bluuebunny/crossref_metadata_2025 huggingface dataset:
Workforce Diversity in Decision-Making Organizations: A Perspective from Agent-Based Computational Economics
Authors: Wall Friederike
Published: July 2021

Diversity in teams has become an important societal and economic issue which is studied in various scientific domains. In organizational sciences, particularly empirical research methods prevail. This paper proposes to explore agent-based computational economics as a research approach for workforce diversity more intensely due to its inherent properties like capturing heterogeneous interacting agents. For highlighting this, this paper presents an agent-based computational model based on the framework of NK fitness landscapes. In the simulations, artificial organizations search for superior levels of organizational performance with search being delegated to several and potentially diverse decision-making agents. The experiments control for the level of task complexity and reflects four different attributes of workplace diversity among agents: cognitive capabilities to (i) generate and (ii) evaluate new solutions, (iii) effort efficiency and (iv) commitment to the overall organizational objective. The results suggest that the effects of workforce diversity differ across task complexity and attributes of diversity. Diversity of commitment has the strongest impact which results from interactions among local maximizers and agents seeking to globally maximize with only local means. Moreover, the results point to nonlinear effects of multi-attributive diversity on organizational performance.
Optimizing Tourniquet Pressure in Upper Extremity Surgery
Authors: Othman Sammy , Pistorio Ashley L. , Lopez Stephen , Orengia Adam , Born Michael W.
Published: July 2021

Background: Pneumatic tourniquet is an effective tool to achieve hemostatic control of the surgical field in upper extremity (UE) operations. Elevated pressures have been associated with adverse effects despite various methods of pressure determination. We aim to demonstrate the usage of reduced tourniquet pressures and examine factors associated with achieving reduced pressures.

Methods: A prospective study was conducted (2016–2018) at a Level 1 Trauma Center and an Outpatient Surgical Center, totaling 226 operations, involving a reduction of cuff pressures over time from a standard baseline of limb occlusion pressure for UE operations.

Results: A gradual reduction of pressures was successfully achieved with a mean pressure of 187 mmHg and average time of tourniquet application being 25 minutes. We found chronological surgical number and patient BMI to be significantly associated with tourniquet pressure (p < 0.05). 4.5% of cases resulted in breakthrough bleeding, but did not reliably occur with any pressure thresholds, patient demographics, or operative factors (p > 0.05, for all).

Conclusions: Reduced tourniquet pressures can mitigate complications associated with tourniquet use. Our research shows reduced pressures are successful in maintaining field visibility and we encourage an adoption of pressures below 200 mm Hg in most procedures that require a tourniquet.
Symbolic Control of Hybrid Systems from Signal Temporal Logic Specifications
Authors: da Silva Rafael Rodrigues , Kurtz Vince , Lin Hai
Published: July 2021

In safety-critical systems, it is desirable to automatically synthesize controllers for complex tasks with guaranteed safety and correctness. Although much progress has been made through controller synthesis from temporal logic specifications, existing approaches generally require conservative assumptions and do not scale well with system dimensionality. We propose a scalable, provably complete algorithm that synthesizes continuous trajectories for hybrid systems to satisfy temporal logic specifications. Specifically, we harness highly efficient Boolean satisfiability (SAT) and Linear Programming (LP) solvers to find trajectories that satisfy non-convex Signal Temporal Logic (STL) specifications for a class of high dimensional hybrid systems. The proposed design algorithms are proven sound and complete, and are validated in simulation experiments.
Beaufort–Mackenzie Tectono-Sedimentary Element
Authors: Chen Zhuoheng , Dietrich Jim , Lane Larry S. , Li Maowen , Dixon Jim
Published: July 2021

