrization of triangular mesh surfaces of arbitrary genus, with or without boundaries, given desired cone points and rational holonomy angles (multiples of 2π/
q
for some positive integer
q
). The basis of the method is an elegant generalization of Tutte's "spring embedding theorem" to this setting. The surface is cut to a disk and a harmonic system with appropriate rotation constraints is solved, resulting in a harmonic global parametrization (HGP) method. We show a remarkable result: that if the triangles adjacent to the cones and boundary are positively oriented, and the correct cone and turning angles are induced, then the resulting map is guaranteed to be locally injective. Guided by this result, we solve the linear system by convex optimization, imposing convexification frames on
only
the boundary and cone triangles, and minimizing a Laplacian energy to achieve harmonicity. We compare HGP to state-of-the-art methods and see that it is the most robust, and is significantly faster than methods with comparable robustness.
Shader components
Authors: He Yong , Foley Theresa , Hofstee Teguh , Long Haomin , Fatahalian Kayvon
Published: July 2017

Modern game engines seek to balance the conflicting goals of high rendering performance and productive software development. To improve CPU performance, the most recent generation of real-time graphics APIs provide new primitives for performing efficient batch updates to shader parameters. However, modern game engines featuring large shader codebases have struggled to take advantage of these benefits. The problem is that even though shader parameters can be organized into efficient modules bound to the pipeline at various frequencies, modern shading languages lack corresponding primitives to organize shader logic (requiring these parameters) into modules as well. The result is that complex shaders are typically compiled to use a monolithic block of parameters, defeating the design, and performance benefits, of the new parameter binding API. In this paper we propose to resolve this mismatch by introducing
shader components
, a first-class unit of modularity in a shader program that encapsulates a unit of shader logic and the parameters that must be bound when that logic is in use. We show that by building sophisticated shaders out of components, we can retain essential aspects of performance (static specialization of the shader logic in use and efficient update of parameters at component granularity) while maintaining the modular shader code structure that is desirable in today's high-end game engines.
Mixed-primary factorization for dual-frame computational displays
Authors: Huang Fu-Chung , Pająk Dawid , Kim Jonghyun , Kautz Jan , Luebke David
Published: July 2017

Increasing resolution and dynamic range of digital color displays is challenging with designs confined by cost and power specifications. This necessitates modern displays to trade-off spatial and temporal resolution for color reproduction capability. In this work we explore the idea of joint hardware and algorithm design to balance such trade-offs. We introduce a system that uses content-adaptive and compressive factorizations to reproduce colors. Each target frame is factorized into two products of high-resolution monochromatic and low-resolution color images, which then get integrated through temporal or spatial multiplexing. As our framework minimizes the error in colorimetric space, the perceived color rendition is high, and thanks to GPU acceleration, the results are generated in real-time. We evaluate our system with a LCD prototype that uses LED backlight array and temporal multiplexing to reproduce color images. Our approach enables high effective resolution and dynamic range without increasing power consumption. We also demonstrate low-cost extensions to hyperspectral and light-field imaging, which are possible due to compressive nature of our system.
Convergence analysis for anisotropic monte carlo sampling spectra
Authors: Singh Gurprit , Jarosz Wojciech
Published: July 2017

Traditional Monte Carlo (MC) integration methods use point samples to numerically approximate the underlying integral. This approximation introduces variance in the integrated result, and this error can depend critically on the sampling patterns used during integration. Most of the well-known samplers used for MC integration in graphics---e.g. jittered, Latin-hypercube (
N
-rooks), multijittered---are anisotropic in nature. However, there are currently no tools available to analyze the impact of such anisotropic samplers on the variance convergence behavior of Monte Carlo integration. In this work, we develop a Fourier-domain mathematical tool to analyze the variance, and subsequently the convergence rate, of Monte Carlo integration using any arbitrary (anisotropic) sampling power spectrum. We also validate and leverage our theoretical analysis, demonstrating that judicious alignment of anisotropic sampling and integrand spectra can improve variance and convergence rates in MC rendering, and that similar improvements can apply to (anisotropic) deterministic samplers.
Audio-driven facial animation by joint end-to-end learning of pose and emotion
Authors: Karras Tero , Aila Timo , Laine Samuli , Herva Antti , Lehtinen Jaakko
Published: July 2017

We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet.

