bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of a glutathione transporter in
A. actinomycetemcomitans
Authors: Klementiev Alexander D. , Garg Neha , Whiteley Marvin
Published: December 2023

Bacteria rely on extracellular chemical cues to sense their environment, interact with one another, and shape their chemical landscape. Understanding this biochemistry can help us elucidate the mechanisms by which bacterial interactions impact community function. By applying mass spectrometry to mono- and co-cultures of the oral pathogen
Aggregatibacter actinomycetemcomitans
and the oral commensal
Streptococcus gordonii
, we identified hundreds of extracellular small molecules produced by these bacteria. We discovered that
A. actinomycetemcomitans
secretes millimolar amounts of glutathione and identified a five-gene operon (called
gttABCDE
) that is important for maximum secretion. The metabolomics data set generated in this study provides a valuable data set for unraveling the importance of individual molecules for mediating polymicrobial interactions in the oral cavity.

IMPORTANCE

Microbes produce a large array of extracellular molecules, which serve as signals and cues to promote polymicrobial interactions and alter the function of microbial communities. This has been particularly well studied in the human oral microbiome, where key metabolites have been shown to impact both health and disease. Here, we used an untargeted mass spectrometry approach to comprehensively assess the extracellular metabolome of the pathogen
Aggregatibacter actinomycetemcomitans
and the commensal
Streptococcus gordonii
during mono- and co-culture. We generated and made publicly available a metabolomic data set that includes hundreds of potential metabolites and leveraged this data set to identify an operon important for glutathione secretion in
A. actinomycetemcomitans
.
Influenza A virus infection alters the resistance profile of gut microbiota to clinically relevant antibiotics
Authors: Robas Marina , Presa Jesús , Arranz-Herrero Javier , Yildiz Soner , Rius-Rocabert Sergio , Llinares-Pinel Francisco , Probanza Agustin , Schmolke Mirco , Jiménez Pedro A. , Nistal-Villan Estanislao
Published: December 2023

Influenza A virus (IAV) infection triggers quantitative and qualitative modifications in lung and intestinal microbiota composition, which contain an important reservoir of antibiotic resistance genes. Analysis of genetic changes is a common practice in studies that analyze microbiota modifications. However, there is little evidence of functional changes linked to such microbiota modifications. This study evaluates some cecal microbiota’s functional changes, comparing sublethal IAV with mock-infected mice. Community-wide phenotypic metabolic profile (Biolog EcoPlatesTM) and relative antibiotic resistance changes to clinically relevant antibiotics (cenoantibiogram) have been performed in this context. Results reveal a temporal association between IAV infection and alterations in nutrient substrate profile usage as well as changes in antibiotic resistance of cecal microbiota. Alterations are transient and predominantly occur at early time points post-IAV infection. There is a functional rebalance in nutrient substrate usage and antibiotic resistance under the established culture conditions, accompanied by a decrease in microbial density of the cecal community on days 5 and 7 after the IAV infection. Our data underline that active IAV infections altering microbial populations are associated with changes in nutrient usage preferences and affect community behaviors toward specific antibiotics. These findings could have implications including activation of nutrient-related metabolic stress at the microbiota community level and additional antibiotic resistance selection mechanisms of clinically relevant infections.

IMPORTANCE

Influenza virus infection affects both lung and intestinal bacterial community composition. Most of the published analyses focus on the characterization of the microbiota composition changes. Here we assess functional alterations of gut microbiota such as nutrient and antibiotic resistance changes during an acute respiratory tract infection. Upon influenza A virus (IAV) infection, cecal microbiota drops accompanied by a decrease in the ability to metabolize some common nutrients under aerobic conditions. At the same time, the cecal community presents an increase in resistance against clinically relevant antibiotics, particularly cephalosporins. Functional characterization of complex communities presents an additional and necessary element of analysis that nowadays is mainly limited to taxonomic description. The consequences of these functional alterations could affect treatment strategies, especially in multimicrobial infections.
Survival of surface bacteriophages and their hosts in
in situ
deep-sea environments
Authors: Wei Wei , Tian Yuan , Cai Lanlan , Xu Yongle , Xiao Xilin , Wang Qiong , Wang Haowen , Dong Chunming , Shao Zongze , Jiao Nianzhi , Zhang Rui
Published: December 2023