The Beaufort–Mackenzie Tectono-Sedimentary Element (TSE) is a post-rift sequence of the Canada Basin that consists of Late Cretaceous–Pleistocene sedimentary strata with a maximum thickness of over 12 km. The tectonics of the Canada Basin formation and the Cenozoic contractional deformation associated with the Cordilleran Orogeny jointly shaped and controlled the architecture and the depositional systems of the TSE. In the west, contractional deformation prevails, whereas in the east extensional deformation dominates, and gravitational deformation associated with growth faults is prominent in the far north. Early successions of Cenomanian–Early Maastrichtian age are composed of thin but commonly organic-rich muds (Boundary Creek, Smoking Hills and Mason River sequences) that were deposited on the continental margin. The main phase of sedimentation started in the Late Maastrichtian with the deposition of a series of large progradational delta complexes, which migrated oceanwards from the SW margin in the Paleogene to the central part of the TSE in the Pliocene in response to the rising Cordilleran orogen in the SW. Coal seams and gas-prone terrestrial-derived organic-rich interbeds in the Cenozoic sequences are the main source rocks for gas generation, whereas organic-rich marine shales in Cretaceous sequences are probably the source rocks for oil generation. The Beaufort–Mackenzie TSE is a proven petroleum province with many significant discoveries and a large undiscovered resource potential in oil, gas and methane hydrate.
Cross-domain Recommendation with Bridge-Item Embeddings
Authors: Gao Chen , Li Yong , Feng Fuli , Chen Xiangning , Zhao Kai , He Xiangnan , Jin Depeng
Published: July 2021

Web systems that provide the same functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the
cross-domain recommendation
task. Despite many research efforts on this task, the main drawback is that they largely assume the data of different systems can be
fully shared
. Such an assumption is unrealistic different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive.

In this work, we consider a more practical scenario to perform cross-domain recommendation. To avoid the leak of user privacy during the data sharing process, we consider sharing only the information of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus (e.g., legal policy) on data sharing since the data to be shared is user-irrelevant and has no explicit semantics. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR (short for

N

eural

A

ttentive

T

ransfer

R

ecommendation
). We perform extensive experiments on two real-world datasets, demonstrating that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the efficacy of NATR in using the transferred item embeddings to alleviate the data sparsity issue.
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
Authors: Liu Fenglin , Wu Xian , Ge Shen , Ren Xuancheng , Fan Wei , Sun Xu , Zou Yuexian
Published: July 2021

Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, various pre-trained V-L models are proposed to learn V-L representations and achieve improved results in many tasks. However, the mainstream models process both vision and language inputs with the same set of attention matrices. As a result, the generated V-L representations are
entangled
in
one common latent space
. To tackle this problem, we propose DiMBERT (short for
Di
sentangled
M
ultimodal-Attention
BERT
), which is a novel framework that applies separated attention spaces for vision and language, and the representations of multi-modalities can thus be disentangled explicitly. To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format. In this manner, visual concepts help to bridge the gap between the two modalities. We pre-train DiMBERT on a large amount of image–sentence pairs on two tasks: bidirectional language modeling and sequence-to-sequence language modeling. After pre-train, DiMBERT is further fine-tuned for the downstream tasks. Experiments show that DiMBERT sets new state-of-the-art performance on three tasks (over four datasets), including both generation tasks (image captioning and visual storytelling) and classification tasks (referring expressions). The proposed DiM (short for
Di
sentangled
M
ultimodal-Attention) module can be easily incorporated into existing pre-trained V-L models to boost their performance, up to a 5% increase on the representative task. Finally, we conduct a systematic analysis and demonstrate the effectiveness of our DiM and the introduced visual concepts.
Free-form scanning of non-planar appearance with neural trace photography
Authors: Ma Xiaohe , Kang Kaizhang , Zhu Ruisheng , Wu Hongzhi , Zhou Kun
Published: July 2021

We propose neural trace photography, a novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance. Our key insight is that free-form appearance scanning can be cast as a geometry learning problem on unstructured point clouds, each of which represents an image measurement and the corresponding acquisition condition. Based on this connection, we carefully design a neural network, to jointly optimize the lighting conditions to be used in acquisition, as well as the spatially independent reconstruction of reflectance from corresponding measurements. Our framework is not tied to a specific setup, and can adapt to various factors in a data-driven manner. We demonstrate the effectiveness of our framework on a number of physical objects with a wide variation in appearance. The objects are captured with a light-weight mobile device, consisting of a single camera and an RGB LED array. We also generalize the framework to other common types of light sources, including a point, a linear and an area light.
MoCap-solver
Authors: Chen Kang , Wang Yupan , Zhang Song-Hai , Xu Sen-Zhe , Zhang Weidong , Hu Shi-Min
Published: July 2021

