bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial resistance ofPasteurella multocidastrains isolated from pigs between 2010 and 2016
Authors: Oh Yoon‐Hee , Moon Dong‐Chan , Lee Young Ju , Hyun Bang‐Hun , Lim Suk‐Kyung
Published: July 2018

Pasteurella multocidais one of the significant causes of respiratory infection outbreaks in the Korean pig industry. Although antimicrobial treatment is an effective strategy for controlling respiratory diseases, limited information is available regarding the antimicrobial susceptibility of the pathogens infecting Korean pigs. Therefore, in this study, we evaluated the antimicrobial resistance ofP multocidaagainst widely used antimicrobials in order to enable the selection of appropriate drugs and to evaluate any trends in resistance. A total of 454 isolates ofP multocidawere collected from all provinces in Korea between 2010 and 2016. Antimicrobial susceptibility of all isolates was determined using a broth microdilution method. The most frequently observed resistance was to sulphadimethoxine (76.0 per cent), followed by oxytetracycline (66.5 per cent), chlortetracycline (36.8 per cent) and florfenicol (18.5 per cent). Although no consistent increase or decrease in resistance was observed for most antimicrobials, resistance to fluoroquinolones tended to increase over the study period. A variety of resistance patterns were observed, most frequently for tetracyclines and sulphonamides. These findings could provide information enabling the selection of optimal antimicrobials for efficient treatment of pneumoniae pasteurellosis in pig farms, which would impede the emergence of antimicrobial resistance.
Synthesis of novel N-functionalized 4-aryl-tetrahydrobiquinoline-2,5-(1H,3H)-diones via one-pot three-component reaction: a joint experimental and computational study
Authors: Zadsirjan Vahideh , Mahdizadeh Sayyed Jalil , Heravi Majid M. , Heydari Masumeh
Published: July 2018

A novel series of N-functionalized 4-aryl-tetrahydrobiquinoline-2,5-(1H,3H)-diones were synthesized in high yields by a one-pot three-component reaction involving 2-chloroquinoline-3-carbaldehydes, Meldrum’s acid, and enaminones (dimedone-based enaminones) in the presence of K2CO3 in CH3CN under reflux condition. To gain a deep insight on the mechanism of the reaction, an extensive series of quantum mechanics calculations in the framework of density functional theory (DFT) were carried out for supporting the suggested reaction pathway.
Zealand hard red spring wheat
Authors: Spaner D. , Iqbal M. , Navabi A. , Strenzke K. , Beres B.
Published: July 2018

Zealand hard red spring wheat (Triticum aestivum L.) was developed using a modified bulk breeding method at the University of Alberta in Edmonton, AB. Zealand is an apically awn-letted, hollow-stemmed cultivar with a combination of high yield potential, tall plant type, large leaves, and early maturity. In three years of testing in the Western Bread Wheat Cooperative Registration Test during 2013–2015, Zealand exhibited grain yield similar to Glenn and Carberry and 5%–6% lower than Unity and AAC Viewfield, though this difference was not significant (p > 0.05). Zealand yielded 37% greater than the highest-yielding Canada Western Red Spring (CWRS) check, CDC Osler, in A-level testing at a certified organic farm. Zealand matured 1–4 d earlier and was taller than all the check cultivars, but exhibited lodging resistance better than Unity and similar to the other checks. The test weight of Zealand (79.1 kg hL−1) was lower than Glenn and similar to the other checks, while its seed mass was in the range of the check cultivars. Overall, Zealand was rated as resistant (R) to the prevalent races of leaf rust, moderately resistant (MR) to stripe rust and loose smut, intermediate (I) to stem rust and leaf spot, and moderately susceptible (MS) to common bunt and Fusarium head blight. Three years of end-use quality evaluation indicated that Zealand is acceptable for the CWRS class, with relatively few weaknesses. The tall plant type, large leaves, and early maturity render Zealand suitable for organic/high weed environments.
Asymmetric magnetized QCD matter realized by scalar and instanton-induced interactions
Authors: Yang Li , Wen Xin-Jian
Published: July 2018

