Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding. Surprisingly, however, there is still no clear understanding of the relation between these two augmentation schemes. Here we propose a novel family of positional encoding schemes which draws a link between the above two approaches and improves over both. The new approach, named Random Feature Propagation (RFP), is inspired by the power iteration method and its generalizations.

Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encoder-based techniques have emerged as a new effective approach for T2I personalization, reducing the need for multiple images and long training times. However, most existing encoders are limited to a single-class domain, which hinders their ability to handle diverse concepts.

A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually. An animated sketch opens another dimension to the expression of ideas and is widely used by designers for a variety of purposes. Animating sketches is a laborious process, requiring extensive experience and professional design skills. In this work, we present a method that automatically adds motion to a single-subject sketch (hence, "breathing life into it"), merely by providing a text prompt indicating the desired motion.

Rendered imagery is presented to us daily. Special effects in movies, video games, scientific visualizations, and marketing catalogs all often rely on images generated through computer graphics. However, with all the possibilities that rendering offers come also a plethora of challenges. This thesis proposes novel ways of evaluating the visual errors caused when some of those challenges are not completely overcome. The thesis also suggests ways to improve on the visual experience observers have when viewing rendered content.

Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output. Specifically, an LLM is utilized to carry out a direct mapping from the N-best hypotheses list generated by an ASR system to the predicted output transcription. However, despite its effectiveness, GER introduces extra data uncertainty since the LLM is trained without taking into account acoustic information available in the speech signal.

DRAM vendors utilize On-Die Error Correction Codes (OD-ECC) to correct random bit errors internally. Meanwhile, system companies utilize Rank-Level ECC (RL-ECC) to protect data against chip errors. Separate protection increases the redundancy ratio to 32.8% in DDR5 and incurs significant performance penalties. This paper proposes a novel RL-ECC, Unity ECC, that can correct both singlechip and double-bit error patterns. Unity ECC corrects doublebit errors using unused syndromes of single-chip correction.

Edge detection is an important process in human visual processing. However, as far as we know, few attempts have been made to map the temporal edge detection filters in human vision. To that end, we devised a user study and collected data from which we derived estimates of human temporal edge detection filters based on three different models, including the derivative of the infinite symmetric exponential function and temporal contrast sensitivity function.