In a conventional optical motion capture (MoCap) workflow, two processes are needed to turn captured raw marker sequences into correct skeletal animation sequences. Firstly, various tracking errors present in the markers must be fixed (
cleaning
or
refining
). Secondly, an agent skeletal mesh must be prepared for the actor/actress, and used to determine skeleton information from the markers (
re-targeting
or
solving
). The whole process, normally referred to as
solving
MoCap data, is extremely time-consuming, labor-intensive, and usually the most costly part of animation production. Hence, there is a great demand for automated tools in industry. In this work, we present MoCap-Solver, a production-ready neural solver for optical MoCap data. It can directly produce skeleton sequences and clean marker sequences from raw MoCap markers, without any tedious manual operations. To achieve this goal, our key idea is to make use of neural encoders concerning three key intrinsic components: the template skeleton, marker configuration and motion, and to learn to predict these latent vectors from imperfect marker sequences containing noise and errors. By decoding these components from latent vectors, sequences of clean markers and skeletons can be directly recovered. Moreover, we also provide a novel normalization strategy based on learning a pose-dependent marker reliability function, which greatly improves system robustness. Experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art on both synthetic and real-world datasets.
RXMesh
Authors: Mahmoud Ahmed H. , Porumbescu Serban D. , Owens John D.
Published: July 2021

We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into
patches
and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with
ribbons
, thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure
RXMesh
: Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications---mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.
Real-time deep dynamic characters
Authors: Habermann Marc , Liu Lingjie , Xu Weipeng , Zollhoefer Michael , Pons-Moll Gerard , Theobalt Christian
Published: July 2021

We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery. In contrast to previous work, our controllable 3D character displays dynamics, e.g., the swing of the skirt, dependent on skeletal body motion in an efficient data-driven way, without requiring complex physics simulation. Our character model also features a learned dynamic texture model that accounts for photo-realistic motion-dependent appearance details, as well as view-dependent lighting effects. During training, we do not need to resort to difficult dynamic 3D capture of the human; instead we can train our model entirely from multi-view video in a weakly supervised manner. To this end, we propose a parametric and differentiable character representation which allows us to model coarse and fine dynamic deformations, e.g., garment wrinkles, as explicit spacetime coherent mesh geometry that is augmented with high-quality dynamic textures dependent on motion and view point. As input to the model, only an arbitrary 3D skeleton motion is required, making it directly compatible with the established 3D animation pipeline. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing, including dynamics, and a neural generative dynamic texture model creates corresponding dynamic texture maps. We show that by merely providing new skeletal motions, our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches, and even in real-time.
Medial IPC
Authors: Lan Lei , Yang Yin , Kaufman Danny , Yao Junfeng , Li Minchen , Jiang Chenfanfu
Published: July 2021

We propose a framework of efficient nonlinear deformable simulation with both fast continuous collision detection and robust collision resolution. We name this new framework
Medial IPC
as it integrates the merits from medial elastics, for an efficient and versatile reduced simulation, as well as incremental potential contact, for a robust collision and contact resolution. We leverage medial axis transform to construct a kinematic subspace. Instead of resorting to projective dynamics, we use classic hyperelastics to embrace real-world nonlinear materials. A novel reduced continuous collision detection algorithm is presented based on the medial mesh. Thanks to unique geometric properties of medial axis and medial primitives, we derive closed-form formulations for identifying between-primitive collision within the reduced medial space. In the meantime, the implicit barrier energy that generates necessary repulsion forces for collision resolution is also formulated with the medial coordinate. In other words, Medial IPC exploits a universal reduced coordinate for simulation, continuous self-/collision detection, and IPC-based collision resolution. Continuous collision detection also allows more aggressive time stepping. In addition, we carefully implement our system with a heterogeneous CPU-GPU deployment such that massively parallelizable computations are carried out on the GPU while few sequential computations are on the CPU. Such implementation also frees us from generating training poses for selecting Cubature points and pre-computing their weights. We have tested our method on complicated deformable models and collision-rich simulation scenarios. Due to the reduced nature of our system, the computation is faster than fullspace IPC or other fullspace methods using continuous collision detection by at least one order. The simulation remains high-quality as the medial subspace captures intriguing and local deformations with sufficient realism.
Constrained willmore surfaces
Authors: Soliman Yousuf , Chern Albert , Diamanti Olga , Knöppel Felix , Pinkall Ulrich , Schröder Peter
Published: July 2021