In a strong magnetic field utilizing a two-flavor NJL model, the isospin symmetry and asymmetry can be realized by altering the fraction parameter [Formula: see text] of the two kinds of interactions, i.e. the scalar coupling and instanton-induced coupling. As [Formula: see text] increases, the [Formula: see text] quark mass decreases while [Formula: see text] quark mass increases monotonously. The two mass lines cross at [Formula: see text] with an isospin symmetry mass [Formula: see text]. Even in the flavor-decoupled state, i.e. the strong isospin asymmetry, the [Formula: see text] and [Formula: see text] mass lines can have a common mass at a critical temperature [Formula: see text]. As the magnetic field becomes stronger, the [Formula: see text] will move towards the chiral restoration phase. The strong isospin asymmetry will lead to a split of the pseudocritical temperatures for [Formula: see text] and [Formula: see text] quarks, which can be reflected by the two peaks of the specific heat and sound velocity.
ARBITRAGE PRICING THEORY IN ERGODIC MARKETS
Authors: FRAHM GABRIEL
Published: July 2018

Traditional approaches to Arbitrage Pricing Theory (APT) propose a factor model, but empirical applications of APT are, nowadays, based on seemingly unrelated regression. I drop the factor model and assume only that the market is ergodic. This enables me to apply the theory of Hilbert spaces in a natural way. The expected return on any asset can always be approximated by an affine-linear function of its betas and we are able to estimate the relative number of assets that violate the APT equation by taking the expected returns and betas in the market into account. I present a simple sufficient condition for the APT equation in its inexact form. Further, I show that the APT equation holds true in its exact form if and only if an equilibrium market is exhaustive, which means that it must be possible to replicate the betas and idiosyncratic risk of each asset by some strategy that diversifies away all approximation errors in the market.
Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework
Authors: Arokiasami Willson Amalraj , Vadakkepat Prahlad , Tan Kay Chen , Srinivasan Dipti
Published: July 2018

Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.
A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles
Authors: Durst Phillip J. , Goodin Christopher T. , Bethel Cindy L. , Anderson Derek T. , Carruth Daniel W. , Lim Hyeona
Published: July 2018

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.
Deep video portraits
Authors: Kim Hyeongwoo , Garrido Pablo , Tewari Ayush , Xu Weipeng , Thies Justus , Niessner Matthias , Pérez Patrick , Richardt Christian , Zollhöfer Michael , Theobalt Christian
Published: July 2018

We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network - thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect.
Point convolutional neural networks by extension operators
Authors: Atzmon Matan , Maron Haggai , Lipman Yaron
Published: July 2018

This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds. The framework consists of two operators: extension and restriction, mapping point cloud functions to volumetric functions and vise-versa. A point cloud convolution is defined by pull-back of the Euclidean volumetric convolution via an extension-restriction mechanism.

The point cloud convolution is computationally efficient, invariant to the order of points in the point cloud, robust to different samplings and varying densities, and translation invariant, that is the same convolution kernel is used at all points. PCNN generalizes image CNNs and allows readily adapting their architectures to the point cloud setting.

Evaluation of PCNN on three central point cloud learning benchmarks convincingly outperform competing point cloud learning methods, and the vast majority of methods working with more informative shape representations such as surfaces and/or normals.
Deep image-based relighting from optimal sparse samples
Authors: Xu Zexiang , Sunkavalli Kalyan , Hadap Sunil , Ramamoorthi Ravi
Published: July 2018

We present an image-based relighting method that can synthesize scene appearance under novel, distant illumination from the visible hemisphere, from only five images captured under pre-defined directional lights. Our method uses a deep convolutional neural network to regress the relit image from these five images; this
relighting network
is trained on a large synthetic dataset comprised of procedurally generated shapes with real-world reflectances. We show that by combining a custom-designed
sampling network
with the relighting network, we can jointly learn both the optimal input light directions and the relighting function. We present an extensive evaluation of our network, including an empirical analysis of reconstruction quality, optimal lighting configurations for different scenarios, and alternative network architectures. We demonstrate, on both synthetic and real scenes, that our method is able to reproduce complex, high-frequency lighting effects like specularities and cast shadows, and outperforms other image-based relighting methods that require an order of magnitude more images.
StrokeAggregator
Authors: Liu Chenxi , Rosales Enrique , Sheffer Alla
Published: July 2018