We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence.
Globally and locally consistent image completion
Authors: Iizuka Satoshi , Simo-Serra Edgar , Ishikawa Hiroshi
Published: July 2017

We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.
Dynamics-aware numerical coarsening for fabrication design
Authors: Chen Desai , Levin David I. W. , Matusik Wojciech , Kaufman Danny M.
Published: July 2017

The realistic simulation of highly-dynamic elastic objects is important for a broad range of applications in computer graphics, engineering and computational fabrication. However, whether simulating flipping toys, jumping robots, prosthetics or quickly moving creatures, performing such simulations in the presence of contact, impact and friction is both time consuming and inaccurate. In this paper we present Dynamics-Aware Coarsening (DAC) and the Boundary Balanced Impact (BBI) model which allow for the accurate simulation of dynamic, elastic objects undergoing both large scale deformation and frictional contact, at rates up to 79 times faster than state-of-the-art methods. DAC and BBI produce simulations that are accurate and fast enough to be used (for the first time) for the computational design of 3D-printable compliant dynamic mechanisms. Thus we demonstrate the efficacy of DAC and BBI by designing and fabricating mechanisms which flip, throw and jump over and onto obstacles as requested.
Hiding of phase-based stereo disparity for ghost-free viewing without glasses
Authors: Fukiage Taiki , Kawabe Takahiro , Nishida Shin'ya
Published: July 2017

When a conventional stereoscopic display is viewed without stereo glasses, image blurs, or 'ghosts', are visible due to the fusion of stereo image pairs. This artifact severely degrades 2D image quality, making it difficult to simultaneously present clear 2D and 3D contents. To overcome this limitation (backward incompatibility), here we propose a novel method to synthesize ghost-free stereoscopic images. Our method gives binocular disparity to a 2D image, and drives human binocular disparity detectors, by the addition of a quadrature-phase pattern that induces spatial subband phase shifts. The disparity-inducer patterns added to the left and right images are identical except for the contrast polarity. Physical fusion of the two images cancels out the disparity-inducer components and makes only the original 2D pattern visible to viewers without glasses. Unlike previous solutions, our method perfectly excludes stereo ghosts without using special hardware. A simple algorithm can transform 3D contents from the conventional stereo format into ours. Furthermore, our method can alter the depth impression of a real object without its being noticed by naked-eye viewers by means of light projection of the disparity-inducer components onto the object's surface. Psychophysical evaluations have confirmed the practical utility of our method.
Facial retargeting with automatic range of motion alignment
Authors: Ribera Roger Blanco i , Zell Eduard , Lewis J. P. , Noh Junyong , Botsch Mario
Published: July 2017

While facial capturing focuses on accurate reconstruction of an actor's performance, facial animation retargeting has the goal to transfer the animation to another character, such that the semantic meaning of the animation remains. Because of the popularity of blendshape animation, this effectively means to compute suitable blendshape weights for the given target character. Current methods either require manually created examples of matching expressions of actor and target character, or are limited to characters with similar facial proportions (i.e., realistic models). In contrast, our approach can automatically retarget facial animations from a real actor to stylized characters. We formulate the problem of transferring the blendshapes of a facial rig to an actor as a special case of manifold alignment, by exploring the similarities of the motion spaces defined by the blendshapes and by an expressive training sequence of the actor. In addition, we incorporate a simple, yet elegant facial prior based on discrete differential properties to guarantee smooth mesh deformation. Our method requires only sparse correspondences between characters and is thus suitable for retargeting marker-less and marker-based motion capture as well as animation transfer between virtual characters.
Computational zoom
Authors: Badki Abhishek , Gallo Orazio , Kautz Jan , Sen Pradeep
Published: July 2017

Capturing a picture that "tells a story" requires the ability to create the right composition. The two most important parameters controlling composition are the camera position and the focal length of the lens. The traditional paradigm is for a photographer to mentally visualize the desired picture, select the capture parameters to produce it, and finally take the photograph, thus committing to a particular composition. We propose to change this paradigm. To do this, we introduce
computational zoom
, a framework that allows a photographer to manipulate several aspects of composition in post-processing from a stack of pictures captured at different distances from the scene. We further define a multi-perspective camera model that can generate compositions that are not physically attainable, thus extending the photographer's control over factors such as the relative size of objects at different depths and the sense of depth of the picture. We show several applications and results of the proposed
computational zoom
framework.
Interactive design space exploration and optimization for CAD models
Authors: Schulz Adriana , Xu Jie , Zhu Bo , Zheng Changxi , Grinspun Eitan , Matusik Wojciech
Published: July 2017

