Scaling blockchain performance through parallel smart contract execution has gained significant attention, as traditional methods remain constrained by the performance of a single virtual machine (VM), even in multi-chain or Layer-2 systems. Parallel VMs offer a compelling solution by enabling concurrent transaction execution within a single smart contract, using multiple CPU cores. However, Ethereum's sequential, shared-everything model limits the efficiency of existing parallel mechanisms, resulting in frequent rollbacks with optimistic methods and high overhead with pessimistic methods due to state dependency analysis and locking.

Deploying an extremely large-scale reconfigurable intelligent surface (XL-RIS) can significantly improve the performance of a RIS-assisted communication system. However, the array aperture and deployment of the XL-RIS affects the radiated field region in which the base station (BS) and the user are located, which in turn affects the performance improvement. In this paper, we have jointly optimized a deployment scheme and phase-shift matrix in XL-RIS-aided communication system, aiming to maximize the user’s received signal-to-noise ratio (SNR). Firstly, we incorporate the far-field and near-field channel into a unified far- or near-field (FoN) model to simplify the SNR analysis and optimization on RIS deployments. Secondly, based on the FoN approach, we derive an expression for the user’s received SNR and formulate an optimization problem to jointly optimize the RIS deployment and phase-shift matrix in order to maximize the user’s received SNR. Thirdly, we summarize the relationship between the RIS array aperture and deployment and the radiated field region in which the BS and the user are located, and propose an optimized closed-form solution for the RIS deployment and phase-shift matrix. Finally, we validate the effectiveness of the proposed scheme through simulation results.

Once deployed, a decentralized blockchain system ensures that it will operate faithfully so that no one can interfere with or manipulate its predefined regulations, such as block size and block creation interval investigated in this paper. However, fixed regulations prevent that system from adapting to the change of the environment, such as increasing the underlying network capacity, and result in sub-optimal performance. For example, Bitcoin remains at 7 TPS (transactions per second), even operating over the current Internet. In this paper, we propose a new paradigm for defining the behavior of a consensus system, named as Meta-Regulation, which allows autonomous evolution of the system behavior. A meta-regulation adjusts the actual behavior of a consensus system in response to the changing capacity of the underlying infrastructure and the community of participants. We demonstrate the effectiveness of the proposed meta-regulation by achieving significantly improved throughput and latency for Bitcoin, adapted to the current capacity of the Internet. Our experimental results show that Meta-Regulation can achieve at least$7\times $performance improvement over Bitcoin network deployed in 2009, resulting in 49.7 TPS or 68% reduction confirmation latency by fully utilizing the bandwidth and the computing power of average network nodes.

Broadcasting plays a vital role in the consensus mechanisms of blockchain systems, since the consensus of each block must wait until the previous block is received by (nearly) all the nodes in the blockchain systems. Therefore, optimizing the performance of broadcasting can significantly improve the performance of the blockchain system. However, compared with other traditional P2P applications such as file downloading or video delivery, the broadcasting in blockchain has two new requirements, namely low redundancy and low propagation latency, which all the existing mechanisms (e.g. flooding, structural DHT etc.) can not meet well. In this paper, we propose Swift, a new broadcasting mechanism for blockchain systems. It optimizes the P2P topology construction and broadcast algorithm in the structured network based on unsupervised learning and greedy algorithm, effectively reducing the propagation latency of the blockchain P2P network while avoiding the waste of redundant bandwidth. We implemented a prototype of Swift and evaluated its performance on a testbed network that consists of 1000 blockchain nodes. The experimental findings show that Swift can reduce propagation latency by 19.8% with similar bandwidth consumption, generating an 18% increase in the throughput performance of the blockchain. Finally, with the increase in connections, Swift can simultaneously achieve low latency and maintain a relatively stable redundant bandwidth waste, instead of linearly increasing in flooding.

A fundamental challenge in video inpainting is the difficulty of generating video contents with fine details, while keeping spatio-temporal coherence in the missing region. Recent studies focus on synthesizing temporally smooth pixels by exploiting the flow information, while ignoring maintaining the semantic structural coherence between frames. This makes them suffer from over-smoothing and blurry contours, which significantly reduce the visual quality of inpainting results. To address this issue, we present a novel structure-guided video inpainting approach that enhances temporal structure coherence to improve video inpainting results. In contrast to directly synthesizing the missing pixel colors, we first complete edges in the missing regions to depict scene structures and object shapes via an edge inpainting network with 3D convolutions. Then, we replenish textures using a coarse-to-fine synthesis network with a structure attention module (SAM), under the guidance of the synthesized edges. Specifically, our SAM is designed to model the semantic correlation between video textures and structural edges to generate more realistic content. Besides, motion flows between neighboring frames are employed to enhance temporal consistency for self-supervision during training the edge inpainting and texture inpainting modules. Consequently, the inpainting results using our approach are visually pleasing with fine details and temporal coherence. Experiments on the YouTubeVOS, DAVIS, and 300VW datasets show that our method obtains state-of-the-art performance under diverse video inpainting settings.