When creating line drawings, artists frequently depict intended curves using multiple, tightly clustered, or overdrawn, strokes. Given such sketches, human observers can readily envision these intended,
aggregate
, curves, and mentally assemble the artist's envisioned 2D imagery. Algorithmic stroke consolidation---replacement of overdrawn stroke clusters by corresponding aggregate curves---can benefit a range of sketch processing and sketch-based modeling applications which are designed to operate on consolidated, intended curves. We propose
StrokeAggregator
, a novel stroke consolidation method that significantly improves on the state of the art, and produces aggregate curve drawings validated to be consistent with viewer expectations. Our framework clusters strokes into groups that jointly define intended aggregate curves by leveraging principles derived from human perception research and observation of artistic practices. We employ these principles within a coarse-to-fine clustering method that starts with an initial clustering based on pairwise stroke compatibility analysis, and then refines it by analyzing interactions both within and in-between clusters of strokes. We facilitate this analysis by computing a common 1D parameterization for groups of strokes via common aggregate curve fitting. We demonstrate our method on a large range of line drawings, and validate its ability to generate consolidated drawings that are consistent with viewer perception via qualitative user evaluation, and comparisons to manually consolidated drawings and algorithmic alternatives.
Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning
Authors: Liu Libin , Hodgins Jessica
Published: July 2018

Basketball is one of the world's most popular sports because of the agility and speed demonstrated by the players. This agility and speed makes designing controllers to realize robust control of basketball skills a challenge for physics-based character animation. The highly dynamic behaviors and precise manipulation of the ball that occur in the game are difficult to reproduce for simulated players. In this paper, we present an approach for learning robust basketball dribbling controllers from motion capture data. Our system decouples a basketball controller into locomotion control and arm control components and learns each component separately. To achieve robust control of the ball, we develop an efficient pipeline based on trajectory optimization and deep reinforcement learning and learn non-linear arm control policies. We also present a technique for learning skills and the transition between skills simultaneously. Our system is capable of learning robust controllers for various basketball dribbling skills, such as dribbling between the legs and crossover moves. The resulting control graphs enable a simulated player to perform transitions between these skills and respond to user interaction.
Learning three-dimensional flow for interactive aerodynamic design
Authors: Umetani Nobuyuki , Bickel Bernd
Published: July 2018

We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which predicts aerodynamic forces and velocity and pressure fields given a three-dimensional shape input. Handling detailed free-form three-dimensional shapes in a data-driven framework is challenging because machine learning approaches usually require a consistent parametrization of input and output. We present a novel PolyCube maps-based parametrization that can be computed for three-dimensional shapes at interactive rates. This allows us to efficiently learn the nonlinear response of the flow using a Gaussian process regression. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body.
Immersion of self-intersecting solids and surfaces
Authors: Li Yijing , Barbič Jernej
Published: July 2018

Self-intersecting, or nearly self-intersecting, meshes are commonly found in 2D and 3D computer graphics practice. Self-intersections occur, for example, in the process of artist manual work, as a by-product of procedural methods for mesh generation, or due to modeling errors introduced by scanning equipment. If the space bounded by such inputs is meshed naively, the resulting mesh joins ("glues") self-overlapping parts, precluding efficient further modeling and animation of the underlying geometry. Similarly, near self-intersections force the simulation algorithm to employ an unnecessarily detailed mesh to separate the nearly self-intersecting regions. Our work addresses both of these challenges, by giving an algorithm to generate an "un-glued" simulation mesh, of arbitrary user-chosen resolution, that properly accounts for self-intersections and near self-intersections. In order to achieve this result, we study the mathematical concept of immersion, and give a deterministic and constructive algorithm to determine if the input self-intersecting triangle mesh is the boundary of an immersion. For near self-intersections, we give a robust algorithm to properly duplicate mesh elements and correctly embed the underlying geometry into the mesh element copies. Both the self-intersections and near self-intersections are combined into one algorithm that permits successful meshing at arbitrary resolution. Applications of our work include volumetric shape editing, physically based simulation and animation, and volumetric weight and geodesic distance computation on self-intersecting inputs.
Dexterous manipulation and control with volumetric muscles
Authors: Lee Seunghwan , Yu Ri , Park Jungnam , Aanjaneya Mridul , Sifakis Eftychios , Lee Jehee
Published: July 2018