Through particles sinking as well as the movement of water masses, a fraction of bacterioplankton and virioplankton can be transported vertically from the surface to the deep oceans, and display significant changes in viral–bacterial interactions. The survival and activity of the sinking prokaryotes and viruses in the deep-sea environment is crucial for our understanding of deep-sea ecosystems and biogeochemical cycles. However, due to the substantial challenge involved
in situ
deep-sea incubation, the effect of the deep-sea environments on the ecology of surface prokaryotes and viruses is poorly studied. To fill this knowledge gap, we used an
in situ
deep-sea long-term incubation device to examine the effect of the natural deep-sea environment on the stability and activity of four viruses and their hosts (
Prochlorococcus
,
Synechococcus
, and heterotrophic bacteria) isolated from the surface ocean. Our results showed that viral particles had still not decayed completely after
in situ
incubation for 1 year, with an average retention rate of 5.69% ± 5.13% (ranging from 1.25% to 13.06%) for infectivity and 68.11% ± 40.50% (ranging from 8.55% to 99.08%) for particles. This suggests that surface viruses probably retain long-term infectivity after sinking and may influence deep-sea microbial populations in terms of activity, function, diversity, and community structure through viral–bacterial interactions and ultimately affect deep-sea biogeochemical cycles.

IMPORTANCE

The survival of the sinking prokaryotes and viruses in the deep-sea environment is crucial for deep-sea ecosystems and biogeochemical cycles. Through an
in situ
deep-sea long-term incubation device, our results showed that viral particles and infectivity had still not decayed completely after
in situ
incubation for 1 year. This suggests that, via infection and lysis, surface viruses with long-term infectious activity
in situ
deep-sea environments may influence deep-sea microbial populations in terms of activity, function, diversity, and community structure and ultimately affect deep-sea biogeochemical cycles, highlighting the need for additional research in this area.
Supplemental Material: Cretaceous magmatism in the northern Lhasa Terrane: Implications for the tectonic evolution and crustal growth tempos of central Tibet
Authors: Tang Wan-Li , Huang Feng , et al.
Published: December 2023

The Supplemental Materials include analytical methods, supplementary figures (Figs. S1 to S3), and data (Table S1-S4).
Comparison of 10 × 1-minute high-intensity interval training (HIIT) versus 4 × 4-minute HIIT on glucose control and variability in females with type 2 diabetes
Authors: Marcotte-Chénard A. , Tremblay R. , Deslauriers L. , Geraldes P. , Gayda M. , Christou D. , Mampuya W. , Little J.P. , Riesco E.
Published: December 2023

Two high-intensity interval training (HIIT) regimens are often used in research and clinical settings. Yet, there has been no direct comparison to determine if one can improve glucose control and variability to a greater extent in individuals living with type 2 diabetes (T2D). Fourteen older females with T2D participated in a semi-randomized control trial where HIIT10 (10 × 1-min intervals at 90% heart rate max; HRmax) and HIIT4 (4 × 4-min intervals at 90% of HRmax) were compared to a control condition (CON; no exercise). Continuous glucose monitoring was used to assess glucose control and variability over 24 h after each condition. Both HIIT10 (−2.1 ± 1.1 mmol/L) and HIIT4 (−2.1 ± 1.3 mmol/L) acutely lowered glucose compared to CON (−0.7 ± 0.8 mmol/L; p = 0.001), with no difference between exercise conditions. This glucose-lowering effect did not persist over the 24-h post-exercise period, as both mean glucose ( p = 0.751) and glucose variability ( p = 0.168) were not significantly different among conditions. However, exploratory analyses focusing on individuals with less optimal glucose control (above median 24-h mean glucose in the CON condition; n = 7) revealed that 24-h mean glucose (7.4 [7.14–8.92] vs. 8.4 [7.5–9.9] mmol/L; p = 0.048), glucose variability ( p = 0.010), and peak glucose ( p = 0.048) were lower following HIIT10 compared to CON, while HIIT4 reduced time spent in moderate hyperglycemia compared to CON ( p = 0.023). Both HIIT10 and HIIT4 acutely lower glycemia, but the effect does not persist over 24 h. However, in individuals with worse glucose control, HIIT10 may improve mean 24-h glucose and glycemic variability, while HIIT4 may reduce time spent in moderate hyperglycemia.
Spontaneous bilayer wrapping of virus particles by a phospholipid Langmuir monolayer
Authors: Torres-Salgado J. F. , Villagrana-Escareño M. V. , Duran-Meza A. L. , Segovia-Gonzalez X. F. , Cadena-Nava R. D. , Gelbart W. M. , Knobler C. M. , Ruiz-García J.
Published: December 2023

