bluuebunny/crossref_metadata_2025 huggingface dataset:
Rosemary extract increases neuronal cell glucose uptake and activates AMPK
Authors: Baron David C. , Marko Daniel M. , Tsiani Evangelia , MacPherson Rebecca E.K.
Published: August 2020

Glucose is the primary metabolic substrate of neurons and is responsible for supporting many vital functions including neuronal signalling. Decreases in glucose uptake and utilization are common characteristics of dementia, particularly Alzheimer’s disease, and thus agents that can restore neuronal glucose availability may be especially valuable to the field. Diets rich in antioxidants and polyphenols have been associated with reductions in the risk of chronic disease that are associated with aging. In previous studies, rosemary extract (RE) has been reported to have antioxidant, anti-inflammatory, anticancer, and antidiabetic properties. The purpose of the present study was to explore the effects of RE on neuronal glucose uptake. Human SH-SY5Y neuroblastoma cells exposed to varied concentrations of RE showed a dose-dependent increase in glucose uptake, with a significant increase observed following treatment with 5 µg/mL RE for 2 h (159% ± 20.81% of control) that was comparable to maximum insulin stimulation (135.6% ± 3.2% of control). This increase in glucose uptake was paralleled by increases in AMP-activated protein kinase (AMPK), but not Akt, phosphorylation/activation. The present study is the first to report that treatment with rosemary extract can stimulate glucose uptake in a neuronal cell line. These results demonstrate the potential of RE to be used as an agent to regulate neuronal glucose homeostasis.

Novelty: RE increases neuronal glucose uptake. RE activates AMPK in neurons. RE increases neuronal glucose uptake independently of insulin signalling.
Effet de la rigidité, du taux du polymère renforcé de fibres (PRF) et de l’armature transversale interne sur la contribution à la résistance à l’effort tranchant d’un renfort en PRF collé en surface : état de l’art et besoins en recherche
Authors: Samb Ndongo , El-Saikaly Georges , Chaallal Omar
Published: August 2020

Le présent article traite du renforcement en cisaillement des poutres en béton armé (BA) à l’aide de matériaux composites à base de polymère renforcé de fibres (PRF) collé en surface. La première partie de l’article fait le point sur l’évolution des codes et des normes nord-américains quant à la résistance à l’effort tranchant des poutres en BA renforcées à l’aide de composite en PRF collé en surface. La seconde partie présente une analyse exhaustive des paramètres majeurs influençant la contribution du PRF à la résistance à l’effort tranchant. Elle présente également une comparaison des prédictions des normes de conception en vigueur (ACI 440.2R 2017; CSA/S6 2019; CSA/S806 2012; fib-TG9.3 2001) avec les résultats des essais expérimentaux rapportés dans la littérature. Il ressort de l’étude que des paramètres majeurs influençant le comportement à l’effort tranchant n’ont fait l’objet, à ce jour, que de peu d’investigations. Parmi ces paramètres, on peut citer en particulier : (i) la présence et le taux de l’acier transversal interne et (ii) le nombre de plis (couches) des PRF collés en surface. Cela peut expliquer les écarts observés entre les résistances prédites par les règles de calcul et celles obtenues à partir de tests. À la suite de ces observations, une base de données extraite à partir des études expérimentales a été élaborée dans le cadre de cette étude. Les résultats obtenus mettent en évidence et confirment l’existence d’une interaction entre le composite en PRF collé en surface et l’acier transversal interne quant à la reprise des efforts tranchants. Ils mettent également en évidence l’existence d’une rigidité optimale du PRF au-delà de laquelle le gain en résistance attribué au PRF collé en surface est plafonné.
Classical analytical solution for Rydberg states of muonic–electronic helium and helium-like ions
Authors: Oks Eugene
Published: August 2020

In our previous papers (Can. J. Phys. 91 (2013) 715; 92 (2014) 1405), we studied Rydberg states of systems consisting of a nucleus of charge Z, a muon, and an electron, both the muon and electron being in circular states. The studies of such quasimolecules μZe were motivated by numerous applications of muonic atoms and molecules, where one of the electrons is substituted by the heavier lepton μ–. We demonstrated that the muonic motion can represent a rapid subsystem, while the electronic motion can represent a slow subsystem. We showed that the spectral lines emitted by the muon in such systems experience a red shift compared to the corresponding spectral lines that would have been emitted by the muon in a muonic hydrogenic atom/ion. In the present paper, we also consider Rydberg states of quasimolecules μZe with Z > 1 (i.e., Rydberg states of muonic–electronic helium and helium-like ions). However, our current approach has important distinctions from our previous papers. The systems considered here are truly stable and the electron orbit is generally elliptical (although the relatively small influence of the electron on the muon is neglected). In our previous papers, the influence of the electron on the muon was taken into account; however, in the rotating frame used in our previous papers, the motion of the muon was only metastable (not truly stable), and furthermore, only circular orbits of the electron were considered in our previous paper. In the present paper, we show that the effective potential energy of the Rydberg electron is mathematically equivalent to the potential energy of a satellite moving around an oblate planet. Based on this, we demonstrate that the unperturbed orbital plane of the Rydberg electron undergoes simultaneously two different precessions: precession within the orbital plane and precession of the orbital plane around the axis of the muonic circular orbit. We provide analytical expressions for the frequencies of both precessions. The shape of the elliptical orbit of the Rydberg electron is not affected by the perturbation, which is the manifestation of the (approximate) conservation of the square of the angular momentum of the Rydberg electron. This means that the above physical systems have a higher than geometrical symmetry (also known as a hidden symmetry) which is a counterintuitive result of general physical interest. We note that the above problem of the motion of the Rydberg electron in muonic–electronic helium atoms or helium-like ions is mathematically equivalent to another problem from atomic physics: a hydrogen Rydberg atom in a linearly-polarized electric field of a high-frequency laser radiation.
Blockade of IL-6 alleviates bone loss induced by modeled microgravity in mice
Authors: He Baorong , Yin Xinhua , Hao Dingjun , Zhang Xuefang , Zhang Zhen , Zhang Ke , Yang Xiaobin
Published: August 2020