We propose a framework for simulation and control of the human musculoskeletal system, capable of reproducing realistic animations of dexterous activities with high-level coordination. We present the first controllable system in this class that incorporates volumetric muscle actuators, tightly coupled with the motion controller, in enhancement of line-segment approximations that prior art is overwhelmingly restricted to. The theoretical framework put forth by our methodology computes all the necessary Jacobians for control, even with the drastically increased dimensionality of the state descriptors associated with three-dimensional, volumetric muscles. The direct coupling of volumetric actuators in the controller allows us to model muscular deficiencies that manifest in shape and geometry, in ways that cannot be captured with line-segment approximations. Our controller is coupled with a trajectory optimization framework, and its efficacy is demonstrated in complex motion tasks such as juggling, and weightlifting sequences with variable anatomic parameters and interaction constraints.
Fluid directed rigid body control using deep reinforcement learning
Authors: Ma Pingchuan , Tian Yunsheng , Pan Zherong , Ren Bo , Manocha Dinesh
Published: July 2018

We present a learning-based method to control a coupled 2D system involving both fluid and rigid bodies. Our approach is used to modify the fluid/rigid simulator's behavior by applying control forces only at the simulation domain boundaries. The rest of the domain, corresponding to the interior, is governed by the Navier-Stokes equation for fluids and Newton-Euler's equation for the rigid bodies. We represent our controller using a general neural-net, which is trained using deep reinforcement learning. Our formulation decomposes a control task into two stages: a precomputation training stage and an online generation stage. We utilize various fluid properties, e.g., the liquid's velocity field or the smoke's density field, to enhance the controller's performance. We set up our evaluation benchmark by letting controller drive fluid jets move on the domain boundary and allowing them to shoot fluids towards a rigid body to accomplish a set of challenging 2D tasks such as keeping a rigid body balanced, playing a two-player ping-pong game, and driving a rigid body to sequentially hit specified points on the wall. In practice, our approach can generate physically plausible animations.
DSCarver
Authors: Zhao Haisen , Zhang Hao , Xin Shiqing , Deng Yuanmin , Tu Changhe , Wang Wenping , Cohen-Or Daniel , Chen Baoquan
Published: July 2018

We present an automatic algorithm for
subtractive
manufacturing of freeform 3D objects using high-speed machining (HSM) via CNC. A CNC machine operates a cylindrical cutter to carve off material from a 3D shape stock, following a tool path, to "expose" the target object. Our method decomposes the input object's surface into a small number of patches each of which is fully accessible and machinable by the CNC machine, in
continuous
fashion, under a fixed cutter-object setup configuration. This is achieved by covering the input surface with a minimum number of
accessible regions
and then extracting a set of machinable patches from each accessible region. For each patch obtained, we compute a continuous, space-filling, and
iso-scallop
tool path which conforms to the patch boundary, enabling efficient carving with high-quality surface finishing. The tool path is generated in the form of
connected Fermat spirals
, which have been generalized from a 2D fill pattern for layered manufacturing to work for curved surfaces. Furthermore, we develop a novel method to control the spacing of Fermat spirals based on directional surface curvature and adapt the heat method to obtain iso-scallop carving. We demonstrate automatic generation of accessible and machinable surface decompositions and iso-scallop Fermat spiral carving paths for freeform 3D objects. Comparisons are made to tool paths generated by commercial software in terms of real machining time and surface quality.
Parametric directional coding for precomputed sound propagation
Authors: Raghuvanshi Nikunj , Snyder John
Published: July 2018

Convincing audio for games and virtual reality requires modeling directional propagation effects. The initial sound's arrival direction is particularly salient and derives from multiply-diffracted paths in complex scenes. When source and listener straddle occluders, the initial sound and multiply-scattered reverberation stream through gaps and portals, helping the listener navigate. Geometry near the source and/or listener reveals its presence through anisotropic reflections. We propose the first precomputed wave technique to capture such directional effects in general scenes comprising millions of polygons. These effects are formally represented with the 9D directional response function of 3D source and listener location, time, and direction at the listener, making memory use the major concern. We propose a novel parametric encoder that compresses this function within a budget of ~100MB for large scenes, while capturing many salient acoustic effects indoors and outdoors. The encoder is complemented with a lightweight signal processing algorithm whose filtering cost is largely insensitive to the number of sound sources, resulting in an immediately practical system.
An extended partitioned method for conservative solid-fluid coupling
Authors: Akbay Muzaffer , Nobles Nicholas , Zordan Victor , Shinar Tamar
Published: July 2018