We report here the spontaneous formation of lipid-bilayer-wrapped virus particles, following the injection of “naked” virus particles into the subphase of a Langmuir trough with a liquid monolayer of lipids at its air–water interface. The virus particles are those of the well-studied cowpea chlorotic mottle virus, CCMV, which are negatively charged at the pH 6 of the subphase; the lipids are a 9:1 mix of neutral DMPC and cationic CTAB molecules. Before adding CCMV particles to the subphase we establish the mixed lipid monolayer in its liquid-expanded state at a fixed pressure (17.5 mN/m) and average area-per-molecule of (41Å2). Keeping the total area fixed, the surface pressure is observed to decrease at about 15 h after adding the virus particles in the subphase; by 37 h it has dropped to zero, corresponding to essentially all the lipid molecules having been removed from the air–water interface. By collecting particles from the subphase and measuring their sizes by atomic force microscopy, we show that the virus particles have been wrapped by lipid bilayers (or by two lipid bilayers). These results can be understood in terms of thermal fluctuations and electrostatic interactions driving the wrapping of the anionic virus particles by the cationic lipids.

Graphical Abstract
Computational Design of LEGO
®
Sketch Art
Authors: Zhou Mingjun , Ge Jiahao , Xu Hao , Fu Chi-Wing
Published: December 2023

This paper presents computational methods to aid the creation of LEGO
®1
sketch models from simple input images. Beyond conventional LEGO
®
mosaics, we aim to improve the expressiveness of LEGO
®
models by utilizing LEGO
®
tiles with sloping and rounding edges, together with rectangular bricks, to reproduce smooth curves and sharp features in the input. This is a challenging task, as we have limited brick shapes to use and limited space to place bricks. Also, the search space is immense and combinatorial in nature. We approach the task by decoupling the LEGO
®
construction into two steps: first approximate the shape with a LEGO
®
-buildable contour then filling the contour polygon with LEGO
®
bricks. Further, we formulate this contour approximation into a graph optimization with our objective and constraints and effectively solve for the contour polygon that best approximates the input shape. Further, we extend our optimization model to handle multi-color and multi-layer regions, and formulate a grid alignment process and various perceptual constraints to refine the results. We employ our method to create a large variety of LEGO
®
models and compare it with humans and baseline methods to manifest its compelling quality and speed.
Analysis and Synthesis of Digital Dyadic Sequences
Authors: Ahmed Abdalla G. M. , Skopenkov Mikhail , Hadwiger Markus , Wonka Peter
Published: December 2023

We explore the space of matrix-generated (0,
m
, 2)-nets and (0, 2)-sequences in base 2, also known as digital dyadic nets and sequences. In computer graphics, they are arguably leading the competition for use in rendering. We provide a complete characterization of the design space and count the possible number of constructions with and without considering possible reorderings of the point set. Based on this analysis, we then show that every digital dyadic net can be reordered into a sequence, together with a corresponding algorithm. Finally, we present a novel family of self-similar digital dyadic sequences, to be named
ξ
-sequences, that spans a subspace with fewer degrees of freedom. Those
ξ
-sequences are extremely efficient to sample and compute, and we demonstrate their advantages over the classic Sobol (0, 2)-sequence.
GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations
Authors: Zhou Yuxiao , Chai Menglei , Pepe Alessandro , Gross Markus , Beeler Thabo
Published: December 2023

Despite recent successes in hair acquisition that fits a high-dimensional hair model to a specific input subject, generative hair models, which establish general embedding spaces for encoding, editing, and sampling diverse hairstyles, are way less explored. In this paper, we present
GroomGen
, the first generative model designed for hair geometry composed of highly-detailed dense strands. Our approach is motivated by two key ideas. First, we construct
hair latent spaces
covering both individual strands and hairstyles. The latent spaces are compact, expressive, and well-constrained for high-quality and diverse sampling. Second, we adopt a
hierarchical hair representation
that parameterizes a complete hair model to three levels: single strands, sparse guide hairs, and complete dense hairs. This representation is critical to the compactness of latent spaces, the robustness of training, and the efficiency of inference. Based on this hierarchical latent representation, our proposed pipeline consists of a
strand-VAE
and a
hairstyle-VAE
that encode an individual strand and a set of guide hairs to their respective latent spaces, and a
hybrid densification step
that populates sparse guide hairs to a dense hair model.
GroomGen
not only enables novel hairstyle sampling and plausible hairstyle interpolation, but also supports interactive editing of complex hairstyles, or can serve as strong data-driven prior for hairstyle reconstruction from images. We demonstrate the superiority of our approach with qualitative examples of diverse sampled hairstyles and quantitative evaluation of generation quality regarding every single component and the entire pipeline.
Towards Garment Sewing Pattern Reconstruction from a Single Image
Authors: Liu Lijuan , Xu Xiangyu , Lin Zhijie , Liang Jiabin , Yan Shuicheng
Published: December 2023