This study investigated the effects of blockade of IL-6 on bone loss induced by modeled microgravity (MG). Adult male mice were exposed to hind-limb suspension (HLS) and treated with IL-6-neutralizing antibody (IL-6 nAb) for 4 weeks. HLS in mice led to upregulation of IL-6 expression in both sera and femurs. IL-6 nAb treatment in HLS mice significantly alleviated bone loss, evidenced by increased bone mineral density of whole tibia, trabecular thickness and number, bone volume fraction of proximal tibiae, and ultimate load and stiffness of femoral diaphysis. IL-6 nAb treatment in HLS mice significantly enhanced levels of osteocalcin in sera and reduced levels of deoxypyridinoline. In MC3T3-E1 cells exposed to MG in vitro, IL-6 nAb treatment increased mRNA expression and activity of alkaline phosphatase, mRNA expression of osteopontin and runt-related transcription factor 2, and protein levels of osteoprotegerin and decreased protein levels of receptor activator of the NF-κB ligand. In RAW254.7 cells exposed to MG, IL-6 nAb treatment downregulated mRNA expression of cathepsin K and tartrate-resistant acid phosphatase (TRAP) and reduced numbers of TRAP-positive multinucleated osteoclasts. In conclusion, blockade of IL-6 alleviated the bone loss induced by MG.
Testing Bose–Einstein condensate dark matter models with the SPARC galactic rotation curves data
Authors: Crăciun Maria , Harko Tiberiu
Published: August 2020

The nature of one of the fundamental components of the Universe, dark matter, is still unknown. One interesting possibility is that dark matter could exist in the form of a self-interacting Bose–Einstein Condensate (BEC). The fundamental properties of dark matter in this model are determined by two parameters only, the mass and the scattering length of the particle. In the present study we investigate the properties of the galactic rotation curves in the BEC dark matter model, with quadratic self-interaction, by using 173 galaxies from the recently published Spitzer Photomery & Accurate Rotation Curves (SPARC) data. We fit the theoretical predictions of the rotation curves in the slowly rotating BEC models with the SPARC data by using genetic algorithms. We provide an extensive set of figures of the rotation curves, and we obtain estimates of the relevant astrophysical parameters of the BEC dark matter halos (central density, angular velocity and static radius). The density profiles of the dark matter distribution are also obtained. It turns out that the BEC model gives a good description of the SPARC data. The presence of the condensate dark matter could also provide a solution for the core–cusp problem.
The CFT$$_6$$ origin of all tree-level 4-point correlators in AdS$$_3 \times S^3$$
Authors: Giusto Stefano , Russo Rodolfo , Tyukov Alexander , Wen Congkao
Published: August 2020

We provide strong evidence that all tree-level 4-point holographic correlators in $$\hbox {AdS}_3 \times S^3$$AdS3×S3 are constrained by a hidden 6D conformal symmetry. This property has been discovered in the $$\hbox {AdS}_5 \times S^5$$AdS5×S5 context and noticed in the tensor multiplet subsector of the AdS$$_3 \times S^3$$3×S3 theory. Here we extend it to general AdS$$_3 \times S^3$$3×S3 correlators which contain also the chiral primary operators of spin zero and one that sit in the gravity multiplet. The key observation is that the 6D conformal primary field associated with these operators is not a scalar but a self-dual 3-form primary. As an example, we focus on the correlators involving two fields in the tensor multiplets and two in the gravity multiplet and show that all such correlators are encoded in a conformal 6D correlator between two scalars and two self-dual 3-forms, which is determined by three functions of the cross ratios. We fix these three functions by comparing with the results of the simplest correlators derived from an explicit supergravity calculation.
Probing sterile neutrinos in B (D) meson decays at Belle II (BESIII)
Authors: Kim C. S. , Kwon Youngjoon , Lee Donghun , Oh Sechul , Sahoo Dibyakrupa
Published: August 2020

We present, how a systematic study of $$B \rightarrow D\ell N$$B→DℓN ($$D \rightarrow K \ell N$$D→KℓN) decays with $$\ell =\mu ,\tau $$ℓ=μ,τ, at Belle II (BESIII) can provide unambiguous signature of a heavy neutrino N and/or constrain its mixing with active neutrinos $$\nu _\ell $$νℓ, which is parameterized by $$| U_{\ell N} |^2$$|UℓN|2. Our constraint on $$\vert U_{\mu N} \vert ^2$$|UμN|2 that can be achieved from the full Belle II data is comparable with what can be obtained from the much larger data set of the upgraded LHCb. Additionally, our method offers better constraint on $$\vert U_{\mu N} \vert ^2$$|UμN|2 for mass of sterile neutrino $$m_N < 2$$mN<2 GeV. We can also probe the Dirac and Majorana nature of N by observing the sequential decay of N, including suppression from observation of a displaced vertex as well as helicity flip, for Majorana N.
A New Study of Parikh Matrices Restricted to Terms
Authors: Chern Zi Jing , Subramanian K. G. , Ahmad Azhana , Teh Wen Chean
Published: August 2020

Parikh matrices as an extension of Parikh vectors are useful tools in arithmetizing words by numbers. This paper presents a further study of Parikh matrices by restricting the corresponding words to terms formed over a signature. Some [Formula: see text]-equivalence preserving rewriting rules for such terms are introduced. A characterization of terms that are only [Formula: see text]-equivalent to themselves is studied for binary signatures. Graphs associated to the equivalence classes of [Formula: see text]-equivalent terms are studied with respect to graph distance. Finally, the preservation of [Formula: see text]-equivalence under the term self-shuffle operator is studied.
A typical stochastic resonance in Landau-type potential field
Authors: Zhang Gang , Xu Hao , Zhang Tianqi
Published: August 2020

Considering the significant influence of the asymmetric structure of the one-dimensional potential field on the stochastic resonance (SR), the research is extended to the two-dimensional asymmetric potential field. The Brownian particles moving in the Landau-type potential field served as the research object. According to the linear response theory and probability flow method, the analytical formula for the spectrum power amplification (SPA) of the physical system concerning the driving frequency is derived. When the SPA is used as a measurement index, the theoretical analysis shows that an atypical SR phenomenon occurs in the system. The reason is that as the noise intensity increases, the response mode changes due to the asymmetric structure of the Landau-type potential field. Moreover, the linear responses and the SPA are inversely proportional to the driving frequency. The conclusions of the theoretical analysis are verified by numerical simulation. Finally, the Landau-type potential system is applied to the bearing fault signal detection. The experimental results show that the Landau-type potential system can effectively diagnose the faults of the inner and outer ring of the bearings, which proves the application value of the Landau-type potential system.
Balancing Assisted Reproductive Technology Dataset for Improving the Efficiency of Incremental Classifiers and Feature Selection Techniques
Authors: Suruliandi A. , Ranjini K. , Raja S. P.
Published: August 2020

Assisted Reproductive Technology (ART) is a set of medical procedures primarily used to address infertility. Success Rate of ART is very low because it is affected by large number of variables. Machine Learning Techniques are now applied to predict ART outcome and to find strategies to improve success rate. For this, determining the best performing classifier for ART is very important. Previously, some classifiers are applied to ART with static data. But, in reality, the datasets are dynamic in nature and require dynamic setup which can be achieved with the help of Incremental Classifiers. Due to low success rate, the ART dataset contains less number of records for positive results that make the dataset imbalanced. This research work first finds the best evaluation metric for classification on imbalanced dataset and then balances the dataset using three different balancing techniques such as undersampling, oversampling and Synthetic Minority Oversampling Technique (SMOTE) and applies five different Incremental Classifiers, namely Stochastic Gradient Descent (SGD), Stochastic Primal Estimated sub-GrAdient SOlver for Support vector machine (SPegasos), Naïve Bayes Updatable, Instance Based (IBk), Averaged One Dependence Estimators (A1DE) Updatable and finds the best balancing technique and suitable classifier for ART outcome prediction. The result shows that for an imbalanced dataset Receiver Operating Characteristics (ROC) Area may be taken as a metric instead of the accuracy. It is found that SMOTE is best method for balancing the ART dataset and IB1 classifier performs well for the balanced data with the high prediction rate of 92.3 for ROC. Finally, various Feature Selection methods are applied to the top three best performing classifiers and suitable feature selection method for each classifier is identified.
Investigations on the study of entrance channel effects in synthesis of superheavy elements using Cr-induced fusion reactions
Authors: Manjunatha H. C. , Manjunatha N. , Seenappa L.
Published: August 2020

We have investigated the synthesis of superheavy elements using Cr-induced fusion reactions. We have studied all possible Cr-induced fusion reactions for the synthesis of super heavy nuclei [Formula: see text]. We have achieved the semi-empirical formula for fusion barrier heights ([Formula: see text]), positions ([Formula: see text]), curvature of the inverted parabola ([Formula: see text]) of Cr-induced fusion reactions for the synthesis of superheavy nuclei with atomic number range [Formula: see text]. The proposed formula produces fusion barriers of Cr-induced fusion reactions for the synthesis of super heavy nuclei with the simple inputs of mass number ([Formula: see text]) and atomic number ([Formula: see text]) of projectile-targets. We have also identified the targets for Cr-induced fusion reactions to synthesis superheavy elements of [Formula: see text]. We have also studied the entrance channel parameters such as mass asymmetry ([Formula: see text]), charge asymmetry ([Formula: see text]), coulomb interaction parameter ([Formula: see text]’), Businaro–Gallone mass asymmetry parameter ([Formula: see text]) and Isospin asymmetry parameter [[Formula: see text]]. We hope that our predictions may be the guide for the future experiments in the synthesis of more superheavy elements using [Formula: see text]Cr-induced fusion reactions.
Size-Dependent Free Vibration of Microbeams Submerged in Fluid
Authors: Li H. C. , Ke L. L. , Yang J. , Kitipornchai S.
Published: August 2020

The size-dependent free vibration of microbeams submerged in fluid is presented in this paper based on the modified couple stress theory. Two different cross-section shapes of microbeams are considered, i.e. the circular cross-section and rectangular cross-section. This nonclassical microbeam model is introduced for capturing the size effect of microstructures. In this fluid and structure coupled system, the effect of hydrodynamic loading on microbeams can be expressed by the added mass method. By using Hamilton’s principle and differential quadrature (DQ) method, we can derive governing equations of microbeams in fluid, and then rewrite them in the discretized form. The frequencies and mode shapes for microbeams are determined by proposing an iterative method. Numerical examples are given to show the effect of fluid depth, fluid density, length scale parameter, slenderness ratio, boundary condition and cross-section shape on the vibration characteristics.
New Method for Numerical Solution of Z-Fractional Differential Equations
Authors: Keshavarz Parisa , Allahviranloo Tofigh , Yaghoobi Farajollah M. , Barahmand Ali
Published: August 2020

In this paper, at first, we introduce fractional differential equations with [Formula: see text]-valuation. Then, we propose a numerical method to approximate the solution. The proposed method is a hybrid method based on the corrected fractional Euler’s method and the probability distribution function. Moreover, the corrected fractional Euler’s method based on the generalized Taylor formula and the modified trapezoidal rule is proposed that this method can be used in the problems’ limitation section of the [Formula: see text]-fractional Initial value problem of order [Formula: see text] with the fuzzy Caputo fractional differential (fractional derivatives are defined on the basis of the Hukuhara differences and the generalized fuzzy derivatives). The probability function is based on exponential distribution function and used to represent the reliability of the problem limitation part. Finally, by two examples, we show that the proposed method can arbitrarily approximate the fractional differential equations with [Formula: see text]-valuation.
An Advanced Arithmetic Approach to GTIFNs and Its Application in Medical Analysis
Authors: Dutta Palash
Published: August 2020

Intuitionistic fuzzy set (IFS) is the straight simplification of fuzzy set theory (FST). Nevertheless, estimation of the arithmetic operation on generalized intuitionistic fuzzy number (GIFNs) is a critical apprehension. This paper presents an attempt to set up a novel method for effectively resolving the drawbacks of conform arithmetic operations on generalized triangular intuitionistic fuzzy numbers (GTIFNs). For this purpose, decomposition theorems for generalized trapezoidal intuitionistic fuzzy numbers (GTrFNs) are studied. Numerical examples are illustrated herewith. Finally, to validate the requirement of a novel elucidation, an application in medical analysis has been carried out under this setting.
On elliptic curves with complex multiplication and root numbers
Authors: Lee Wan , Yu Myungjun
Published: August 2020

Let [Formula: see text] be an elliptic curve defined over a number field [Formula: see text]. Suppose that [Formula: see text] has complex multiplication over [Formula: see text], i.e. [Formula: see text] is an imaginary quadratic field. With the aid of CM theory, we find elliptic curves whose quadratic twists have a constant root number.
On the intersections of exceptional sets in Borel’s normal number theorem and Erdös–Rényi limit theorem
Authors: Zhang Mengjie
Published: August 2020

For any [Formula: see text], let [Formula: see text] be the partial summation of the first [Formula: see text] digits in the binary expansion of [Formula: see text] and [Formula: see text] be its run-length function. The classical Borel’s normal number theorem tells us that for almost all [Formula: see text], the limit of [Formula: see text] as [Formula: see text] goes to infinity is one half. On the other hand, the Erdös–Rényi limit theorem shows that [Formula: see text] increases to infinity with the logarithmic speed [Formula: see text] as [Formula: see text] for almost every [Formula: see text] in [Formula: see text]. In this paper, we are interested in the intersections of exceptional sets arising in the above two famous theorems. More precisely, for any [Formula: see text] and [Formula: see text], we completely determine the Hausdorff dimension of the following set: [Formula: see text] where [Formula: see text] and [Formula: see text] After some minor modifications, our result still holds if we replace the denominator [Formula: see text] in [Formula: see text] with any increasing function [Formula: see text] satisfying [Formula: see text] tending to [Formula: see text] and [Formula: see text]. As a result, we also obtain that the set of points for which neither the sequence [Formula: see text] nor [Formula: see text] converges has full Hausdorff dimension.
Strongly continuous quasi semigroups in optimal control problems for non-autonomous systems
Authors: Sutrima Sutrima , Indrati Christiana Rini , Aryati Lina
Published: August 2020

This paper addresses linear quadratic control optimal problems for non-autonomous linear control systems using strongly continuous quasi semigroups. Riccati equations are implemented to investigate the control optimal problems of cost functional with finite and infinite horizons. The unique optimal pair for the cost functional is determined by the mild solution of the associated closed-loop problem and the feedback control of the solution of the corresponding Riccati equation. In addition for the infinite horizon, the stabilizability of the system is a sufficiency for the solvability to the Riccati equation. An application in a parabolic system is proposed.
A congestion bottleneck analysis model for tourist block street network
Authors: Dong Xiaoqin , Sun Xianbin , He Jiangquan , Yan Xiaofeng
Published: August 2020

The development of the tourism industry has led to increased pressure of people flow in tourist blocks. Therefore, it is critical to ease the traffic pressure in these blocks. This paper aims to identify the bottleneck links of street networks in tourist blocks to achieve the effective prevention of congestion accidents. A logit stochastic user equilibrium model combined with spatial syntax is presented to study the travelers’ route choice behavior. The nonlinear Bureau of Public Roads function is applied to calculate the time impedance of each street. A case analysis of the Chongqing Ciqikou tourist block shows that the bottleneck link has the features of high integration and a large degree of negative time impedance evolution. The research’s results are more consistent with practical circumstances because the influence of the road network topological structure on pedestrian path selection has been considered.
Efficient Latency Control in Fog Deployments via Hardware-Accelerated Popularity Estimation
Authors: Enguehard Marcel , Desmouceaux Yoann , Carofiglio Giovanna
Published: August 2020

Introduced as an extension of the Cloud at the network edge for computing and storage purposes, the Fog is increasingly considered a key enabler for Internet-of-Things applications whose latency requirements are not compatible with a Cloud-only approach. Unlike Cloud platforms, which can elastically accommodate large numbers of requests, Fog deployments are usually dimensioned for an average traffic load and, thus, unable to handle sudden bursts of requests without violating latency guarantees. In this article, we address the problem of efficiently controlling Fog admission to guarantee application response time. We propose request-aware admission control (AC) strategies maximizing the number of Fog-handled requests by means of dynamic popularity estimation. In particular, the
LRU-AC
, an AC strategy based on online learning of the request popularity distribution via a Least Recently Used (LRU) filter, is introduced. We contribute an analytical model for assessing LRU-AC performance and quantifying the incurred reduction of Cloud offload cost, w.r.t. both an ideal oracle-based and a request-oblivious AC strategy. Further, we propose a feasible implementation design of LRU-AC on FPGA hardware using Aging Bloom Filters (ABF) to mimic the function of the LRU-AC, while providing a compact memory representation. The use of ABFs for LRU-AC is theoretically validated and verified through simulation. The current implementation shows a throughput of 16.7 Mpps and a processing latency of less than 3μ
s
while multiplying the Fog acceptance-rate by 10 in the evaluated scenario.
Phong deformation
Authors: James Doug L.
Published: August 2020

Physics-based simulations of deforming tetrahedral meshes are widely used to animate detailed embedded geometry. Unfortunately most practitioners still use linear interpolation (or other low-order schemes) on tetrahedra, which can produce undesirable visual artifacts, e.g., faceting and shading artifacts, that necessitate increasing the simulation's spatial resolution and, unfortunately, cost.

In this paper, we propose
Phong Deformation
, a simple, robust and practical vertex-based quadratic interpolation scheme that, while still only
C
0
continuous like linear interpolation, greatly reduces visual artifacts for embedded geometry. The method first averages element-based linear deformation models to vertices, then barycentrically interpolates the vertex models while also averaging with the traditional linear interpolation model. The method is a fast, robust, and easily implemented replacement for linear interpolation that produces visually better results for embedded deformation with irregular tetrahedral meshes.
A scalable approach to control diverse behaviors for physically simulated characters
Authors: Won Jungdam , Gopinath Deepak , Hodgins Jessica
Published: August 2020

Human characters with a broad range of natural looking and physically realistic behaviors will enable the construction of compelling interactive experiences. In this paper, we develop a technique for learning controllers for a large set of heterogeneous behaviors. By dividing a reference library of motion into clusters of like motions, we are able to construct
experts
, learned controllers that can reproduce a simulated version of the motions in that cluster. These experts are then combined via a second learning phase, into a general controller with the capability to reproduce any motion in the reference library. We demonstrate the power of this approach by learning the motions produced by a motion graph constructed from eight hours of motion capture data and containing a diverse set of behaviors such as dancing (ballroom and breakdancing), Karate moves, gesturing, walking, and running.
Interferometric transmission probing with coded mutual intensity
Authors: Kotwal Alankar , Levin Anat , Gkioulekas Ioannis
Published: August 2020

We introduce a new interferometric imaging methodology that we term interferometry with coded mutual intensity, which allows selectively imaging photon paths based on attributes such as their length and endpoints. At the core of our methodology is a new technical result that shows that manipulating the spatial coherence properties of the light source used in an interferometric system is equivalent, through a Fourier transform, to implementing light path probing patterns. These patterns can be applied to either the coherent transmission matrix, or the incoherent light transport matrix describing the propagation of light in a scene. We test our theory by building a prototype inspired by the Michelson interferometer, extended to allow for programmable phase and amplitude modulation of the illumination injected in the interferometer. We use our prototype to perform experiments such as visualizing complex fields, capturing direct and global transport components, acquiring light transport matrices, and performing anisotropic descattering, both in steady-state imaging and, by combining our technique with optical coherence tomography, in transient imaging.
DeepFaceDrawing
Authors: Chen Shu-Yu , Su Wanchao , Gao Lin , Xia Shihong , Fu Hongbo
Published: August 2020

Recent deep image-to-image translation techniques allow fast generation of face images from freehand sketches. However, existing solutions tend to overfit to sketches, thus requiring professional sketches or even edge maps as input. To address this issue, our key idea is to implicitly model the shape space of plausible face images and synthesize a face image in this space to approximate an input sketch. We take a local-to-global approach. We first learn feature embeddings of key face components, and push corresponding parts of input sketches towards underlying component manifolds defined by the feature vectors of face component samples. We also propose another deep neural network to learn the mapping from the embedded component features to realistic images with multi-channel feature maps as intermediate results to improve the information flow. Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches. Our tool is easy to use even for non-artists, while still supporting fine-grained control of shape details. Both qualitative and quantitative evaluations show the superior generation ability of our system to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
Image-based acquisition and modeling of polarimetric reflectance
Authors: Baek Seung-Hwan , Zeltner Tizian , Ku Hyun Jin , Hwang Inseung , Tong Xin , Jakob Wenzel , Kim Min H.
Published: August 2020

Realistic modeling of the bidirectional reflectance distribution function (BRDF) of scene objects is a vital prerequisite for any type of physically based rendering. In the last decades, the availability of databases containing real-world material measurements has fueled considerable innovation in the development of such models. However, previous work in this area was mainly focused on increasing the visual realism of images, and hence ignored the effect of scattering on the polarization state of light, which is normally imperceptible to the human eye. Existing databases thus only capture scattered flux, or polarimetric BRDF datasets are too directionally sparse (e.g., in-plane) to be usable for simulation.

While subtle to human observers, polarization is easily perceived by any optical sensor (e.g., using polarizing filters), providing a wealth of additional information about shape and material properties of the object under observation. Given the increasing application of rendering in the solution of inverse problems via analysis-by-synthesis and differentiation, the ability to realistically model polarized radiative transport is thus highly desirable.

Polarization depends on the wavelength of the spectrum, and thus we provide the first polarimetric BRDF (pBRDF) dataset that captures the polarimetric properties of real-world materials over the full angular domain, and at multiple wavelengths. Acquisition of such reflectance data is challenging due to the extremely large space of angular, spectral, and polarimetric configurations that must be observed, and we propose a scheme combining image-based acquisition with spectroscopic ellipsometry to perform measurements in a realistic amount of time. This process yields raw Mueller matrices, which we subsequently transform into Rusinkiewicz-parameterized pBRDFs that can be used for rendering.

Our dataset provides 25 isotropic pBRDFs spanning a wide range of appearances: diffuse/specular, metallic/dielectric, rough/smooth, and different color albedos, captured in five wavelength ranges covering the visible spectrum. We demonstrate usage of our data-driven pBRDF model in a physically based renderer that accounts for polarized interreflection, and we investigate the relationship of polarization and material appearance, providing insights into the behavior of characteristic real-world pBRDFs.
Graph2Plan
Authors: Hu Ruizhen , Huang Zeyu , Tang Yuhan , Van Kaick Oliver , Zhang Hao , Huang Hui
Published: August 2020

We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human users to provide sparse design constraints. Such constraints are represented by a
layout graph.
The core component of our learning framework is a deep neural network, Graph2Plan, which converts a layout graph, along with a building boundary, into a floorplan that fulfills both the layout and boundary constraints. Given an input building boundary, we allow a user to specify room counts and other layout constraints, which are used to retrieve a set of floorplans, with their associated layout graphs, from a database. For each retrieved layout graph, along with the input boundary, Graph2Plan first generates a corresponding raster floorplan image, and then a refined set of boxes representing the rooms. Graph2Plan is trained on RPLAN, a large-scale dataset consisting of 80K annotated floorplans. The network is mainly based on convolutional processing over both the layout graph, via a graph neural network (GNN), and the input building boundary, as well as the raster floorplan images, via conventional image convolution. We demonstrate the quality and versatility of our floorplan generation framework in terms of its ability to cater to different user inputs. We conduct both qualitative and quantitative evaluations, ablation studies, and comparisons with state-of-the-art approaches.
Fast and scalable turbulent flow simulation with two-way coupling
Authors: Li Wei , Chen Yixin , Desbrun Mathieu , Zheng Changxi , Liu Xiaopei
Published: August 2020

Despite their cinematic appeal, turbulent flows involving fluid-solid coupling remain a computational challenge in animation. At the root of this current limitation is the numerical dispersion from which most accurate Navier-Stokes solvers suffer: proper coupling between fluid and solid often generates artificial dispersion in the form of local, parasitic trains of velocity oscillations, eventually leading to numerical instability. While successive improvements over the years have led to conservative and detail-preserving fluid integrators, the dispersive nature of these solvers is rarely discussed despite its dramatic impact on fluid-structure interaction. In this paper, we introduce a novel low-dissipation and low-dispersion fluid solver that can simulate two-way coupling in an efficient and scalable manner, even for turbulent flows. In sharp contrast with most current CG approaches, we construct our solver from a kinetic formulation of the flow derived from statistical mechanics. Unlike existing lattice Boltzmann solvers, our approach leverages
high-order moment relaxations
as a key to controlling both dissipation and dispersion of the resulting scheme. Moreover, we combine our new fluid solver with the immersed boundary method to easily handle fluid-solid coupling through time adaptive simulations. Our kinetic solver is highly parallelizable by nature, making it ideally suited for implementation on single- or multi-GPU computing platforms. Extensive comparisons with existing solvers on synthetic tests and real-life experiments are used to highlight the multiple advantages of our work over traditional and more recent approaches, in terms of accuracy, scalability, and efficiency.
Single image HDR reconstruction using a CNN with masked features and perceptual loss
Authors: Santos Marcel Santana , Ren Tsang Ing , Kalantari Nima Khademi
Published: August 2020

Digital cameras can only capture a limited range of real-world scenes' luminance, producing images with saturated pixels. Existing single image high dynamic range (HDR) reconstruction methods attempt to expand the range of luminance, but are not able to hallucinate plausible textures, producing results with artifacts in the saturated areas. In this paper, we present a novel learning-based approach to reconstruct an HDR image by recovering the saturated pixels of an input LDR image in a visually pleasing way. Previous deep learning-based methods apply the same convolutional filters on wellexposed and saturated pixels, creating ambiguity during training and leading to checkerboard and halo artifacts. To overcome this problem, we propose a feature masking mechanism that reduces the contribution of the features from the saturated areas. Moreover, we adapt the VGG-based perceptual loss function to our application to be able to synthesize visually pleasing textures. Since the number of HDR images for training is limited, we propose to train our system in two stages. Specifically, we first train our system on a large number of images for image inpainting task and then fine-tune it on HDR reconstruction. Since most of the HDR examples contain smooth regions that are simple to reconstruct, we propose a sampling strategy to select challenging training patches during the HDR fine-tuning stage. We demonstrate through experimental results that our approach can reconstruct visually pleasing HDR results, better than the current state of the art on a wide range of scenes.
ARAnimator
Authors: Ye Hui , Kwan Kin Chung , Su Wanchao , Fu Hongbo
Published: August 2020

Creating animated virtual AR characters closely interacting with real environments is interesting but difficult. Existing systems adopt video see-through approaches to indirectly control a virtual character in mobile AR, making close interaction with real environments not intuitive. In this work we use an AR-enabled mobile device to directly control the position and motion of a virtual character situated in a real environment. We conduct two guessability studies to elicit user-defined motions of a virtual character interacting with real environments, and a set of user-defined motion gestures describing specific character motions. We found that an SVM-based learning approach achieves reasonably high accuracy for gesture classification from the motion data of a mobile device. We present
ARAnimator
, which allows novice and casual animation users to directly represent a virtual character by an AR-enabled mobile phone and control its animation in AR scenes using motion gestures of the device, followed by animation preview and interactive editing through a video see-through interface. Our experimental results show that with
ARAnimator
, users are able to easily create in-situ character animations closely interacting with different real environments.
A perceptual model of motion quality for rendering with adaptive refresh-rate and resolution
Authors: Denes Gyorgy , Jindal Akshay , Mikhailiuk Aliaksei , Mantiuk Rafał K.
Published: August 2020

Limited GPU performance budgets and transmission bandwidths mean that real-time rendering often has to compromise on the spatial resolution or temporal resolution (refresh rate). A common practice is to keep either the resolution or the refresh rate constant and dynamically control the other variable. But this strategy is non-optimal when the velocity of displayed content varies. To find the best trade-off between the spatial resolution and refresh rate, we propose a perceptual visual model that predicts the quality of motion given an object velocity and predictability of motion. The model considers two motion artifacts to establish an overall quality score: non-smooth (juddery) motion, and blur. Blur is modeled as a combined effect of eye motion, finite refresh rate and display resolution. To fit the free parameters of the proposed visual model, we measured eye movement for predictable and unpredictable motion, and conducted psychophysical experiments to measure the quality of motion from 50 Hz to 165 Hz. We demonstrate the utility of the model with our on-the-fly motion-adaptive rendering algorithm that adjusts the refresh rate of a G-Sync-capable monitor based on a given rendering budget and observed object motion. Our psychophysical validation experiments demonstrate that the proposed algorithm performs better than constant-refresh-rate solutions, showing that motion-adaptive rendering is an attractive technique for driving variable-refresh-rate displays.
Homogenized yarn-level cloth
Authors: Sperl Georg , Narain Rahul , Wojtan Chris
Published: August 2020