Smooth curves and surfaces can be characterized as minimizers of squared curvature bending energies subject to constraints. In the univariate case with an isometry (length) constraint this leads to classic non-linear splines. For surfaces, isometry is too rigid a constraint and instead one asks for minimizers of the Willmore (squared mean curvature) energy subject to a conformality constraint. We present an efficient algorithm for (conformally) constrained Willmore surfaces using triangle meshes of arbitrary topology with or without boundary. Our conformal class constraint is based on the discrete notion of conformal equivalence of triangle meshes. The resulting non-linear constrained optimization problem can be solved efficiently using the competitive gradient descent method together with appropriate Sobolev metrics. The surfaces can be represented either through point positions or differential coordinates. The latter enable the realization of abstract metric surfaces without an initial immersion. A versatile toolkit for extrinsic conformal geometry processing, suitable for the construction and manipulation of smooth surfaces, results through the inclusion of additional point, area, and volume constraints.
Control strategies for physically simulated characters performing two-player competitive sports
Authors: Won Jungdam , Gopinath Deepak , Hodgins Jessica
Published: July 2021

In two-player competitive sports, such as
boxing
and
fencing
, athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many degrees-of-freedom. Our framework uses a two step-approach, learning basic skills and learning bout-level strategies, with deep reinforcement learning, which is inspired by the way that people how to learn competitive sports. We develop a policy model based on an encoder-decoder structure that incorporates an autoregressive latent variable, and a mixture-of-experts decoder. To show the effectiveness of our framework, we implemented two competitive sports,
boxing
and
fencing
, and demonstrate control policies learned by our framework that can generate both tactical and natural-looking behaviors. We also evaluate the control policies with comparisons to other learning configurations and with ablation studies.
HodgeNet
Authors: Smirnov Dmitriy , Solomon Justin
Published: July 2021

Constrained by the limitations of learning toolkits engineered for other applications, such as those in image processing, many mesh-based learning algorithms employ data flows that would be atypical from the perspective of conventional geometry processing. As an alternative, we present a technique for learning from meshes built from standard geometry processing modules and operations. We show that low-order eigenvalue/eigenvector computation from operators parameterized using discrete exterior calculus is amenable to efficient approximate backpropagation, yielding spectral per-element or per-mesh features with similar formulas to classical descriptors like the heat/wave kernel signatures. Our model uses few parameters, generalizes to high-resolution meshes, and exhibits performance and time complexity on par with past work.
Orienting point clouds with dipole propagation
Authors: Metzer Gal , Hanocka Rana , Zorin Denis , Giryes Raja , Panozzo Daniele , Cohen-Or Daniel
Published: July 2021

Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both
local
and
global
shape characteristics. The normal direction of a point is a function of the
local
surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the
local
and
global
components into two different sub-problems. In the local phase, we train a neural network to learn a
coherent
normal direction per patch (
i.e.
, consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.
Using isometries for computational design and fabrication
Authors: Jiang Caigui , Wang Hui , Ceballos Inza Victor , Dellinger Felix , Rist Florian , Wallner Johannes , Pottmann Helmut
Published: July 2021

We solve the task of representing free forms by an arrangement of panels that are manufacturable by precise isometric bending of surfaces made from a small number of molds. In fact we manage to solve the paneling task with surfaces of constant Gaussian curvature alone. This includes the case of developable surfaces which exhibit zero curvature. Our computations are based on an existing discrete model of isometric mappings between surfaces which for this occasion has been refined to obtain higher numerical accuracy. Further topics are interesting connections of the paneling problem with the geometry of Killing vector fields, designing and actuating isometries, curved folding in the double-curved case, and quad meshes with rigid faces that are nevertheless flexible.
Foldover-free maps in 50 lines of code
Authors: Garanzha Vladimir , Kaporin Igor , Kudryavtseva Liudmila , Protais François , Ray Nicolas , Sokolov Dmitry
Published: July 2021

Mapping a triangulated surface to 2D space (or a tetrahedral mesh to 3D space) is an important problem in geometry processing. In computational physics, untangling plays an important role in mesh generation: it takes a mesh as an input, and moves the vertices to get rid of foldovers. In fact, mesh untangling can be considered as a special case of mapping where the geometry of the object is to be defined in the map space and the geometric domain is not explicit, supposing that each element is regular. In this paper, we propose a mapping method inspired by the untangling problem and compare its performance to the state of the art. The main advantage of our method is that the untangling aims at producing locally injective maps, which is the major challenge of mapping. In practice, our method produces locally injective maps in very difficult settings, both in 2D and 3D. We demonstrate it on a large reference database as well as on more difficult stress tests. For a better reproducibility, we publish the code in Python for a basic evaluation, and in C++ for more advanced applications.
Neural scene graph rendering
Authors: Granskog Jonathan , Schnabel Till N. , Rousselle Fabrice , Novák Jan
Published: July 2021

We present a neural scene graph---a modular and controllable representation of scenes with elements that are learned from data. We focus on the forward rendering problem, where the scene graph is provided by the user and references learned elements. The elements correspond to geometry and material definitions of scene objects and constitute the leaves of the graph; we store them as high-dimensional vectors. The position and appearance of scene objects can be adjusted in an artist-friendly manner via familiar transformations, e.g. translation, bending, or color hue shift, which are stored in the inner nodes of the graph. In order to apply a (non-linear) transformation to a learned vector, we adopt the concept of linearizing a problem by lifting it into higher dimensions: we first encode the transformation into a high-dimensional matrix and then apply it by standard matrix-vector multiplication. The transformations are encoded using neural networks. We render the scene graph using a streaming neural renderer, which can handle graphs with a varying number of objects, and thereby facilitates scalability. Our results demonstrate a precise control over the learned object representations in a number of animated 2D and 3D scenes. Despite the limited visual complexity, our work presents a step towards marrying traditional editing mechanisms with learned representations, and towards high-quality, controllable neural rendering.
Highlight-aware two-stream network for single-image SVBRDF acquisition
Authors: Guo Jie , Lai Shuichang , Tao Chengzhi , Cai Yuelong , Wang Lei , Guo Yanwen , Yan Ling-Qi
Published: July 2021

This paper addresses the task of estimating spatially-varying reflectance (i.e., SVBRDF) from a single, casually captured image. Central to our method is a highlight-aware (HA) convolution operation and a two-stream neural network equipped with proper training losses. Our HA convolution, as a novel variant of standard (ST) convolution, directly modulates convolution kernels under the guidance of automatically learned masks representing potentially overexposed highlight regions. It helps to reduce the impact of strong specular highlights on diffuse components and at the same time, hallucinates plausible contents in saturated regions. Considering that variation of saturated pixels also contains important cues for inferring surface bumpiness and specular components, we design a two-stream network to extract features from two different branches stacked by HA convolutions and ST convolutions, respectively. These two groups of features are further fused in an attention-based manner to facilitate feature selection of each SVBRDF map. The whole network is trained end to end with a new perceptual adversarial loss which is particularly useful for enhancing the texture details. Such a design also allows the recovered material maps to be disentangled. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to recover clear SVBRDFs from a single casually captured image, and performs favorably against state-of-the-arts. Since we impose very few constraints on the capture process, even a non-expert user can create high-quality SVBRDFs that cater to many graphical applications.
Transfer matrix based layered materials rendering
Authors: Randrianandrasana Joël , Callet Patrick , Lucas Laurent
Published: July 2021

A statistical multi-lobe approach was recently introduced in order to efficiently handle layered materials rendering as an alternative to expensive general-purpose approaches. However, this approach poorly supports scattering volumes as the method does not account for back-scattering and resorts to single scattering approximations. In this paper, we address these limitations with an efficient solution based upon a transfer matrix approach which leverages the properties of the Henyey-Greenstein phase function. Under this formalism, each scattering component of the stack is described through a lightweight matrix, layering operations are reduced to simple matrix products and the statistics of each BSDF lobe accounting for multiple scattering effects are obtained through matrix operators. Based on this representation, we leverage the versatility of the transfer matrix approach to efficiently handle forward and backward scattering which occurs in arbitrary layered materials. The resulting model enables the reproduction of a wide range of layered structures embedding scattering volumes of arbitrary depth, in constant computation time and with low variance.
StyleCariGAN
Authors: Jang Wonjong , Ju Gwangjin , Jung Yucheol , Yang Jiaolong , Tong Xin , Lee Seungyong
Published: July 2021

