Tootfinder

M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
Chuan He, Yongchao Liu, Qiang Li, Wenliang Zhong, Chuntao Hong, Xinwei Yao
https://arxiv.org/abs/2508.00452

M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
Cold-start item recommendation is a significant challenge in recommendation systems, particularly when new items are introduced without any historical interaction data. While existing methods leverage multi-modal content to alleviate the cold-start issue, they often neglect the inherent multi-view structure of modalities, the distinction between shared and modality-specific features. In this paper, we propose Multi-Modal Multi-View Variational AutoEncoder (M^2VAE), a generative model that addre…

Better Call Claude: Can LLMs Detect Changes of Writing Style?
Johannes R\"omisch, Svetlana Gorovaia, Mariia Halchynska, Gleb Schmidt, Ivan P. Yamshchikov
https://arxiv.org/abs/2508.00680

Better Call Claude: Can LLMs Detect Changes of Writing Style?
This article explores the zero-shot performance of state-of-the-art large language models (LLMs) on one of the most challenging tasks in authorship analysis: sentence-level style change detection. Benchmarking four LLMs on the official PAN~2024 and 2025 "Multi-Author Writing Style Analysis" datasets, we present several observations. First, state-of-the-art generative models are sensitive to variations in writing style - even at the granular level of individual sentences. Second, their accuracy …

A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models
Sadia Kamal, Lalu Prasad Yadav Prakash, S M Rafiuddin, Mohammed Rakib, Arunkumar Bagavathi, Atriya Sen, Sagnik Ray Choudhury
https://arxiv.org/abs/2506.22493

A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models
Political Compass Test (PCT) or similar questionnaires have been used to quantify LLM's political leanings. Building on a recent line of work that examines the validity of PCT tests, we demonstrate that variation in standard generation parameters does not significantly impact the models' PCT scores. However, external factors such as prompt variations and fine-tuning individually and in combination affect the same. Finally, we demonstrate that when models are fine-tuned on text datasets with hig…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Seventh Wave:
🎵 Aether Anthem
#SeventhWave
Please 🔁 BOOST to share what you like
- your followers don't see if you ⭐ favourite a post

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization
Alejandro Golfe, Natalia P. Garc\'ia-de-la-puente, Adri\'an Colomer, Valery Naranjo
https://arxiv.org/abs/2506.13843

BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization
Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent sp…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Autour de Lucie:
🎵 Selon l’humeur
#AutourdeLucie
https://autourdeluciemh.bandcamp.com/track/selon-l-humeur
https://open.spotify.com/track/5RubgfYwhrBMglt1Hvoc43

The noisy voter model with general initial conditions
Patrizio Caddeo, Eyal Lubetzky
https://arxiv.org/abs/2507.16188 https://arxiv.o…

The noisy voter model with general initial conditions
We study the noisy voter model with $q\geq 2$ states and noise probability $θ$ on arbitrary bounded-degree $n$-vertex graphs $G$ with subexponential growth of balls (e.g., finite subsets of $\mathbb{Z}^d$). Cox, Peres and Steif (2016) showed for the binary case $q=2$ (and a wider class of chains) that, when starting from a worst-case initial state, this Markov chain has total variation cutoff at $t_n=\frac1{2θ}\log n$. The second author and Sly (2021) analyzed faster initial conditions for Gl…

🇺🇦 #NowPlaying on #KEXP's #VarietyMix
Lauren Auder:
🎵 730Kingfisher
#LaurenAuder
https://laurenauder.bandcamp.com/track/730kingfisher
https://open.spotify.com/track/52xdTe02MQDQO9Kl9y34BB