We propose TPP-SD, a novel approach that accelerates Transformer temporal point process (TPP) sampling by adapting speculative decoding (SD) techniques from language models. By identifying the structural similarities between thinning algorithms for TPPs and speculative decoding for language models, we develop an efficient sampling framework that leverages a smaller draft model to generate multiple candidate events, which are then verified by the larger target model. TPP-SD maintains the same output distribution as autoregressive sampling while achieving significant acceleration. Experiments on both synthetic and real datasets demonstrate that our approach produces samples from identical distributions as standard methods, but with 2-6$\times$ speedup. Our ablation studies analyze the impact of hyperparameters such as draft length and draft model size on sampling efficiency. TPP-SD bridges the gap between powerful Transformer TPP models and the practical need for rapid sequence generation.

This paper presents the design and implementation of a hardware-based memory game developed on an FPGA platform to promote cognitive stimulation among older adults. The game features a 3×3 LED and switch matrix that displays pseudo-random light patterns that users must recall and replicate. To enhance randomness, a seed is extracted from ambient electromagnetic noise using an Analog-to-Digital (ADC) and fed into a multi-Linear Feedback Shift Register (LFSR) pseudo-random number generator (PRNG). The PRNG employs XOR-combined LFSRs initialized with non-overlapping seed transformations to ensure statistical independence. A finite state machine (FSM) governs the game logic, translating 9-bit PRNG outputs into spatial LED patterns and evaluating user input for correctness. Statistical analyses of PRNG outputs, using normality tests and Central Limit Theorem-based smoothing, verify the Gaussian conformity and entropy quality of generated sequences. The fully integrated system includes custom PCBs for ADC input, a mechanical button interface, and an LED display, housed in a 3D-printed enclosure. Results from both simulation and hardware validation confirm the system's effectiveness in generating high-quality randomness and delivering a simple, tactile, and accessible cognitive exercise platform.

This project focused on the development of a Tetris-like game that incorporates a pseudo-random number generator (PRNG) implemented on a Field Programmable Gate Array (FPGA) to enhance the gameplay experience. By utilizing a hardware-generated seed instead of relying solely on software-based randomness, the project introduces a novel approach to determining the sequence in which Tetris blocks appear on the screen. This integration of hardware and software not only adds a layer of complexity to the game but also showcases the seamless real-time communication between a digital system powered by an FPGA and an interactive Python-based gaming application. Overall, this project represents a significant step forward in the realm of game development by showcasing the potential of integrating hardware and software technologies to enhance gameplay and create more engaging experiences for players. The fusion of FPGA-driven hardware components with a Python-based game exemplifies the innovative spirit driving advancements in the gaming industry, paving the way for future developments that blur the lines between virtual and physical gaming environments.

The integration of satellite and terrestrial networks has ushered in a new era of communication capabilities. In satellite-terrestrial integrated network (STIN), full-frequency reuse introduces substantial inter-satellite interference and inter-beam interference, leading to a reduction in system capacity. Considering the extensive coverage area and high mobility of low Earth orbit (LEO) satellites, the acquisition of satellite-ground channel state information (CSI) is inherently imprecise, significantly affecting the performance gains of beamforming techniques. We propose a Graph Attention Network(GAT)-based approach to reduce system interference by optimizing beamforming. In detail, we represent the network topology as a graph, where nodes represent desired links and edges signify interference links. This transforms the NP-hard problem into a graph optimization task. In GAT, it can uncover the concealed influences of inter-satellite and inter-beam interference. This entails learning high-dimensional feature information from networks with similar structures while disregarding state variations in low-dimensional spaces. This approach enhances system robustness. Through simulation results, our proposed beamforming method demonstrates effective interference suppression with low computational complexity, leading to increased system throughput. Moreover, the method exhibits robustness in the presence of corrupted input data.