Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-end Procedural Material Capture with Proxy-Free Mixed-Integer Optimization

Node-graph-based procedural materials are vital to 3D content creation within the computer graphics industry. Leveraging the expressive representation of procedural materials, artists can effortlessly generate diverse appearances by altering the graph structure or node parameters. However, manually reproducing a specific appearance is a challenging task that demands extensive domain knowledge and labor. Previous research has sought to automate this process by converting artist-created material graphs into differentiable programs and optimizing node parameters against a photographed material appearance using gradient descent. These methods involve implementing differentiable filter nodes [Shi et al. 2020] and training differentiable neural proxies for generator nodes to optimize continuous and discrete node parameters [Hu et al. 2022a] jointly. Nevertheless, Neural Proxies exhibits critical limitations, such as long training times, inaccuracies, fixed resolutions, and confined parameter ranges, which hinder their scalability towards the broad spectrum of production-grade material graphs. These constraints fundamentally stem from the absence of faithful and efficient implementations of generic noise and pattern generator nodes, both differentiable and non-differentiable. Such deficiency prevents the direct optimization of continuous and discrete generator node parameters without relying on surrogate models. We present Diffmat v2, an improved differentiable procedural material library, along with a fully-automated, end-to-end procedural material capture framework that combines gradient-based optimization and gradient-free parameter search to match existing production-grade procedural materials against user-taken flash photos. Diffmat v2 expands the range of differentiable material graph nodes in Diffmat [Shi et al. 2020] by adding generic noise/pattern generator nodes and user-customizable per-pixel filter nodes. This allows for the complete translation and optimization of procedural materials across various categories without the need for external proprietary tools or pre-cached noise patterns. Consequently, our method can capture a considerably broader array of materials, encompassing those with highly regular or stochastic geometries. We demonstrate that our end-to-end approach yields a closer match to the target than MATch [Shi et al. 2020] and Neural Proxies [Hu et al. 2022a] when starting from initially unmatched continuous and discrete parameters.
Autoencoder Induced Deep Spiking Neural Network

Spiking neural networks (SNNs) obtain impressive good performance on various applications due to their powerful computing capacity for encoding spatio-temporal information. However, most existing spiking neural networks remain shallow structures, lacking effective structures to handle real-world tasks. In this work, we propose an autoencoder induced deep spiking neural network (AE-DSN) to improve the representative capacity. Specifically, AE-DSN consists of three coding modules shared the same structure. Each module contains an autoencoder and a spiking coding layer. In particular, we present a progressive training strategy to train these modules one-by-one. For one module, the spiking coding layer is trained using ReSuMe algorithm, guided by the encoded information from the autoencoder, which would be dropped when training the subsequent module. The entire training process terminates when the final spiking coding layer is trained well. Experimental results show that the proposed AE-DSN could effectively extract discriminative features for the input images to achieve superior classification performance.
Patternshop: Editing Point Patterns by Image Manipulation

Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embedding for point correlations. This embedding can map point patterns to common three-channel raster images, enabling manipulation with off-the-shelf image editing software. To synthesize back point patterns, we propose a novel edge-aware objective that carefully handles sharp variations in density and correlation. The resulting framework allows intuitive and backward-compatible manipulation of point patterns, such as recoloring, relighting to even texture synthesis that have not been available to 2D point pattern design before. Effectiveness of our approach is tested in several user experiments. Code is available at https://github.com/xchhuang/patternshop.
Masonry Shell Structures with Discrete Equivalence Classes

This paper proposes a method to model masonry shell structures where the shell elements fall into a set of discrete equivalence classes. Such shell structure can reduce the fabrication cost and simplify the physical construction due to reuse of a few template shell elements. Given a freeform surface, our goal is to generate a small set of template shell elements that can be reused to produce a seamless and buildable structure that closely resembles the surface. The major technical challenge in this process is balancing the desire for high reusability of template elements with the need for a seamless and buildable final structure. To address the challenge, we define three error metrics to measure the seamlessness and buildability of shell structures made from discrete equivalence classes and develop a hierarchical cluster-and-optimize approach to generate a small set of template elements that produce a structure closely approximating the surface with low error metrics. We demonstrate the feasibility of our approach on various freeform surfaces and geometric patterns, and validate buildability of our results with four physical prototypes. Code and data of this paper are at https://github.com/Linsanity81/TileableShell.
Winding Numbers on Discrete Surfaces