In order to detect tea diseases quickly and meet the needs of practical production, a method was proposed for the classification and identification of tea diseases under natural conditions. According to the characteristic factors, the foreground main blade region was segmented, and then an improved image segmentation algorithm is used to segment each color component. The$\pmb{H}, \pmb{a}^{*},$and$\pmb{b}^{\star}$components were superimposed to obtain an ideal lesion area. A total of 34 features were extracted from three aspects of color, shape, and texture of lesion area. The first 9 principal components were selected as the modeling variables by principal component analysis. Diseases were classified by back propagation (BP) neural network, random forest, and support vector machine (SVM), respectively. The results show that the recognition rate of SVM classification algorithm is 87.3%, which is superior to BP neural network and random forest, and its operating speed is faster. In order to improve the recognition rate, particle swarm optimization (PSO) algorithm to optimize SVM was proposed, which improved the recognition rate of SVM to 91.7%. The experimental results show that PSO optimized SVM algorithm can quickly realize the classification and identification of tea diseases under natural background, and provide a valuable reference for the intelligent agricultural informatization of tea garden agriculture.

We propose a novel framework that automatically learns the lighting patterns for efficient reflectance acquisition, as well as how to faithfully reconstruct spatially varying anisotropic BRDFs and local frames from measurements under such patterns. The core of our framework is an asymmetric deep autoencoder, consisting of a nonnegative, linear encoder which directly corresponds to the lighting patterns used in physical acquisition, and a stacked, nonlinear decoder which computationally recovers the BRDF information from captured photographs. The autoencoder is trained with a large amount of synthetic reflectance data, and can adapt to various factors, including the geometry of the setup and the properties of appearance. We demonstrate the effectiveness of our framework on a wide range of physical materials, using as few as 16 ~ 32 lighting patterns, which correspond to 12 ~ 25 seconds of acquisition time. We also validate our results with the ground truth data and captured photographs. Our framework is useful for increasing the efficiency in both novel and existing acquisition setups.

Raster images are the standard format for texture mapping, but they suffer from limited resolution. Vector graphics are resolution-independent but are less general and more difficult to implement on a GPU. We propose a hybrid representation called vector regression functions (VRFs), which compactly approximate any point-sampled image and support GPU texture mapping, including random access and filtering operations. Unlike standard GPU texture compression, (VRFs) provide a variable-rate encoding in which piecewise smooth regions compress to the square root of the original size. Our key idea is to represent images using the multilayer perceptron , allowing general encoding via regression and efficient decoding via a simple GPU pixel shader. We also propose a content-aware spatial partitioning scheme to reduce the complexity of the neural network model. We demonstrate benefits of our method including its quality, size, and runtime speed.

We present radiance regression functions for fast rendering of global illumination in scenes with dynamic local light sources. A radiance regression function (RRF) represents a non-linear mapping from local and contextual attributes of surface points, such as position, viewing direction, and lighting condition, to their indirect illumination values. The RRF is obtained from precomputed shading samples through regression analysis, which determines a function that best fits the shading data. For a given scene, the shading samples are precomputed by an offline renderer. The key idea behind our approach is to exploit the nonlinear coherence of the indirect illumination data to make the RRF both compact and fast to evaluate. We model the RRF as a multilayer acyclic feed-forward neural network, which provides a close functional approximation of the indirect illumination and can be efficiently evaluated at run time. To effectively model scenes with spatially variant material properties, we utilize an augmented set of attributes as input to the neural network RRF to reduce the amount of inference that the network needs to perform. To handle scenes with greater geometric complexity, we partition the input space of the RRF model and represent the subspaces with separate, smaller RRFs that can be evaluated more rapidly. As a result, the RRF model scales well to increasingly complex scene geometry and material variation. Because of its compactness and ease of evaluation, the RRF model enables real-time rendering with full global illumination effects, including changing caustics and multiple-bounce high-frequency glossy interreflections.

Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.

We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF. We introduce a BRDF chart , analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance. We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart. We demonstrate convincing results obtained after less than 30 seconds of data capture, using commercial mobile phone cameras in a casual environment.

