Tootfinder

The properties of the nitrogen-vacancy center in milled chemical vapor deposition nanodiamonds
Alessandro Mameli, Giannis Thalassinos, Marco Capelli, Johannes Ackermann, Edwin Mayes, Hiroshi Abe, Takeshi Ohshima, Tingpeng Luo, Volker Cimalla, Peter Knittel, Brant Gibson, Jan Jeske, Nikolai Dontschuk, Anke Krueger, Alastair Stacey, Alexander Healey, Philipp Reineck

The properties of the nitrogen-vacancy center in milled chemical vapor deposition nanodiamonds
Fluorescent nanodiamonds (FNDs) containing negatively charged nitrogen-vacancy (NV-) centers are vital for many emerging quantum sensing applications from magnetometry to intracellular sensing in biology. However, developing a scalable fabrication method for FNDs hosting color centers with consistent bulk-like photoluminescence (PL) and spin coherence properties remains a highly desired but unrealized goal. Here, we investigate optimized ball milling of single-crystal diamonds produced via chem…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jinpu Yang, Hongqing Liang, Yan Hu, Benyou Wang
https://arxiv.org/abs/2509.09307

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
Materials characterization is fundamental to acquiring materials information, revealing the processing-microstructure-property relationships that guide material design and optimization. While multimodal large language models (MLLMs) have recently shown promise in generative and predictive tasks within materials science, their capacity to understand real-world characterization imaging data remains underexplored. To bridge this gap, we present MatCha, the first benchmark for materials characteriz…

Att vara Sida-chef är alltså att vara en simpel mellanchef nuförtiden, om man ska tro generaldirektören. https://www.dn.se/sverige/granit-om-stormen-pa-sida-det-ar-regeringen-som-styr/

Sidas Jakob Granit om turerna med Diana Janse
Sidas generaldirektör Jakob Granit har ändrat hur biståndspengar ska ges till organisationer och FN. Beror generaldirektörens agerande på påtryckningar?

I’m so glad sane, rational leadership from the US Federal Government lead to smooth sailing for trade between most of Europe and most of North America.
Gratifying to see intelligible, useful agreement that won’t needlessly jeopardize the livelihoods of millions. /s
https://www.

EU and US spin conflicting versions of trade deal
The European Commission and the White House are making contradictory claims about the content and scope of the EU-US trade deal. #EuropeNews

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Centroid Approximation with Multidimensional Approximate Agreement Protocols
Melanie Cambus, Darya Melnyk

“We kissed Trump’s ass and kowtowed to a fascist.” https://ec.social-network.europa.eu/@EUCommission/115067151811326127

European Commission (@EUCommission@ec.social-network.europa.eu)
Attached: 1 image 🇪🇺🤝🇺🇸 Today, the EU and US moved forward in restoring stability and predictability in transatlantic trade. This agreement brings: ✅ Predictability and stability for businesses and consumers ✅ Protection for millions of EU jobs and long-term economic growth ✅ Opportunities for industries on both sides of the Atlantic, including the car sector Fair, balanced, and mutually beneficial, this deal avoids harmful escalation and strengthens the transatlantic …

Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
Latent Labs Team, Alex Bridgland, Jonathan Crabb\'e, Henry Kenlay, Daniella Pretorius, Sebastian M. Schmon, Agrin Hilmkil, Rebecca Bartke-Croughan, Robin Rombach, Michael Flashman, Tomas Matteson, Simon Mathis, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl
https:/…

Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
Traditional drug discovery relies on rounds of screening millions of candidate molecules with low success rates, making drug discovery time and resource intensive. To overcome this screening bottleneck, we introduce Latent-X, an all-atom protein design model that enables a new paradigm of precision AI design. Given a target protein epitope, Latent-X jointly generates the all atom structure and sequence of the protein binder and target, directly modelling the non-covalent interactions essential …

Efficient and Reuseable Cloud Configuration Search Using Discovery Spaces
Michael Johnston, Burkhard Ringlein, Christoph Hagleitner, Alessandro Pomponio, Vassilis Vassiliadis, Christian Pinto, Srikumar Venugopal
https://arxiv.org/abs/2506.21467

Efficient and Reuseable Cloud Configuration Search Using Discovery Spaces
Finding the optimal set of cloud resources to deploy a given workload at minimal cost while meeting a defined service level agreement is an active area of research. Combining tens of parameters applicable across a large selection of compute, storage, and services offered by cloud providers with similar numbers of application-specific parameters leads to configuration spaces with millions of deployment options. In this paper, we propose Discovery Space, an abstraction that formalizes the descr…