bluuebunny/crossref_metadata_2025 huggingface dataset:
An Optimized 2.4GHz RF Energy Harvester for Energizing Low-Power Wireless Sensor Platforms
Authors: Shekhar Chandra , Varma Shirshu
Published: July 2018

The lifetime of battery-operated sensor platforms (i.e., sensor nodes) is a critical issue. The replacement of their batteries is quite a challenging task if these platforms are deployed for detecting events in inaccessible geographical areas (e.g., forest). This paper describes an optimized RF energy harvester/scavenger (consisting of an antenna, impedance matching circuit and rectifier) for energizing low-power sensor platforms (electronic systems). Few nonmatched rectifiers (using HSMS-285X Schottky diodes) are fabricated to characterize the input impedance for different sets of parameters. After characterization a proper impedance matching circuit is integrated for the maximum power transfer from antenna to rectifier. It is shown that a single stage of RF rectifier is enough to produce output voltage of 1.8[Formula: see text]V. Very few realizations of RF energy harvester are reported in the literature under 2.4[Formula: see text]GHz ISM band category. Furthermore, high-gain microstrip patch array antennas are fabricated to capture the maximum power from the surroundings. The maximum harvesting range of 0.92[Formula: see text]m is obtained at 27[Formula: see text]dBm transmitting power level.
Mapping the Field of Knowledge Management: Bibliometric and Content Analysis of Journal of Information & Knowledge Management for the Period from 2002–2016
Authors: Alajmi Bibi , Alhaji Talal
Published: July 2018

This study aims to overview the research trends in Knowledge Management (KM) using a bibliometric and content analysis. Several research questions pertaining to the annual growth of KM publications, the key topics discussed, popular methodologies, country of origin, and citations analysis are posed and answered. In total, 475 articles published in the Journal of Information & Knowledge Management (JIKM) were retrieved from the Scopus bibliographical database. The results showed a relatively steady growth in the number of articles published in JIKM between 2002 and 2016. US scholars authored 21% of the articles published in JIKM, followed by those from India (almost 10%), Australia (8%), and the UK (7.5%). It is also noteworthy that scientific papers published in JIKM are referenced in highly cited journals of computer science, business, and library and information science. Topics such as knowledge discovery, taxonomy and ontology, and knowledge representation were the major KM research trends to arise from the current phenomena of “big data” and data analytics, as reflected in the subject matter of published works. A comparison between the counts for qualitative and quantitative research methodologies showed a clear tendency toward the latter, including questionnaires, while case studies, content analysis, and framework and conceptual papers also proved to be important for KM researchers.
An Approach Towards Establishing Reference Linking in Desktop Reference Manager
Authors: Kiran Mandava Kranthi , Reddy K. Thammi
Published: July 2018

The growth in the availability of number of research journal articles in a digital form is explosive. So more and more researchers have turned towards the publications in digital form, download and maintain them in their standalone personal computers. This increase in a number of publications in digital form has made it difficult for researchers, who have to face the burden of managing, linking and searching the research journal articles on their personal computers. Many reference manager softwares like RefWorks, Zotero, EndNote, and Mendeley are available in the market, which provide an easy way for a researcher to organize and manage the research journal articles. These above said reference managers take the help of extracted basic metadata such as Title, Author, Abstract, etc. to organize the journal articles and maintain a link between them. But the essential feature of “reference linking” is not focused. Reference linking is a feature where a cited article can be tracked from the article citing it, from a large volume of journal article collection. This feature is mostly available for online, but not for offline standalone personal computers. This paper addresses this problem in detail, explores the existing reference linking features which exist for online scholarlty literature, presents the algorithms for retrieving the metadata along with references at the end of each journal article and a way for storing and linking them to the full texts present in the reference management software that works on a standalone personal computer, using semantic technology. A reference management software “sodhanaRef” which includes the reference linking feature and semantic search has been built along with an explanation about its architecture.
Impact of Knowledge Management on Innovation: Evidence from a South Asian Country
Authors: Yousaf Muhammad Jahanzaib , Ali Qamar
Published: July 2018