Turning Rust into Gold is inspired by a Chinese antique Mao-Kung Ting (cauldron) treasured by the National Palace Museum in Taiwan. Having a five-hundred-character inscription cast inside, and its weathered appearance made the Mao-Kung very unique. Motivated by revealing the great nature of the artifact and interpreting it into a meaningful narrative, we have proposed an interactive multimedia system that facilitates effective communication between museum audiences and the Mao-Kung Ting. Three technologies have been implemented to emphasize the weathered appearance of the bronze. De-/weathering simulation techniques have been deployed to revive the bronze to its original shiny gold color; while breath-based biofeedback and haptic technology have been utilized as user interfaces to trigger the de-weathering process of the Mao-Kung Ting. Also, the interactive scenarios have been designed with the Chinese cultural context and philosophy Qi, enabling users more easily fall into the Chinese civilization. The paper aims to present the development of the artwork Turing Rust into Gold, in order to further contribute to the feasibility of incorporating new media art in a historical museum context, and bring a new horizon in the museum sector.

Abstract We present a technique for rapid capture of high quality bidirectional reflection distribution functions(BRDFs) of surface points. Our method represents the BRDF at each point by a generalized microfacet model with tabulated normal distribution function (NDF) and assumes that the BRDF is symmetrical. A compact and light‐weight reflectometry apparatus is developed for capturing reflectance data from each surface point within one second. The device consists of a pair of condenser lenses, a video camera, and six LED light sources. During capture, the reflected rays from a surface point lit by a LED lighting are refracted by a condenser lenses and efficiently collected by the camera CCD. Taking advantage of BRDF symmetry, our reflectometry apparatus provides an efficient optical design to improve the measurement quality. We also propose a model fitting algorithm for reconstructing the generalized microfacet model from the sparse BRDF slices captured from a material surface point. Our new algorithm addresses the measurement errors and generates more accurate results than previous work. Our technique provides a practical and efficient solution for BRDF acquisition, especially for materials with anisotropic reflectance. We test the accuracy of our approach by comparing our results with ground truth. We demonstrate the efficiency of our reflectometry by measuring materials with different degrees of specularity, values of Fresnel factor, and angular variation.

Abstract We present a real‐time algorithm for rendering translucent objects of arbitrary shapes. We approximate the scattering of light inside the objects using the diffusion equation, which we solve on‐the‐fly using the GPU. Our algorithm is general enough to handle arbitrary geometry, heterogeneous materials, deformable objects and modifications of lighting, all in real‐time. In a pre‐processing step, we discretize the object into a regular 4‐connected structure (QuadGraph). Due to its regular connectivity, this structure is easily packed into a texture and stored on the GPU. At runtime, we use the QuadGraph stored on the GPU to solve the diffusion equation, in real‐time, taking into account the varying input conditions: Incoming light, object material and geometry. We handle deformable objects, provided the deformation does not change the topological structure of the objects.

Many real world surfaces exhibit translucent appearance due to subsurface scattering. Although various methods exists to measure, edit and render subsurface scattering effects, no solution exists for manufacturing physical objects with desired translucent appearance. In this paper, we present a complete solution for fabricating a material volume with a desired surface BSSRDF. We stack layers from a fixed set of manufacturing materials whose thickness is varied spatially to reproduce the heterogeneity of the input BSSRDF. Given an input BSSRDF and the optical properties of the manufacturing materials, our system efficiently determines the optimal order and thickness of the layers. We demonstrate our approach by printing a variety of homogenous and heterogenous BSSRDFs using two hardware setups: a milling machine and a 3D printer.

Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms a low-dimensional manifold. It provides a high-resolution result in both the spatial and angular domains by decomposing reflectance measurement into two lower-dimensional phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension but sampled densely over the surface. We develop a hand-held, high-speed BRDF capturing device for phase one measurements. A condenser-based optical setup collects a dense hemisphere of rays emanating from a single point on the target sample as it is manually scanned over it, yielding 10 BRDF point measurements per second. Lighting directions from 6 LEDs are applied at each measurement; these are amplified to a full 4D BRDF using the general (NDF-tabulated) microfacet model. The second phase captures N =20-200 images of the entire sample from a fixed view and lit by a varying area source. We show that the resulting N -dimensional keys capture much of the distance information in the original BRDF space, so that they effectively discriminate among representatives, though they lack sufficient angular detail to reconstruct the SVBRDF by themselves. At each surface position, a local linear combination of a small number of neighboring representatives is computed to match each key, yielding a high-resolution SVBRDF. A quick capture session (10-20 minutes) on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.

Abstract We present a new model, called the dual‐microfacet, for those materials such as paper and plastic formed by a thin, transparent slab lying between two surfaces of spatially varying roughness. Light transmission through the slab is represented by a microfacet‐based BTDF which tabulates the microfacet's normal distribution (NDF) as a function of surface location. Though the material is bounded by two surfaces of different roughness, we approximate light transmission through it by a virtual slab determined by a single spatially‐varying NDF. This enables efficient capturing of spatially variant transparent slices. We describe a device for measuring this model over a flat sample by shining light from a CRT behind it and capturing a sequence of images from a single view. Our method captures both angular and spatial variation in the BTDF and provides a good match to measured materials.

Abstract We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearance from one texture and distribution from the other. Texture splicing involves self‐similarity search to extract the distribution, distribution warping, context‐dependent warping, and finally, texture refinement to preserve overall appearance. We show a variety of results to illustrate this operation.

Abstract We propose an efficient method for editing bidirectional texture functions (BTFs) based on edit propagation scheme. In our approach, users specify sparse edits on a certain slice of BTF. An edit propagation scheme is then applied to propagate edits to the whole BTF data. The consistency of the BTF data is maintained by propagating similar edits to points with similar underlying geometry/reflectance. For this purpose, we propose to use view independent features including normals and reflectance features reconstructed from each view to guide the propagation process. We also propose an adaptive sampling scheme for speeding up the propagation process. Since our method needn't any accurate geometry and reflectance information, it allows users to edit complex BTFs with interactive feedback.

We propose a kernel Nyström method for reconstructing the light transport matrix from a relatively small number of acquired images. Our work is based on the generalized Nyström method for low rank matrices. We introduce the light transport kernel and incorporate it into the Nyström method to exploit the nonlinear coherence of the light transport matrix. We also develop an adaptive scheme for efficiently capturing the sparsely sampled images from the scene. Our experiments indicate that the kernel Nyström method can achieve good reconstruction of the light transport matrix with a few hundred images and produce high quality relighting results. The kernel Nyström method is effective for modeling scenes with complex lighting effects and occlusions which have been challenging for existing techniques.

We describe a technique for real-time rendering of dynamic, spatially-varying BRDFs in static scenes with all-frequency shadows from environmental and point lights. The 6D SVBRDF is represented with a general microfacet model and spherical lobes fit to its 4D spatially-varying normal distribution function (SVNDF). A sum of spherical Gaussians (SGs) provides an accurate approximation with a small number of lobes. Parametric BRDFs are fit on-the-fly using simple analytic expressions; measured BRDFs are fit as a preprocess using nonlinear optimization. Our BRDF representation is compact, allows detailed textures, is closed under products and rotations, and supports reflectance of arbitrarily high specularity. At run-time, SGs representing the NDF are warped to align the half-angle vector to the lighting direction and multiplied by the microfacet shadowing and Fresnel factors. This yields the relevant 2D view slice on-the-fly at each pixel, still represented in the SG basis. We account for macro-scale shadowing using a new, nonlinear visibility representation based on spherical signed distance functions (SSDFs). SSDFs allow per-pixel interpolation of high-frequency visibility without ghosting and can be multiplied by the BRDF and lighting efficiently on the GPU.

Light emitting diodes (LEDs) can be used as light detectors and as light emitters. In this paper, we present a novel BRDF measurement device consisting exclusively of LEDs. Our design can acquire BRDFs over a full hemisphere, or even a full sphere (for the bidirectional transmittance distribution function BTDF), and can also measure a (partial) multi-spectral BRDF. Because we use no cameras, projectors, or even mirrors, our design does not suffer from occlusion problems. It is fast, significantly simpler, and more compact than existing BRDF measurement designs.

Abstract The appearance manifold [WTL*06] is an efficient approach for modeling and editing time‐variant appearance of materials from the BRDF data captured at single time instance. However, this method is difficult to apply in images in which weathering and shading variations are combined. In this paper, we present a technique for modeling and editing the weathering effects of an object in a single image with appearance manifolds. In our approach, we formulate the input image as the product of reflectance and illuminance. An iterative method is then developed to construct the appearance manifold in color space (i.e., Lab space) for modeling the reflectance variations caused by weathering. Based on the appearance manifold, we propose a statistical method to robustly decompose reflectance and illuminance for each pixel. For editing, we introduce a “pixel‐walking” scheme to modify the pixel reflectance according to its position on the manifold, by which the detailed reflectance variations are well preserved. We illustrate our technique in various applications, including weathering transfer between two images that is first enabled by our technique. Results show that our technique can produce much better results than existing methods, especially for objects with complex geometry and shading effects.