We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed
StyleCariGAN
, automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control.
Mixture of volumetric primitives for efficient neural rendering
Authors: Lombardi Stephen , Simon Tomas , Schwartz Gabriel , Zollhoefer Michael , Sheikh Yaser , Saragih Jason
Published: July 2021

Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications. Existing methods have a number of drawbacks we aim to address with our work. Triangle meshes have difficulty modeling thin structures like hair, volumetric representations like Neural Volumes are too low-resolution given a reasonable memory budget, and high-resolution implicit representations like Neural Radiance Fields are too slow for use in real-time applications. We present Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, e.g., point-based or mesh-based methods. Our approach achieves this by leveraging spatially shared computation with a convolutional architecture and by minimizing computation in empty regions of space with volumetric primitives that can move to cover only occupied regions. Our parameterization supports the integration of correspondence and tracking constraints, while being robust to areas where classical tracking fails, such as around thin or translucent structures and areas with large topological variability. MVP is a hybrid that generalizes both volumetric and primitive-based representations. Through a series of extensive experiments we demonstrate that it inherits the strengths of each, while avoiding many of their limitations. We also compare our approach to several state-of-the-art methods and demonstrate that MVP produces superior results in terms of quality and runtime performance.
Thin-film smoothed particle hydrodynamics fluid
Authors: Wang Mengdi , Deng Yitong , Kong Xiangxin , Prasad Aditya H. , Xiong Shiying , Zhu Bo
Published: July 2021

We propose a particle-based method to simulate thin-film fluid that jointly facilitates aggressive surface deformation and vigorous tangential flows. We build our dynamics model from the surface tension driven Navier-Stokes equation with the dimensionality reduced using the asymptotic lubrication theory and customize a set of differential operators based on the weakly compressible Smoothed Particle Hydrodynamics (SPH) for evolving pointset surfaces. The key insight is that the compressible nature of SPH, which is unfavorable in its typical usage, is helpful in our application to co-evolve the thickness, calculate the surface tension, and enforce the fluid incompressibility on a thin film. In this way, we are able to two-way couple the surface deformation with the in-plane flows in a physically based manner. We can simulate complex vortical swirls, fingering effects due to Rayleigh-Taylor instability, capillary waves, Newton's interference fringes, and the Marangoni effect on liberally deforming surfaces by presenting both realistic visual results and numerical validations. The particle-based nature of our system also enables it to conveniently handle topology changes and codimension transitions, allowing us to marry the thin-film simulation with a wide gamut of 3D phenomena, such as pinch-off of unstable catenoids, dripping under gravity, merging of droplets, as well as bubble rupture.
Incompressible flow simulation on vortex segment clouds
Authors: Xiong Shiying , Tao Rui , Zhang Yaorui , Feng Fan , Zhu Bo
Published: July 2021

We propose a novel Lagrangian geometric representation using segment clouds to simulate incompressible fluid exhibiting strong anisotropic vortical features. The central component of our approach is a cloud of discrete segments enhanced by a set of local segment reseeding operations to facilitate both the geometrical evolution and the topological updates of vortical flow. We build a vortex dynamics solver with the support for dynamic solid boundaries based on discrete segment primitives. We demonstrate the efficacy of our approach by simulating a broad range of challenging flow phenomena, such as reconnection of non-closed vortex tubes and vortex shedding behind a rotating object.
Boundary-sampled halfspaces
Authors: Du Xingyi , Zhou Qingnan , Carr Nathan , Ju Tao
Published: July 2021

We present a novel representation of solid models for shape design. Like Constructive Solid Geometry (CSG), the solid shape is constructed from a set of halfspaces without the need for an explicit boundary structure. Instead of using Boolean expressions as in CSG, the shape is defined by sparsely placed samples on the boundary of each halfspace. This representation, called Boundary-Sampled Halfspaces (BSH), affords greater agility and expressiveness than CSG while simplifying the reverse engineering process. We discuss theoretical properties of the representation and present practical algorithms for boundary extraction and conversion from other representations. Our algorithms are demonstrated on both 2D and 3D examples.
Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstruction
Authors: Cho In-Young , Huo Yuchi , Yoon Sung-Eui
Published: July 2021