Ever growing need for innovation has become a big challenge for organisations. Consequently, Knowledge management (KM) is gaining importance in the business world as one of the critical enablers for innovation. This study answers the question whether KM philosophy helps organisations develop capabilities to induce innovation, especially in the context of developing economies. Based on the empirical evidence from the service sector of Pakistan, we show that knowledge acquisition, knowledge dissemination, and responsiveness to knowledge significantly influence an organisation’s capabilities to produce innovative outputs. We also bring to the fore a mutual relationship of KM components with each other, thereby highlighting that knowledge acquisition is important for knowledge dissemination which in turn determines an organisation’s responsiveness to knowledge. We used structural equation modelling (SEM) to develop a model among three endogenous KM factors and one exogenous factor, innovation. Our findings show a significant relationship of all variables, though the strength of each varies, hence confirming the hypothesis that KM, if implemented effectively, improves organisation’s ability to innovate.
Influence of Screen Size and Field of View on Perceived Brightness
Authors: Chapiro Alexandre , Kunkel Timo , Atkins Robin , Daly Scott
Published: July 2018

We present a study into the perception of display brightness as related to the physical size and distance of the screen from the observer. Brightness perception is a complex topic, which is influenced by a number of lower- and higher-order factors—with empirical evidence from the cinema industry suggesting that display size may play a significant role. To test this hypothesis, we conducted a series of user studies exploring brightness perception for a range of displays and distances from the observer that span representative use scenarios. Our results suggest that retinal size is not sufficient to explain the range of discovered brightness variations, but is sufficient in combination with physical distance from the observer. The resulting model can be used as a step toward perceptually correcting image brightness perception based on target display parameters. This can be leveraged for energy management and the preservation of artistic intent. A pilot study suggests that adaptation luminance is an additional factor for the magnitude of the effect.
Efficient reflectance capture using an autoencoder
Authors: Kang Kaizhang , Chen Zimin , Wang Jiaping , Zhou Kun , Wu Hongzhi
Published: July 2018

We propose a novel framework that automatically learns the lighting patterns for efficient reflectance acquisition, as well as how to faithfully reconstruct spatially varying anisotropic BRDFs and local frames from measurements under such patterns. The core of our framework is an asymmetric deep autoencoder, consisting of a nonnegative, linear encoder which directly corresponds to the lighting patterns used in physical acquisition, and a stacked, nonlinear decoder which computationally recovers the BRDF information from captured photographs. The autoencoder is trained with a large amount of synthetic reflectance data, and can adapt to various factors, including the geometry of the setup and the properties of appearance. We demonstrate the effectiveness of our framework on a wide range of physical materials, using as few as 16 ~ 32 lighting patterns, which correspond to 12 ~ 25 seconds of acquisition time. We also validate our results with the ground truth data and captured photographs. Our framework is useful for increasing the efficiency in both novel and existing acquisition setups.
Dynamic kelvinlets
Authors: De Goes Fernando , James Doug L.
Published: July 2018

We introduce
Dynamic Kelvinlets
, a new analytical technique for real-time physically based animation of virtual elastic materials. Our formulation is based on the dynamic response to time-varying force distributions applied to an infinite elastic medium. The resulting displacements provide the plausibility of volumetric elasticity, the dynamics of compressive and shear waves, and the interactivity of closed-form expressions. Our approach builds upon the work of de Goes and James [2017] by presenting an extension of the regularized Kelvinlet solutions from elastostatics to the elastodynamic regime. To finely control our elastic deformations, we also describe the construction of compound solutions that resolve pointwise and keyframe constraints. We demonstrate the versatility and efficiency of our method with a series of examples in a production grade implementation.
Creating and chaining camera moves for quadrotor videography
Authors: Xie Ke , Yang Hao , Huang Shengqiu , Lischinski Dani , Christie Marc , Xu Kai , Gong Minglun , Cohen-Or Daniel , Huang Hui
Published: July 2018

Capturing aerial videos with a quadrotor-mounted camera is a challenging creative task, as it requires the simultaneous control of the quadrotor's motion and the mounted camera's orientation. Letting the drone follow a pre-planned trajectory is a much more appealing option, and recent research has proposed a number of tools designed to automate the generation of feasible camera motion plans; however, these tools typically require the user to specify and edit the camera path, for example by providing a complete and ordered sequence of key viewpoints.