We present a novel extended partitioned method for two-way solid-fluid coupling, where the fluid and solid solvers are treated as black boxes with limited exposed interfaces, facilitating modularity and code reusability. Our method achieves improved stability and extended range of applicability over standard partitioned approaches through three techniques. First, we couple the black-box solvers through a small, reduced-order monolithic system, which is constructed on the fly from input/output pairs generated by the solid and fluid solvers. Second, we use a conservative, impulse-based interaction term to couple the solid and fluid rather than typical pressure-based forces. We show that both of these techniques significantly improve stability and reduce the number of iterations needed for convergence. Finally, we propose a novel boundary pressure projection method that allows for the partitioned simulation of a fully enclosed fluid coupled to a dynamic solid, a scenario that has been problematic for partitioned methods. We demonstrate the benefits of our extended partitioned method by coupling Eulerian fluid solvers for smoke and water to Lagrangian solid solvers for volumetric and thin deformable and rigid objects in a variety of challenging scenarios. We further demonstrate our method by coupling a Lagrangian SPH fluid solver to a rigid body solver.
Robust optimization for topological surface reconstruction
Authors: Lazar Roee , Dym Nadav , Kushinsky Yam , Huang Zhiyang , Ju Tao , Lipman Yaron
Published: July 2018

Surface reconstruction is one of the central problems in computer graphics. Existing research on this problem has primarily focused on improving the geometric aspects of the reconstruction (e.g., smoothness, features, element quality, etc.), and little attention has been paid to ensure it also has desired topological properties (e.g., connectedness and genus). In this paper, we propose a novel and general optimization method for surface reconstruction under topological constraints. The input to our method is a prescribed genus for the reconstructed surface, a partition of the ambient volume into cells, and a set of possible surface candidates and their associated energy within each cell. Our method computes one candidate per cell so that their union is a connected surface with the prescribed genus that minimizes the total energy. We formulate the task as an integer program, and propose a novel solution that combines convex relaxations within a branch and bound framework. As our method is oblivious of the type of input cells, surface candidates, and energy, it can be applied to a variety of reconstruction scenarios, and we explore two of them in the paper: reconstruction from cross-section slices and iso-surfacing an intensity volume. In the first scenario, our method outperforms an existing topology-aware method particularly for complex inputs and higher genus constraints. In the second scenario, we demonstrate the benefit of topology control over classical topology-oblivious methods such as Marching Cubes.
High-fidelity facial reflectance and geometry inference from an unconstrained image
Authors: Yamaguchi Shugo , Saito Shunsuke , Nagano Koki , Zhao Yajie , Chen Weikai , Olszewski Kyle , Morishima Shigeo , Li Hao
Published: July 2018

We present a deep learning-based technique to infer high-quality facial reflectance and geometry given a single unconstrained image of the subject, which may contain partial occlusions and arbitrary illumination conditions. The reconstructed high-resolution textures, which are generated in only a few seconds, include high-resolution skin surface reflectance maps, representing both the diffuse and specular albedo, and medium- and high-frequency displacement maps, thereby allowing us to render compelling digital avatars under novel lighting conditions. To extract this data, we train our deep neural networks with a high-quality skin reflectance and geometry database created with a state-of-the-art multi-view photometric stereo system using polarized gradient illumination. Given the raw facial texture map extracted from the input image, our neural networks synthesize complete reflectance and displacement maps, as well as complete missing regions caused by occlusions. The completed textures exhibit consistent quality throughout the face due to our network architecture, which propagates texture features from the visible region, resulting in high-fidelity details that are consistent with those seen in visible regions. We describe how this highly underconstrained problem is made tractable by dividing the full inference into smaller tasks, which are addressed by dedicated neural networks. We demonstrate the effectiveness of our network design with robust texture completion from images of faces that are largely occluded. With the inferred reflectance and geometry data, we demonstrate the rendering of high-fidelity 3D avatars from a variety of subjects captured under different lighting conditions. In addition, we perform evaluations demonstrating that our method can infer plausible facial reflectance and geometric details comparable to those obtained from high-end capture devices, and outperform alternative approaches that require only a single unconstrained input image.
Skaterbots
Authors: Geilinger Moritz , Poranne Roi , Desai Ruta , Thomaszewski Bernhard , Coros Stelian
Published: July 2018