Image-space auxiliary features such as surface normal have significantly contributed to the recent success of Monte Carlo (MC) reconstruction networks. However, path-space features, another essential piece of light propagation, have not yet been sufficiently explored. Due to the curse of dimensionality, information flow between a regression loss and high-dimensional path-space features is sparse, leading to difficult training and inefficient usage of path-space features in a typical reconstruction framework. This paper introduces a contrastive manifold learning framework to utilize path-space features effectively. The proposed framework employs weakly-supervised learning that converts reference pixel colors to dense pseudo labels for light paths. A convolutional path-embedding network then induces a low-dimensional manifold of paths by iteratively clustering intra-class embeddings, while discriminating inter-class embeddings using gradient descent. The proposed framework facilitates path-space exploration of reconstruction networks by extracting low-dimensional yet meaningful embeddings within the features. We apply our framework to the recent image- and sample-space models and demonstrate considerable improvements, especially on the sample space. The source code is available at https://github.com/Mephisto405/WCMC.
Optimizing dyadic nets
Authors: Ahmed Abdalla G. M. , Wonka Peter
Published: July 2021

We explore the space of (0,
m
, 2)-nets in base 2 commonly used for sampling. We present a novel constructive algorithm that can exhaustively generate all nets --- up to
m
-bit resolution --- and thereby compute the exact number of distinct nets. We observe that the construction algorithm holds the key to defining a transformation operation that lets us transform one valid net into another one. This enables the optimization of digital nets using arbitrary objective functions. For example, we define an analytic energy function for blue noise, and use it to generate nets with high-quality blue-noise frequency power spectra. We also show that the space of (0, 2)-sequences is significantly smaller than nets with the same number of points, which drastically limits the optimizability of sequences.
Learning active quasistatic physics-based models from data
Authors: Srinivasan Sangeetha Grama , Wang Qisi , Rojas Junior , Klár Gergely , Kavan Ladislav , Sifakis Eftychios
Published: July 2021

Humans and animals can control their bodies to generate a wide range of motions via low-dimensional action signals representing high-level goals. As such, human bodies and faces are prime examples of
active
objects, which can affect their shape via an internal actuation mechanism. This paper explores the following proposition: given a training set of example poses of an active deformable object, can we learn a low-dimensional control space that could reproduce the training set and generalize to new poses? In contrast to popular machine learning methods for dimensionality reduction such as auto-encoders, we model our active objects in a physics-based way. We utilize a differentiable, quasistatic, physics-based simulation layer and combine it with a decoder-type neural network. Our differentiable physics layer naturally fits into deep learning frameworks and allows the decoder network to learn actuations that reach the desired poses after physics-based simulation. In contrast to modeling approaches where users build anatomical models from first principles, medical literature or medical imaging, we do not presume knowledge of the underlying musculature, but learn the structure and control of the actuation mechanism directly from the input data. We present a training paradigm and several scalability-oriented enhancements that allow us to train effectively while accommodating high-resolution volumetric models, with as many as a quarter million simulation elements. The prime demonstration of the efficacy of our example-driven modeling framework targets facial animation, where we train on a collection of input expressions while generalizing to unseen poses, drive detailed facial animation from sparse motion capture input, and facilitate expression sculpting via direct manipulation.
Authoring consistent landscapes with flora and fauna
Authors: Ecormier-Nocca Pierre , Cordonnier Guillaume , Carrez Philippe , Moigne Anne-Marie , Memari Pooran , Benes Bedrich , Cani Marie-Paule
Published: July 2021

We present a novel method for authoring landscapes with flora and fauna while considering their mutual interactions. Our algorithm outputs a steady-state ecosystem in the form of density maps for each species, their daily circuits, and a modified terrain with eroded trails from a terrain, climatic conditions, and species with related biological information. We introduce the Resource Access Graph, a new data structure that encodes both interactions between food chain levels and animals traveling between resources over the terrain. A novel competition algorithm operating on this data progressively computes a steady-state solution up the food chain, from plants to carnivores. The user can explore the resulting landscape, where plants and animals are instantiated on the fly, and interactively edit it by over-painting the maps. Our results show that our system enables the authoring of consistent landscapes where the impact of wildlife is visible through animated animals, clearings in the vegetation, and eroded trails. We provide quantitative validation with existing ecosystems and a user-study with expert paleontologist end-users, showing that our system enables them to author and compare different ecosystems illustrating climate changes over the same terrain while enabling relevant visual immersion into consistent landscapes.
Lost in Transduction: Transductive Transfer Learning in Text Classification
Authors: Moreo Alejandro , Esuli Andrea , Sebastiani Fabrizio
Published: July 2021

