Intuitive and Accurate Material Appearance Design and Editing

Creating and editing high-quality materials for photorealistic rendering can be a difficult task due to the diversity and complexity of material appearance. Material design is the process by which artists specify the reflectance properties of a surface, such as its diffuse color and specular roughness. Even with the support of commercial software packages, material design can be a time-consuming trial-and-error task due to the counter-intuitive nature of the complex reflectance models. Moreover, many material design tasks require the physical realization of virtually designed materials as the final step, which makes the process even more challenging due to rendering artifacts and the limitations of fabrication. In this dissertation, we propose a series of studies and novel techniques to improve the intuitiveness and accuracy of material design and editing. Our goal is to understand how humans visually perceive materials, simplify user interaction in the design process and, and improve the accuracy of the physical fabrication of designs. Our first work focuses on understanding the perceptual dimensions for measured material data. We build a perceptual space based on a low-dimensional reflectance manifold that is computed from crowd-sourced data using a multi-dimensional scaling model. Our analysis shows the proposed perceptual space is consistent with the physical interpretation of the measured data. We also put forward a new material editing interface that takes advantage of the proposed perceptual space. We visualize each dimension of the manifold to help users understand how it changes the material appearance. Our second work investigates the relationship between translucency and glossiness in material perception. We conduct two human subject studies to test if subsurface scattering impacts gloss perception and examine how the shape of an object influences this perception. Based on our results, we discuss why it is necessary to include transparent and translucent media for future research in gloss perception and material design. Our third work addresses user interaction in the material design system. We present a novel Augmented Reality (AR) material design prototype, which allows users to visualize their designs against a real environment and lighting. We believe introducing AR technology can make the design process more intuitive and improve the authenticity of the results for both novice and experienced users. To test this assumption, we conduct a user study to compare our prototype with the traditional material design system with gray-scale background and synthetic lighting. The results demonstrate that with the help of AR techniques, users perform better in terms of objectively measured accuracy and time and they are subjectively more satisfied with their results. Finally, our last work turns to the challenge presented by the physical realization of designed materials. We propose a learning-based solution to map the virtually designed appearance to a meso-scale geometry that can be easily fabricated. Essentially, this is a fitting problem, but compared with previous solutions, our method can provide the fabrication recipe with higher reconstruction accuracy for a large fitting gamut. We demonstrate the efficacy of our solution by comparing the reconstructions with existing solutions and comparing fabrication results with the original design. We also provide an application of bi-scale material editing using the proposed method

Tactile mesh saliency

While the concept of visual saliency has been previously explored in the areas of mesh and image processing, saliency detection also applies to other sensory stimuli. In this paper, we explore the problem of tactile mesh saliency, where we define salient points on a virtual mesh as those that a human is more likely to grasp, press, or touch if the mesh were a real-world object. We solve the problem of taking as input a 3D mesh and computing the relative tactile saliency of every mesh vertex. Since it is difficult to manually define a tactile saliency measure, we introduce a crowdsourcing and learning framework. It is typically easy for humans to provide relative rankings of saliency between vertices rather than absolute values. We thereby collect crowdsourced data of such relative rankings and take a learning-to-rank approach. We develop a new formulation to combine deep learning and learning-to-rank methods to compute a tactile saliency measure. We demonstrate our framework with a variety of 3D meshes and various applications including material suggestion for rendering and fabricatio

Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network, such that the network not only conforms to the measurements, initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing PINN in stiff ODE systems. Consequently, we employ Quasi-Steady-State-Assumptions (QSSA) to reduce the stiffness of the ODE systems, and the PINN then can be successfully applied to the converted non/mild-stiff systems. Therefore, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed Stiff-PINN approach that utilizes QSSA to enable PINN to solve stiff chemical kinetics shall open the possibility of applying PINN to various reaction-diffusion systems involving stiff dynamics

Comparative Analysis of Complete Chloroplast Genomes of Nine Species of Litsea (Lauraceae): Hypervariable Regions, Positive Selection, and Phylogenetic Relationships

