bluuebunny/crossref_metadata_2025 huggingface dataset:
Advances in multiscale modeling for novel and emerging technologies
Authors: Verkhovtsev Alexey V. , Solov’yov Ilia A. , Solov’yov Andrey V.
Published: July 2021

Computational multiscale modeling encompasses a wide range of end-products and a great number of technological applications. This paper provides an overview of the computational multiscale modeling approach based on utilization of MBN Explorer and MBN Studio software packages, the universal and powerful tools for computational modeling in different areas of challenging research arising in connection with the development of novel and emerging technologies. Three illustrative case studies of multiscale modeling are reviewed in relation to: (i) the development of novel sources of monochromatic high-energy radiation based on the crystalline undulators, (ii) controlled fabrication of nanostructures using the focused electron-beam induced deposition, and (iii) ion-beam cancer therapy. These examples illustrate the key algorithms and unique methodologies implemented in the software.

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Quantum battles in attoscience: tunnelling
Authors: Hofmann Cornelia , Bray Alexander , Koch Werner , Ni Hongcheng , Shvetsov-Shilovski Nikolay I.
Published: July 2021

What is the nature of tunnelling? This yet unanswered question is as pertinent today as it was at the dawn of quantum mechanics. This article presents a cross section of current perspectives on the interpretation, computational modelling, and numerical investigation of tunnelling processes in attosecond physics as debated in the Quantum Battles in Attoscience virtual workshop 2020.

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FT-IR measurement on the damage of Japanese paper induced by PIXE analysis
Authors: Oguri Y. , Hasegawa J. , Fukuda H. , Hagura N.
Published: July 2021

Analysis of cultural heritage samples by PIXE involves the risk of damaging precious samples due to MeV-proton irradiation. To investigate appropriate methods to detect invisible damage due to PIXE analysis, we analyzed the change in chemical bonding of the sample surface subjected to PIXE and RBS measurement by Fourier Transform InfraRed spectroscopy (FT-IR). We used Japanese hemp paper as a simulated cultural property sample. The proton irradiation for the PIXE/RBS measurement was performed in a vacuum with an incident beam energy of 2.5 MeV, a beam current of 1 nA, and an irradiation time up to 10 min. The corresponding beam flux and fluence were 0.06 nA/mm2 and 4 [Formula: see text]Coulomb/cm2, respectively. When the irradiation time was 10 min, the absorbed dose was 480 kGy on the sample surface. We identified neither change of elemental composition nor visible change such as discoloration due to irradiation. However, we found changes in peak heights in the measured FT-IR spectra, which suggest the destruction of chemical bonds such as O–H and C–O due to proton-induced radiation damage.
An Iterated Local Search Heuristic for the Staff Scheduling Problem for Part-Time Employees in Japan
Authors: Wu Wei , Katoh Naoaki , Ikegami Atsuko
Published: July 2021

In this paper, we introduce a mathematical programming model for solving a staff scheduling problem based on one-day duties (task patterns) of individual staff members. The model can accommodate various service types, management policies, and staff preferences. We first enumerate all feasible one-day duties and propose an iterated local search approach that incorporates various methodologies, including a size-reduction method and a very large-scale neighborhood search. For the very large-scale neighborhood search, we design a dynamic programming method that aims to find the most improved schedule and can be used in the rescheduling stage. Computational results show that the model and the proposed algorithm perform well for real-world instances in Japan.
Construction of optical solitons for conformable generalized model in nonlinear media
Authors: Az-Zo’bi Emad , Akinyemi Lanre , Alleddawi Ahmed O.
Published: July 2021

In the current analysis, the conformable generalized Kudryashov equation of pulses propagation with power non-linearity is processed. The considered higher order equation represents a generalized mathematical model of many well-known ones in nonlinear media. A variety of multiple kinks, bi-symmetry, periodic, singular, bright and dark optical solitons are extracted via the generalized Riccati equation mapping method. Basing on the Riccati differential equation, the theoretical algorithm extracts a number of empirical solutions that do not exist in the literature. The obtained results showed that the present technique is an effective and strong tool for solving nonlinear fractional partial differential equations and produces a very large number of solutions.
Value-Driven Multiple Criteria Sorting with Probabilistic Linguistic Information Considering Uncertain Assignment Examples
Authors: Sun Shuxian , Liao Huchang
Published: July 2021