Computer Aided Design (CAD) is a multi-billion dollar industry used by almost every mechanical engineer in the world to create practically every existing manufactured shape. CAD models are not only widely available but also extremely useful in the growing field of fabrication-oriented design because they are parametric by construction and capture the engineer's design intent, including manufacturability. Harnessing this data, however, is challenging, because generating the geometry for a given parameter value requires time-consuming computations. Furthermore, the resulting meshes have different combinatorics, making the mesh data inherently discontinuous with respect to parameter adjustments. In our work, we address these challenges and develop tools that allow interactive exploration and optimization of parametric CAD data. To achieve interactive rates, we use precomputation on an adaptively sampled grid and propose a novel scheme for interpolating in this domain where each sample is a mesh with different combinatorics. Specifically, we extract partial correspondences from CAD representations for local mesh morphing and propose a novel interpolation method for adaptive grids that is both
continuous/smooth
and
local
(i.e., the influence of each sample is constrained to the local regions where mesh morphing can be computed). We show examples of how our method can be used to interactively visualize and optimize objects with a variety of physical properties.
k
-curves
Authors: Yan Zhipei , Schiller Stephen , Wilensky Gregg , Carr Nathan , Schaefer Scott
Published: July 2017

We present a method for constructing almost-everywhere curvature-continuous, piecewise-quadratic curves that interpolate a list of control points and have local maxima of curvature only at the control points. Our premise is that salient features of the curve should occur only at control points to avoid the creation of features unintended by the artist. While many artists prefer to use interpolated control points, the creation of artifacts, such as loops and cusps, away from control points has limited the use of these types of curves. By enforcing the maximum curvature property, loops and cusps cannot be created unless the artist intends for them to be.

To create such curves, we focus on piecewise quadratic curves, which can have only one maximum curvature point. We provide a simple, iterative optimization that creates quadratic curves, one per interior control point, that meet with
G
2
continuity everywhere except at inflection points of the curve where the curves are
G
1
. Despite the nonlinear nature of curvature, our curves only obtain local maxima of the absolute value of curvature only at interpolated control points.
Beyond points and beams
Authors: Bitterli Benedikt , Jarosz Wojciech
Published: July 2017

We develop a theory of volumetric density estimation which generalizes prior photon point (0D) and beam (1D) approaches to a broader class of estimators using "
n
D" samples along photon and/or camera subpaths. Volumetric photon mapping performs density estimation by point sampling propagation distances within the medium and performing density estimation over the generated points (0D). Beam-based (1D) approaches consider the expected value of this distance sampling process along the last camera and/or light subpath segments. Our theory shows how to replace propagation distance sampling steps across
multiple bounces
to form higher-dimensional samples such as photon planes (2D), photon volumes (3D), their camera path equivalents, and beyond. We perform a theoretical error analysis which reveals that in scenarios where beams already outperform points, each additional dimension of
n
D samples compounds these benefits further. Moreover, each additional sample dimension reduces the required dimensionality of the blurring needed for density estimation, allowing us to formulate, for the first time,
fully unbiased
forms of volumetric photon mapping. We demonstrate practical implementations of several of the new estimators our theory predicts, including both biased and unbiased variants, and show that they outperform state-of-the-art beam-based volumetric photon mapping by a factor of 2.4--40×.
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
Authors: Bako Steve , Vogels Thijs , Mcwilliams Brian , Meyer Mark , NováK Jan , Harvill Alex , Sen Pradeep , Derose Tony , Rousselle Fabrice
Published: July 2017

Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit to noise in the input. For this reason, supervised learning methods have been proposed that train on a large collection of reference examples, but they use explicit filters that limit their denoising ability. To address these problems, we propose a novel, supervised learning approach that allows the filtering kernel to be more complex and general by leveraging a deep convolutional neural network (CNN) architecture. In one embodiment of our framework, the CNN directly predicts the final denoised pixel value as a highly non-linear combination of the input features. In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors. We train and evaluate our networks on production data and observe improvements over state-of-the-art MC denoisers, showing that our methods generalize well to a variety of scenes. We conclude by analyzing various components of our architecture and identify areas of further research in deep learning for MC denoising.
Real-time planning for automated multi-view drone cinematography
Authors: Nägeli Tobias , Meier Lukas , Domahidi Alexander , Alonso-Mora Javier , Hilliges Otmar
Published: July 2017

