bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidation of secondary hydroperoxides via αC–H abstraction to form ketones and hydroxyl radicals: fluorene autoxidation as a model system
Authors: Sollin Mélanie , Hosseininasab Seyedehsan , Malenfant Jason , El-Akhrass Mohamed , Lopez de Arbina Amaia , Montulet Alicia , Frenette Mathieu
Published: July 2023

Organic and biological molecules with relatively weak C–H bonds can react with O2 via a free-radical reaction called autoxidation. The primary products of these peroxyl-radical-driven reactions are hydroperoxides, R2CHOOH. If autoxidation continues, the secondary oxidation of hydroperoxides is known to form ketones, R2C═O, but this mechanism is not well characterized. Importantly, we find that ketone formation produces a highly reactive hydroxyl radical, HO•. We can trap HO• using benzene as a solvent to form quantifiable amounts of phenol. Fluorene was chosen as a model system to study this secondary oxidation in great detail. Kinetic modeling allowed the measurement of rate constants for the primary and secondary autoxidation reactions as 11.3 and 25 mol L−1s−1, respectively. Density functional theory modeling likewise predicts a faster oxidation for the secondary autoxidation. This type of kinetic measurement and modeling approach could be useful to study the autoxidation of plastics, petrochemicals, and lipids.
Response of soil moisture and water use efficiency to straw mulching amount and mulching period in black soil zone of northeast China
Authors: Liu Jilong , Liu Qianqian , Li Jiawen , Zhang Lingling , Cao Xiaoqiang , Li Jizhen , Cao Ran , Lv Hang , Wang Zhizhuo , Dong Ze
Published: July 2023

To reveal the mechanisms of straw mulching amounts and mulching periods on soil moisture in the black soil zone of Northeast China. Three types of straw mulching (0.4, 0.8, and 1.2 kg/m2) and two mulching periods (sowing stage and three-leaf stage) were set up as interactive experiments, and no straw mulching was used as the control (CK) to analyze the response mechanisms of soil moisture content, water consumption, and water use efficiency to straw mulching periods and mulching amounts at different depths. The results showed that straw mulching improved the moisture storage capacity compared with no straw mulching, and the straw mulching rate of 0.8 kg/m2 at the three-leaf stage improved the moisture storage capacity and reduced the water consumption capacity compared with the straw mulching at the sowing stage, CK and other straw mulching treatments at the three-leaf stage; the crop yield and water use efficiency increased and then decreased with the increase of straw mulching. The increase of crop yield and water use efficiency under the straw mulching treatments at the three-leaf stage was higher than that under the same straw mulching treatments at the sowing stage. The analysis of the dual effects of straw mulching amounts and mulching periods on moisture gain and loss showed that the straw mulching amounts of 0.8 kg/m2 at the three-leaf stage had the best effect on soil moisture characteristics and water use efficiency.
Some observable physical properties of the higher dimensional dS/AdS black holes in Einstein-bumblebee gravity theory
Authors: Uniyal Akhil , Kanzi Sara , Sakallı İzzet
Published: July 2023

We study the greybody factors, quasinormal modes, and shadow of the higher dimensional de-Sitter (dS)/anti de-Sitter (AdS) black hole spacetimes derived from the Einstein-bumblebee gravity theory within the Lorentz symmetry breaking (LSB) framework. We specifically apply the semi-analytical WKB method and the time domain approach to study the scalar and Dirac perturbations of the black hole. In-depth researches are done on the effects of the LSB and dimensionality on the bosonic/fermionic greybody factors, quasinormal modes, and shadow of the higher dimensional bumblebee black hole. The results obtained are discussed, tabulated, and illustrated graphically.
Decidability Boundaries for the Finite-Image Property of Weighted Finite Automata
Authors: Droste Manfred , Fülöp Zoltán , Kószó Dávid
Published: July 2023

A weighted finite automaton has the finite-image property if the image of the weighted language associated with it is finite. We show two undecidability results concerning the finite-image property of weighted finite automata over semirings, respectively strong bimonoids. Firstly, we give a computable idempotent commutative past-finite ordered semiring such that it is undecidable, for an arbitrary deterministic weighted finite automaton [Formula: see text] over that semiring, whether [Formula: see text] has the finite-image property. Secondly, we give a computable commutative past-finite monotonic ordered strong bimonoid such that it is undecidable, for an arbitrary weighted finite automaton [Formula: see text] over that strong bimonoid, whether [Formula: see text] has the finite-image property. This shows that recent decidability results for suitable weighted finite automata over past-finite monotonic strong bimonoids cannot be extended to natural classes of ordered semirings and ordered strong bimonoids without further assumptions.
Online Predictions for Online TSP on the Line
Authors: Shao Jian-Xi , Liang Ya-Chun , Liao Chung-Shou
Published: July 2023

With a rapidly growing attention on the development of learning-augmented algorithms, we revisit the classical online traveling salesman problem (OLTSP) from the perspective of such learning approaches. A learning-augmented online algorithm, or simply an online algorithm with predictions, considers how to improve its competitive performance with theoretical guarantees by forecasting the information of future requests, e.g., their release time or locations in the OLTSP. In this study, we investigate the OLTSP on the real line, motivated by the parcel picking problem in a smart warehouse, which aims at scheduling a route for a server (saying, a Kiva robot), starting at the origin, serving all online requests and returning to the origin such that the completion time is minimized. Each online request is released over time on the real line and the server moves back and forth along the line with unit speed.

