Tootfinder

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

This https://arxiv.org/abs/2502.07404 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the psychological and practical factors critical to successful collaboration. Specifically, we investigate…

This https://arxiv.org/abs/2411.14464 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data
Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low. Results: We introduce in this paper a novel paradigm (JESTR) for annotation. Unlike prior approaches that explicitly construct molecular fingerprints or spectra, JESTR leverages the insight that molecules and their corresponding spec…

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Zixuan Jiang, Renjing Xu
https://arxiv.org/abs/2506.07035 https://

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal fra…

As promised, the @… fundraising campaign seems to be storming ahead, so here's a new - and rather hastily edited - video from NW #Greenland fieldwork just up on @… Just in case anyone finds it interesting and fun. I made this in shotcut. At some point I'll work out how to annotate them properly I guess...
Enjoy!
https://tilvids.com/w/nY8US6AsGDnsVeUXPqBixq

Antithetic Noise in Diffusion Models
Jing Jia, Sifan Liu, Bowen Song, Wei Yuan, Liyue Shen, Guanyang Wang
https://arxiv.org/abs/2506.06185 https://

Antithetic Noise in Diffusion Models
We initiate a systematic study of antithetic initial noise in diffusion models. Across unconditional models trained on diverse datasets, text-conditioned latent-diffusion models, and diffusion-posterior samplers, we find that pairing each initial noise with its negation consistently yields strongly negatively correlated samples. To explain this phenomenon, we combine experiments and theoretical analysis, leading to a symmetry conjecture that the learned score function is approximately affine an…

Impact of Label Noise from Large Language Models Generated Annotations on Evaluation of Diagnostic Model Performance
Mohammadreza Chavoshi, Hari Trivedi, Janice Newsome, Aawez Mansuri, Chiratidzo Rudado Sanyika, Rohan Satya Isaac, Frank Li, Theo Dapamede, Judy Gichoya
https://arxiv.org/abs/2506.07273

Impact of Label Noise from Large Language Models Generated Annotations on Evaluation of Diagnostic Model Performance
Large language models (LLMs) are increasingly used to generate labels from radiology reports to enable large-scale AI evaluation. However, label noise from LLMs can introduce bias into performance estimates, especially under varying disease prevalence and model quality. This study quantifies how LLM labeling errors impact downstream diagnostic model evaluation. We developed a simulation framework to assess how LLM label errors affect observed model performance. A synthetic dataset of 10,000 cas…

Is there a tool to set up #GitAnnex to calculate archive unpackings yet? Something™ could look at a repo's .zip / .tar.gz files, check whether any file in the repo is also in there, and annotate it as computed / computable. (I'm not aiming to restore the zip files from the content –that'd be pristine-tar territory. I'd already be happy if the compute path would be there so that the…

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata