Interior design often struggles to capture the subtleties of client experience, leaving gaps between what clients feel and what designers can act upon. We present AIDED, a designer-AI co-design workflow that integrates multimodal client data into generative AI (GAI) design processes. In a within-subjects study with twelve professional designers, we compared four modalities: baseline briefs, gaze heatmaps, questionnaire visualizations, and AI-predicted overlays. Results show that questionnaire data were trusted, creativity-enhancing, and satisfying; gaze heatmaps increased cognitive load; and AI-predicted overlays improved GAI communication but required natural language mediation to establish trust. Interviews confirmed that an authenticity-interpretability trade-off is central to balancing client voices with professional control. Our contributions are: (1) a system that incorporates experiential client signals into GAI design workflows; (2) empirical evidence of how different modalities affect design outcomes; and (3) implications for future AI tools that support human-data interaction in creative practice.

Time-lapse videos usually perform eye-catching appearances but are often hard to create. In this paper, we propose a self-supervised end-to-end model to generate the time-lapse video from a single image and a reference video. Our key idea is to extract both the style and the features of temporal variation from the reference video, and transfer them onto the input image. To ensure both the temporal consistency and realness of our resultant videos, we introduce several novel designs in our architecture, including classwise NoiseAdaIN, flow loss, and the video discriminator. In comparison to the baselines of state-of-the-art style transfer approaches, our proposed method is not only efficient in computation but also able to create more realistic and temporally smooth time-lapse video of a still image, with its temporal variation consistent to the reference.

Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are originated from the content inconsistency between the original image and its stylized output. Therefore, in this paper we advance to keep the content information of the input image during the process of style transfer by the power of steganography, with two approaches proposed: a two-stage model and an end-to-end model. We conduct extensive experiments to successfully verify the capacity of our models, in which both of them are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image as well as performing multiple times of style transfer in series.

In this paper, two programmable voltage-detect charge balance controls (CBC) are implemented on a buck converter. The proposed CBC simultaneously optimizes both load transient and dynamic voltage scaling (DVS) to minimize output voltage spikes and settling time. Firstly, this paper introduces a computational method that is equipped with high resolution analog-to-digital (ADC) converter to achieve better CBC performance. Secondly, an auto-tuning mechanism is introduced to achieve the same performance but with less cost for the circuit. The proposed computational CBC and auto-tuning CBC were tested on the MATLAB Simulink/ Modelsim co-simulation platform to verify the effectiveness of transient response. Simulation results show that the output voltage (Vout) spontaneously follows the DVS function at various Vout levels (from 0.9V to 1.2V) with very fast transient response.