We mainly focus on zealous algorithms, a special type of online algorithms for the OLTSP which never allow the sever to wait if there are still unserved requests, and exploit a specific forecasting strategy, called online predictions, which makes a sequence of predictions one by one in an online manner. In order to ensure better competitive performance, we particularly explore the worst-case scenarios that restrict the power of such predictions. Based on the discussion, we make an assumption in which online predictions are guaranteed to be useful, and devise a learning-augmented algorithm with [Formula: see text]-robustness and [Formula: see text]-consistency, [Formula: see text], comparing to the previous lower bound of [Formula: see text], where [Formula: see text] and [Formula: see text] which indicate prediction errors are sufficiently small constants. Moreover, we also conduct numerical experiments to demonstrate the practical effectiveness of the proposed algorithm.
Study of propagation of skew cosh Gaussian laser beam in attenuated magneto plasma
Authors: Takale Prasad , Khandale Kalyani , Patil Sanyogita , Patil Sandip , Takale Mansing
Published: July 2023

Self-focusing of a laser beam is one of the major consequences of nonlinear interaction of laser with plasma. In this paper, self-focusing of skew cosh Gaussian (skew-ChG) laser beam is studied in anisotropic, collisional plasma where in externally exponentially decreasing magnetic field is applied. Skewness parameters in the field profile of skew-ChG laser beam eventually determine intensity profile of the beam. The symmetry in skewness parameter in two transverse directions simplifies the calculations considerably. Second order, nonlinear, ordinary, coupled differential equations for Beam Width Parameters (BWP) are obtained using Akhmanovs approach under Wentzel–Kramers–Brillouin (WKB) and paraxial approximations which are solved numerically. For given amplitude of applied magnetic field, attenuated oscillatory self-focusing is observed with respect to change in attenuation coefficient.
DYNAMIC STUDY OF A RATIO-DEPENDENT PREDATOR–PREY MODEL WITH STRONG ALLEE EFFECT AND NONLINEAR HARVESTING
Authors: VERMA MAITRI , SINGH DIVAKAR VIKRAM
Published: July 2023

The harvesting of species occurs in terrestrial and aquatic habitats across the world. It not only causes alteration in the population structure of the species subjected to harvesting but also of the species in interaction with the harvested species. The present work investigates the effect of nonlinear prey harvesting on the dynamics of a ratio-dependent predator–prey system with a strong Allee effect in prey population. It is found that the system exhibits a rich spectrum of dynamics including saddle-node bifurcation, Hopf bifurcation and homoclinic bifurcation with respect to the parameters that shape the nonlinear harvesting rate, namely, the maximum harvesting rate and a half-saturation constant that represents the prey density at which half of the maximum harvesting rate is reached. It is found that the basin of attraction of the stable coexistence state shrinks as the harvesting rate increases and if the harvesting rate is above a threshold value at which saddle-node bifurcation occurs, the stable coexistence of predator and prey populations is not possible for any initial start. It is also found that the harvesting policies in which the harvesting rate increases less rapidly at low prey population size are more favorable for the stable coexistence of species. The presence of Allee effect in the prey population is found to increase the chances of extinction of both species by reducing the threshold value of the harvesting rate at which the unconditional extinction occurs. Numerical simulations are carried out to support the analytical findings.
A NEW FRACTAL MODEL FOR PREDICTING SATURATED SOIL PERMEABILITY UNDER DIFFERENT DEFORMATION
Authors: TAO GAOLIANG , ZHANG FAN , ZHAO WEI , XIAO HENGLIN , CHEN QINGSHENG , BASACK SUDIP , LIU LISHENG
Published: July 2023