In this article, we propose techniques for modeling and rendering of heterogeneous translucent materials that enable acquisition from measured samples, interactive editing of material attributes, and real-time rendering. The materials are assumed to be optically dense such that multiple scattering can be approximated by a diffusion process described by the diffusion equation. For modeling heterogeneous materials, we present the inverse diffusion algorithm for acquiring material properties from appearance measurements. This modeling algorithm incorporates a regularizer to handle the ill-conditioning of the inverse problem, an adjoint method to dramatically reduce the computational cost, and a hierarchical GPU implementation for further speedup. To render an object with known material properties, we present the polygrid diffusion algorithm , which solves the diffusion equation with a boundary condition defined by the given illumination environment. This rendering technique is based on representation of an object by a polygrid, a grid with regular connectivity and an irregular shape, which facilitates solution of the diffusion equation in arbitrary volumes. Because of the regular connectivity, our rendering algorithm can be implemented on the GPU for real-time performance. We demonstrate our techniques by capturing materials from physical samples and performing real-time rendering and editing with these materials.

We present a new technique for the visual modeling of spatiallyvarying anisotropic reflectance using data captured from a single view. Reflectance is represented using a microfacet-based BRDF which tabulates the facets' normal distribution (NDF) as a function of surface location. Data from a single view provides a 2D slice of the 4D BRDF at each surface point from which we fit a partial NDF. The fitted NDF is partial because the single view direction coupled with the set of light directions covers only a portion of the "half-angle" hemisphere. We complete the NDF at each point by applying a novel variant of texture synthesis using similar, overlapping partial NDFs from other points. Our similarity measure allows azimuthal rotation of partial NDFs, under the assumption that reflectance is spatially redundant but the local frame may be arbitrarily oriented. Our system includes a simple acquisition device that collects images over a 2D set of light directions by scanning a linear array of LEDs over a flat sample. Results demonstrate that our approach preserves spatial and directional BRDF details and generates a visually compelling match to measured materials.

We present a visual simulation technique called appearance manifolds for modeling the time-variant surface appearance of a material from data captured at a single instant in time. In modeling time-variant appearance, our method takes advantage of the key observation that concurrent variations in appearance over a surface represent different degrees of weathering. By reorganizing these various appearances in a manner that reveals their relative order with respect to weathering degree, our method infers spatial and temporal appearance properties of the material's weathering process that can be used to convincingly generate its weathered appearance at different points in time. Results with natural non-linear reflectance variations are demonstrated in applications such as visual simulation of weathering on 3D models, increasing and decreasing the weathering of real objects, and material transfer with weathering effects.

Many translucent materials consist of evenly-distributed heterogeneous elements which produce a complex appearance under different lighting and viewing directions. For these quasi-homogeneous materials, existing techniques do not address how to acquire their material representations from physical samples in a way that allows arbitrary geometry models to be rendered with these materials. We propose a model for such materials that can be readily acquired from physical samples. This material model can be applied to geometric models of arbitrary shapes, and the resulting objects can be efficiently rendered without expensive subsurface light transport simulation. In developing a material model with these attributes, we capitalize on a key observation about the subsurface scattering characteristics of quasi-homogeneous materials at different scales. Locally, the non-uniformity of these materials leads to inhomogeneous subsurface scattering. For subsurface scattering on a global scale, we show that a lengthy photon path through an even distribution of heterogeneous elements statistically resembles scattering in a homogeneous medium. This observation allows us to represent and measure the global light transport within quasi-homogeneous materials as well as the transfer of light into and out of a material volume through surface mesostructures. We demonstrate our technique with results for several challenging materials that exhibit sophisticated appearance features such as transmission of back illumination through surface mesostructures.

We propose a texture function for realistic modeling and efficient rendering of materials that exhibit surface mesostructures, translucency and volumetric texture variations. The appearance of such complex materials for dynamic lighting and viewing directions is expensive to calculate and requires an impractical amount of storage to precompute. To handle this problem, our method models an object as a shell layer, formed by texture synthesis of a volumetric material sample, and a homogeneous inner core. To facilitate computation of surface radiance from the shell layer, we introduce the shell texture function (STF) which describes voxel irradiance fields based on precomputed fine-level light interactions such as shadowing by surface mesostructures and scattering of photons inside the object. Together with a diffusion approximation of homogeneous inner core radiance, the STF leads to fast and detailed raytraced renderings of complex materials.