Garment sewing pattern represents the intrinsic rest shape of a garment, and is the core for many applications like fashion design, virtual try-on, and digital avatars. In this work, we explore the challenging problem of recovering garment sewing patterns from daily photos for augmenting these applications. To solve the problem, we first synthesize a versatile dataset, named SewFactory, which consists of around 1M images and ground-truth sewing patterns for model training and quantitative evaluation. SewFactory covers a wide range of human poses, body shapes, and sewing patterns, and possesses realistic appearances thanks to the proposed human texture synthesis network. Then, we propose a two-level Transformer network called Sewformer, which significantly improves the sewing pattern prediction performance. Extensive experiments demonstrate that the proposed framework is effective in recovering sewing patterns and well generalizes to casually-taken human photos. Code, dataset, and pre-trained models will be released.
CamP: Camera Preconditioning for Neural Radiance Fields
Authors: Park Keunhong , Henzler Philipp , Mildenhall Ben , Barron Jonathan T. , Martin-Brualla Ricardo
Published: December 2023

Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D scene reconstructions of objects and large-scale scenes. However, NeRFs require accurate camera parameters as input --- inaccurate camera parameters result in blurry renderings. Extrinsic and intrinsic camera parameters are usually estimated using Structure-from-Motion (SfM) methods as a pre-processing step to NeRF, but these techniques rarely yield perfect estimates. Thus, prior works have proposed jointly optimizing camera parameters alongside a NeRF, but these methods are prone to local minima in challenging settings. In this work, we analyze how different camera parameterizations affect this joint optimization problem, and observe that standard parameterizations exhibit large differences in magnitude with respect to small perturbations, which can lead to an ill-conditioned optimization problem. We propose using a proxy problem to compute a whitening transform that eliminates the correlation between camera parameters and normalizes their effects, and we propose to use this transform as a
preconditioner
for the camera parameters during joint optimization. Our preconditioned camera optimization significantly improves reconstruction quality on scenes from the Mip-NeRF 360 dataset: we reduce error rates (RMSE) by 67% compared to state-of-the-art NeRF approaches that do not optimize for cameras like Zip-NeRF, and by 29% relative to state-of-the-art joint optimization approaches using the camera parameterization of SCNeRF. Our approach is easy to implement, does not significantly increase runtime, can be applied to a wide variety of camera parameterizations, and can straightforwardly be incorporated into other NeRF-like models.
A Neural Space-Time Representation for Text-to-Image Personalization
Authors: Alaluf Yuval , Richardson Elad , Metzer Gal , Cohen-Or Daniel
Published: December 2023

A key aspect of text-to-image personalization methods is the manner in which the target concept is represented within the generative process. This choice greatly affects the visual fidelity, downstream editability, and disk space needed to store the learned concept. In this paper, we explore a new text-conditioning space that is dependent on both the denoising process timestep
(time)
and the denoising U-Net layers
(space)
and showcase its compelling properties. A single concept in the space-time representation is composed of hundreds of vectors, one for each combination of time and
space
, making this space challenging to optimize directly. Instead, we propose to implicitly represent a concept in this space by optimizing a small neural mapper that receives the current
time
and
space
parameters and outputs the matching token embedding. In doing so, the entire personalized concept is represented by the parameters of the learned mapper, resulting in a compact, yet expressive, representation. Similarly to other personalization methods, the output of our neural mapper resides in the input space of the text encoder. We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a
textual bypass
, where our neural mapper additionally outputs a residual that is added to the
output
of the text encoder. Finally, we show how one can impose an importance-based ordering over our implicit representation, providing users control over the reconstruction and editability of the learned concept using a single trained model. We demonstrate the effectiveness of our approach over a range of concepts and prompts, showing our method's ability to generate high-quality and controllable compositions without fine-tuning any parameters of the generative model itself.
VASCO: Volume and Surface Co-Decomposition for Hybrid Manufacturing
Authors: Zhong Fanchao , Zhao Haisen , Li Haochen , Yan Xin , Liu Jikai , Chen Baoquan , Lu Lin
Published: December 2023