Obtaining high-quality labelled data for training a classifier in a new application domain is often costly.
Transfer Learning
(a.k.a. “Inductive Transfer”) tries to alleviate these costs by transferring, to the “target” domain of interest, knowledge available from a different “source” domain. In transfer learning the lack of labelled information from the target domain is compensated by the availability at training time of a set of unlabelled examples from the target distribution.
Transductive Transfer Learning
denotes the transfer learning setting in which the only set of target documents that we are interested in classifying is known and available at training time. Although this definition is indeed in line with Vapnik’s original definition of “transduction”, current terminology in the field is confused. In this article, we discuss how the term “transduction” has been misused in the transfer learning literature, and propose a clarification consistent with the original characterization of this term given by Vapnik. We go on to observe that the above terminology misuse has brought about misleading experimental comparisons, with inductive transfer learning methods that have been incorrectly compared with transductive transfer learning methods. We then, give empirical evidence that the difference in performance between the inductive version and the transductive version of a transfer learning method can indeed be statistically significant (i.e., that knowing at training time the only data one needs to classify indeed gives an advantage). Our clarification allows a reassessment of the field, and of the relative merits of the major, state-of-the-art algorithms for transfer learning in text classification.
CrowdTC: Crowd-powered Learning for Text Classification
Authors: Yang Keyu , Gao Yunjun , Liang Lei , Bian Song , Chen Lu , Zheng Baihua
Published: July 2021

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.
Learning to see the physical world
Authors: Wu Jiajun
Published: July 2021

I am fascinated by how rich and flexible human intelligence is. From a quick glance at the scenes in Figure 1A, we effortlessly recognize the 3D geometry and texture of the objects within, reason about how they support each other, and when they move, track and predict their trajectories. Stacking blocks, picking up fruits---we also plan and interact with scenes and objects in many ways.
CHIEv
Authors: Leithner Manuel , Simos Dimitris E.
Published: July 2021

Researchers and practitioners in the fields of testing, security assessment and web development seeking to evaluate a given web application often have to rely on the existence of a model of the respective system, which is then used as input to task-specific tools. Such models may include information on HTTP endpoints and their parameters, available user actions/event listeners and required assets. Unfortunately, this data is often unavailable in practice, as only rigorous development practices or manual analysis guarantee their existence and correctness. Crawlers based on static analysis have traditionally been used to extract required information from existing sites. Regrettably, these tools can not accurately account for the dynamic behavior introduced by technologies such as JavaScript that are prevalent on modern sites. While methods based on dynamic analysis exist, they are often not fully capable of identifying event listeners and their effects. In an earlier work, we presented XIEv, an approach for dynamic analysis of web applications that produces an execution trace usable for the extraction of navigation graphs, identification of bugs at runtime and enumeration of resources. It offers improved recognition and selection of event listeners as well as a greater range of observed effects compared to existing approaches. While the evaluation of our research prototype implementation confirmed the capabilities of XIEv, it was generally out-performed by static crawlers in terms of speed. This work introduces CHIEv, an approach that augments XIEv by enabling concurrent processing as well as incorporating the results of a static crawler in real-time. Our results indicate a significant increase in performance, particularly when applied to larger sites.
The Ethics of Consent in a Shifting Genomic Ecosystem
Authors: Lee Sandra Soo-Jin
Published: July 2021

The collection and use of human genetic data raise important ethical questions about how to balance individual autonomy and privacy with the potential for public good. The proliferation of local, national, and international efforts to collect genetic data and create linkages to support large-scale initiatives in precision medicine and the learning health system creates new demands for broad data sharing that involve managing competing interests and careful consideration of what constitutes appropriate ethical trade-offs. This review describes these emerging ethical issues with a focus on approaches to consent and issues related to justice in the shifting genomic research ecosystem.