In this paper, we propose a higher level tool designed to enable even novice users to easily capture compelling aerial videos of large-scale outdoor scenes. Using a coarse 2.5D model of a scene, the user is only expected to specify starting and ending viewpoints and designate a set of landmarks, with or without a particular order. Our system automatically generates a diverse set of candidate local
camera moves
for observing each landmark, which are collision-free, smooth, and adapted to the shape of the landmark. These moves are guided by a landmark-centric view quality field, which combines visual interest and frame composition. An optimal global camera trajectory is then constructed that chains together a sequence of local camera moves, by choosing one move for each landmark and connecting them with suitable transition trajectories. This task is formulated and solved as an instance of the Set Traveling Salesman Problem.
Predictive and generative neural networks for object functionality
Authors: Hu Ruizhen , Yan Zihao , Zhang Jingwen , Van Kaick Oliver , Shamir Ariel , Zhang Hao , Huang Hui
Published: July 2018

Humans can predict the functionality of an object even without any surroundings, since their knowledge and experience would allow them to "hallucinate" the interaction or usage scenarios involving the object. We develop
predictive
and
generative
deep convolutional neural networks to replicate this feat. Specifically, our work focuses on functionalities of man-made 3D objects characterized by human-object or object-object interactions. Our networks are trained on a database of scene contexts, called
interaction contexts
, each consisting of a central object and one or more surrounding objects, that represent object functionalities. Given a 3D object
in isolation
, our
functional similarity network
(fSIM-NET), a variation of the triplet network, is trained to predict the functionality of the object by inferring functionality-revealing interaction contexts. fSIM-NET is complemented by a generative network (iGEN-NET) and a segmentation network (iSEG-NET). iGEN-NET takes a single voxelized 3D object with a functionality label and synthesizes a voxelized surround, i.e., the interaction context which visually demonstrates the corresponding functionality. iSEG-NET further separates the interacting objects into different groups according to their interaction types.
Efficient rendering of layered materials using an atomic decomposition with statistical operators
Authors: Belcour Laurent
Published: July 2018

We derive a novel framework for the efficient analysis and computation of light transport within layered materials. Our derivation consists in two steps. First, we decompose light transport into a set of atomic operators that act on its directional statistics. Specifically, our operators consist of reflection, refraction, scattering, and absorption, whose combinations are sufficient to describe the statistics of light scattering multiple times within layered structures. We show that the first three directional moments (energy, mean and variance) already provide an accurate summary. Second, we extend the adding-doubling method to support arbitrary combinations of such operators efficiently. During shading, we map the directional moments to BSDF lobes. We validate that the resulting BSDF closely matches the ground truth in a lightweight and efficient form. Unlike previous methods we support an arbitrary number of textured layers, and demonstrate a practical and accurate rendering of layered materials with both an offline and real-time implementation that are free from per-material precomputation.
Visemenet
Authors: Zhou Yang , Xu Zhan , Landreth Chris , Kalogerakis Evangelos , Maji Subhransu , Singh Karan
Published: July 2018

We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.
Towards virtual reality infinite walking
Authors: Sun Qi , Patney Anjul , Wei Li-Yi , Shapira Omer , Lu Jingwan , Asente Paul , Zhu Suwen , Mcguire Morgan , Luebke David , Kaufman Arie
Published: July 2018

Redirected walking techniques can enhance the immersion and visual-vestibular comfort of virtual reality (VR) navigation, but are often limited by the size, shape, and content of the physical environments.

We propose a redirected walking technique that can apply to small physical environments with static or dynamic obstacles. Via a head- and eye-tracking VR headset, our method detects saccadic suppression and redirects the users during the resulting temporary blindness. Our dynamic path planning runs in real-time on a GPU, and thus can avoid static and dynamic obstacles, including walls, furniture, and other VR users sharing the same physical space. To further enhance saccadic redirection, we propose subtle gaze direction methods tailored for VR perception.