Multiple criteria sorting (MCS) dedicates to assigning alternatives to one of the predefined ordered categories according to their evaluation information on multiple criteria. The utility (value) function-based sorting is a popular MCS procedure, which requires decision-makers to express their preferences through assignment examples. By taking the assignment examples as reference alternatives, the additive value function, as the preferred model of a decision maker, can be built using the preference disaggregation technique. However, the existing literature hardly considered people’s hesitancy when determining assignment examples, and ignored applying linguistic evaluation information on qualitative criteria. To fill these research gaps, this study proposes a value-driven MCS procedure with probabilistic linguistic information considering uncertain assignment examples. Specifically, the probability linguistic term set, as a flexible information representation tool, is introduced to express the hesitancy of decision-makers regarding assignment examples and the performance of alternatives on qualitative criteria. Besides, to comprehensively reflect the preference of a decision-maker, a weighted additive value function is proposed based on the preference disaggregation technique to calculate the comprehensive scores of alternatives in which the weights are determined by the best-worst method. Finally, a case study on the sorting of down coats for sale demonstrates the applicability and superiority of our proposed method.
A Critical Review on Synthesis, Characterization and Multifunctional Applications of Reduced Graphene Oxide (rGO)/Composites
Authors: Kesarwani Shivi , Verma Rajesh Kumar
Published: July 2021

In this paper, various research works have been performed on synthesis and functionalization after the invention of graphene (Gr) and its spectral properties. The reinforcing of Gr family members into the matrix leads to the development of novel composites. Its characterization in terms of morphological and physicochemical properties is required for the discovery of wide-ranging applications. This paper reviews the various emerging features and future scope for reduced graphene oxide (rGO). The possibilities of composites development through the different matrix are proposed to evaluate application potential, various fabrication techniques for Gr, and its role in improving composite properties. The work initially examines multiple ways to synthesize rGO. Its addition to different materials includes metals, metal oxides, ceramics, polymers, and organic compounds through various composite preparation techniques. Finally, this paper gives an overview of the enhancement in properties obtained due to the addition of rGO, which opens the door for a broad set of applications. This paper concludes with a comparative study that defines a suitable preparation technique according to the required material composites, desired properties, and specific applications. An attempt has been proposed to examine the exceptional feature of rGO/composite for cost-effective functions in manufacturing sectors.
Outcomes of Isolated Digital Flexor Tenolysis: A Systematic Review
Authors: Kher Sachin , Graham David J. , Symes Michael , Lawson Richard , Sivakumar Brahman S.
Published: July 2021

Background: Flexor tendon injuries form a significant proportion of hand trauma presentations. Insult to the flexor tendon and surrounding sheath may lead to the formation of adhesions between these structures during the repair process. Tenolysis is a surgical release of these adhesions which requires careful consideration. This systematic review aims to report on the functional outcomes following isolated digital flexor tenolysis.

Methods: We searched four online databases in December 2019. Eligibility criteria for studies were: English language; described patients undergoing digital flexor tenolysis; reported functional outcomes such as total active motion (TAM). Five articles were included. The selected studies were of limited quality (level IV evidence).

Results: Of the final cohort 79.4% were male. Three studies reported patient age, with a mean age of 36.8 years [4 years to 58 years] in that subset. One hundred and three digits underwent flexor tenolysis only. Mean duration to surgery from the index operation or injury was 15.1 months [2.3 months–240 months]. Average follow-up from the tenolysis procedure was 18.6 months [3 months to 120 months]. Four studies assessing motion via Strickland classification reported 78.9% excellent or good outcomes. The remaining study reported 80% of patients reporting excellent or good motion as per Buck-Gramcko score. The complication rate reported was 15.3%.

