All Too Well

All Too Well is a 2D animation graduate thesis film, with a length of 6 minutes and 49 seconds. The production phase went from September 2018 to January 2020. A father who lost his son in a car accident recreates the family by bringing an artificial intelligence boy into the home to replace the lost son. Not only does their relationship fail to heal the father\u27s broken heart, but it also often brings back painful memories of his real boy. This only makes him realize that the robot son cannot replace his real son. So, he turns the power off of the robot son and deeply grieves. The mother witnesses her husband’s actions, approaches him, and then turns his power off. Themes of loss and healing in the age of artificial intelligence and the use of robots to fill our need for love and normal relationships are central to this abstracted narrative. The major software used during the entire production process include: Adobe Photoshop, Adobe After Effects, Adobe Premiere, TVPaint and MOHO. The original music is a score by KaMan Chang. The final output format is 1080HD. This paper will retrace the making of All Too Well and share the personal experience during its creation

Two-Dimensional Halide Perovskites for Emerging New- Generation Photodetectors

Compared to their conventional three-dimensional (3D) counterparts, two-dimensional (2D) halide perovskites have attracted more interests recently in a variety of areas related to optoelectronics because of their unique structural characteristics and enhanced performances. In general, there are two distinct types of 2D halide perovskites. One represents those perovskites with an intrinsic layered crystal structure (i.e. MX6 layers, M = metal and X = Cl, Br, I), the other defines the perovskites with a 2D nanostructured morphology such as nanoplatelets and nanosheets. Recent studies have shown that 2D halide perovskites hold promising potential for the development of new-generation photodetectors, mainly arising from their highly efficient photoluminescence and absorbance, color tunability in the visible-light range and relatively high stability. In this chapter, we present the summary and highlights of latest researches on these two types of 2D halide perovskites for developing photodetectors, with an emphasis on synthesis methods, structural characterization, optoelectronic properties, and theoretical analysis and simulations. We also discuss the current challenging issues and future perspective. We hope this chapter would add new elements for understanding halide perovskite-based 2D materials and for developing their more efficient optoelectronic devices

Z∗Z^*: Zero-shot Style Transfer via Attention Rearrangement

Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging. In contrast to existing learning/tuning methods, this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content image without retraining. Specifically, we adopt dual denoising paths to represent content/style references in latent space and then guide the content image denoising process with style latent codes. We further reveal that the cross-attention mechanism in latent diffusion models tends to blend the content and style images, resulting in stylized outputs that deviate from the original content image. To overcome this limitation, we introduce a cross-attention rearrangement strategy. Through theoretical analysis and experiments, we demonstrate the effectiveness and superiority of the diffusion-based $\underline{Z}$ero-shot $\underline{S}$tyle $\underline{T}$ransfer via $\underline{A}$ttention $\underline{R}$earrangement, Z-STAR

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page