We demonstrate that saccades can significantly increase the rotation gains during redirection without introducing visual distortions or simulator sickness. This allows our method to apply to large open virtual spaces and small physical environments for room-scale VR. We evaluate our system via numerical simulations and real user studies.
The human touch
Authors: Pai Dinesh K. , Rothwell Austin , Wyder-Hodge Pearson , Wick Alistair , Fan Ye , Larionov Egor , Harrison Darcy , Neog Debanga Raj , Shing Cole
Published: July 2018

Simulating how the human body deforms in contact with external objects, tight clothing, or other humans is of central importance to many fields. Despite great advances in numerical methods, the material properties required to accurately simulate the body of a real human have been sorely lacking. Here we show that mechanical properties of the human body can be directly measured using a novel hand-held device. We describe a complete pipeline for measurement, modeling, parameter estimation, and simulation using the finite element method. We introduce a phenomenological model (the sliding thick skin model) that is effective for both simulation and parameter estimation. Our data also provide new insights into how the human body actually behaves. The methods described here can be used to create personalized models of an individual human or of a population. Consequently, our methods have many potential applications in computer animation, product design, e-commerce, and medicine.
Animating fluid sediment mixture in particle-laden flows
Authors: Gao Ming , Pradhana Andre , Han Xuchen , Guo Qi , Kot Grant , Sifakis Eftychios , Jiang Chenfanfu
Published: July 2018

In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.
DeepMimic
Authors: Peng Xue Bin , Abbeel Pieter , Levine Sergey , van de Panne Michiel
Published: July 2018

A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.
Single-photon 3D imaging with deep sensor fusion
Authors: Lindell David B. , O'Toole Matthew , Wetzstein Gordon
Published: July 2018

Sensors which capture 3D scene information provide useful data for tasks in vehicle navigation, gesture recognition, human pose estimation, and geometric reconstruction. Active illumination time-of-flight sensors in particular have become widely used to estimate a 3D representation of a scene. However, the maximum range, density of acquired spatial samples, and overall acquisition time of these sensors is fundamentally limited by the minimum signal required to estimate depth reliably. In this paper, we propose a data-driven method for photon-efficient 3D imaging which leverages sensor fusion and computational reconstruction to rapidly and robustly estimate a dense depth map from low photon counts. Our sensor fusion approach uses measurements of single photon arrival times from a low-resolution single-photon detector array and an intensity image from a conventional high-resolution camera. Using a multi-scale deep convolutional network, we jointly process the raw measurements from both sensors and output a high-resolution depth map. To demonstrate the efficacy of our approach, we implement a hardware prototype and show results using captured data. At low signal-to-background levels, our depth reconstruction algorithm with sensor fusion outperforms other methods for depth estimation from noisy measurements of photon arrival times.
The layer laboratory
Authors: Zeltner Tizian , Jakob Wenzel
Published: July 2018

We present a versatile computational framework for modeling the reflective and transmissive properties of arbitrarily layered anisotropic material structures. Given a set of input layers, our model synthesizes an effective BSDF of the entire structure, which accounts for all orders of internal scattering and is efficient to sample and evaluate in modern rendering systems.

Our technique builds on the insight that reflectance data is sparse when expanded into a suitable frequency-space representation, and that this property extends to the class of anisotropic materials. This sparsity enables an efficient matrix calculus that admits the entire space of BSDFs and considerably expands the scope of prior work on layered material modeling. We show how both measured data and the popular class of microfacet models can be expressed in our representation, and how the presence of anisotropy leads to a weak coupling between Fourier orders in frequency space.

In addition to additive composition, our models supports subtractive composition, a fascinating new operation that reconstructs the BSDF of a material that can only be observed indirectly through another layer with known reflectance properties. The operation produces a new BSDF of the desired layer as if measured in isolation. Subtractive composition can be interpreted as a type of deconvolution that removes both internal scattering and blurring due to transmission through the known layer.

We experimentally demonstrate the accuracy and scope of our model and validate both additive and subtractive composition using measurements of real-world layered materials. Both implementation and data will be released to ensure full reproducibility of all of our results.
1
Fast winding numbers for soups and clouds
Authors: Barill Gavin , Dickson Neil G. , Schmidt Ryan , Levin David I. W. , Jacobson Alec
Published: July 2018