Conclusions: This review found digital flexor tenolysis significantly improves ROM, however these benefits must be considered in the light of potential complications. A detailed discussion with each individual patient must occur prior to such an undertaking.
Aneurysmal Bone Cyst of Pisiform Bone: Diagnosis and Treatment of a Rare Entity: Case Report and a Review of the Literature
Authors: Aksu Ali Emre , Üstün Galip Gencay , Uzun Hakan , Gedikoğlu Gökhan
Published: July 2021

Aneurysmal bone cyst (ABC) is a benign expansile bone tumor without metastasis capability. Only 3–4% of ABCs occur in the hand and they mainly take place in metaphysis’ of long bones like metacarpals or phalanges. Carpal ABCs have been reported as individual case reports in the literature due to rarity. A patient presented with pain in her right wrist. Magnetic resonance imaging revealed a well circumscribed one cm sized mass in the pisiform bone that resembled an aneurysmal bone cyst. Total pisiformectomy was performed. Treatment options are total excision or curettaging in ABCs. But rarity of these lesions may delay the diagnosis process for the inexperienced surgeon.
BRDF importance sampling for polygonal lights
Authors: Peters Christoph
Published: July 2021

With the advent of real-time ray tracing, there is an increasing interest in GPU-friendly importance sampling techniques. We present such methods to sample convex polygonal lights approximately proportional to diffuse and specular BRDFs times the cosine term. For diffuse surfaces, we sample the polygons proportional to projected solid angle. Our algorithm partitions the polygon suitably and employs inverse function sampling for each part. Inversion of the distribution function is challenging. Using algebraic geometry, we develop a special iterative procedure and an initialization scheme. Together, they achieve high accuracy in all possible situations with only two iterations. Our implementation is numerically stable and fast. For specular BRDFs, this method enables us to sample the polygon proportional to a linearly transformed cosine. We combine these diffuse and specular sampling strategies through novel variants of optimal multiple importance sampling. Our techniques render direct lighting from Lambertian polygonal lights with almost no variance outside of penumbrae and support shadows and textured emission. Additionally, we propose an algorithm for solid angle sampling of polygons. It is faster and more stable than existing methods.
Fast diffraction pathfinding for dynamic sound propagation
Authors: Schissler Carl , Mückl Gregor , Calamia Paul
Published: July 2021

In the context of geometric acoustic simulation, one of the more perceptually important yet difficult to simulate acoustic effects is diffraction, a phenomenon that allows sound to propagate around obstructions and corners. A significant bottleneck in real-time simulation of diffraction is the enumeration of high-order diffraction propagation paths in scenes with complex geometry (e.g. highly tessellated surfaces). To this end, we present a dynamic geometric diffraction approach that consists of an extensive mesh preprocessing pipeline and complementary runtime algorithm. The preprocessing module identifies a small subset of edges that are important for diffraction using a novel silhouette edge detection heuristic. It also extends these edges with planar diffraction geometry and precomputes a graph data structure encoding the visibility between the edges. The runtime module uses bidirectional path tracing against the diffraction geometry to probabilistically explore potential paths between sources and listeners, then evaluates the intensities for these paths using the Uniform Theory of Diffraction. It uses the edge visibility graph and the A* pathfinding algorithm to robustly and efficiently find additional high-order diffraction paths. We demonstrate how this technique can simulate 10th-order diffraction up to 568 times faster than the previous state of the art, and can efficiently handle large scenes with both high geometric complexity and high numbers of sources.
Knit sketching
Authors: Kaspar Alexandre , Wu Kui , Luo Yiyue , Makatura Liane , Matusik Wojciech
Published: July 2021

We present a novel workflow to design and program knitted garments for industrial whole-garment knitting machines. Inspired by traditional garment making based on cutting and sewing, we propose a sketch representation with additional annotations necessary to model the knitting process. Our system bypasses complex editing operations in 3D space, which allows us to achieve interactive editing of both the garment shape and its underlying time process. We provide control of the local knitting direction, the location of important course interfaces, as well as the placement of stitch irregularities that form seams in the final garment. After solving for the constrained knitting time process, the garment sketches are automatically segmented into a minimal set of simple regions that can be knitted using simple knitting procedures. Finally, our system optimizes a stitch graph hierarchically while providing control over the tradeoff between accuracy and simplicity. We showcase different garments created with our web interface.
Guaranteed globally injective 3D deformation processing
Authors: Fang Yu , Li Minchen , Jiang Chenfanfu , Kaufman Danny M.
Published: July 2021