We propose a method for automated aerial videography in dynamic and cluttered environments. An online receding horizon optimization formulation facilitates the planning process for novices and experts alike. The algorithm takes high-level plans as input, which we dub virtual rails, alongside interactively defined aesthetic framing objectives and
jointly
solves for 3D quadcopter motion plans and associated velocities. The method generates control inputs subject to constraints of a non-linear quadrotor model and dynamic constraints imposed by actors moving in an a priori unknown way. The output plans are physically feasible, for the horizon length, and we apply the resulting control inputs directly at each time-step, without requiring a separate trajectory tracking algorithm. The online nature of the method enables incorporation of feedback into the planning and control loop, makes the algorithm robust to disturbances. Furthermore, we extend the method to include coordination between multiple drones to enable dynamic multi-view shots, typical for action sequences and live TV coverage. The algorithm runs in real-time on standard hardware and computes motion plans for several drones in the order of milliseconds. Finally, we evaluate the approach qualitatively with a number of challenging shots, involving multiple drones and actors and qualitatively characterize the computational performance experimentally.
Infinite continuous adaptivity for incompressible SPH
Authors: Winchenbach Rene , Hochstetter Hendrik , Kolb Andreas
Published: July 2017

In this paper we introduce a novel method to adaptive incompressible SPH simulations. Instead of using a scheme with a number of fixed particle sizes or levels, our approach allows continuous particle sizes. This enables us to define optimal particle masses with respect to, e.g., the distance to the fluid's surface. A required change in mass due to the dynamics of the fluid is properly and stably handled by our scheme of mass redistribution. This includes temporally smooth changes in particle masses as well as sudden mass variations in regions of high flow dynamics. Our approach guarantees low spatial variations in particle size, which is a core property in order to achieve large adaptivity ratios for incompressible fluid simulations. Conceptually, our approach allows for infinite continuous adaptivity, practically we achieved adaptivity ratios up to 5 orders of magnitude, while still being mass preserving and numerically stable, yielding unprecedented vivid surface detail at comparably low computational cost and moderate particle counts.
eLearn Magazine: Advancing forward
Authors: Conceicao Simone , Samuel Anita
Published: July 2017

eLearn Magazine is entering a new period and exciting changes lie ahead.
Rock Art and Ontology
Authors: Jones Andrew Meirion
Published: July 2017

This article reviews recent ontological debates in archaeology and examines how ontology has been discussed in rock art studies. It questions the prevailing symbolic analysis of rock art and critically questions the epistemological foundations of “informed” and “formal” approaches to rock art. The article evaluates ontological debates within rock art studies and argues for a committed approach to ontology that uses anthropological understandings of ontology as an analytical tool and a method for generating fresh concepts. The article then reviews the ontological dimensions of a series of aspects of rock art studies, including the production of rock art images, their placement on the rock surface, their position in the landscape, and their relationship to formation processes. The article concludes by arguing that ontological questions not only relate to the interpretation of rock art images, but touch on all aspects of rock art.
Reconstructing Climate from Glaciers
Authors: Mackintosh Andrew N. , Anderson Brian M. , Pierrehumbert Raymond T.
Published: July 2017

Glaciers offer the potential to reconstruct past climate over timescales from decades to millennia. They are found on nearly every continent, and at the Last Glacial Maximum, glaciers were larger in all regions on Earth. The physics of glacier-climate interaction are relatively well understood, and glacier models can be used to reconstruct past climate from geological evidence of past glacier extent. This can lead to significant insights regarding past, present, and future climate. For example, glacier modeling has demonstrated that the near-ubiquitous global pattern of glacier retreat during the last few centuries resulted from a global-scale climate warming of ∼1°C, consistent with instrumental data and climate proxy records. Climate reconstructions from glaciers have also demonstrated that the tropics were colder at the Last Glacial Maximum than was originally inferred from sea surface temperature reconstructions. Future efforts to reconstruct climate from glaciers may provide new constraints on climate sensitivity to CO2 forcing, polar amplification of climate change, and more.
Lethal avian influenza A (H5N1) virus induces ataxic breathing in mice with apoptosis of pre-Botzinger complex neurons expressing neurokinin-1 receptor
Authors: Zhuang Jianguo , Zang Na , Ye Chunyan , Xu Fadi
Published: July 2017