In the plane, the winding number is the number of times a curve wraps around a given point. Winding numbers are a basic component of geometric algorithms such as point-in-polygon tests, and their generalization to data with noise or topological errors has proven valuable for geometry processing tasks ranging from surface reconstruction to mesh booleans. However, standard definitions do not immediately apply on surfaces, where not all curves bound regions. We develop a meaningful generalization, starting with the well-known relationship between winding numbers and harmonic functions. By processing the derivatives of such functions, we can robustly filter out components of the input that do not bound any region. Ultimately, our algorithm yields (i) a closed, completed version of the input curves, (ii) integer labels for regions that are meaningfully bounded by these curves, and (iii) the complementary curves that do not bound any region. The main computational cost is solving a standard Poisson equation, or for surfaces with nontrivial topology, a sparse linear program. We also introduce special basis functions to represent singularities that naturally occur at endpoints of open curves.
Evolutionary Piecewise Developable Approximations

We propose a novel method to compute high-quality piecewise developable approximations for triangular meshes. Central to our approach is an evolutionary genetic algorithm for optimizing the combinatorial and discontinuous fitness function, including the approximation error, the number of patches, the patch boundary length, and the penalty for small patches and narrow regions within patches. The genetic algorithm's operations (i.e., initialization, selection, mutation, and crossover) are explicitly designed to minimize the fitness function. The main challenge is evaluating the fitness function's approximation error as it requires developable patches, which are difficult or time-consuming to obtain. Resolving the challenge is based on a critical observation: the approximation error and the mapping distortion between an input surface and its developable approximation are positively correlated empirically. To efficiently measure distortion without explicitly generating developable shapes, we creatively use conformal mapping techniques. Then, we control the mapping distortion at a relatively low level to achieve high shape similarity in the genetic algorithm. The feasibility and effectiveness of our method are demonstrated over 240 complex examples. Compared with the state-of-the-art methods, our results have much smaller approximation errors, fewer patches, shorter patch boundaries, and fewer small patches and narrow regions.
Skin-Screen: A Computational Fabrication Framework for Color Tattoos

Tattoos are a highly popular medium, with both artistic and medical applications. Although the mechanical process of tattoo application has evolved historically, the results are reliant on the artisanal skill of the artist. This can be especially challenging for some skin tones, or in cases where artists lack experience. We provide the first systematic overview of tattooing as a computational fabrication technique. We built an automated tattooing rig and a recipe for the creation of silicone sheets mimicking realistic skin tones, which allowed us to create an accurate model predicting tattoo appearance. This enables several exciting applications including tattoo previewing, color retargeting, novel ink spectra optimization, color-accurate prosthetics, and more.
Endothelin Induces DNA Polymerase α Activity in Ovaries of Hypophysectomized Estrogen-Treated Immature Rats

S. Usuki1, S. Otani1, K. Goto , H. Matsumoto, N. Suzuki, M. Yanagisawa4 and T. Masaki4 Department of Obstetrics and Gynecology, Institute of Clinical Medicine and Department of Pharmacology, Institute of Basic Sciences, University of Tsukuba, Ibaraki 3Tsukuba Research Laboratories, Takeda Chemical Industries, Ltd., Wadai, Tsukuba, Ibaraki Department of Pharmacology, Faculty of Medicine, Kyoto University, Kyoto, Japan
INVESTIGATION OF VAN DER WAALS COMPLEXES IN A FREE EXPANSION OF C2H2C2H2/X (X=NOBLE GAS) USING CW CAVITY RING-DOWN SPECTROSCOPY IN THE OVERTONE RANGE

Results are reported on a search for decays of a pseudoscalar A boson into a Z boson and a light scalar h boson, where the Z boson decays into a pair of oppositely-charged electrons or muons, and the h boson decays into b anti-b. The search is based on data from proton-proton collisions at a center-of-mass energy sqrt(s)=8 TeV collected with the CMS detector, corresponding to an integrated luminosity of 19.7 inverse femtobarns. The h boson is assumed to be the standard model-like Higgs boson with a mass of 125 GeV. With no evidence for signal, upper limits are obtained on the product of the production cross section and the branching fraction of the A boson in the Zh channel. Results are also interpreted in the context of two Higgs doublet models. Document type : Article de périodique (Journal article) Référence bibliographique CMS ; Basegmez, Suzan ; Beluffi, Camille ; Bondu, Olivier ; Bruno, Giacomo Luca ; et. al. INVESTIGATION OF VAN DER WAALS COMPLEXES IN A FREE EXPANSION OF C2H2C2H2/ X (X=NOBLE GAS) USING CW CAVITY RING-DOWN SPECTROSCOPY IN THE OVERTONE RANGE. In: Physics Letters. Section B: Nuclear, Elementary Particle and High-Energy Physics, Vol. B748, p. 221-243 (2015) DOI : 10.1016/j.physletb. 2015.07. 010 EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN) CERN-PH-EP/2013-037 2015/07/24
Flow Theory and Offline Shopping Behaviour of Gen X