We extend recent advances in the numerical time-integration of contacting elastodynamics [Li et al. 2020] to build a new framework, called Injective Deformation Processing (IDP), for the robust solution of a wide range of mesh deformation problems requiring injectivity. IDP solves challenging 3D (and 2D) geometry processing and animation tasks on meshes, via artificial time integration, with guarantees of both non-inversion and non-overlap. To our knowledge IDP is the first framework for 3D deformation processing that can efficiently guarantee globally injective deformation without geometric locking. We demonstrate its application on a diverse set of problems and show its significant improvement over state-of-the-art for globally injective 3D deformation.
PhotoApp
Authors: R Mallikarjun B , Tewari Ayush , Dib Abdallah , Weyrich Tim , Bickel Bernd , Seidel Hans-Peter , Pfister Hanspeter , Matusik Wojciech , Chevallier Louis , Elgharib Mohamed , Theobalt Christian
Published: July 2021

Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting, and do not allow camera viewpoint editing. Thus, they only capture and control a subset of the reflectance field. Recently, portrait editing has been demonstrated by operating in the generative model space of StyleGAN. While such approaches do not require direct supervision, there is a significant loss of quality when compared to the supervised approaches. In this paper, we present a method which learns from limited supervised training data. The training images only include people in a fixed neutral expression with eyes closed, without much hair or background variations. Each person is captured under 150 one-light-at-a-time conditions and under 8 camera poses. Instead of training directly in the image space, we design a supervised problem which learns transformations in the latent space of StyleGAN. This combines the best of supervised learning and generative adversarial modeling. We show that the StyleGAN prior allows for generalisation to different expressions, hairstyles and backgrounds. This produces high-quality photorealistic results for in-the-wild images and significantly outperforms existing methods. Our approach can edit the illumination and pose simultaneously, and runs at interactive rates.
SP-GAN
Authors: Li Ruihui , Li Xianzhi , Hui Ka-Hei , Fu Chi-Wing
Published: July 2021

We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN is able to synthesize diverse and high-quality shapes with fine details and promote controllability for part-aware shape generation and manipulation, yet trainable without any parts annotations. In SP-GAN, we incorporate a global prior (uniform points on a sphere) to spatially guide the generative process and attach a local prior (a random latent code) to each sphere point to provide local details. The key insight in our design is to disentangle the complex 3D shape generation task into a global shape modeling and a local structure adjustment, to ease the learning process and enhance the shape generation quality. Also, our model forms an implicit dense correspondence between the sphere points and points in every generated shape, enabling various forms of structure-aware shape manipulations such as part editing, part-wise shape interpolation, and multi-shape part composition, etc., beyond the existing generative models. Experimental results, which include both visual and quantitative evaluations, demonstrate that our model is able to synthesize diverse point clouds with fine details and less noise, as compared with the state-of-the-art models.
AgileGAN
Authors: Song Guoxian , Luo Linjie , Liu Jing , Ma Wan-Chun , Lai Chunpong , Zheng Chuanxia , Cham Tat-Jen
Published: July 2021

Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images. Such StyleGAN-based methods have focused on finding the best latent inversion mapping for reconstructing input images; however, our key insight is that this does not lead to good generalization to different portrait styles. Hence we propose AgileGAN, a framework that can generate high quality stylistic portraits via inversion-consistent transfer learning. We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. To better capture attribute-dependent stylization of facial features, we also present an attribute-aware generator and adopt an early stopping strategy to avoid overfitting small training datasets. Our approach provides greater agility in creating high quality and high resolution (1024×1024) portrait stylization models, requiring only a limited number of style exemplars (~100) and short training time (~1 hour). We collected several style datasets for evaluation including 3D cartoons, comics, oil paintings and celebrities. We show that we can achieve superior portrait stylization quality to previous state-of-the-art methods, with comparisons done qualitatively, quantitatively and through a perceptual user study. We also demonstrate two applications of our method, image editing and motion retargeting.
Swept volumes via spacetime numerical continuation
Authors: Sellán Silvia , Aigerman Noam , Jacobson Alec
Published: July 2021

