Global illumination combines direct and indirect lighting to create realistic lighting effects, bringing virtual scenes closer to reality. Static global illumination is a crucial component of virtual scene rendering, leveraging precomputation and baking techniques to significantly reduce runtime computational costs. Unfortunately, many existing works prioritize visual quality by relying on extensive texture storage and massive pixel-level texture sampling, leading to large performance overhead. In this paper, we introduce an illumination reconstruction method that effectively reduces sampling in fragment shader and avoids additional render passes, making it well-suited for low-end platforms. To achieve high-quality global illumination with reduced memory usage, we adopt a spherical harmonics fitting approach for baking effective illumination information and propose an inverse probe distribution method that generates unique probe associations for each mesh. This association, which can be generated offline in the local space, ensures consistent lighting quality across all instances of the same mesh. As a consequence, our method delivers highly competitive lighting effects while using only approximately 5% of the memory required by mainstream industry techniques.

With the proliferation of IoT devices today, the need to strengthen the security of these devices is becoming increasingly urgent, particularly the need to review the security of lightweight block ciphers. SIMON is a lightweight block cipher proposed by the National Security Agency (NSA) of US to provide efficient and secure encryption for resource-constrained devices in IoT systems. This paper aims to evaluate the security of SIMON against mixture differential cryptanalysis, which was proposed in Eurocrypt 2017 to launch the best key-recovery attacks on the most widely used encryption standard AES. Though there have been intensive studies on this cryptanalysis method, its current targets are all aligned block ciphers. Whether the numerous bitwise block ciphers, including SIMON, have weaknesses regarding this method remains unknown. In this paper, we extend the mixture differential cryptanalysis to bit-wise ciphers and develop an SAT-based automatic tool to search for such distinguishers. We interpret the bit-wise mixture differential distinguisher as a variant of differential distinguisher in the multi-key setting with 2-3n as the boundary (n:block size), potentially boosting rounds or improving the signal-to-noise ratio of previous boomerang or classical differential distinguisher. Using SIMON as an example, we discover multi-key distinguishers for up to 17-round SIMON32, 18-round SIMON48, and 23-round SIMON64, which outperform previous results in terms of the number of rounds. This paper reconciles the disparity between mixture differential cryptanalysis applied to word-oriented target ciphers and its application to bit-oriented targets, thereby extending the mixture differential cryptanalysis to a broader range of block ciphers.

Stereo-electroencephalography (SEEG) creates unique opportunities for clinical applications, brain-machine-interface (BMI) studies and neuroscientific research. However, all of these directions have been under-explored because the lack of an easy-to-use and multi-functional software to accurately localize the SEEG electrodes in 3D space. In this work, we present, for the first time, an SEEG-specific MATLAB-based toolbox (SEEGview) for electrode localization that identifies 3D coordinates and anatomical information for all SEEG contacts within each individual brain, generates cortical activation maps, and maps SEEG contacts from different subjects into one standard brain model. This article first describes how each of these four functions (3D localization, anatomical localization, cortical activation mapping, and SEEG contact standardization) is implemented through SEEGview. We then present localization results based on SEEG data, thereby demonstrating the high localization accuracy and viability for SEEG research.