We present a computation-driven approach to design optimization and motion synthesis for robotic creatures that locomote using arbitrary arrangements of legs and wheels. Through an intuitive interface, designers first create unique robots by combining different types of servomotors, 3D printable connectors, wheels and feet in a mix-and-match manner. With the resulting robot as input, a novel trajectory optimization formulation generates walking, rolling, gliding and skating motions. These motions emerge naturally based on the components used to design each individual robot. We exploit the particular structure of our formulation and make targeted simplifications to significantly accelerate the underlying numerical solver without compromising quality. This allows designers to interactively choreograph stable, physically-valid motions that are agile and compelling. We furthermore develop a suite of user-guided, semi-automatic, and fully-automatic optimization tools that enable motion-aware edits of the robot's physical structure. We demonstrate the efficacy of our design methodology by creating a diverse array of hybrid legged/wheeled mobile robots which we validate using physics simulation and through fabricated prototypes.
Rapid deployment of curved surfaces via programmable auxetics
Authors: Konaković-Luković Mina , Panetta Julian , Crane Keenan , Pauly Mark
Published: July 2018

Deployable structures are physical mechanisms that can easily transition between two or more geometric configurations; such structures enable industrial, scientific, and consumer applications at a wide variety of scales. This paper develops novel deployable structures that can approximate a large class of doubly-curved surfaces and are easily actuated from a flat initial state via inflation or gravitational loading. The structures are based on two-dimensional rigid mechanical linkages that implicitly encode the curvature of the target shape via a user-programmable pattern that permits locally isotropic scaling under load. We explicitly characterize the shapes that can be realized by such structures---in particular, we show that they can approximate target surfaces of positive mean curvature and bounded scale distortion relative to a given reference domain. Based on this observation, we develop efficient computational design algorithms for approximating a given input geometry. The resulting designs can be rapidly manufactured via digital fabrication technologies such as laser cutting, CNC milling, or 3D printing. We validate our approach through a series of physical prototypes and present several application case studies, ranging from surgical implants to large-scale deployable architecture.
Laplacian kernel splatting for efficient depth-of-field and motion blur synthesis or reconstruction
Authors: Leimkühler Thomas , Seidel Hans-Peter , Ritschel Tobias
Published: July 2018

Simulating combinations of depth-of-field and motion blur is an important factor to cinematic quality in synthetic images but can take long to compute. Splatting the point-spread function (PSF) of every pixel is general and provides high quality, but requires prohibitive compute time. We accelerate this in two steps: In a pre-process we optimize for sparse representations of the Laplacian of all possible PSFs that we call
spreadlets.
At runtime, spreadlets can be splat efficiently to the Laplacian of an image. Integrating this image produces the final result. Our approach scales faithfully to strong motion and large out-of-focus areas and compares favorably in speed and quality with off-line and interactive approaches. It is applicable to both synthesizing from pinhole as well as reconstructing from stochastic images, with or without layering.
Slang
Authors: He Yong , Fatahalian Kayvon , Foley Theresa
Published: July 2018

Designers of real-time rendering engines must balance the conflicting goals of maintaining clear, extensible shading systems and achieving high rendering performance. In response, engine architects have established effective design patterns for authoring shading systems, and developed engine-specific code synthesis tools, ranging from preprocessor hacking to domain-specific shading languages, to productively implement these patterns. The problem is that proprietary tools add significant complexity to modern engines, lack advanced language features, and create additional challenges for learning and adoption. We argue that the advantages of engine-specific code generation tools can be achieved using the underlying GPU shading language directly, provided the shading language is extended with a small number of best-practice principles from modern, well-established programming languages. We identify that adding generics with interface constraints, associated types, and interface/structure extensions to existing C-like GPU shading languages enables real-time Tenderer developers to build shading systems that are extensible, maintainable, and execute efficiently on modern GPUs without the need for additional domain-specific tools. We embody these ideas in an extension of HLSL called Slang, and provide a reference design for a large, extensible shader library implemented using Slang's features. We rearchitect an open source Tenderer to use this library and Slang's compiler services, and demonstrate the resulting shading system is substantially simpler, easier to extend with new features, and achieves higher rendering performance than the original HLSL-based implementation.
An empirical rig for jaw animation
Authors: Zoss Gaspard , Bradley Derek , Bérard Pascal , Beeler Thabo
Published: July 2018