Given a solid 3D shape and a trajectory of it over time, we compute its
swept volume
- the union of all points contained within the shape at some moment in time. We consider the representation of the input and output as implicit functions, and lift the problem to 4D spacetime, where we show the problem gains a continuous structure which avoids expensive global searches. We exploit this structure via a continuation method which marches and reconstructs the zero level set of the swept volume, using the temporal dimension to avoid erroneous solutions. We show that, compared to other methods, our approach is not restricted to a limited class of shapes or trajectories, is extremely robust, and its asymptotic complexity is an order lower than standards used in the industry, enabling its use in applications such as modeling, constructive solid geometry, and path planning.
Computing minimal surfaces with differential forms
Authors: Wang Stephanie , Chern Albert
Published: July 2021

We describe a new algorithm that solves a classical geometric problem: Find a surface of minimal area bordered by an arbitrarily prescribed boundary curve. Existing numerical methods face challenges due to the non-convexity of the problem. Using a representation of curves and surfaces via differential forms on the ambient space, we reformulate this problem as a convex optimization. This change of variables overcomes many difficulties in previous numerical attempts and allows us to find the global minimum across all possible surface topologies. The new algorithm is based on differential forms on the ambient space and does not require handling meshes. We adopt the Alternating Direction Method of Multiplier (ADMM) to find global minimal surfaces. The resulting algorithm is simple and efficient: it boils down to an alternation between a Fast Fourier Transform (FFT) and a pointwise shrinkage operation. We also show other applications of our solver in geometry processing such as surface reconstruction.
Acorn
Authors: Martel Julien N. P. , Lindell David B. , Lin Connor Z. , Chan Eric R. , Monteiro Marco , Wetzstein Gordon
Published: July 2021

Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorporated into differentiable learning-based pipelines. While recent improvements to neural representations now make it possible to represent signals with fine details at moderate resolutions (e.g., for images and 3D shapes), adequately representing large-scale or complex scenes has proven a challenge. Current neural representations fail to accurately represent images at resolutions greater than a megapixel or 3D scenes with more than a few hundred thousand polygons. Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest. Our approach uses a multiscale block-coordinate decomposition, similar to a quadtree or octree, that is optimized during training. The network architecture operates in two stages: using the bulk of the network parameters, a coordinate encoder generates a feature grid in a single forward pass. Then, hundreds or thousands of samples within each block can be efficiently evaluated using a lightweight feature decoder. With this hybrid implicit-explicit network architecture, we demonstrate the first experiments that fit gigapixel images to nearly 40 dB peak signal-to-noise ratio. Notably this represents an increase in scale of over 1000X compared to the resolution of previously demonstrated image-fitting experiments. Moreover, our approach is able to represent 3D shapes significantly faster and better than previous techniques; it reduces training times from days to hours or minutes and memory requirements by over an order of magnitude.
3D weaving with curved ribbons
Authors: Ren Yingying , Panetta Julian , Chen Tian , Isvoranu Florin , Poincloux Samuel , Brandt Christopher , Martin Alison , Pauly Mark
Published: July 2021

Basket weaving is a traditional craft for creating curved surfaces as an interwoven array of thin, flexible, and initially straight ribbons. The three-dimensional shape of a woven structure emerges through a complex interplay of the elastic bending behavior of the ribbons and the contact forces at their crossings. Curvature can be injected by carefully placing topological singularities in the otherwise regular weaving pattern. However, shape control through topology is highly non-trivial and inherently discrete, which severely limits the range of attainable woven geometries. Here, we demonstrate how to construct arbitrary smooth free-form surface geometries by weaving carefully optimized
curved
ribbons. We present an optimization-based approach to solving the inverse design problem for such woven structures. Our algorithm computes the ribbons' planar geometry such that their interwoven assembly closely approximates a given target design surface in equilibrium. We systematically validate our approach through a series of physical prototypes to show a broad range of new woven geometries that is not achievable by existing methods. We anticipate our computational approach to significantly enhance the capabilities for the design of new woven structures. Facilitated by modern digital fabrication technology, we see potential applications in material science, bio- and mechanical engineering, art, design, and architecture.
NeuMIP
Authors: Kuznetsov Alexandr , Mullia Krishna , Xu Zexiang , Hašan Miloš , Ramamoorthi Ravi
Published: July 2021

We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which enables rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.
A non-exponential transmittance model for volumetric scene representations
Authors: Vicini Delio , Jakob Wenzel , Kaplanyan Anton
Published: July 2021