Additive and subtractive hybrid manufacturing (ASHM) involves the alternating use of additive and subtractive manufacturing techniques, which provides unique advantages for fabricating complex geometries with otherwise inaccessible surfaces. However, a significant challenge lies in ensuring tool accessibility during both fabrication procedures, as the object shape may change dramatically, and different parts of the shape are interdependent. In this study, we propose a computational framework to optimize the planning of additive and subtractive sequences while ensuring tool accessibility. Our goal is to minimize the switching between additive and subtractive processes to achieve efficient fabrication while maintaining product quality. We approach the problem by formulating it as a Volume-And-Surface-CO-decomposition (VASCO) problem. First, we slice volumes into slabs and build a dynamic-directed graph to encode manufacturing constraints, with each node representing a slab and direction reflecting operation order. We introduce a novel geometry property called hybrid-fabricability for a pair of additive and subtractive procedures. Then, we propose a beam-guided top-down block decomposition algorithm to solve the VASCO problem. We apply our solution to a 5-axis hybrid manufacturing platform and evaluate various 3D shapes. Finally, we assess the performance of our approach through both physical and simulated manufacturing evaluations.
Warped-Area Reparameterization of Differential Path Integrals
Authors: Xu Peiyu , Bangaru Sai , Li Tzu-Mao , Zhao Shuang
Published: December 2023

Physics-based differentiable rendering is becoming increasingly crucial for tasks in inverse rendering and machine learning pipelines. To address discontinuities caused by geometric boundaries and occlusion, two classes of methods have been proposed: 1) the edge-sampling methods that directly sample light paths at the scene discontinuity boundaries, which require nontrivial data structures and precomputation to select the edges, and 2) the reparameterization methods that avoid discontinuity sampling but are currently limited to hemispherical integrals and unidirectional path tracing.

We introduce a new mathematical formulation that enjoys the benefits of both classes of methods. Unlike previous reparameterization work that focused on hemispherical integral, we derive the reparameterization in the path space. As a result, to estimate derivatives using our formulation, we can apply advanced Monte Carlo rendering methods, such as bidirectional path tracing, while avoiding explicit sampling of discontinuity boundaries. We show differentiable rendering and inverse rendering results to demonstrate the effectiveness of our method.
Meshes with Spherical Faces
Authors: Kilian Martin , Cisneros Anthony S Ramos , Müller Christian , Pottmann Helmut
Published: December 2023

Discrete surfaces with spherical faces are interesting from a simplified manufacturing viewpoint when compared to other double curved face shapes. Furthermore, by the nature of their definition they are also appealing from the theoretical side leading to a Möbius invariant discrete surface theory. We therefore systematically describe so called sphere meshes with spherical faces and circular arcs as edges where the Möbius transformation group acts on all of its elements. Driven by aspects important for manufacturing, we provide the means to cluster spherical panels by their radii. We investigate the generation of sphere meshes which allow for a geometric support structure and characterize all such meshes with triangular combinatorics in terms of non-Euclidean geometries. We generate sphere meshes with hexagonal combinatorics by intersecting tangential spheres of a reference surface and let them evolve - guided by the surface curvature - to visually convex hexagons, even in negatively curved areas. Furthermore, we extend meshes with circular faces of all combinatorics to sphere meshes by filling its circles with suitable spherical caps and provide a remeshing scheme to obtain quadrilateral sphere meshes with support structure from given sphere congruences. By broadening polyhedral meshes to sphere meshes we exploit the additional degrees of freedom to minimize intersection angles of neighboring spheres enabling the use of spherical panels that provide a softer perception of the overall surface.
Learning Based 2D Irregular Shape Packing
Authors: Yang Zeshi , Pan Zherong , Li Manyi , Wu Kui , Gao Xifeng
Published: December 2023

2D irregular shape packing is a necessary step to arrange UV patches of a 3D model within a texture atlas for memory-efficient appearance rendering in computer graphics. Being a joint, combinatorial decision-making problem involving all patch positions and orientations, this problem has well-known NP-hard complexity. Prior solutions either assume a heuristic packing order or modify the upstream mesh cut and UV mapping to simplify the problem, which either limits the packing ratio or incurs robustness or generality issues. Instead, we introduce a learning-assisted 2D irregular shape packing method that achieves a high packing quality with minimal requirements from the input. Our method iteratively selects and groups subsets of UV patches into near-rectangular super patches, essentially reducing the problem to bin-packing, based on which a joint optimization is employed to further improve the packing ratio. In order to efficiently deal with large problem instances with hundreds of patches, we train deep neural policies to predict nearly rectangular patch subsets and determine their relative poses, leading to linear time scaling with the number of patches. We demonstrate the effectiveness of our method on three datasets for UV packing, where our method achieves a higher packing ratio over several widely used baselines with competitive computational speed.
Authoring and Simulating Meandering Rivers
Authors: Paris Axel , Guérin Eric , Collon Pauline , Galin Eric
Published: December 2023