The permeability coefficient and air-entry value of saturated soil are important hydraulic properties, which play an important role in engineering applications. Subsoil supporting foundation is subjected to stress and undergoes deformation; the saturated permeability coefficient of such deformed soil is of practical importance. With the help of fractal theory, based on the different fractal forms of Tao–Kong model, CCG model, Mualem model, and soil–water characteristic curve, this paper derives the saturated permeability coefficient models under four deformation conditions, considering the saturation permeability coefficient is inversely proportional to the square of the air-entry value and directly proportional to the square of the maximum aperture of the soil. Combining the prediction method of air-entry value under deformation conditions, four prediction methods for the permeability coefficient of deformed saturated soil are established and the method proposed in this paper is validated by the measured value of the saturated permeability coefficient of the deformed soil. As observed, the predicted values of the four methods for clay, silt loam, sandy loam and sandy soil under deformed conditions are in acceptable agreement with the measured values, and the prediction results of the second prediction method are the closest to the measured values.
A NUMERICAL STUDY OF COMPLEX DYNAMICS OF A CHEMOSTAT MODEL UNDER FRACTAL-FRACTIONAL DERIVATIVE
Authors: KHAN ZAREEN A. , SHAH KAMAL , ABDALLA BAHAAELDIN , ABDELJAWAD THABET
Published: July 2023

In this paper, we study the existence of numerical solution and stability of a chemostat model under fractal-fractional order derivative. First, we investigate the positivity and roundedness of the solution of the considered system. Second, we find the existence of a solution of the considered system by employing the Banach and Schauder fixed-point theorems. Furthermore, we obtain a sufficient condition that allows the existence of the stabling of solutions by using the numerical-functional analysis. We find that the proposed system exists as a unique positive solution that obeys the criteria of Ulam–Hyers (U-H) and generalized U-H stability. We also establish a numerical analysis for the proposed system by using a numerical scheme based on the Lagrange interpolation procedure. Finally, we provide two numerical examples to verify the correctness of the theoretical results. We remark that the structure described by the considered model is also sometimes called side capacity or cross-flow model. The structure considered here can be also seen as a limiting case of the pattern chemostats in parallel with diffusion connection. Moreover, the said model forms in natural and engineered systems and can significantly affect the hydrodynamics in porous media. Fractal calculus is an excellent tool to discuss fractal characteristics of porous media and the characteristic method of the porous media.
THEORETICAL AND NUMERICAL COMPUTATIONS OF CONVEXITY ANALYSIS FOR FRACTIONAL DIFFERENCES USING LOWER BOUNDEDNESS
Authors: MOHAMMED PSHTIWAN OTHMAN , BALEANU DUMITRU , AL-SARAIRAH EMAN , ABDELJAWAD THABET , CHORFI NEJMEDDINE
Published: July 2023

This study focuses on the analytical and numerical solutions of the convexity analysis for fractional differences with exponential and Mittag-Leffler kernels involving negative and nonnegative lower bounds. In the analytical part of the paper, we will give a new formula for [Formula: see text] of the discrete fractional differences, which can be useful to obtain the convexity results. The correlation between the nonnegativity and negativity of both of the discrete fractional differences, [Formula: see text] with the convexity of the functions will be examined. In light of the main lemmas, we will define the two decreasing subsets of [Formula: see text], namely [Formula: see text] and [Formula: see text]. The decrease of these sets enables us to obtain the relationship between the negative lower bound of [Formula: see text] and the convexity of the function on a finite time set given by [Formula: see text] for some [Formula: see text] Besides, the numerical part of the paper is dedicated to examine the validity of the sets [Formula: see text] and [Formula: see text] in certain regions of the solutions for different values of [Formula: see text] and [Formula: see text]. For this reason, we will illustrate the domain of the solutions by means of several figures in which the validity of the main theorems are explained.
RESEARCH ON THE FLOW FIELD OF THE PEMFC BIPOLAR PLATE BASED ON THE TREE-LIKE FRACTAL THEORY
Authors: WAN XINYU , CAO JIYIN , YANG XIA , WANG LONGFEI , CHEN YUANCHAO , CHENG BEIBEI
Published: July 2023

Tree-like branching structures occur in both natural and artificial transport systems, which have fascinated multidisciplinary researchers to study and apply the transport mechanisms of tree-like branching structures for decades. In this paper, the flow field of a proton exchange membrane fuel cell (PEMFC) with a Y-shaped tree-like fractal structure was studied utilizing Murray’s law in fractal theory. The polarization curve, gas concentration distribution, water concentration distribution, pressure drop distribution, and current density distribution of the PEMFC are numerically simulated in this research, and the transmission law is analyzed with different channel branching angles [Formula: see text]. The results demonstrate that the optimal branching angle of the designed tree-like fractal flow field is [Formula: see text]. When compared to a parallel flow field under identical conditions, the maximum output power density of the fractal flow field with a branching angle of [Formula: see text] is 26.7% higher. The optimal angle of [Formula: see text] for symmetric branching flow derived from Murray’s law was shown to be applicable to the flow field design of the fuel cell, improving the transport characteristics of the reaction gases and the overall performance of the PEMFC. This research may provide further references for the design of flow fields in fuel cells.
Surface expression of Late Caledonian magmatic lithium concentration, in the Rhynie Chert, UK
Authors: Parnell J. , Armstrong J. G. T.
Published: July 2023