We introduce a novel transmittance model to improve the volumetric representation of 3D scenes. The model can represent opaque surfaces in the volumetric light transport framework. Volumetric representations are useful for complex scenes, and become increasingly popular for level of detail and scene reconstruction. The traditional exponential transmittance model found in volumetric light transport cannot capture correlations in visibility across volume elements. When representing opaque surfaces as volumetric density, this leads to both bloating of silhouettes and light leaking artifacts. By introducing a parametric non-exponential transmittance model, we are able to approximate these correlation effects and significantly improve the accuracy of volumetric appearance representation of opaque scenes. Our parametric transmittance model can represent a continuum between the linear transmittance that opaque surfaces exhibit and the traditional exponential transmittance encountered in participating media and unstructured geometries. This covers a large part of the spectrum of geometric structures encountered in complex scenes. In order to handle the spatially varying transmittance correlation effects, we further extend the theory of non-exponential participating media to a heterogeneous transmittance model. Our model is compact in storage and computationally efficient both for evaluation and for reverse-mode gradient computation. Applying our model to optimization algorithms yields significant improvements in volumetric scene appearance quality. We further show improvements for relevant applications, such as scene appearance prefiltering, image-based scene reconstruction using differentiable rendering, neural representations, and compare it to a conventional exponential model.
A unified second-order accurate in time MPM formulation for simulating viscoelastic liquids with phase change
Authors: Su Haozhe , Xue Tao , Han Chengguizi , Jiang Chenfanfu , Aanjaneya Mridul
Published: July 2021

We assume that the viscous forces in any liquid are simultaneously
local
and
non-local
, and introduce the
extended POM-POM model
[McLeish and Larson 1998; Oishi et al. 2012; Verbeeten et al. 2001] to computer graphics to design a unified constitutive model for viscosity that generalizes prior models, such as Oldroyd-B, the Upper-convected Maxwell (UCM) model [Sadeghy et al. 2005], and classical Newtonian viscosity under one umbrella, recovering each of them with different parameter values. Implicit discretization of our model via backward Euler recovers the variational Stokes solver of [Larionov et al. 2017] for Newtonian viscosity. For greater accuracy, however, we introduce the second-order accurate Generalized Single Step Single Solve (GS4) scheme [Tamma et al. 2000; Zhou and Tamma 2004] to computer graphics, which recovers all prior second-order accurate time integration schemes to date. Using GS4 and our generalized constitutive model, we present a Material Point Method (MPM) for simulating various viscoelastic liquid behaviors, such as classical liquid rope coiling, buckling, folding, and shear thinning/thickening. In addition, we show how to couple our viscoelastic liquid simulator with the recently introduced non-Fourier heat diffusion solver [Xue et al. 2020] for simulating problems with phase change, such as melting chocolate and digital fabrication with 3D printing. While the discretization of heat diffusion is slightly different within GS4, we show that it can still be efficiently solved using an assembly-free Multigrid-preconditioned Conjugate Gradients solver. We present end-to-end 3D simulations to demonstrate the versatility of our framework.
ShapeMOD
Authors: Jones R. Kenny , Charatan David , Guerrero Paul , Mitra Niloy J. , Ritchie Daniel
Published: July 2021

A popular way to create detailed yet easily controllable 3D shapes is via procedural modeling, i.e. generating geometry using programs. Such programs consist of a series of instructions along with their associated parameter values. To fully realize the benefits of this representation, a shape program should be compact and only expose degrees of freedom that allow for meaningful manipulation of output geometry. One way to achieve this goal is to design higher-level
macro
operators that, when executed, expand into a series of commands from the base shape modeling language. However, manually authoring such macros, much like shape programs themselves, is difficult and largely restricted to domain experts. In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs. ShapeMOD operates on shape programs expressed in an imperative, statement-based language. It is designed to discover macros that make programs more compact by minimizing the number of function calls and free parameters required to represent an input shape collection. We run ShapeMOD on multiple collections of programs expressed in a domain-specific language for 3D shape structures. We show that it automatically discovers a concise set of macros that abstract out common structural and parametric patterns that generalize over large shape collections. We also demonstrate that the macros found by ShapeMOD improve performance on downstream tasks including shape generative modeling and inferring programs from point clouds. Finally, we conduct a user study that indicates that ShapeMOD's discovered macros make interactive shape editing more efficient.
Learning time-critical responses for interactive character control
Authors: Lee Kyungho , Min Sehee , Lee Sunmin , Lee Jehee
Published: July 2021