Purpose: The purpose of this research paper is to observe different determinants of shopping behaviour and the moderated effect of ethnic groups in Indian culture. It uses flow theory to understand the shopping behaviour determinants. So, the importance of offline retailing is generalized among Generation X consumers in Indian aspect. Research methods and design: The data was collected from six religious groups (Sikh, Hindu, Muslim, Christian and Buddhist), and scale was developed for multi-dimensional shopping behaviour. The samples are belong to Generation X (age 35 to 55 years) Findings: The research model investigates the relevant relationship between these constructs by using a structural equation modeling approach, which reveals that status consciousness, need for touch and companion opinion are significant determinants of shopping behaviour. Conclusions: The results reveal that the Indian culture is composite in nature and because of which there is no difference between different religious groups in their preference for three shopping behaviour determinants, viz., status consciousness, need for touch and companion opinion. Significance of the study: Organized retailers working in India, and especially in Jammu region have lack of cultural preference and knowledge, because of adaptation of western world marketing philosophies in multicultural societies like India. The outbreak of shopping behaviour as a multidimensional constructs help the retailers to understand the religious group preferences for FMGC goods, white goods, apparel, jewellery, watches and shoes.
Model order reduction in thermoacoustic stability analysis

Modern gas-turbines are typically operated under lean, premixed combustion conditions due to emission restrictions. In this operating range, the flame is susceptible to acoustic perturbations. Once perturbed, the heat-release fluctuates, which acts as a monopole sound-source. Due to interaction with the compressor, the turbine and the combustor housing, these acoustic waves can be reflected back to the flame, which establishes a feedback cycle. When constructive interference occurs, the amplitudes of this mechanism can grow and a thermoacoustic instability develops that might limit the operating range, increase material wear and even cause fatal damage.
A comparison of discrete and continuum models of cardiac electrophysiology

When modelling tissue-level cardiac electrophysiology, a continuum approximation to the discrete cell-level equations, known as the bidomain equations, is often used to maintain computational tractability. The bidomain equations are derived from the discrete equations using a mathematical technique known as homogenisation. As part of this derivation conductivity tensors are specified for use in the continuum model. Analysing the derivation of the bidomain equations allows us to investigate how microstructure, in particular gap junctions that electrically connect cells, affect tissue-level conductivity properties and model solutions. We perform two distinct but related strands of investigation in this thesis. In the first, we consider the effect of including gap junctions on the results of both discrete and continuum simulations, and identify when the continuum model fails to be a good approximation to the discrete model. Secondly, we perform a comprehensive study into how cell-level microstructure properties, such as cell shape, impact the homogenised conductivities to be used in a tissue-level continuum model. This will allow us to predict how the onset of a disease or a change in cellular microstructure will affect the propagation of action potentials. To do this, we first derive a modified version of the bidomain equations that explicitly takes gap junctions into account. We then derive analytic solutions for the homogenised conductivity tensors on a simplified two-dimensional geometry and find that diseased gap junctions have a large impact on the results of homogenisation. On this same geometry we compare the results of discrete and continuum simulations and find a significant discrepancy between model conduction velocities when we introduce gap junctions with lower coupling strength, or when we consider elongated cells. From this, we conclude that the bidomain equations are less likely to give an accurate representation of the underlying discrete system when modelling diseased states whose symptoms include reduced gap junction coupling or an increase in myocyte length. We then use a more realistic two-dimensional geometry and numerically approximate the homogenised conductivity tensors on this geometry. We discover that the packing of cells has a substantial effect on conduction, with a brick-wall geometry particularly beneficial for fast propagation, and that gap junctions also have a large effect on conduction. Finally, we consider a three-dimensional cellular geometry and show that the effect of changing gap junction properties is different when compared to two dimensions, and discover that the anisotropy ratios of the tissue are highly sensitive to changes in gap junction parameters. Overall, we conclude that gap junctions and cell structure have a large effect on discrete and continuum model results, and on homogenised conductivity calculations in tissue-level cardiac electrophysiology.
Vertical Spectra of Temperature in the Free Troposphere at Meso-and-Small Scales According to the Flow Regime: Observations and Interpretation