Litsea is a group of evergreen trees or shrubs in the laurel family, Lauraceae. Species of the genus are widely used for a wide range of medicinal and industrial aspects. At present, most studies related to the gene resources of Litsea are restricted to morphological analyses or features of individual genomes, and currently available studies of select molecular markers are insufficient. In this study, we assembled and annotated the complete chloroplast genomes of nine species in Litsea, carried out a series of comparative analyses, and reconstructed phylogenetic relationships within the genus. The genome length ranged from 152,051 to 152,747 bp and a total of 128 genes were identified. High consistency patterns of codon bias, repeats, divergent analysis, single nucleotide polymorphisms (SNP) and insertions and deletions (InDels) were discovered across the genus. Variations in gene length and the presence of the pseudogene ycf1Ψ, resulting from IR contraction and expansion, are reported. The hyper-variable gene rpl16 was identified for its exceptionally high Ka/Ks and Pi values, implying that those frequent mutations occurred as a result of positive selection. Phylogenetic relationships were recovered for the genus based on analyses of full chloroplast genomes and protein-coding genes. Overall, both genome sequences and potential molecular markers provided in this study enrich the available genomic resources for species of Litsea. Valuable genomic resources and divergent analysis are also provided for further research of the evolutionary patterns, molecular markers, and deeper phylogenetic relationships of Litsea

A Low-Dimensional Perceptual Space for Intuitive BRDF Editing

International audienceUnderstanding and characterizing material appearance based on human perception is challenging because of the highdimensionality and nonlinearity of reflectance data. We refer to the process of identifying specific characteristics of material appearance within the same category as material estimation, in contrast to material categorization which focuses on identifying inter-category differences [FNG15]. In this paper, we present a method to simulate the material estimation process based on human perception. We create a continuous perceptual space for measured tabulated data based on its underlying low-dimensional manifold. Unlike many previous works that only address individual perceptual attributes (such as gloss), we focus on extracting all possible dimensions that can explain the perceived differences between appearances. Additionally, we propose a new material editing interface that combines image navigation and sliders to visualize each perceptual dimension and facilitate the editing of tabulated BRDFs. We conduct a user study to evaluate the efficacy of the perceptual space and the interface in terms of appearance matching

P21cip-Overexpression in the Mouse β Cells Leads to the Improved Recovery from Streptozotocin-Induced Diabetes

Under normal conditions, the regeneration of mouse β cells is mainly dependent on their own duplication. Although there is evidence that pancreatic progenitor cells exist around duct, whether non-β cells in the islet could also potentially contribute to β cell regeneration in vivo is still controversial. Here, we developed a novel transgenic mouse model to study the pancreatic β cell regeneration, which could specifically inhibit β cell proliferation by overexpressing p21cip in β cells via regulation of the Tet-on system. We discovered that p21 overexpression could inhibit β-cell duplication in the transgenic mice and these mice would gradually suffer from hyperglycemia. Importantly, the recovery efficiency of the p21-overexpressing mice from streptozotocin-induced diabetes was significantly higher than control mice, which is embodied by better physiological quality and earlier emergence of insulin expressing cells. Furthermore, in the islets of these streptozotocin-treated transgenic mice, we found a large population of proliferating cells which expressed pancreatic duodenal homeobox 1 (PDX1) but not markers of terminally differentiated cells. Transcription factors characteristic of early pancreatic development, such as Nkx2.2 and NeuroD1, and pancreatic progenitor markers, such as Ngn3 and c-Met, could also be detected in these islets. Thus, our work showed for the first time that when β cell self-duplication is repressed by p21 overexpression, the markers for embryonic pancreatic progenitor cells could be detected in islets, which might contribute to the recovery of these transgenic mice from streptozotocin-induced diabetes. These discoveries could be important for exploring new diabetes therapies that directly promote the regeneration of pancreatic progenitors to differentiate into islet β cells in vivo

TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining

A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for Text, Image, and Layout Fusion in Argument Mining), is designed to handle this mixed data. It excels at not only understanding text but also detecting optical characters and recognizing layout details in images. Our model significantly outperforms existing baselines, earning our team, KnowComp, the 1st place in the leaderboard of Argumentative Stance Classification subtask in this shared task.Comment: Accepted to the 10th Workshop on Argument Mining, co-located with EMNLP 202

