We introduce r-loopy Weisfeiler-Leman (r-lWL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-lMPNN, that can count cycles up to length r+2. Most notably, we show that r-lWL can count homomorphisms of cactus graphs. This extends 1-WL, which can only count homomorphisms of trees and, in fact, we prove that r-lWL is incomparable to k-WL for any fixed k. We empirically validate the expressive and counting power of r-lMPNN on several synthetic datasets and demonstrate the scalability and strong performance on various real-world datasets, particularly on sparse graphs. Our code is available on GitHub.

Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal. Although DDGMs are well studied, it is still unclear how the small amount of noise is transformed during the backward diffusion process. Here, we focus on analyzing this problem to gain more insight into the behavior of DDGMs and their denoising and generative capabilities. We observe a fluid transition point that changes the functionality of the backward diffusion process from generating a (corrupted) image from noise to denoising the corrupted image to the final sample. Based on this observation, we postulate to divide a DDGM into two parts: a denoiser and a generator. The denoiser could be parameterized by a denoising auto-encoder, while the generator is a diffusion-based model with its own set of parameters.

In addition to the essential LED flashlight, the other major reason I always have a Garmin watch on one wrist is the comprehensive ecosystem powered by Garmin Connect. Garmin Connect, both via its smartphone app and website, will continue to provide a free, personalized experience. However, Garmin is launching another option for people who want more from their devices. Garmin Connect Plus is an optional subscription service for 6.99 monthly or 69.99 annually. There's a free 30-day trial to test the service and see if it satisfies your needs.

Recently powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide. However, their strong capabilities and vast world knowledge do not come without associated privacy risks. In this work, we focus on the emerging privacy threat LLMs pose - the ability to accurately infer personal information from online texts. Despite the growing importance of LLM-based author profiling, research in this area has been hampered by a lack of suitable public datasets, largely due to ethical and privacy concerns associated with real personal data. We take two steps to address this problem: (i) we construct a simulation framework for the popular social media platform Reddit using LLM agents seeded with synthetic personal profiles; (ii) using this framework, we generate SynthPAI, a diverse synthetic dataset of over 7800 comments manually labeled for personal attributes. We validate our dataset with a human study showing that humans barely outperform random guessing on the task of distinguishing our synthetic comments from real ones. Further, we verify that our dataset enables meaningful personal attribute inference research by showing across 18 state-of-theart LLMs that our synthetic comments allow us to draw the same conclusions as real-world data. Combined, our experimental results, dataset and pipeline form a strong basis for future privacy-preserving research geared towards understanding and mitigating inference-based privacy threats that LLMs pose.

SAVE 50: As of March 27, eufy E10 RGBWW Smart Floor Lamp is available for 49.99 during Amazon's Spring Sale. I'm not usually the type to get excited over a floor lamp, but this eufy E10 is doing things I didn't know lamps could do -- and it's only 49.99 during Amazon's Spring Sale. For something that's part mood-setter, part light show, and part futuristic room accessory, that's a ridiculous value. I'm talking smooth transitions between 16 million colors, preset lighting modes for holidays like Christmas or Halloween, and even AI-powered "themes" that adapt to your mood. If your lamp can't do that, it's officially basic.

Getting an AI to write software for you? Want to write software, but haven't got the first clue where to start? Enter "vibe coding", a term that has swept the internet to describe the use of AI tools, including large language models (LLMs) like ChatGPT, to generate computer code even if you can't program. "Vibe coding basically refers to using generative AI not just to assist with coding, but to generate the entire code for an app," says Noah Giansiracusa at Bentley University in Waltham, Massachusetts. Users ask, or prompt, LLM-based models such as ChatGPT, Claude or Copilot to produce the code for an app or service, and the AI system does all the work.