We present a method for interactively authoring and simulating meandering river networks. Starting from a terrain with an initial low-resolution network encoded as a directed graph, we simulate the evolution of the path of the different river channels using a physically-based migration equation augmented with control terms. The curvature-based terms in the equation allow us to reproduce phenomena identified in geomorphology, such as downstream migration of bends. Control terms account for the influence of the landscape topography and user-defined river trajectory constraints. Our model implements abrupt events that shape meandering networks, such as cutoffs forming oxbow lakes and avulsions. We visually show the effectiveness of our method and compare the generated networks quantitatively to river data by analyzing sinuosity and wavelength metrics. Our vector-based model runs at interactive rates, allowing for efficient authoring of large-scale meandering networks.
OpenSVBRDF: A Database of Measured Spatially-Varying Reflectance
Authors: Ma Xiaohe , Xu Xianmin , Zhang Leyao , Zhou Kun , Wu Hongzhi
Published: December 2023

We present the first large-scale database of measured spatially-varying anisotropic reflectance, consisting of 1,000 high-quality near-planar SVBRDFs, spanning 9 material categories such as wood, fabric and metal. Each sample is captured in 15 minutes, and represented as a set of high-resolution texture maps that correspond to spatially-varying BRDF parameters and local frames. To build this database, we develop a novel integrated system for robust, high-quality and -efficiency reflectance acquisition and reconstruction. Our setup consists of 2 cameras and 16,384 LEDs. We train 64 lighting patterns for efficient acquisition, in conjunction with a network that predicts per-point reflectance in a neural representation from carefully aligned two-view measurements captured under the patterns. The intermediate results are further fine-tuned with respect to the photographs acquired under 63 effective linear lights, and finally fitted to a BRDF model. We report various statistics of the database, and demonstrate its value in the applications of material generation, classification as well as sampling. All related data, including future additions to the database, can be downloaded from https://opensvbrdf.github.io/.
Rectifying Strip Patterns
Authors: Wang Bolun , Wang Hui , Schling Eike , Pottmann Helmut
Published: December 2023

Straight flat strips of inextensible material can be bent into curved strips aligned with arbitrary space curves. The large shape variety of these so-called rectifying strips makes them candidates for shape modeling, especially in applications such as architecture where simple elements are preferred for the fabrication of complex shapes. In this paper, we provide computational tools for the design of shapes from rectifying strips. They can form various patterns and fulfill constraints which are required for specific applications such as gridshells or shading systems. The methodology is based on discrete models of rectifying strips, a discrete level-set formulation and optimization-based constrained mesh design and editing. We also analyse the geometry at nodes and present remarkable quadrilateral arrangements of rectifying strips with torsion-free nodes.
Projective Sampling for Differentiable Rendering of Geometry
Authors: Zhang Ziyi , Roussel Nicolas , Jakob Wenzel
Published: December 2023

Discontinuous visibility changes at object boundaries remain a persistent source of difficulty in the area of differentiable rendering. Left untreated, they bias computed gradients so severely that even basic optimization tasks fail.

Prior path-space methods addressed this bias by decoupling boundaries from the interior, allowing each part to be handled using specialized Monte Carlo sampling strategies. While conceptually powerful, the full potential of this idea remains unrealized since existing methods often fail to adequately sample the boundary proportional to its contribution.

This paper presents theoretical and algorithmic contributions. On the theoretical side, we transform the boundary derivative into a remarkably simple local integral that invites present and future developments.

Building on this result, we propose a new strategy that projects ordinary samples produced during forward rendering onto nearby boundaries. The resulting projections establish a variance-reducing guiding distribution that accelerates convergence of the subsequent differential phase.

We demonstrate the superior efficiency and versatility of our method across a variety of shape representations, including triangle meshes, implicitly defined surfaces, and cylindrical fibers based on Bézier curves.
Enhancing Diffusion Models with 3D Perspective Geometry Constraints
Authors: Upadhyay Rishi , Zhang Howard , Ba Yunhao , Yang Ethan , Gella Blake , Jiang Sicheng , Wong Alex , Kadambi Achuta
Published: December 2023