Functional Characterization of Two Putative DAHP Synthases of AroG1 and AroG2 and Their Links With Type III Secretion System in Ralstonia solanacearum

Type three secretion system (T3SS) is essential for Ralstonia solanacearum to cause disease in host plants and we previously screened AroG1 as a candidate with impact on the T3SS expression. Here, we focused on two putative DAHP synthases of AroG1 and AroG2, which control the first step of the shikimate pathway, a common route for biosynthesis of aromatic amino acids (AAA), to characterize their functional roles and possible links with virulence in R. solanacearum. Deletion of aroG1/2 or aroG1, but not aroG2, significantly impaired the T3SS expression both in vitro and in planta, and the impact of AroG1 on T3SS was mediated with a well-characterized PrhA signaling cascade. Virulence of the aroG1/2 or aroG1 mutants was completely diminished or significantly impaired in tomato and tobacco plants, but not the aroG2 mutants. The aroG1/2 mutants failed to grow in limited medium, but grew slowly in planta. This significantly impaired growth was also observed in the aroG1 mutants both in planta and limited medium, but not in aroG2 mutants. Complementary aroG1 significantly restored the impaired or diminished bacterial growth, T3SS expression and virulence. Supplementary AAA or shikimic acid, an important intermediate of the shikimate pathway, significantly restored diminished growth in limited medium. The promoter activity assay showed that expression of aroG1 and aroG2 was greatly increased to 10-20-folder higher levels with deletion of the other. All these results demonstrated that both AroG1 and AroG2 are involved in the shikimate pathway and cooperatively essential for AAA biosynthesis in R. solanacearum. The AroG1 plays a major role on bacterial growth, T3SS expression and pathogenicity, while the AroG2 is capable to partially carry out the function of AroG1 in the absence of AroG1

Lagrangian actuator model for wind turbine wake aerodynamics

As a continuation of authors’ previous work, this work extends and hackles the numerical method for wind turbine wakes based on the vortex method, and proposes the Lagrangian actuator model (LAM) which is used for the representation of the wind turbine rotor under the Lagarangian framework. This paper provides two examples of the LAM, the Lagrangian actuator line (LAL) model and the Lagrangian actuator disc (LAD) model, and constructs matching numerical methods for wake predictions respectively. Those methods have high computation efficiency, and the results coincide with the wind tunnel test data well. Moreover, based on that, a vorticity description framework centered on vortex geometric structures is established to illustrate wind turbine wake phenomena and explore the wake evolution mechanism

Decoding the influence of bacterial community structure and algicidal bacteria in a Karenia longicanalis bloom event

IntroductionHarmful algal blooms (HABs) have been increasing in frequency and expanding their ranges on coastlines worldwide in recent decades. Algicidal bacteria play a pivotal role in eliminating HABs, yet the characteristics of bacterial communities and their algicidal activity during a Karenia longicanalis bloom remain poorly understood.MethodsIn this study, we investigated bacterial communities using 16S rRNA sequencing during a K. longicanalis bloom to identify bacteria with high algicidal activity that could be isolated. Five sampling sites in Tongxin Bay, located in Lianjiang County, China, including TX1 to TX5, were selected based on the concentration of K. longicanalis cells.ResultsOur 16S rRNA sequencing results revealed that the TX4 site was enriched with genera known to contain algicidal bacteria, such as Pseudoalteromonas and Alteromonas, which are members of the Gammaproteobacteria class, while Sulfitobacter, a member of the Alphaproteobacteria class, was enriched in the TX5 site. Among the 100 cultivable bacteria isolated from the 5 sampling sites, 6 exhibited an algicidal rate of over 80%, with FDHY-MQ5, isolated from the TX4 site, exhibiting an algicidal rate of approximately 100% against Karenia mikimotoi after 48 hours of challenge with 2% (v/v) bacterial volume (OD600=4.5) concentration. Our 16S rRNA sequencing result showed FDHY-MQ5 was a member of the Pseudoalteromonas genus, and this bacterium also demonstrated high algicidal activity against Heterosigma akashiwo and Alexandrium tamarense.DiscussionOur findings shed light on the changes in bacterial community structure and the algicidal behavior of bacteria towards algae during a K. longicanalis bloom, providing a research basis for a better understanding of HAB management