In computer graphics the motion of the jaw is commonly modelled by up-down and left-right rotation around a fixed pivot plus a forward-backward translation, yielding a three dimensional rig that is highly suited for intuitive artistic control. The anatomical motion of the jaw is, however, much more complex since the joints that connect the jaw to the skull exhibit both rotational and translational components. In reality the jaw does not move in a three dimensional subspace but on a constrained manifold in six dimensions. We analyze this manifold in the context of computer animation and show how the manifold can be parameterized with three degrees of freedom, providing a novel jaw rig that preserves the intuitive control while providing more accurate jaw positioning. The chosen parameterization furthermore places anatomically correct limits on the motion, preventing the rig from entering physiologically infeasible poses. Our new jaw rig is empirically designed from accurate capture data, and we provide a simple method to retarget the rig to new characters, both human and fantasy.
Voxel cores
Authors: Yan Yajie , Letscher David , Ju Tao
Published: July 2018

We present a novel algorithm for computing the medial axes of 3D shapes. We make the observation that the medial axis of a voxel shape can be simply yet faithfully approximated by the interior Voronoi diagram of the boundary vertices, which we call the
voxel core.
We further show that voxel cores can approximate the medial axes of any smooth shape with homotopy equivalence and geometric convergence. These insights motivate an algorithm that is simple, efficient, numerically stable, and equipped with theoretical guarantees. Compared with existing voxel-based methods, our method inherits their simplicity but is more scalable and can process significantly larger inputs. Compared with sampling-based methods that offer similar theoretical guarantees, our method produces visually comparable results but more robustly captures the topology of the input shape.
Understanding Crowdsourcing Systems from a Multiagent Perspective and Approach
Authors: Jiang Jiuchuan , An Bo , Jiang Yichuan , Lin Donghui , Bu Zhan , Cao Jie , Hao Zhifeng
Published: July 2018

Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.
Shared Last-Level Cache Management and Memory Scheduling for GPGPUs with Hybrid Main Memory
Authors: Wang Guan , Zang Chuanqi , Ju Lei , Zhao Mengying , Cai Xiaojun , Jia Zhiping
Published: July 2018

Memory intensive workloads become increasingly popular on general purpose graphics processing units (GPGPUs), and impose great challenges on the GPGPU memory subsystem design. On the other hand, with the recent development of non-volatile memory (NVM) technologies, hybrid memory combining both DRAM and NVM achieves high performance, low power, and high density simultaneously, which provides a promising main memory design for GPGPUs. In this article, we explore the shared last-level cache management for GPGPUs with consideration of the underlying hybrid main memory. To improve the overall memory subsystem performance, we exploit the characteristics of both the asymmetric read/write latency of the hybrid main memory architecture, as well as the memory coalescing feature of GPGPUs. In particular, to reduce the average cost of L2 cache misses, we prioritize cache blocks from DRAM or NVM based on observations that operations to NVM part of main memory have a large impact on the system performance. Furthermore, the cache management scheme also integrates the GPU memory coalescing and cache bypassing techniques to improve the overall system performance. To minimize the impact of memory divergence behaviors among simultaneously executed groups of threads, we propose a hybrid main memory and warp aware memory scheduling mechanism for GPGPUs. Experimental results show that in the context of a hybrid main memory system, our proposed L2 cache management policy and memory scheduling mechanism improve performance by 15.69% on average for memory intensive benchmarks, whereas the maximum gain can be up to 29% and achieve an average memory subsystem energy reduction of 21.27%.
Presentation Attack Detection for Iris Recognition
Authors: Czajka Adam , Bowyer Kevin W.
Published: July 2018