Creating agile and responsive characters from a collection of unorganized human motion has been an important problem of constructing interactive virtual environments. Recently, learning-based approaches have successfully been exploited to learn deep network policies for the control of interactive characters. The agility and responsiveness of deep network policies are influenced by many factors, such as the composition of training datasets, the architecture of network models, and learning algorithms that involve many threshold values, weights, and hyper-parameters. In this paper, we present a novel teacher-student framework to learn time-critically responsive policies, which guarantee the time-to-completion between user inputs and their associated responses regardless of the size and composition of the motion databases. We demonstrate the effectiveness of our approach with interactive characters that can respond to the user's control quickly while performing agile, highly dynamic movements.
FovVideoVDP
Authors: Mantiuk Rafał K. , Denes Gyorgy , Chapiro Alexandre , Kaplanyan Anton , Rufo Gizem , Bachy Romain , Lian Trisha , Patney Anjul
Published: July 2021

FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.
Consistent depth of moving objects in video
Authors: Zhang Zhoutong , Cole Forrester , Tucker Richard , Freeman William T. , Dekel Tali
Published: July 2021

We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a
geometrically and temporally consistent
solution to this under-constrained problem: the depth predictions of corresponding points across frames should induce plausible, smooth motion in 3D. We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to impose multi-view consistency constraints with wide baselines. We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars), as well as camera motion. Our depth maps give rise to a number of depth-and-motion aware video editing effects such as object and lighting insertion.
Total relighting
Authors: Pandey Rohit , Escolano Sergio Orts , Legendre Chloe , Häne Christian , Bouaziz Sofien , Rhemann Christoph , Debevec Paul , Fanello Sean
Published: July 2021

We propose a novel system for portrait relighting and background replacement, which maintains high-frequency boundary details and accurately synthesizes the subject's appearance as lit by novel illumination, thereby producing realistic composite images for any desired scene. Our technique includes foreground estimation via alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. known background or known illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance, producing relit portraits with convincingly rendered non-Lambertian effects like specular highlights. Multiple experiments and comparisons show the effectiveness of the proposed approach when applied to in-the-wild images.
TryOnGAN
Authors: Lewis Kathleen M , Varadharajan Srivatsan , Kemelmacher-Shlizerman Ira
Published: July 2021

Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.
Endless loops
Authors: Halperin Tavi , Hakim Hanit , Vantzos Orestis , Hochman Gershon , Benaim Netai , Sassy Lior , Kupchik Michael , Bibi Ofir , Fried Ohad
Published: July 2021

We present an algorithm for producing a seamless animated loop from a single image. The algorithm detects periodic structures, such as the windows of a building or the steps of a staircase, and generates a non-trivial displacement vector field that maps each segment of the structure onto a neighboring segment along a user- or auto-selected main direction of motion. This displacement field is used, together with suitable temporal and spatial smoothing, to warp the image and produce the frames of a continuous animation loop. Our cinemagraphs are created in under a second on a mobile device. Over 140,000 users downloaded our app and exported over 350,000 cinemagraphs. Moreover, we conducted two user studies that show that users prefer our method for creating surreal and structured cinemagraphs compared to more manual approaches and compared to previous methods.
Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation
Authors: Wang Lichen , Ding Zhengming , Fu Yun
Published: July 2021

Multi-label learning recovers multiple labels from a single instance. It is a more challenging task compared with single-label manner. Most multi-label learning approaches need large-scale well-labeled samples to achieve high accurate performance. However, it is expensive to build such a dataset. In this work, we propose a generic multi-label learning framework based on Adaptive Graph and Marginalized Augmentation (AGMA) in a semi-supervised scenario. Generally speaking, AGMA makes use of a small amount of labeled data associated with a lot of unlabeled data to boost the learning performance. First, an adaptive similarity graph is learned to effectively capture the intrinsic structure within the data. Second, marginalized augmentation strategy is explored to enhance the model generalization and robustness. Third, a feature-label autoencoder is further deployed to improve inferring efficiency. All the modules are jointly trained to benefit each other. State-of-the-art benchmarks in both traditional and zero-shot multi-label learning scenarios are evaluated. Experiments and ablation studies illustrate the accuracy and efficiency of our AGMA method.
Events
Authors: Samarasinghe Dilini
Published: July 2021