Lethal influenza A (H5N1) induces respiratory failure in humans. Although it also causes death at 7 days postinfection (dpi) in mice, the development of the respiratory failure and the viral impact on pre-Botzinger complex (PBC) neurons expressing neurokinin 1 receptor (NK1R), which is the respiratory rhythm generator, have not been explored. Body temperature, weight, ventilation, and arterial blood pH and gases were measured at 0, 2, 4, and 6 dpi in control, lethal HK483, and nonlethal HK486 viral-infected mice. Immunoreactivities (IR) of PBC NK1R, H5N1 viral nucleoprotein (NP), and active caspase-3 (CASP3; a marker for apoptosis) were detected at 6 dpi. HK483, but not HK486, mice showed the following abnormalities: 1) gradual body weight loss and hypothermia; 2) tachypnea at 2–4 dpi and ataxic breathing with long-lasting apneas and hypercapnic hypoxemia at 6 dpi; and 3) viral replication in PBC NK1R neurons with NK1R-IR reduced by 75% and CASP3-IR colabeled at 6 dpi. Lethal H5N1 viral infection causes tachypnea at the early stage and ataxic breathing and apneas (hypercapnic hypoxemia) leading to death at the late stage. Its replication in the PBC induces apoptosis of local NK1R neurons, contributing to ataxic breathing and respiratory failure.
Ethanol Extract of Mylabris phalerata Inhibits M2 Polarization Induced by Recombinant IL‐4 and IL‐13 in Murine Macrophages
Authors: Chung Hwan-Suck , Lee Bong-Seon , Ma Jin Yeul
Published: July 2017

Mylabris phalerata (MP) is an insect used in oriental herbal treatments for tumor, tinea infections, and stroke. Recent studies have shown that tumor‐associated macrophages (TAM) have detrimental roles such as tumor progression, angiogenesis, and metastasis. Although TAM has phenotypes and characteristics in common with M2‐polarized macrophages, M1 macrophages have tumor suppression and immune stimulation effects. Medicines polarizing macrophages to M1 have been suggested to have anticancer effects via the modulation of the tumor microenvironment. In this line, we screened oriental medicines to find M1 polarizing medicines in M2‐polarized macrophages. Among approximately 400 types of oriental medicine, the ethanol extract of M. phalerata (EMP) was the most proficient in increasing TNF‐α secretion in M2‐polarized macrophages and TAM. Although EMP enhanced the levels of an M1 cytokine (TNF‐α) and a marker (CD86), it significantly reduced the levels of an M2 marker (arginase‐1) in M2‐polarized macrophages. In addition, EMP‐treated macrophages increased the levels of M1 markers (Inos and Tnf-α) and reduced those of the enhanced M2 markers (Fizz-1, Ym-1, and arginase-1). EMP‐treated macrophages significantly reduced Lewis lung carcinoma cell migration in a transwell migration assay and inhibited EL4‐luc2 lymphoma proliferation. In our mechanism study, EMP was found to inhibit STAT3 phosphorylation in M2‐polarized macrophages. These results suggest that EMP is effective in treating TAM‐mediated tumor progression and metastasis.
Surface Modification of Carbon Nanofibers and Graphene Platelets Mixtures by Plasma Polymerization of Propylene
Authors: Covarrubias-Gordillo Carlos Andrés , Soriano-Corral Florentino , Ávila-Orta Carlos Alberto , Cruz-Delgado Victor Javier , Neira-Velázquez María Guadalupe , Hernández-Hernández Ernesto , Hernández-Gámez José Francisco , De León-Martínez Patricia Adriana
Published: July 2017

Carbon nanofibers (CNFs), graphene platelets (GPs), and their mixtures were treated by plasma polymerization of propylene. The carbon nanoparticles (CNPs) were previously sonicated in order to deagglomerate and increase the surface area. Untreated and plasma treated CNPs were analyzed by dynamic light scattering (DLS), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and thermogravimetric analysis (TGA). DLS analysis showed a significant reduction of average particle size, due to the sonication pretreatment. Plasma polymerized propylene was deposited on the CNPs surface; the total amount of polymerized propylene was from 4.68 to 6.58 wt-%. Raman spectroscopy indicates an increase in the sp3 hybridization of the treated samples, which suggest that the polymerized propylene is grafted onto the CNPs.
A Hypoxia-Independent Hypoxia-Inducible Factor-1 Activation Pathway Induced by Phosphatidylinositol-3 Kinase/Akt in HER2 Overexpressing Cells
Authors: Li Yan M. , Zhou Binhua P. , Deng Jiong , Pan Yong , Hay Nissim , Hung Mien-Chie
Published: July 2017