The Lower Devonian plant-bearing Rhynie Chert, Aberdeenshire, UK, consists of sinters deposited by a hot spring system. Like many modern hot springs, the Rhynie geothermal system was lithium-rich, and its silica deposits are richer in lithium than other current or fossil sinters. Twenty samples of Rhynie sinter have a mean content of 255 ppm lithium. The high values imply exceptional lithium contents in the spring waters. Together with pegmatites and granites in the same region, the chert is related to a lithium-rich Late Caledonian magmatic suite, of which it is a surface expression. The measurements suggest that ancient hot spring deposits could provide valuable data during the exploration for lithium.

Supplementary material:
Supplementary Table 1 (compositional data for Late Caledonian granites) is available at
https://doi.org/10.6084/m9.figshare.c.6756506
GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents
Authors: Ao Tenglong , Zhang Zeyi , Liu Libin
Published: July 2023

The automatic generation of stylized co-speech gestures has recently received increasing attention. Previous systems typically allow style control via predefined text labels or example motion clips, which are often not flexible enough to convey user intent accurately. In this work, we present GestureDiffuCLIP, a neural network framework for synthesizing realistic, stylized co-speech gestures with flexible style control. We leverage the power of the large-scale Contrastive-Language-Image-Pre-training (CLIP) model and present a novel CLIP-guided mechanism that extracts efficient style representations from multiple input modalities, such as a piece of text, an example motion clip, or a video. Our system learns a latent diffusion model to generate high-quality gestures and infuses the CLIP representations of style into the generator via an adaptive instance normalization (AdaIN) layer. We further devise a gesture-transcript alignment mechanism that ensures a semantically correct gesture generation based on contrastive learning. Our system can also be extended to allow fine-grained style control of individual body parts. We demonstrate an extensive set of examples showing the flexibility and generalizability of our model to a variety of style descriptions. In a user study, we show that our system outperforms the state-of-the-art approaches regarding human likeness, appropriateness, and style correctness.
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
Authors: Yi Xinyu , Zhou Yuxiao , Habermann Marc , Golyanik Vladislav , Pan Shaohua , Theobalt Christian , Xu Feng
Published: July 2023

Human and environment sensing are two important topics in Computer Vision and Graphics. Human motion is often captured by inertial sensors, while the environment is mostly reconstructed using cameras. We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera. On one hand, inertial mocap suffers from large translation drift due to the lack of the global positioning signal. EgoLo-cate leverages image-based simultaneous localization and mapping (SLAM) techniquesto locate the human in the reconstructed scene. Onthe other hand, SLAM often fails when the visual feature is poor. EgoLocate involves inertial mocap to provide a strong prior for the camera motion. Experiments show that localization, a key challenge for both two fields, is largely improved by our technique, compared with the state of the art of the two fields. Our codes are available for research at https://xinyu-yi.github.io/EgoLocate/.
AvatarReX: Real-time Expressive Full-body Avatars
Authors: Zheng Zerong , Zhao Xiaochen , Zhang Hongwen , Liu Boning , Liu Yebin
Published: July 2023

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face together, but also supports real-time animation and rendering. To this end, we propose a compositional avatar representation, where the body, hands and the face are separately modeled in a way that the structural prior from parametric mesh templates is properly utilized without compromising representation flexibility. Furthermore, we disentangle the geometry and appearance for each part. With these technical designs, we propose a dedicated deferred rendering pipeline, which can be executed at a real-time framerate to synthesize high-quality free-view images. The disentanglement of geometry and appearance also allows us to design a two-pass training strategy that combines volume rendering and surface rendering for network training. In this way, patch-level supervision can be applied to force the network to learn sharp appearance details on the basis of geometry estimation. Overall, our method enables automatic construction of expressive full-body avatars with real-time rendering capability, and can generate photo-realistic images with dynamic details for novel body motions and facial expressions.
ScanBot: Autonomous Reconstruction via Deep Reinforcement Learning
Authors: Cao Hezhi , Xia Xi , Wu Guan , Hu Ruizhen , Liu Ligang
Published: July 2023

Autoscanning of an unknown environment is the key to many AR/VR and robotic applications. However, autonomous reconstruction with both high efficiency and quality remains a challenging problem. In this work, we propose a reconstruction-oriented autoscanning approach, called ScanBot, which utilizes hierarchical deep reinforcement learning techniques for global
region-of-interest
(ROI) planning to improve the scanning efficiency and local
next-best-view
(NBV) planning to enhance the reconstruction quality. Given the partially reconstructed scene, the global policy designates an ROI with insufficient exploration or reconstruction. The local policy is then applied to refine the reconstruction quality of objects in this region by planning and scanning a series of NBVs. A novel mixed 2D-3D representation is designed for these policies, where a 2D quality map with tailored quality channels encoding the scanning progress is consumed by the global policy, and a coarse-to-fine 3D volumetric representation that embodies both local environment and object completeness is fed to the local policy. These two policies iterate until the whole scene has been completely explored and scanned. To speed up the learning of complex environmental dynamics and enhance the agent's memory for spatial-temporal inference, we further introduce two novel auxiliary learning tasks to guide the training of our global policy. Thorough evaluations and comparisons are carried out to show the feasibility of our proposed approach and its advantages over previous methods. Code and data are available at https://github.com/HezhiCao/Scanbot.
Computational Long Exposure Mobile Photography
Authors: Tabellion Eric , Karnad Nikhil , Glaser Noa , Weiss Ben , Jacobs David E. , Pritch Yael
Published: July 2023

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers.
Dense, Interlocking-Free and Scalable Spectral Packing of Generic 3D Objects
Authors: Cui Qiaodong , Rong Victor , Chen Desai , Matusik Wojciech
Published: July 2023

Packing 3D objects into a known container is a very common task in many industries such as packaging, transportation, and manufacturing. This important problem is known to be NP-hard and even approximate solutions are challenging. This is due to the difficulty of handling interactions between objects with arbitrary 3D geometries and a vast combinatorial search space. Moreover, the packing must be
interlocking-free
for real-world applications. In this work, we first introduce a novel packing algorithm to search for placement locations given an object. Our method leverages a discrete voxel representation. We formulate collisions between objects as correlations of functions computed efficiently using Fast Fourier Transform (FFT). To determine the best placements, we utilize a novel cost function, which is also computed efficiently using FFT. Finally, we show how interlocking detection and correction can be addressed in the same framework resulting in interlocking-free packing. We propose a challenging benchmark with thousands of 3D objects to evaluate our algorithm. Our method demonstrates state-of-the-art performance on the benchmark when compared to existing methods in both density and speed.
End-to-end Procedural Material Capture with Proxy-Free Mixed-Integer Optimization
Authors: Li Beichen , Shi Liang , Matusik Wojciech
Published: July 2023

Node-graph-based procedural materials are vital to 3D content creation within the computer graphics industry. Leveraging the expressive representation of procedural materials, artists can effortlessly generate diverse appearances by altering the graph structure or node parameters. However, manually reproducing a specific appearance is a challenging task that demands extensive domain knowledge and labor. Previous research has sought to automate this process by converting artist-created material graphs into differentiable programs and optimizing node parameters against a photographed material appearance using gradient descent. These methods involve implementing differentiable filter nodes [Shi et al. 2020] and training differentiable neural proxies for generator nodes to optimize continuous and discrete node parameters [Hu et al. 2022a] jointly. Nevertheless, Neural Proxies exhibits critical limitations, such as long training times, inaccuracies, fixed resolutions, and confined parameter ranges, which hinder their scalability towards the broad spectrum of production-grade material graphs. These constraints fundamentally stem from the absence of faithful and efficient implementations of generic noise and pattern generator nodes, both differentiable and non-differentiable. Such deficiency prevents the direct optimization of continuous and discrete generator node parameters without relying on surrogate models.

We present
Diffmat v2
, an improved differentiable procedural material library, along with a fully-automated, end-to-end procedural material capture framework that combines gradient-based optimization and gradient-free parameter search to match existing production-grade procedural materials against user-taken flash photos. Diffmat v2 expands the range of differentiable material graph nodes in Diffmat [Shi et al. 2020] by adding generic noise/pattern generator nodes and user-customizable per-pixel filter nodes. This allows for the complete translation and optimization of procedural materials across various categories without the need for external proprietary tools or pre-cached noise patterns. Consequently, our method can capture a considerably broader array of materials, encompassing those with highly regular or stochastic geometries. We demonstrate that our end-to-end approach yields a closer match to the target than MATch [Shi et al. 2020] and Neural Proxies [Hu et al. 2022a] when starting from initially unmatched continuous and discrete parameters.
Scratch-based Reflection Art via Differentiable Rendering
Authors: Shen Pengfei , Li Ruizeng , Wang Beibei , Liu Ligang
Published: July 2023

The 3D visual optical arts create fascinating special effects by carefully designing interactions between objects and light sources. One of the essential types is 3D reflection art, which aims to create reflectors that can display different images when viewed from different directions. Existing works produce impressive visual effects. Unfortunately, previous works discretize the reflector surface with regular grids/facets, leading to a large parameter space and a high optimization time cost. In this paper, we introduce a new type of 3D reflection art -scratch-based reflection art, which allows for a more compact parameter space, easier fabrication, and computationally efficient optimization. To design a 3D reflection art with scratches, we formulate it as a multi-view optimization problem and introduce differentiable rendering to enable efficient gradient-based optimizers. For that, we propose an analytical scratch rendering approach, together with a high-performance rendering pipeline, allowing efficient differentiable rendering. As a consequence, we could display multiple images on a single metallic board with only several minutes for optimization. We demonstrate our work by showing virtual objects and manufacturing our designed reflectors with a carving machine.
∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization
Authors: Lai Zeqiang , Wei Kaixuan , Fu Ying , Härtel Philipp , Heide Felix
Published: July 2023

Tasks across diverse application domains can be posed as large-scale optimization problems, these include graphics, vision, machine learning, imaging, health, scheduling, planning, and energy system forecasting. Independently of the application domain, proximal algorithms have emerged as a formal optimization method that successfully solves a wide array of existing problems, often exploiting problem-specific structures in the optimization. Although model-based formal optimization provides a principled approach to problem modeling with convergence guarantees, at first glance, this seems to be at odds with black-box deep learning methods. A recent line of work shows that, when combined with learning-based ingredients, model-based optimization methods are effective, interpretable, and allow for generalization to a wide spectrum of applications with little or no extra training data. However, experimenting with such hybrid approaches for different tasks by hand requires domain expertise in both proximal optimization and deep learning, which is often error-prone and time-consuming. Moreover, naively unrolling these iterative methods produces lengthy compute graphs, which when differentiated via autograd techniques results in exploding memory consumption, making batch-based training challenging. In this work, we introduce ∇-Prox, a domain-specific modeling language and compiler for large-scale optimization problems using differentiable proximal algorithms. ∇-Prox allows users to specify optimization objective functions of unknowns concisely at a high level, and intelligently compiles the problem into compute and memory-efficient differentiable solvers. One of the core features of ∇-Prox is its full differentiability, which supports hybrid model- and learning-based solvers integrating proximal optimization with neural network pipelines. Example applications of this methodology include learning-based priors and/or sample-dependent inner-loop optimization schedulers, learned with deep equilibrium learning or deep reinforcement learning. With a few lines of code, we show ∇-Prox can generate performant solvers for a range of image optimization problems, including end-to-end computational optics, image deraining, and compressive magnetic resonance imaging. We also demonstrate ∇-Prox can be used in a completely orthogonal application domain of energy system planning, an essential task in the energy crisis and the clean energy transition, where it outperforms state-of-the-art CVXPY and commercial Gurobi solvers.
PolyStokes: A Polynomial Model Reduction Method for Viscous Fluid Simulation
Authors: Panuelos Jonathan , Goldade Ryan , Grinspun Eitan , Levin David , Batty Christopher
Published: July 2023

Standard liquid simulators apply operator splitting to independently solve for pressure and viscous stresses, a decoupling that induces incorrect free surface boundary conditions. Such methods are unable to simulate fluid phenomena reliant on the balance of pressure and viscous stresses, such as the liquid rope coil instability exhibited by honey. By contrast, unsteady Stokes solvers retain coupling between pressure and viscosity, thus resolving these phenomena, albeit using a much larger and thus more computationally expensive linear system compared to the decoupled approach. To accelerate solving the unsteady Stokes problem, we propose a reduced fluid model wherein interior regions are represented with incompressible polynomial vector fields. Sets of standard grid cells are consolidated into super-cells, each of which are modelled using a quadratic field of 26 degrees of freedom. We demonstrate that the reduced field must necessarily be at least quadratic, with the affine model being unable to correctly capture viscous forces. We reproduce the liquid rope coiling instability, as well as other simulated examples, to show that our reduced model is able to reproduce the same fluid phenomena at a smaller computational cost. Futhermore, we performed a crowdsourced user survey to verify that our method produces imperceptible differences compared to the full unsteady Stokes method.
Towards Material Digitization with a Dual-scale Optical System
Authors: Garces Elena , Arellano Victor , Rodriguez-Pardo Carlos , Pascual-Hernandez David , Suja Sergio , Lopez-Moreno Jorge
Published: July 2023

Existing devices for measuring material appearance in spatially-varying samples are limited to a single scale, either micro or mesoscopic. This is a practical limitation when the material has a complex multi-scale structure. In this paper, we present a system and methods to digitize materials at two scales, designed to include high-resolution data in spatially-varying representations at larger scales. We design and build a hemispherical light dome able to digitize flat material samples up to 11x11cm. We estimate geometric properties, anisotropic reflectance and transmittance at the microscopic level using polarized directional lighting with a single orthogonal camera. Then, we propagate this structured information to the mesoscale, using a neural network trained with the data acquired by the device and image-to-image translation methods. To maximize the compatibility of our digitization, we leverage standard BSDF models commonly adopted in the industry. Through extensive experiments, we demonstrate the precision of our device and the quality of our digitization process using a set of challenging real-world material samples and validation scenes. Further, we demonstrate the optical resolution and potential of our device for acquiring more complex material representations by capturing microscopic attributes which affect the global appearance: we characterize the properties of textile materials such as the yarn twist or the shape of individual fly-out fibers. We also release the SEDDIDOME dataset of materials, including raw data captured by the machine and optimized parameteres.
Building a Virtual Weakly-Compressible Wind Tunnel Testing Facility
Authors: Lyu Chaoyang , Bai Kai , Wu Yiheng , Desbrun Mathieu , Zheng Changxi , Liu Xiaopei
Published: July 2023

Virtual wind tunnel testing is a key ingredient in the engineering design process for the automotive and aeronautical industries as well as for urban planning: through visualization and analysis of the simulation data, it helps optimize lift and drag coefficients, increase peak speed, detect high pressure zones, and reduce wind noise at low cost prior to manufacturing. In this paper, we develop an efficient and accurate virtual wind tunnel system based on recent contributions from both computer graphics and computational fluid dynamics in high-performance kinetic solvers. Running on one or multiple GPUs, our massively-parallel lattice Boltzmann model meets industry standards for accuracy and consistency while exceeding current mainstream industrial solutions in terms of efficiency --- especially for unsteady turbulent flow simulation at very high Reynolds number (on the order of 10
7
) --- due to key contributions in improved collision modeling and boundary treatment, automatic construction of multiresolution grids for complex models, as well as performance optimization. We demonstrate the efficacy and reliability of our virtual wind tunnel testing facility through comparisons of our results to multiple benchmark tests, showing an increase in both accuracy and efficiency compared to state-of-the-art industrial solutions. We also illustrate the fine turbulence structures that our system can capture, indicating the relevance of our solver for both VFX and industrial product design.
Boundary Value Caching for Walk on Spheres
Authors: Miller Bailey , Sawhney Rohan , Crane Keenan , Gkioulekas Ioannis
Published: July 2023

Grid-free Monte Carlo methods such as
walk on spheres
can be used to solve elliptic partial differential equations without mesh generation or global solves. However, such methods independently estimate the solution at every point, and hence do not take advantage of the high spatial regularity of solutions to elliptic problems. We propose a fast caching strategy which first estimates solution values and derivatives at randomly sampled points along the boundary of the domain (or a local region of interest). These cached values then provide cheap, output-sensitive evaluation of the solution (or its gradient) at interior points, via a boundary integral formulation. Unlike classic boundary integral methods, our caching scheme introduces zero statistical bias and does not require a dense global solve. Moreover we can handle imperfect geometry (e.g., with self-intersections) and detailed boundary/source terms without repairing or resampling the boundary representation. Overall, our scheme is similar in spirit to
virtual point light
methods from photorealistic rendering: it suppresses the typical salt-and-pepper noise characteristic of independent Monte Carlo estimates, while still retaining the many advantages of Monte Carlo solvers: progressive evaluation, trivial parallelization, geometric robustness,
etc.
We validate our approach using test problems from visual and geometric computing.
Fast Complementary Dynamics via Skinning Eigenmodes
Authors: Benchekroun Otman , Zhang Jiayi Eris , Chaudhuri Siddartha , Grinspun Eitan , Zhou Yi , Jacobson Alec
Published: July 2023

We propose a reduced-space elastodynamic solver that is well suited for augmenting rigged character animations with secondary motion. At the core of our method is a novel deformation subspace based on Linear Blend Skinning that overcomes many of the shortcomings prior subspace methods face. Our skinning subspace is parameterized entirely by a set of scalar weights, which we can obtain through a small, material-aware and rig-sensitive generalized eigenvalue problem. The resulting subspace can easily capture rotational motion and guarantees that the resulting simulation is rotation equivariant. We further propose a simple local-global solver for linear co-rotational elasticity and propose a clustering method to aggregate per-tetrahedra nonlinear energetic quantities. The result is a compact simulation that is fully decoupled from the complexity of the mesh.
Inkjet 4D Print: Self-folding Tessellated Origami Objects by Inkjet UV Printing
Authors: Narumi Koya , Koyama Kazuki , Suto Kai , Noma Yuta , Sato Hiroki , Tachi Tomohiro , Sugimoto Masaaki , Igarashi Takeo , Kawahara Yoshihiro
Published: July 2023

We propose Inkjet 4D Print, a self-folding fabrication method of 3D origami tessellations by printing 2D patterns on both sides of a heat-shrinkable base sheet, using a commercialized inkjet ultraviolet (UV) printer. Compared to the previous folding-based 4D printing approach using fused deposition modeling (FDM) 3D printers [An et al. 2018], our method has merits in (1) more than 1200 times higher resolution in terms of the number of self-foldable facets, (2) 2.8 times faster printing speed, and (3) optional full-color decoration. This paper describes the material selection, the folding mechanism, the heating condition, and the printing patterns to self-fold both known and freeform tessellations. We also evaluated the self-folding resolution, the printing and transformation speed, and the shape accuracy of our method. Finally, we demonstrated applications enabled by our self-foldable tessellated objects.
Galaxy Maps: Localized Foliations for Bijective Volumetric Mapping
Authors: Hinderink Steffen , Campen Marcel
Published: July 2023

A method is presented to compute volumetric maps and parametrizations of objects over 3D domains. As a key feature, continuity and bijectivity are ensured by construction. Arbitrary objects of ball topology, represented as tetrahedral meshes, are supported. Arbitrary convex as well as star-shaped domains are supported. Full control over the boundary mapping is provided. The method is based on the technique of simplicial foliations, generalized to a broader class of domain shapes and applied adaptively in a novel localized manner. This increases flexibility as well as efficiency over the state of the art, while maintaining reliability in guaranteeing map bijectivity.
PCBend: Light Up Your 3D Shapes With Foldable Circuit Boards
Authors: Freire Marco , Bhargava Manas , Schreck Camille , Hugron Pierre-Alexandre , Bickel Bernd , Lefebvre Sylvain
Published: July 2023

We propose a computational design approach for covering a surface with individually addressable RGB LEDs, effectively forming a low-resolution surface screen. To achieve a low-cost and scalable approach, we propose creating designs from flat PCB panels bent in-place along the surface of a 3D printed core. Working with standard rigid PCBs enables the use of established PCB manufacturing services, allowing the fabrication of designs with several hundred LEDs.

Our approach optimizes the PCB geometry for folding, and then jointly optimizes the LED packing, circuit and routing, solving a challenging layout problem under strict manufacturing requirements. Unlike paper, PCBs cannot bend beyond a certain point without breaking. Therefore, we introduce parametric cut patterns acting as hinges, designed to allow bending while remaining compact. To tackle the joint optimization of placement, circuit and routing, we propose a specialized algorithm that splits the global problem into one sub-problem per triangle, which is then individually solved.

Our technique generates PCB blueprints in a completely automated way. After being fabricated by a PCB manufacturing service, the boards are bent and glued by the user onto the 3D printed support. We demonstrate our technique on a range of physical models and virtual examples, creating intricate surface light patterns from hundreds of LEDs.

The code and data for this paper are available at https://github.com/mfremer/pcbend.
A Full-Wave Reference Simulator for Computing Surface Reflectance
Authors: Yu Yunchen , Xia Mengqi , Walter Bruce , Michielssen Eric , Marschner Steve
Published: July 2023

Computing light reflection from rough surfaces is an important topic in computer graphics. Reflection models developed based on geometric optics fail to capture wave effects such as diffraction and interference, while existing models based on physical optics approximations give erroneous predictions under many circumstances (e.g. when multiple scattering from the surface cannot be ignored). We present a scalable 3D full-wave simulator for computing reference solutions to surface scattering problems, which can be used to evaluate and guide the development of approximate models for rendering. We investigate the range of validity for some existing wave optics based reflection models; our results confirm these models for low-roughness surfaces but also show that prior rendering methods do not accurately predict the scattering behavior of some types of surfaces.

Our simulator is based on the boundary element method (BEM) and accelerated using the adaptive integral method (AIM), and is implemented to execute on modern GPUs. We demonstrate the simulator on domains up to 60 × 60 × 10 wavelengths, involving surface samples with significant height variations. Furthermore, we propose a new system for efficiently computing BRDF values for large numbers of incident and outgoing directions at once, by combining small simulations to characterize larger areas. Our simulator will be released as an open-source toolkit for computing surface scattering.
FactorMatte: Redefining Video Matting for Re-Composition Tasks
Authors: Gu Zeqi , Xian Wenqi , Snavely Noah , Davis Abe
Published: July 2023

We propose
Factor Matting
, an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of a video into independent components, each representing a counterfactual version of the scene where the contents of other components have been removed. We show that factor matting maps well to a more general Bayesian framing of the matting problem that accounts for complex conditional interactions between layers. Based on this observation, we present a method for solving the factor matting problem that learns augmented patch-based appearance priors to produce useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections. Our method is trained per-video and does not require external training data or any knowledge about the 3D structure of the scene. Through extensive experiments, we show that it is able to produce useful decompositions of scenes with such complex interactions while performing competitively on classical matting tasks as well. We also demonstrate the benefits of our approach on a wide range of downstream video editing tasks. Our project website is at: https://factormatte.github.io/.