Tootfinder

I feel like this Masto post will be evidence in yet another claim against Air Canada for wrecking someone’s mobility aid.
https://mastodon.social/@mollyanglin/115228115467133057

Molly Anglin (@mollyanglin@mastodon.social)
Attached: 1 image Sitting on the tarmac for an Air Canada flight from Toronto to Ottawa… The ground crew spent 2 hrs trying to fit a wheelchair into the cargo. After a very long time, they took it off the plane - but appeared to have detached the battery and maybe broken it. 😱 There were maybe 7-10 people out there trying to fix it. I feel for the owner of that chair! #accessibility #aircanada

Bears, star CB Jaylon Johnson mulling options with season-ending surgery possible https://www.foxsports.com/articles/nfl/bears-star-cb-jaylon-johnson-mulling-options-with-seasonending-surgery-possible

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

A coalition of patriotic groups is again leading a day of mass demonstrations across the US to protest against the Trump authoritarian onslaught.
The coalition spearheaded a previous attempt #NoKings protest day in June,
drawing millions to the streets to speak out against Trumpism on the same day Trump held a military parade in Washington.
Today JD Vance is predictably holding a military…

Controlling Multimodal LLMs via Reward-guided Decoding
Oscar Ma\~nas, Pierluca D'Oro, Koustuv Sinha, Adriana Romero-Soriano, Michal Drozdzal, Aishwarya Agrawal
https://arxiv.org/abs/2508.11616

Controlling Multimodal LLMs via Reward-guided Decoding
As Multimodal Large Language Models (MLLMs) gain widespread applicability, it is becoming increasingly desirable to adapt them for diverse user needs. In this paper, we study the adaptation of MLLMs through controlled decoding. To achieve this, we introduce the first method for reward-guided decoding of MLLMs and demonstrate its application in improving their visual grounding. Our method involves building reward models for visual grounding and using them to guide the MLLM's decoding process. Co…

Rund 15 % der britischen Bevölkerung waren laut einer Umfrage bereits von der #Flut eingeschlossen – oft wegen Fehleinschätzungen.
Viele wissen nicht, dass sich #Gezeiten täglich verschieben und sich örtlich stark unterscheiden. Gerade auf

Misunderstanding the tide is putting millions at risk on UK coasts – here’s what you need to know
Four in ten people surveyed were unaware that tides come in twice daily, that they vary in timing each day, and that they differ in height across the country.

Muovi hajoaa ympäristössä mikromuoveiksi, mistä seuraa ongelmia sekä luonnolle että ihmisille. Muovin aiheuttamat terveyskulut ovat maailmassa melkoiset. https://www.maaseuduntulevaisuus.fi/koti/28eb0d3c-3b7c-4464-bd5f-eed14b3f3531

Tiesitkö? Tässä suomalaisturistien suosimassa meressä on muovia hurjat määrät
Muovi hajoaa ympäristössä mikromuoveiksi, mistä seuraa ongelmia sekä luonnolle että ihmisille. Muovin aiheuttamat terveyskulut ovat maailmassa melkoiset.

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
Qian Liang, Yujia Wu, Kuncheng Li, Jiwei Wei, Shiyuan He, Jinyu Guo, Ning Xie
https://arxiv.org/abs/2508.11433

MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
Multimodal Large Language Models (MLLMs) with unified architectures excel across a wide range of vision-language tasks, yet aligning them with personalized image generation remains a significant challenge. Existing methods for MLLMs are frequently subject-specific, demanding a data-intensive fine-tuning process for every new subject, which limits their scalability. In this paper, we introduce MM-R1, a framework that integrates a cross-modal Chain-of-Thought (X-CoT) reasoning strategy to unlock …

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…