We present a method for animating yarn-level cloth effects using a thin-shell solver. We accomplish this through numerical homogenization: we first use a large number of yarn-level simulations to build a model of the potential energy density of the cloth, and then use this energy density function to compute forces in a thin shell simulator. We model several yarn-based materials, including both woven and knitted fabrics. Our model faithfully reproduces expected effects like the stiffness of woven fabrics, and the highly deformable nature and anisotropy of knitted fabrics. Our approach does not require any real-world experiments nor measurements; because the method is based entirely on simulations, it can generate entirely new material models quickly, without the need for testing apparatuses or human intervention. We provide data-driven models of several woven and knitted fabrics, which can be used for efficient simulation with an off-the-shelf cloth solver.
Neural subdivision
Authors: Liu Hsueh-Ti Derek , Kim Vladimir G. , Chaudhuri Siddhartha , Aigerman Noam , Jacobson Alec
Published: August 2020

This paper introduces
Neural Subdivision
, a novel framework for data-driven coarse-to-fine geometry modeling. During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch. This approach enables us to learn complex non-linear subdivision schemes, beyond simple linear averaging used in classical techniques. One of our key contributions is a novel self-supervised training setup that only requires a set of high-resolution meshes for learning network weights. For any training shape, we stochastically generate diverse low-resolution discretizations of coarse counterparts, while maintaining a bijective mapping that prescribes the exact target position of every new vertex during the subdivision process. This leads to a very efficient and accurate loss function for conditional mesh generation, and enables us to train a method that generalizes across discretizations and favors preserving the manifold structure of the output. During training we optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. Our network encodes patch geometry in a local frame in a rotation- and translation-invariant manner. Jointly, these design choices enable our method to generalize well, and we demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
Error-bounded compatible remeshing
Authors: Yang Yang , Zhang Wen-Xiang , Liu Yuan , Liu Ligang , Fu Xiao-Ming
Published: August 2020

We present a novel method to construct compatible surface meshes with bounded approximation errors. Given two oriented and topologically equivalent surfaces and a sparse set of corresponding landmarks, our method contains two steps: (1) generate compatible meshes with bounded approximation errors and (2) reduce mesh complexity while ensuring that approximation errors are always bounded. Central to the first step is a parameterization-based remeshing technique, which is capable of isotropically remeshing the input surfaces to be compatible and error-bounded. By iteratively performing a novel edge-based compatible remeshing and increasing the compatible target edge lengths, the second step effectively reduces mesh complexity while explicitly maintaining compatibility, regularity, and bounding approximation errors. Tests on various pairs of complex models demonstrate the efficacy and practicability of our method for constructing high-quality compatible meshes with bounded approximation errors.
Efficient bijective parameterizations
Authors: Su Jian-Ping , Ye Chunyang , Liu Ligang , Fu Xiao-Ming
Published: August 2020

We propose a novel method to efficiently compute bijective parameterizations with low distortion on disk topology meshes. Our method relies on a second-order solver. To design an efficient solver, we develop two key techniques. First, we propose a coarse shell to substantially reduce the number of collision constraints that are used to guarantee overlap-free boundaries. During the optimization process, the shell ensures the Hessian matrix with a fixed nonzero structure and a low density, thereby significantly accelerating the optimization. The second is a triangle inequality-based barrier function that effectively ensures non-intersecting boundaries. Our barrier function is
C
∞
inside the locally supported region and its convex second-order approximation is able to be analytically obtained. Compared to state-of-the-art methods for optimizing bijective parameterizations, our method exhibits better scalability and is about six times faster. The performance of our bijective parameterization algorithm is comparable to state-of-the-art methods of locally flip-free parameterizations. A large number of experimental results have shown the capability and feasibility of our method.
CNNs on surfaces using rotation-equivariant features
Authors: Wiersma Ruben , Eisemann Elmar , Hildebrandt Klaus
Published: August 2020

This paper is concerned with a fundamental problem in geometric deep learning that arises in the construction of convolutional neural networks on surfaces. Due to curvature, the transport of filter kernels on surfaces results in a rotational ambiguity, which prevents a uniform alignment of these kernels on the surface. We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features. The equivariance property makes it possible to locally align features, which were computed in arbitrary coordinate systems, when aggregating features in a convolution layer. The resulting network is agnostic to the choices of coordinate systems for the tangent spaces on the surface. We implement our approach for triangle meshes. Based on circular harmonic functions, we introduce convolution filters for meshes that are rotation-equivariant at the discrete level. We evaluate the resulting networks on shape correspondence and shape classifications tasks and compare their performance to other approaches.