This article addresses the properties of stably-stratified and unstable layers in the free troposphere. Thorpe’s method of analysis has been applied to potential temperature (PT) profiles obtained from the raw measurements of operational radiosondes. In principle, this method distinguishes stably stratified and unstable regions. The background static stability, quantified by the square Brunt-Väisälä frequency estimated on the sorted PT profiles (stable everywhere), is observed to be significantly smaller in the unstable regions, likely due to turbulent mixing. The vertical power spectral densities (PSDs) of temperature fluctuations are shown to be proportional to m − p , where p is in the average 2.8 ± 0.2 in the stably stratified regions, and is 1.7 ± 0.3 in the unstable regions, for wavenumbers m in the range [ 10 − 2 , 10 − 1 ] m − 1 . Such findings validate the Thorpe analysis when applied to radiosondes. Also, the distribution of thicknesses h of unstable layers is observed to approximately follow a power law, varying as h − r with r ≈ 2.1 ± 0.1 . PT profiles for the entire troposphere have also been analyzed as the sum of a sorted profile and an anomaly profile. The PSDs of the sorted PT profiles are scaled as m − 3 down to a few meters on the vertical scale. Simple stochastic models based on random walks with increments having the property of flicker noise are shown to reproduce the spectral properties of the sorted PT profiles, i.e., of the vertical stratification of the free atmosphere.
Inhibition of Acidification Rate in Cultured Fibroblasts by Glucocorticoids

Summary A recently-developed semiconducter-based instrument, the silicon microphysiometer, allows for real time, sensitive quantification of cellular metabolism in small numbers of cultured cells with relative ease. This is accom plished by detecting the extrusion into the extracellular space of acidic metabolic products of glycolysis, respiration, and ATP hydrolysis, including lactic acid, CO2, and protons. In the present report, we use microphysiometry to observe that glucocorticoids inhibit metabolic rate (as assessed indirectly by a change in the extracellular acidification rate) in fibro blasts (minimal effective dose of 1 nM of corticosterone), whereas 1 uM each estradiol, progesterone and testosterone failed to do so. We suggest that this inhibition of metabolism is secondary to the well-established inhibition of glucose transport and of protein synthesis in fibroblasts by glucocor ticoids.
Khovanov–Rozansky homology for infinite multicolored braids

Abstract We define a limiting ${\mathfrak {sl}_N}$ Khovanov–Rozansky homology for semi-infinite positive multicolored braids. For a large class of such braids, we show that this limiting homology categorifies a highest-weight projector in the tensor product of fundamental representations determined by the coloring of the braid. This effectively completes the extension of Cautis’ similar result for infinite twist braids, begun in our earlier papers with Islambouli and Abel. We also present several similar results for other families of semi-infinite and bi-infinite multicolored braids.
Target recognition for synthetic aperture radar imagery based on convolutional neural network feature fusion

Abstract. Driven by the great success of deep convolutional neural networks (CNNs) that are currently used by quite a few computer vision applications, we extend the usability of visual-based CNNs into the synthetic aperture radar (SAR) data domain without employing transfer learning. Our SAR automatic target recognition (ATR) architecture efficiently extends the pretrained Visual Geometry Group CNN from the visual domain into the X-band SAR data domain by clustering its neuron layers, bridging the visual—SAR modality gap by fusing the features extracted from the hidden layers, and by employing a local feature matching scheme. Trials on the moving and stationary target acquisition dataset under various setups and nuisances demonstrate a highly appealing ATR performance gaining 100% and 99.79% in the 3-class and 10-class ATR problem, respectively. We also confirm the validity, robustness, and conceptual coherence of the proposed method by extending it to several state-of-the-art CNNs and commonly used local feature similarity/match metrics.
A boundary-curvature-aware and continuous-curvature path generation method for car-like robots

In order to guide the car-like robots run smoothly and steadily, we propose a boundary-curvature-aware and continuous-curvature path generation method based on prior researches. The main contribution of this paper is that the curvature value constraints at both ends of generated paths are well satisfied according to the given start and desired target configurations at the same time of keeping continuous curvature. Experiments show that paths generated by the proposed method keep with fine curvature character.