BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs

Parametric Bidirectional Scattering Distribution Functions (BSDFs) are pervasively used because of their flexibility to represent a large variety of material appearances by simply tuning the parameters. While efficient evaluation of parametric BSDFs has been well-studied, high-quality importance sampling techniques for parametric BSDFs are still scarce. Existing sampling strategies either heavily rely on approximations, resulting in high variance, or solely perform sampling on a portion of the whole BSDF slice. Moreover, many of the sampling approaches are specifically paired with certain types of BSDFs. In this paper, we seek an efficient and general way for importance sampling parametric BSDFs. We notice that the nature of importance sampling is the mapping between a uniform distribution and the target distribution. Specifically, when BSDF parameters are given, the mapping that performs importance sampling on a BSDF slice can be simply recorded as a 2D image that we name as importance map. Following this observation, we accurately precompute the importance maps using a mathematical tool named optimal transport. Then we propose a lightweight neural network to efficiently compress the precomputed importance maps. In this way, we have brought parametric BSDF important sampling to the precomputation stage, avoiding heavy runtime computation. Since this process is similar to light baking where a set of images are precomputed, we name our method importance baking. Together with a BSDF evaluation network and a PDF (probability density function) query network, our method enables full multiple importance sampling (MIS) without any revision to the rendering pipeline. Our method essentially performs perfect importance sampling. Compared with previous methods, we demonstrate reduced noise levels on rendering results with a rich set of appearances

Causal relationships involving brain imaging-derived phenotypes based on UKB imaging cohort: a review of Mendelian randomization studies

The UK Biobank (UKB) has the largest adult brain imaging dataset, which encompasses over 40,000 participants. A significant number of Mendelian randomization (MR) studies based on UKB neuroimaging data have been published to validate potential causal relationships identified in observational studies. Relevant articles published before December 2023 were identified following the PRISMA protocol. Included studies (n = 34) revealed that there were causal relationships between various lifestyles, diseases, biomarkers, and brain image-derived phenotypes (BIDPs). In terms of lifestyle habits and environmental factors, there were causal relationships between alcohol consumption, tea intake, coffee consumption, smoking, educational attainment, and certain BIDPs. Additionally, some BIDPs could serve as mediators between leisure/physical inactivity and major depressive disorder. Regarding diseases, BIDPs have been found to have causal relationships not only with Alzheimer’s disease, stroke, psychiatric disorders, and migraine, but also with cardiovascular diseases, diabetes, poor oral health, osteoporosis, and ankle sprain. In addition, there were causal relationships between certain biological markers and BIDPs, such as blood pressure, LDL-C, IL-6, telomere length, and more

Topological edge and corner states in Bi fractals on InSb

Topological materials hosting metallic edges characterized by integer quantized conductivity in an insulating bulk have revolutionized our understanding of transport in matter. The topological protection of these edge states is based on symmetries and dimensionality. However, only integer-dimensional models have been classified, and the interplay of topology and fractals, which may have a non-integer dimension, remained largely unexplored. Quantum fractals have recently been engineered in metamaterials, but up to present no topological states were unveiled in fractals realized in real materials. Here, we show theoretically and experimentally that topological edge and corner modes arise in fractals formed upon depositing thin layers of bismuth on an indium antimonide substrate. Scanning tunneling microscopy reveals the appearance of (nearly) zero-energy modes at the corners of Sierpi\'nski triangles, as well as the formation of outer and inner edge modes at higher energies. Unexpectedly, a robust and sharp depleted mode appears at the outer and inner edges of the samples at negative bias voltages. The experimental findings are corroborated by theoretical calculations in the framework of a continuum muffin-tin and a lattice tight-binding model. The stability of the topological features to the introduction of a Rashba spin-orbit coupling and disorder is discussed. This work opens the perspective to novel electronics in real materials at non-integer dimensions with robust and protected topological states.Comment: Main manuscript 14 pages, supplementary material 34 page

Coarse graining molecular dynamics with graph neural networks

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems

Effects of Kiwifruit Dietary Fibers on Pasting Properties and In Vitro Starch Digestibility of Wheat Starch

In this study, the roles of kiwifruit soluble/insoluble dietary fiber (SDF/IDF, respectively) in the pasting characteristics and in vitro digestibility of wheat starch were explored. According to RVA and rheological tests, the IDF enhanced the wheat starch viscosity, decreased the gelatinization degree of the starch granules, and exacerbated starch retrogradation. The addition of SDF in high quantities could reduce the starch gelatinization level, lower the system viscosity, and exacerbate starch retrogradation. Through determining the leached amylose content and conducing scanning electron microscopy, the IDF and SDF added in high quantities was combined with the leached amylose wrapped around the starch granules, which reduced the leached amylose content and decreased the gelatinization degree of the starch granules. The Fourier transform infrared results showed that the addition of both the IDF and SDF resulted in an enhancement in hydrogen bonding formed by the hydroxyl groups of the system. The in vitro digestion results strongly suggested that both the IDF and SDF reduced the wheat starch digestibility. The above findings are instructive for the application of both IDF and SDF in starchy functional foods

A long-lived Indian Ocean slab: Deep dip reversal induced by the African LLSVP

A slab-like high seismic velocity anomaly (referred as SEIS) has been inferred beneath the central-southern Indian Ocean in a recent tomographic inversion. Although subduction has previously been suggested regionally by surface observations, the new inversion is consistent with a north-dipping slab extending from the upper mantle to the core mantle boundary (CMB). We propose that SEIS anomaly originated from an oceanic plate in the Paleo-Tethys that was consumed by a south-dipping intra-oceanic subduction zone during the Triassic and Jurassic periods. SEIS challenges traditional concepts of the dynamics of slab descent by its relatively shallow depths and a present-day polarity opposite to the geometry of subduction. Geodynamic models show the upwelling mantle flow exerted by a thermochemical pile can hold and stagnate the descending SEIS slab at shallow depths for more than 100 Myr. The spatial distribution of resistance from the upwelling mantle flow can reverse the slab dip, producing a structure consistent with seismic inversions as well as with our proposed tectonic scenario and geology constraining the Tethyan tectonic domain. The results suggest that slabs can descend through the lower mantle at rates substantially lower than 1 cm/yr, and even reverse their polarity through interactions with background mantle flow