While perspective is a well-studied topic in art, it is generally taken for granted in images. However, for the recent wave of high-quality image synthesis methods such as latent diffusion models, perspective accuracy is not an explicit requirement. Since these methods are capable of outputting a wide gamut of possible images, it is difficult for these synthesized images to adhere to the principles of linear perspective. We introduce a novel geometric constraint in the training process of generative models to enforce perspective accuracy. We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images. Subjective human trials show that images generated with latent diffusion models trained with our constraint are preferred over images from the Stable Diffusion V2 model 70% of the time. SOTA monocular depth estimation models such as DPT and PixelFormer, fine-tuned on our images, outperform the original models trained on real images by up to 7.03% in RMSE and 19.3% in SqRel on the KITTI test set for zero-shot transfer.
Metric Optimization in Penner Coordinates
Authors: Capouellez Ryan , Zorin Denis
Published: December 2023

Many parametrization and mapping-related problems in geometry processing can be viewed as metric optimization problems, i.e., computing a metric minimizing a functional and satisfying a set of constraints, such as flatness.

Penner coordinates
are global coordinates on the space of metrics on meshes with a fixed vertex set and topology, but varying connectivity, making it homeomorphic to the Euclidean space of dimension equal to the number of edges in the mesh, without any additional constraints imposed. These coordinates play an important role in the theory of discrete conformal maps, enabling recent development of highly robust algorithms with convergence and solution existence guarantees for computing such maps.

We demonstrate how Penner coordinates can be used to solve a general class of optimization problems involving metrics, including optimization and interpolation, while retaining the key solution existence guarantees available for discrete conformal maps.
Depolarized Holography with Polarization-Multiplexing Metasurface
Authors: Nam Seung-Woo , Kim Youngjin , Kim Dongyeon , Jeong Yoonchan
Published: December 2023

The evolution of computer-generated holography (CGH) algorithms has prompted significant improvements in the performances of holographic displays. Nonetheless, they start to encounter a limited degree of freedom in CGH optimization and physical constraints stemming from the coherent nature of holograms. To surpass the physical limitations, we consider polarization as a new degree of freedom by utilizing a novel optical platform called metasurface. Polarization-multiplexing metasurfaces enable incoherent-like behavior in holographic displays due to the mutual incoherence of orthogonal polarization states. We leverage this unique characteristic of a metasurface by integrating it into a holographic display and exploiting polarization diversity to bring an additional degree of freedom for CGH algorithms. To minimize the speckle noise while maximizing the image quality, we devise a fully differentiable optimization pipeline by taking into account the metasurface proxy model, thereby jointly optimizing spatial light modulator phase patterns and geometric parameters of metasurface nanostructures. We evaluate the metasurface-enabled depolarized holography through simulations and experiments, demonstrating its ability to reduce speckle noise and enhance image quality.
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Authors: Roessle Barbara , Müller Norman , Porzi Lorenzo , Bulò Samuel Rota , Kontschieder Peter , Niessner Matthias
Published: December 2023

Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.
A Quality Assessment of Strawberry Nectar Stabilized by Thermal and High-Pressure Processing Conditions
Authors: Lacey Karen Louise , Moreno-Barreto Andres , Pavón-Vargas Darío , Cattani Luca , Rinaldi Massimiliano , Rainieri Sara , Dhenge Rohini
Published: December 2023

This study is aimed at evaluating the quality of strawberry nectar following high-pressure processing (HPP) at different pressure levels (400, 500, and 600 MPa for 3 minutes) and thermal pasteurization (TT) at 75°C for 15 s. Key parameters include total colour difference (

Δ
E
∗

), aparent viscosity, colour analysis via CIELAB coordinates, and pH. Additionally, residual enzymatic activity (RA%) of polyphenol oxidase (PPO) and peroxidase (POD) was measured. Strawberry nectar (12°Brix) was prepared and subjected to processing, with thermal treatment in 50 mL tubes and HPP in 40 mL polyethylene bags. Samples were stored under refrigeration during 21 days, with microbial analysis conducted to assess microbiological stability during storage. TT exhibited a more pronounced reduction in total aerobic microbial count (TAMC) compared to HPP, which showed no detectable yeast and mould. Apparent viscosity varied among samples, with HPP at 600 MPa displaying a significant increase of 52.1% compared to the control. The pseudoplastic behavior, characterized by

n

values lower than 1, was consistent across all treatments. TT led to a reduction in

L
∗

brightness value during storage, whereas HPP-treated samples underwent colour changes over time with

