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.

We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do.

We consider the concatenation of a convolutional code (CC) with an optimized cyclic redundancy check (CRC) code as a promising paradigm for good short blocklength codes. The resulting CRC-aided convolutional code naturally permits the use of serial list Viterbi decoding (SLVD) to achieve maximum-likelihood decoding. The convolutional encoder of interest is of rate-$1/\omega $and the convolutional code is either zero-terminated (ZT) or tail-biting (TB). The resulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To design a good CRC-aided convolutional code, we propose thedistance-spectrum optimal (DSO)CRC polynomial. A DSO CRC search algorithm for the TBCC is provided. Our analysis reveals that the complexity of SLVD is governed by the expected list rank which converges to 1 at high SNR. This allows a good performance to be achieved with a small increase in complexity. In this paper, we focus on transmitting 64 information bits with a rate-1/2 convolutional encoder. For a target error probability$10^{-4}$, simulations show that the best CRC-ZTCC approaches the random-coding union (RCU) bound within 0.4 dB. Several CRC-TBCCs outperform the RCU bound at moderate SNR values.

In this paper, we consider variable-length coding over the memoryless binary asymmetric channel (BAC) with full noiseless feedback, including the binary symmetric channel (BSC) as a special case. In 2012, Naghshvar et al. introduced a coding scheme, which we refer to as the small-enough-difference (SED) coding scheme. For symmetric binary-input channels, the deterministic variable-length feedback (VLF) code constructed with the SED coding scheme asymptotically achieves both capacity and Burnashev’s optimal error exponent. Building on the work of Naghshvar et al., this paper extends the SED coding scheme to the BAC and develops a non-asymptotic VLF achievability bound that is shown to achieve both capacity and the optimal error exponent. For the specific case of the BSC, we develop an additional non-asymptotic VLF achievability bound using a two-phase analysis that leverages both a submartingale synthesis and a Markov chain time of first passage analysis. Numerical evaluations show that both new VLF achievability bounds outperform Polyanskiy’s achievability bound for variable-length stop-feedback codes.

Abstract In this paper we present a fractional parametric splitting scheme for Reyes‐style adaptive tessellation. Our parallel algorithm generates crack‐free tessellation from a parametric surface, which is also free of sudden temporal changes under animation. Continuous level of detail is not addressed by existing Reyes‐style methods, since these aim to produce subpixel‐sized micropolygons, where topology changes are no longer noticeable. Using our method, rendering pipelines that use larger triangles, thus sensitive to geometric popping, may also benefit from the quality of the split‐dice tessellation stages of Reyes. We demonstrate results on a real‐time GPU implementation, going beyond the limited quality and resolution of the hardware tessellation unit. In contrast to previous split‐dice methods, our split stage is compatible with the fractional hardware tessellation scheme that has been designed for continuous level of detail.

This paper presents a new technique for real-time relighting of static scenes with all-frequency shadows from complex lighting and highly specular reflections from spatially varying BRDFs. The key idea is to depict the boundaries of visible regions using piecewise linear functions, and convert the shading computation into double product integrals—the integral of the product of lighting and BRDF on visible regions. By representing lighting and BRDF with spherical Gaussians and approximating their product using Legendre polynomials locally in visible regions, we show that such double product integrals can be evaluated in an analytic form. Given the precomputed visibility, our technique computes the visibility boundaries on the fly at each shading point, and performs the analytic integral to evaluate the shading color. The result is a real-time all-frequency relighting technique for static scenes with dynamic, spatially varying BRDFs, which can generate more accurate shadows than the state-of-the-art real-time PRT methods.

Traditional gradient domain seamless image cloning is a time consuming task, requiring the solving of Poisson’s equations whenever the shape or position of the cloned region changes. Recently, a more efficient alternative, the mean-value coordinates (MVCs) based approach, was proposed to interpolate interior pixels by a weighted combination of values along the boundary. However, this approach cannot faithfully preserve the gradient in the cloning region. In this paper, we introduce harmonic cloning, which uses harmonic coordinates (HCs) instead of MVCs in image cloning. Benefiting from the non-negativity and interior locality of HCs, our interpolation generates a more accurate harmonic field across the cloned region, to preserve the results with as high a quality as with Poisson cloning. Furthermore, with optimizations and implementation on a graphic processing unit (GPU), we demonstrate that, compared with the method using MVCs, our harmonic cloning gains better quality while retaining real-time performance.

Abstract Solving aliasing artifacts is an essential problem in shadow mapping approaches. Many works have been proposed, however, most of them focused on removing the texel‐level aliasing that results from the limited resolution of shadow maps. Little work has been done to solve the pixel‐level shadow aliasing that is produced by the rasterization on the screen plane. In this paper, we propose a fast, sub‐pixel antialiased shadowing algorithm to solve the pixel aliasing problem. Our work is based on the alias‐free shadow maps, which is capable of computing accurate per‐pixel shadow, and only incurs little cost to extend to sub‐pixel accuracy. Instead of direct supersampling the screen space, we take facets to approximate pixels in shadow testing. The shadowed area of one facet is rapidly evaluated by projecting blocker geometry onto a supersampled 2D occlusion mask with bitmasks fusion. It provides a sub‐pixel occlusion sampling so as to capture fine shadow details and features. Furthermore, we introduce the silhouette mask map that limits visibility evaluation to pixels only on the silhouette, which greatly reduces the computation cost. Our algorithm runs entirely on the GPU, achieving real‐time performance and is an order of magnitude faster than the brute‐force supersampling method to produce comparable 32× antialiased shadows.

This paper presents a GPU-based method for interactive global illumination that integrates complex effects such as multi-bounce indirect lighting, glossy reflections, caustics, and arbitrary specular paths. Our method builds upon scattered data sampling and interpolation on the GPU. We start with raytraced shading points and partition them into coherent shading clusters using adaptive seeding followed by k-means. At each cluster center we apply final gather to evaluate its incident irradiance using GPU-based photon mapping. We approximate the entire photon tree as a compact illumination cut, thus reducing the final gather cost for each ray. The sampled irradiance values are then interpolated at all shading points to produce rendering. Our method exploits the spatial coherence of illumination to reduce sampling cost. We sample sparsely and the distribution of sample points conforms with the underlying illumination changes. Therefore our method is both fast and preserves high rendering quality. Although the same property has been exploited by previous caching and adaptive sampling methods, these methods typically require sequential computation of sample points, making them ill-suited for the GPU. In contrast, we select sample points adaptively in a single pass, enabling parallel computation. As a result, our algorithm runs entirely on the GPU, achieving interactive rates for scenes with complex illumination effects.

Abstract In this paper, we introduce a new representation – radiance transfer fields (RTF) – for rendering interreflections in dynamic scenes under low frequency illumination. The RTF describes the radiance transferred by an individual object to its surrounding space as a function of the incident radiance. An important property of RTF is its independence of the scene configuration, enabling interreflection computation in dynamic scenes. Secondly, RTFs naturally fit in with the rendering framework of precomputed shadow fields, incurring negligible cost to add interreflection effects. In addition, RTFs can be used to compute interreflections for both diffuse and glossy objects. We also show that RTF data can be highly compressed by clustered principal component analysis (CPCA), which not only reduces the memory cost but also accelerates rendering. Finally, we present some experimental results demonstrating our techniques.

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.