Inside-outside determination is a basic building block for higher-level geometry processing operations. Generalized winding numbers provide a robust answer for triangle meshes, regardless of defects such as self-intersections, holes or degeneracies. In this paper, we further generalize the winding number to point clouds. Previous methods for evaluating the winding number are slow for completely disconnected surfaces, such as triangle soups or-in the extreme case- point clouds. We propose a tree-based algorithm to reduce the asymptotic complexity of generalized winding number computation, while closely approximating the exact value. Armed with a fast evaluation, we demonstrate the winding number in a variety of new applications: voxelization, signing distances, generating 3D printer paths, defect-tolerant mesh booleans and point set surfaces.
Bayesian online regression for adaptive direct illumination sampling
Authors: Vévoda Petr , Kondapaneni Ivo , Křivánek Jaroslav
Published: July 2018

Direct illumination calculation is an important component of any physically-based Tenderer with a substantial impact on the overall performance. We present a novel adaptive solution for unbiased Monte Carlo direct illumination sampling, based on online learning of the light selection probability distributions. Our main contribution is a formulation of the learning process as Bayesian regression, based on a new, specifically designed statistical model of direct illumination. The net result is a set of regularization strategies to prevent over-fitting and ensure robustness even in early stages of calculation, when the observed information is sparse. The regression model captures spatial variation of illumination, which enables aggregating statistics over relatively large scene regions and, in turn, ensures a fast learning rate. We make the method scalable by adopting a light clustering strategy from the Lightcuts method, and further reduce variance through the use of control variates. As a main design feature, the resulting algorithm is virtually free of any preprocessing, which enables its use for interactive progressive rendering, while the online learning still enables super-linear convergence.
A material point method for thin shells with frictional contact
Authors: Guo Qi , Han Xuchen , Fu Chuyuan , Gast Theodore , Tamstorf Rasmus , Teran Joseph
Published: July 2018

We present a novel method for simulation of thin shells with frictional contact using a combination of the Material Point Method (MPM) and subdivision finite elements. The shell kinematics are assumed to follow a continuum shell model which is decomposed into a Kirchhoff-Love motion that rotates the mid-surface normals followed by shearing and compression/extension of the material along the mid-surface normal. We use this decomposition to design an elastoplastic constitutive model to resolve frictional contact by decoupling resistance to contact and shearing from the bending resistance components of stress. We show that by resolving frictional contact with a continuum approach, our hybrid Lagrangian/Eulerian approach is capable of simulating challenging shell contact scenarios with hundreds of thousands to millions of degrees of freedom. Without the need for collision detection or resolution, our method runs in a few minutes per frame in these high resolution examples. Furthermore we show that our technique naturally couples with other traditional MPM methods for simulating granular and related materials.
Tetrahedral meshing in the wild
Authors: Hu Yixin , Zhou Qingnan , Gao Xifeng , Jacobson Alec , Zorin Denis , Panozzo Daniele
Published: July 2018

We propose a novel tetrahedral meshing technique that is unconditionally robust, requires no user interaction, and can directly convert a triangle soup into an analysis-ready volumetric mesh. The approach is based on several core principles: (1) initial mesh construction based on a fully robust, yet efficient, filtered exact computation (2) explicit (automatic or user-defined) tolerancing of the mesh relative to the surface input (3) iterative mesh improvement with guarantees, at every step, of the output validity. The quality of the resulting mesh is a direct function of the target mesh size and allowed tolerance: increasing allowed deviation from the initial mesh and decreasing the target edge length both lead to higher mesh quality.

Our approach enables "black-box" analysis, i.e. it allows to automatically solve partial differential equations on geometrical models available in the wild, offering a robustness and reliability comparable to, e.g., image processing algorithms, opening the door to automatic, large scale processing of real-world geometric data.
Variational surface cutting
Authors: Sharp Nicholas , Crane Keenan
Published: July 2018

This paper develops a global variational approach to cutting curved surfaces so that they can be flattened into the plane with low metric distortion. Such cuts are a critical component in a variety of algorithms that seek to parameterize surfaces over flat domains, or fabricate structures from flat materials. Rather than evaluate the quality of a cut solely based on properties of the curve itself (
e.g.
, its length or curvature), we formulate a flow that directly optimizes the distortion induced by cutting and flattening. Notably, we do not have to explicitly parameterize the surface in order to evaluate the cost of a cut, but can instead integrate a simple evolution equation defined on the cut curve itself. We arrive at this flow via a novel application of
shape derivatives
to the
Yamabe equation
from conformal geometry. We then develop an Eulerian numerical integrator on triangulated surfaces, which does not restrict cuts to mesh edges and can incorporate user-defined data such as importance or occlusion. The resulting cut curves can be used to drive distortion to arbitrarily low levels, and have a very different character from cuts obtained via purely discrete formulations. We briefly explore potential applications to computational design, as well as connections to space filling curves and the problem of uniform heat distribution.
Looking to listen at the cocktail party
Authors: Ephrat Ariel , Mosseri Inbar , Lang Oran , Dekel Tali , Wilson Kevin , Hassidim Avinatan , Freeman William T. , Rubinstein Michael
Published: July 2018