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings is suggested.
AI education matters
Authors: Neller Todd W.
Published: July 2018

In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students.
AI policy matters
Authors: Medsker Larry
Published: July 2018

AI Policy is a regular column in AI Matters featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog (https://sigai.acm.org/aimatters/blog/). Selected posts are summarized in issues of
AI Matters
.
Versatile event correlation with algebraic effects
Authors: Bračevac Oliver , Amin Nada , Salvaneschi Guido , Erdweg Sebastian , Eugster Patrick , Mezini Mira
Published: July 2018

We present the first language design to uniformly express variants of
n
-way joins over asynchronous event streams from different domains, e.g., stream-relational algebra, event processing, reactive and concurrent programming. We model asynchronous reactive programs and joins in direct style, on top of algebraic effects and handlers. Effect handlers act as modular interpreters of event notifications, enabling fine-grained control abstractions and customizable event matching. Join variants can be considered as cartesian product computations with ”degenerate” control flow, such that unnecessary tuples are not materialized a priori. Based on this computational interpretation, we decompose joins into a generic, naive enumeration procedure of the cartesian product, plus variant-specific extensions, represented in terms of user-supplied effect handlers. Our microbenchmarks validate that this extensible design avoids needless materialization. Alongside a formal semantics for joining and prototypes in Koka and multicore OCaml, we contribute a systematic comparison of the covered domains and features.
Compositional soundness proofs of abstract interpreters
Authors: Keidel Sven , Poulsen Casper Bach , Erdweg Sebastian
Published: July 2018

Abstract interpretation is a technique for developing static analyses. Yet, proving abstract interpreters sound is challenging for interesting analyses, because of the high
proof complexity
and
proof effort
. To reduce complexity and effort, we propose a framework for abstract interpreters that makes their soundness proof compositional. Key to our approach is to capture the similarities between concrete and abstract interpreters in a single shared interpreter, parameterized over an arrow-based interface. In our framework, a soundness proof is reduced to proving reusable soundness lemmas over the concrete and abstract instances of this interface; the soundness of the overall interpreters follows from a generic theorem.

To further reduce proof effort, we explore the relationship between soundness and parametricity. Parametricity not only provides us with useful guidelines for how to design non-leaky interfaces for shared interpreters, but also provides us soundness of shared pure functions as
free theorems
. We implemented our framework in Haskell and developed a
k
-CFA analysis for PCF and a tree-shape analysis for Stratego. We were able to prove both analyses sound compositionally with manageable complexity and effort, compared to a conventional soundness proof.
What you needa know about Yoneda: profunctor optics and the Yoneda lemma (functional pearl)
Authors: Boisseau Guillaume , Gibbons Jeremy
Published: July 2018

Profunctor optics are a neat and composable representation of bidirectional data accessors, including lenses, and their dual, prisms. The profunctor representation exploits higher-order functions and higher-kinded type constructor classes, but the relationship between this and the familiar representation in terms of "getter" and "setter" functions is not at all obvious. We derive the profunctor representation from the concrete representation, making the relationship clear. It turns out to be a fairly direct application of the Yoneda Lemma, arguably the most important result in category theory. We hope this derivation aids understanding of the profunctor representation. Conversely, it might also serve to provide some insight into the Yoneda Lemma.
Generic deriving of generic traversals
Authors: Kiss Csongor , Pickering Matthew , Wu Nicolas
Published: July 2018

Functional programmers have an established tradition of using traversals as a design pattern to work with recursive data structures. The technique is so prolific that a whole host of libraries have been designed to help in the task of automatically providing traversals by analysing the generic structure of data types. More recently, lenses have entered the functional scene and have proved themselves to be a simple and versatile mechanism for working with product types. They make it easy to focus on the salient parts of a data structure in a composable and reusable manner.

This paper uses the combination of lenses and traversals to give rise to a library with unprecedented expressivity and flexibility for querying and modifying complex data structures. Furthermore, since lenses and traversals are based on the generic shape of data, this information is used to generate code that is as efficient as hand-optimised versions. The technique leverages the structure of data to produce generic abstractions that are then eliminated by the standard workhorses of modern functional compilers: inlining and specialisation.