This section features information about upcoming events relevant to the readers of AI Matters, including those supported by SIGAI. We would love to hear from you if you are are organizing an event and would be interested in cooperating with SIGAI.For more information about conference support visit sigai.acm.org/activities/requesting sponsorship. html.
Conference reports
Authors: Dennis Louise A.
Published: July 2021

This section features brief reports from recent events sponsored or run in cooperation with ACM SIGAI.
Decoding AI
Authors: Srivastava Vandana
Published: July 2021

The "Decoding AI" event was conducted online by ViSER for high school students and adults between Oct 10 to Oct 31, 2020 and was sponsored by ACM SIGAI . The proposal for the event was selected by ACM SIGAI from a pool of entries. It was advertised on different social media platforms and registration was available using Google forms and Eventbrite.
A framework for the specification and validation of dynamic reconfigurable systems
Authors: El-Hokayem Antoine , Bozga Marius , Sifakis Joseph
Published: July 2021

We study a framework for the specification and validation of dynamic reconfigurable systems. The framework is based on configuration logic for the description of architecture styles which are families of architectures sharing common connectivity features. We express specifications in the Temporal Configuration Logic (TCL), a linear time temporal logic built from atomic formulas characterizing system configurations and temporal modalities. Two non-trivial benchmarks are introduced to show the adequacy of TCL for the specification of dynamic reconfigurable systems. We study an effective model-checking procedure based on SMT techniques for a non-trivial fragment of TCL which has been implemented in a prototype runtime verification tool. We provide preliminary experimental results illustrating the capabilities of the tool on the considered benchmark systems.
Illuminating the Virosphere Through Global Metagenomics
Authors: Call Lee , Nayfach Stephen , Kyrpides Nikos C.
Published: July 2021

Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily alongside much of modern biology. In terms of outright numbers of novel viruses discovered, however, the last few years have been by far the most transformative for the field. Advances in methods for identifying viral sequences in genomic and metagenomic datasets, coupled to the exponential growth of environmental sequencing, have greatly expanded the catalog of known viruses and fueled the tremendous growth of viral sequence databases. Development and implementation of new standards, along with careful study of the newly discovered viruses, have transformed and will continue to transform our understanding of microbial evolution, ecology, and biogeochemical cycles, leading to new biotechnological innovations across many diverse fields, including environmental, agricultural, and biomedical sciences.
What Do We Know About Corporate Bond Returns?
Authors: Huang Jing-Zhi , Shi Zhan
Published: July 2021

Recently, there has been a fast-growing literature on the determinants of corporate bond returns, in particular, the driving force of cross-sectional return variation. In this review, we first survey recent empirical studies on this important topic. We discuss cross-sectional evidence as well as time-series evidence. We then present a model-based analysis of individual corporate bond returns using the structural approach for credit risk modeling. We show, among other things, that the expected corporate bond return implied by the Merton model predicts 1-month-ahead corporate bond returns in the cross section.
Replicability in Empirical Legal Research
Authors: Chin Jason M. , Zeiler Kathryn
Published: July 2021

As part of a broader methodological reform movement, scientists are increasingly interested in improving the replicability of their research. Replicability allows others to perform replications to explore potential errors and statistical issues that might call the original results into question. Little attention, however, has been paid to the state of replicability in the field of empirical legal research (ELR). Quality is especially important in this field because empirical legal researchers produce work that is regularly relied upon by courts and other legal bodies. In this review, we summarize the current state of ELR relative to the broader movement toward replicability in the social sciences. As part of that aim, we summarize recent collective replication efforts in ELR and transparency and replicability guidelines adopted by journals that publish ELR. Based on this review, ELR seems to be lagging other fields in implementing reforms. We conclude with suggestions for reforms that might encourage improved replicability.