We present a joint audio-visual model for isolating a single speech signal from a mixture of sounds such as other speakers and background noise. Solving this task using only audio as input is extremely challenging and does not provide an association of the separated speech signals with speakers in the video. In this paper, we present a deep network-based model that incorporates both visual and auditory signals to solve this task. The visual features are used to "focus" the audio on desired speakers in a scene and to improve the speech separation quality. To train our joint audio-visual model, we introduce AVS
peech
, a new dataset comprised of thousands of hours of video segments from the Web. We demonstrate the applicability of our method to classic speech separation tasks, as well as real-world scenarios involving heated interviews, noisy bars, and screaming children, only requiring the user to specify the face of the person in the video whose speech they want to isolate. Our method shows clear advantage over state-of-the-art audio-only speech separation in cases of mixed speech. In addition, our model, which is speaker-independent (trained once, applicable to any speaker), produces better results than recent audio-visual speech separation methods that are speaker-dependent (require training a separate model for each speaker of interest).
Curved optimal delaunay triangulation
Authors: Feng Leman , Alliez Pierre , Busé Laurent , Delingette Hervé , Desbrun Mathieu
Published: July 2018

Meshes with curvilinear elements hold the appealing promise of enhanced geometric flexibility and higher-order numerical accuracy compared to their commonly-used straight-edge counterparts. However, the generation of curved meshes remains a computationally expensive endeavor with current meshing approaches: high-order parametric elements are notoriously difficult to conform to a given boundary geometry, and enforcing a smooth and non-degenerate Jacobian everywhere brings additional numerical difficulties to the meshing of complex domains. In this paper, we propose an extension of Optimal Delaunay Triangulations (ODT) to curved and graded isotropic meshes. By exploiting a continuum mechanics interpretation of ODT instead of the usual approximation theoretical foundations, we formulate a very robust geometry and topology optimization of Bézier meshes based on a new simple functional promoting isotropic and uniform Jacobians throughout the domain. We demonstrate that our resulting curved meshes can adapt to complex domains with high precision even for a small count of elements thanks to the added flexibility afforded by more control points and higher order basis functions.
Mode-adaptive neural networks for quadruped motion control
Authors: Zhang He , Starke Sebastian , Komura Taku , Saito Jun
Published: July 2018

Quadruped motion includes a wide variation of gaits such as walk, pace, trot and canter, and actions such as jumping, sitting, turning and idling. Applying existing data-driven character control frameworks to such data requires a significant amount of data preprocessing such as motion labeling and alignment. In this paper, we propose a novel neural network architecture called Mode-Adaptive Neural Networks for controlling quadruped characters. The system is composed of the motion prediction network and the gating network. At each frame, the motion prediction network computes the character state in the current frame given the state in the previous frame and the user-provided control signals. The gating network dynamically updates the weights of the motion prediction network by selecting and blending what we call the expert weights, each of which specializes in a particular movement. Due to the increased flexibility, the system can learn consistent expert weights across a wide range of non-periodic/periodic actions, from unstructured motion capture data, in an end-to-end fashion. In addition, the users are released from performing complex labeling of phases in different gaits. We show that this architecture is suitable for encoding the multi-modality of quadruped locomotion and synthesizing responsive motion in real-time.
A high-performance software graphics pipeline architecture for the GPU
Authors: Kenzel Michael , Kerbl Bernhard , Schmalstieg Dieter , Steinberger Markus
Published: July 2018