HER2 overexpression, a known prognostic factor in many human cancers, can activate phosphatidylinositol-3 kinase (PI-3K)/Akt pathways and plays an important role in mediating cell survival and tumor development. Hypoxia-inducible factors (HIFs) promote angiogenesis and energy metabolism and thereby enhance tumor growth and metastasis. HIFs, composed of α and β subunits, are activated in most human cancers, including those that overexpress HER2. Previous reports have suggested that increased PI-3K/Akt or decreased PTEN activity may activate the HIF pathway in various tumors, but the detailed mechanism is still not completely understood. Here we reported an interaction between the HIF and PI-3K/Akt pathways in HER2-overexpressing cancer cells. Our results indicate that HER2 overexpression, which results in constitutively active Akt, turns on HIF-1α independently of hypoxia, and this activation is weaker than that under hypoxic condition. Further investigation showed that Akt is required for the hypoxia-independent HIF activity. The PI-3K/Akt pathway did not affect the HIF-1α binding with its E3 ligase von Hippel-Lindau but enhanced the binding affinity between the HIF-1 α and β subunits. Furthermore, we found that Akt interacts with HIF-1β and regulates HIF activity. Our results indicated that HER2 can induce HIF activation via the activation of Akt suggesting that activation of HER2/Akt pathway may promote angiogenesis independent of hypoxia, which may have important implications for the oncogenic activity of HER2 and Akt.
DNA Vaccine Expressing the Mimotope of GD2 Ganglioside Induces Protective GD2 Cross-reactive Antibody Responses
Authors: Bolesta Elizabeth , Kowalczyk Aleksandra , Wierzbicki Andrzej , Rotkiewicz Piotr , Bambach Barbara , Tsao Chun-Yen , Horwacik Irena , Kolinski Andrzej , Rokita Hanna , Brecher Martin , Wang Xinhui , Ferrone Soldano , Kozbor Danuta
Published: July 2017

The GD2 ganglioside expressed on neuroectodermally derived tumors, including neuroblastoma and melanoma, is weakly immunogenic in tumor-bearing patients and induces predominantly immunoglobulin (Ig)-M antibody responses in the immunized host. Here, we investigated whether interconversion of GD2 into a peptide mimetic form would induce GD2 cross-reactive IgG antibody responses in mice. Screening of the X15 phage display peptide library with the anti-GD2 monoclonal antibody (mAb) 14G2a led to isolation of mimetic peptide 47, which inhibited the binding of 14G2a antibody to GD2-positive tumor cells. The peptide was also recognized by GD2-specific serum antibodies from a patient with neuroblastoma, suggesting that it bears an internal image of GD2 ganglioside expressed on the tumor cells. The molecular basis for antigenicity of the GD2 mimetic peptide, established by molecular modeling and mutagenesis studies, led to the generation of a 47-LDA mutant with an increased mimicry to GD2. Immunization of mice with peptide 47-LDA–encoded plasmid DNA elicited GD2 cross-reactive IgG antibody responses, which were increased on subsequent boost with GD2 ganglioside. The vaccine-induced antibodies recognized GD2-positive tumor cells, mediated complement-dependent cytotoxicity, and exhibited protection against s.c. human GD2-positive melanoma growth in the severe combined immunodeficient mouse xenograft model. The results from our studies provide insights into approaches for boosting GD2 cross-reactive IgG antibody responses by minigene vaccination with a protective epitope of GD2 ganglioside.
SV40 Enhances the Risk of Malignant Mesothelioma among People Exposed to Asbestos: A Molecular Epidemiologic Case-Control Study
Authors: Cristaudo Alfonso , Foddis Rudy , Vivaldi Agnese , Buselli Rodolfo , Gattini Vittorio , Guglielmi Giovanni , Cosentino Francesca , Ottenga Franco , Ciancia Eugenio , Libener Roberta , Filiberti Rosangela , Neri Monica , Betta PierGiacomo , Tognon Mauro , Mutti Luciano , Puntoni Riccardo
Published: July 2017