Δ
E
∗

of 3.1 for 400 MPa, 3 minutes, at 21 days. For PPO and POD, the 600 MPa treatment exhibited the highest inhibition (44% and 4% RA, respectively). This comprehensive study provides insights into how different HPP processing conditions affect the quality attributes, enzymatic activity, and microbiological stability of strawberry nectar during production and refrigerated storage. The research’s significance lies in its holistic comparison of HPP with traditional thermal pasteurization, offering valuable information for both industry and consumers.
Green Investment Decisions and Coordination in a Green Agri-Product Supply Chain considering Risk Aversion and Bargaining Power under Different Channel Power Structures
Authors: Bai Shizhen , Wang Yonggan , Zheng Shenghua , He Hao
Published: December 2023

With eco-friendly green agriculture becoming the development trend of modern agriculture, how to make green investments and how to coordinate the supply chain become the key issues of agricultural green development. Using game theory and optimization theory, this paper studies the green investment decision in a two-echelon agricultural supply chain composed of a risk-averse farmer and a risk-neutral retailer under different power structures including three kinds of decentralized decision making and three kinds of cooperative decision making and conducts the supply chain coordination based on generalized Nash bargaining model. The results show that under decentralized decision making, Nash vertical, farmer-led, and retailer-led maximizes green investment level, the expected utility of farmer and retailer, respectively. In addition, the cooperative decision increases the marginal revenue, sales price, and the expected utility of the retailer and decreases the expectations of farmers. Except for retailer-led cooperative decisions, all cooperative decisions have increased the level of green investment and wholesale prices; among the six decision models, the green investment level is negatively correlated with risk aversion, while it is positively correlated with the cost-sharing contract. The optimal cost-sharing ratio is positively correlated with risk aversion and bargaining power. The cost-sharing contracts are invalid when farmers have full bargaining power. Numerical analysis shows that a cost-sharing contract with equal bargaining power can achieve perfect coordination in the supply chain.
Table S2 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary table 2 is the injury scoring rubric
Figure S9 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S9 shows gene expression analysis of mouse and human sarcomas that arose in irradiated tissue compared to sporadic sarcomas
Figure S8 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S8 shows immune infiltration analysis of radiation-induced sarcomas
Figure S7 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S7 shows gene expression analysis of radiation-induced sarcomas compared to normal muscles
Figure S3 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S3 shows histological examination of mouse radiation-induced injuries
Figure S1 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S1 shows irradiated hind limbs of control and p53KD mice
FIGURE 5 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Comparison of CNV and expression of specific oncogenes. Boxplot showing gene expression of Yap1 (A), Met (B), and Cdk4 (C) in radiation-induced sarcomas where the gene amplification status is known to be amplified (pink), not amplified (orange), or unknown (gray) compared with normal muscle (blue).
FIGURE 3 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Radiation-induced chronic injuries increase the risk of sarcomagenesis. A, The final injury scores of the control and p53KD mice that received 30 or 40 Gy are plotted. The final injury scores of mice that did not develop a radiation-induced sarcoma (blue) are compared with the scores of mice that did develop a radiation-induced sarcoma (red). P value is from a t test. B, Kaplan–Meier curves show radiation-induced sarcoma-free survival of the control and p53KD mice irradiated with 30 or 40 Gy to the hind limb. Mice with chronic injury scores equal to or less than 3 are compared with mice with chronic injury scores greater than 3. P value is from a cox proportional hazards model.
FIGURE 1 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Temporary reduction in p53 expression during irradiation increases sarcomagenesis. A, Schematic showing mice fed dox diet for 10 days to drive expression of shRNA to knockdown p53, irradiated with 30 or 40 Gy to the hind limb, returned to normal chow, and followed for sarcoma development and normal tissue injury in the radiation field. B, Kaplan–Meier curves show radiation-induced sarcoma-free survival of control and p53KD mice irradiated with 30 or 40 Gy to the hind limb. P value is from a log-rank test. C, Representative images of a H&E-stained radiation-induced sarcoma subtypes and table with the number of tumors from each subtype. D, Quantitation and representative IHC images of mouse muscle tissue from control and p53KD mice 4 hours after 30 Gy irradiation stained with antibodies recognizing cleaved caspase 3. The number of positive cells per tissue section is graphed. E, Representative flow cytometry dot plots of GFP+ muscle satellite cells isolated from unirradiated (left) and irradiated (right) limbs from control (top) or p53KD (bottom) mice. F, The relative ratio of GFP+ cells in the irradiated limb (30 Gy) over the unirradiated limb is graphed (±SEM). Each dot represents one mouse. P value is from a t test. G, Schematic showing that cells with functional p53 undergo apoptosis or cell-cycle arrest and repair after irradiation, while damaged cells with impaired p53 function are protected from cell death and persist. Figure generated using BioRender.