In this paper, we present a real-time graphics pipeline implemented entirely in software on a modern GPU. As opposed to previous work, our approach features a fully-concurrent, multi-stage, streaming design with dynamic load balancing, capable of operating efficiently within bounded memory. We address issues such as primitive order, vertex reuse, and screen-space derivatives of dependent variables, which are essential to real-world applications, but have largely been ignored by comparable work in the past. The power of a software approach lies in the ability to tailor the graphics pipeline to any given application. In exploration of this potential, we design and implement four novel pipeline modifications. Evaluation of the performance of our approach on more than 100 real-world scenes collected from video games shows rendering speeds within one order of magnitude of the hardware graphics pipeline as well as significant improvements over previous work, not only in terms of capabilities and performance, but also robustness.
Integer-only cross field computation
Authors: Farchi Nahum , Ben-Chen Mirela
Published: July 2018

We propose a new iterative algorithm for computing smooth cross fields on triangle meshes that is simple, easily parallelizable on the GPU, and finds solutions with lower energy and fewer cone singularities than state-of-the-art methods. Our approach is based on a formal equivalence, which we prove, between two formulations of the optimization problem. This equivalence allows us to eliminate the real variables and design an efficient grid search algorithm for the cone singularities. We leverage a recent graph-theoretical approximation of the
resistance distance matrix
of the triangle mesh to speed up the computation and enable a trade-off between the computation time and the smoothness of the output.
Differentiable programming for image processing and deep learning in halide
Authors: Li Tzu-Mao , Gharbi Michaël , Adams Andrew , Durand Frédo , Ragan-Kelley Jonathan
Published: July 2018

Gradient-based optimization has enabled dramatic advances in computational imaging through techniques like deep learning and nonlinear optimization. These methods require gradients not just of simple mathematical functions, but of general
programs
which encode complex transformations of images and graphical data. Unfortunately, practitioners have traditionally been limited to either hand-deriving gradients of complex computations, or composing programs from a limited set of coarse-grained operators in deep learning frameworks. At the same time, writing programs with the level of performance needed for imaging and deep learning is prohibitively difficult for most programmers.

We extend the image processing language Halide with general reverse-mode automatic differentiation (AD), and the ability to automatically optimize the implementation of gradient computations. This enables automatic computation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. A key challenge is to structure the gradient code to retain parallelism. We define a simple algorithm to automatically schedule these pipelines, and show how Halide's existing scheduling primitives can express and extend the key AD optimization of "checkpointing."

Using this new tool, we show how to easily define new neural network layers which automatically compile to high-performance GPU implementations, and how to solve nonlinear inverse problems from computational imaging. Finally, we show how differentiable programming enables dramatically improving the quality of even traditional, feed-forward image processing algorithms, blurring the distinction between classical and deep methods.
Numerical coarsening using discontinuous shape functions
Authors: Chen Jiong , Bao Hujun , Wang Tianyu , Desbrun Mathieu , Huang Jin
Published: July 2018

In this paper, an efficient and scalable approach for simulating inhomogeneous and non-linear elastic objects is introduced. Our numerical coarsening approach consists in optimizing non-conforming and matrix-valued shape functions to allow for predictive simulation of heterogeneous materials with non-linear constitutive laws even on coarse grids, thus saving orders of magnitude in computational time compared to traditional finite element computations. The set of local shape functions over coarse elements is carefully tailored in a preprocessing step to balance geometric continuity and local material stiffness. In particular, we do not impose continuity of our material-aware shape functions between neighboring elements to significantly reduce the fictitious numerical stiffness that conforming bases induce; however, we enforce crucial geometric and physical properties such as partition of unity and exact reproduction of representative fine displacements to eschew the use of discontinuous Galerkin methods. We demonstrate that we can simulate, with no parameter tuning, inhomogeneous and non-linear materials significantly better than previous approaches that traditionally try to homogenize the constitutive model instead.
Generalized motorcycle graphs for imperfect quad-dominant meshes
Authors: Schertler Nico , Panozzo Daniele , Gumhold Stefan , Tarini Marco
Published: July 2018

We introduce a practical pipeline to create UV T-layouts for real-world quad dominant semi-regular meshes. Our algorithm creates large rectangular patches by relaxing the notion of motorcycle graphs and making it insensitive to local irregularities in the mesh structure such as non-quad elements, redundant irregular vertices, T-junctions, and others. Each surface patch, which can contain multiple singularities and/or polygonal elements, is mapped to an axis-aligned rectangle, leading to a simple and efficient UV layout, which is ideal for texture mapping (allowing for mipmapping and artifact-free bilinear interpolation). We demonstrate that our algorithm is an ideal solution for both recent semi-regular, quad-dominant meshing methods, and for the low-poly meshes typically used in games and movies.
Learning symmetric and low-energy locomotion
Authors: Yu Wenhao , Turk Greg , Liu C. Karen
Published: July 2018

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function [Duan et al. 2016]. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning.
Validated and Numerically Efficient Chebyshev Spectral Methods for Linear Ordinary Differential Equations
Authors: Bréhard Florent , Brisebarre Nicolas , Joldeş Mioara
Published: July 2018

In this work, we develop a validated numeric method for the solution of linear ordinary differential equations (LODEs). A wide range of algorithms (i.e., Runge-Kutta, collocation, spectral methods) exist for numerically computing approximations of the solutions. Most of these come with proofs of asymptotic convergence, but usually, provided error bounds are nonconstructive. However, in some domains like critical systems and computer-aided mathematical proofs, one needs validated effective error bounds. We focus on both the theoretical and practical complexity analysis of a so-called a posteriori quasi-Newton validation method, which mainly relies on a fixed-point argument of a contracting map. Specifically, given a polynomial approximation, obtained by some numerical algorithm and expressed on a Chebyshev basis, our algorithm efficiently computes an accurate and rigorous error bound. For this, we study theoretical properties like compactness, convergence, and invertibility of associated linear integral operators and their truncations in a suitable coefficient space of Chebyshev series. Then, we analyze the almost-banded matrix structure of these operators, which allows for very efficient numerical algorithms for both numerical solutions of LODEs and rigorous computation of the approximation error. Finally, several representative examples show the advantages of our algorithms as well as their theoretical and practical limits.
BLASFEO
Authors: Frison Gianluca , Kouzoupis Dimitris , Sartor Tommaso , Zanelli Andrea , Diehl Moritz
Published: July 2018

Basic Linear Algebra Subroutines for Embedded Optimization (BLASFEO) is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization and small-scale high-performance computing, in general. A key difference with respect to existing high-performance implementations of BLAS is that the computational performance is optimized for small- to medium-scale matrices, i.e., for sizes up to a few hundred. BLASFEO comes with three different implementations: a high-performance implementation aimed at providing the highest performance for matrices fitting in cache, a reference implementation providing portability and embeddability and optimized for very small matrices, and a wrapper to standard BLAS and LAPACK providing high performance on large matrices. The three implementations of BLASFEO together provide high-performance dense linear algebra routines for matrices ranging from very small to large. Compared to both open-source and proprietary highly tuned BLAS libraries, for matrices of size up to about 100, the high-performance implementation of BLASFEO is about 20--30% faster than the corresponding level 3 BLAS routines and two to three times faster than the corresponding LAPACK routines.
A Survey of Machine Learning for Big Code and Naturalness
Authors: Allamanis Miltiadis , Barr Earl T. , Devanbu Premkumar , Sutton Charles
Published: July 2018

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.
ROPTLIB
Authors: Huang Wen , Absil P.-A. , Gallivan Kyle A. , Hand Paul
Published: July 2018

Riemannian optimization is the task of finding an optimum of a real-valued function defined on a Riemannian manifold. Riemannian optimization has been a topic of much interest over the past few years due to many applications including computer vision, signal processing, and numerical linear algebra. The substantial background required to successfully design and apply Riemannian optimization algorithms is a significant impediment for many potential users. Therefore, multiple packages, such as Manopt (in Matlab) and Pymanopt (in Python), have been developed. This article describes ROPTLIB, a C++ library for Riemannian optimization. Unlike prior packages, ROPTLIB simultaneously achieves the following goals: (i) it has user-friendly interfaces in Matlab, Julia, and C++; (ii) users do not need to implement manifold- and algorithm-related objects; (iii) it provides efficient computational time due to its C++ core; (iv) it implements state-of-the-art generic Riemannian optimization algorithms, including quasi-Newton algorithms; and (v) it is based on object-oriented programming, allowing users to rapidly add new algorithms and manifolds.