We conducted a case-control study on asbestos exposure and presence of SV40 in tumor samples of malignant mesotheliomas (MMs) and bladder urotheliomas (BUs). PCR analysis revealed the presence of SV40 DNA (SV40+) in eight (42.1%) MMs and 6 (33.3%) BUs. The odds ratio for MM Asb− and SV40+ was 0.4 [95% confidence interval (95% CI), 0.03-4.0], for Asb+ and SV40− was 3.6 (95% CI, 0.6-21.0), and for Asb+ and SV40+ was 12.6 (95% CI, 1.2-133.9). Our results suggest that SV40 increases the risk of MM among individuals exposed to asbestos.
Inhibition of a New Differentiation Pathway in Neuroblastoma by Copy Number Defects of N-myc, Cdc42, and nm23 Genes
Authors: Valentijn Linda J. , Koppen Arjen , van Asperen Ronald , Root Heather A. , Haneveld Franciska , Versteeg Rogier
Published: July 2017

The best studied oncogenic mechanisms are inactivating defects in both alleles of tumor suppressor genes and activating mutations in oncogenes. Chromosomal gains and losses are frequent in human tumors, but for many regions, like 1p36 and 17q in neuroblastoma, no mutated tumor suppressor genes or oncogenes were identified. Amplification of N-myc in neuroblastoma is strongly correlated with loss of 1p36 and gain of 17q. Here we report that N-myc down-regulates the mRNA expression of many genes with a role in cell architecture. One of them is the 1p36 gene Cdc42. Restoring the Cdc42 expression in neuroblastoma cells strongly induced differentiation. N-myc also inhibited Cdc42 functioning at the protein level. This was mediated by nm23-H1 and nm23-H2, which are located in the amplified 17q region. Nm23-H1 and nm23-H2 are strongly up-regulated downstream targets of N-myc. Nm23-H1 was shown to bind Cdc42 and prevented the induction of differentiation. Overexpression of Nm23 due to gain of 17q and induction by N-myc combined with weak expression of Cdc42 due to loss of 1p36 and down-regulation by N-myc can thus block differentiation. Although this marks Cdc42 as a candidate tumor suppressor gene, no mutations were found. Further silencing of Cdc42 by small interfering RNA induced massive apoptosis, indicating that tumor cell survival requires a minimal Cdc42 activity. Three regions of chromosomal gain and loss thus affect genes functioning in one pathway in neuroblastoma. They converge to bring the pathway out of balance and prevent Cdc42 mediated differentiation.
Loss of Heterozygosity in Childhood Acute Lymphoblastic Leukemia Detected by Genome-Wide Microarray Single Nucleotide Polymorphism Analysis
Authors: Irving Julie A.E. , Bloodworth Lisa , Bown Nick P. , Case Marian C. , Hogarth Linda A. , Hall Andrew G.
Published: July 2017

Loss of heterozygosity (LOH) is detectable in many forms of malignancy, including leukemia, using techniques such as microsatellite analysis and comparative genomic hybridization. However, these techniques are laborious and require the use of relatively large amounts of DNA if the whole genome is to be examined. Here we describe the use of oligonucleotide microarrays to characterize single nucleotide polymorphisms (SNPs) in lymphoblasts isolated from children with acute lymphoblastic leukemia for the pan-genomic mapping of LOH with a resolution of 100 to 200 kb. Results were compared with DNA obtained during remission and on relapse. Abnormalities were seen in 8 of 10 cases. The two cases with no abnormalities and one case that showed identical changes at relapse and presentation remain in remission 1 to 9 years following retreatment. The remaining seven patients died following relapse. In four cases, LOH was only detectable at relapse suggesting that progressive LOH may be a cause of disease progression and/or drug resistance. This was supported by detailed analysis of one case in which LOH involving the glucocorticoid receptor was associated with mutation of the remaining allele. The most frequent abnormality detected involved chromosome 9p. In each of the four cases where this was observed LOH included the INK4 locus. In three of the four cases, INK4 loss was only observed at relapse, suggesting that this abnormality may be commonly associated with treatment failure. These observations show that SNP array analysis is a powerful new tool for the analysis of allelic imbalance in leukemic blasts.