"Wax fire" often refers to a specific type of fire that can occur in environments where flammable wax is present, such as candle-making or candle-burning scenarios. It can also sometimes refer to incidents involving wax or waxy substances catching fire, primarily due to heat sources or improper handling. In a more general sense, wax is a combustible material, and if it reaches its flash point, it can ignite.
The "List of Nuclides" refers to a comprehensive catalog of all known isotopes (nuclides) of the chemical elements, including both stable and radioactive forms. Each nuclide is characterized by its atomic number (the number of protons), mass number (the total number of protons and neutrons), and sometimes its specific energy states and half-lives if it is radioactive.
A segmented, narrow table of nuclides is a graphical representation that organizes nuclides (different isotopes of elements) according to their atomic number (protons) and mass number (protons plus neutrons). The table is often segmented to reflect various properties of the nuclides, such as stability, mode of decay, or type of nuclear interactions.
Fermium is a synthetic, radioactive element with the symbol **Fm** and atomic number **100**. It belongs to the actinide series in the periodic table and is named after the physicist Enrico Fermi. Fermium was first discovered in 1952 in the debris of a thermonuclear explosion, specifically during the testing of nuclear weapons.
Rutherfordium is a synthetic element in the periodic table with the symbol Rf and atomic number 104. It is classified as a radioactive transition metal and is part of the transactinide elements. Rutherfordium was first synthesized in 1964 by a team of Russian physicists at the Joint Institute for Nuclear Research in Dubna, and it was named in honor of the physicist Ernest Rutherford, who is known for his pioneering work in nuclear physics.
14 million images with more than 20k categories, typically denoting prominent objects in the image, either common daily objects, or a wild range of animals. About 1 million of them also have bounding boxes for the objects. The images have different sizes, they are not all standardized to a single size like MNIST[ref].
Each image appears to have a single label associated to it. Care must have been taken somehow with categories, since some images contain severl possible objects, e.g. a person and some object.
Official project page: www.image-net.org/
The data license is restrictive and forbids commercial usage: www.image-net.org/download.php. Also as a result you have to login to download the dataset. Super annoying.
How to visualize: datascience.stackexchange.com/questions/111756/where-can-i-view-the-imagenet-classes-as-a-hierarchy-on-wordnet
Stereospecificity refers to the property of a chemical reaction in which the formation of products occurs in such a way that the spatial arrangement of atoms is specifically determined by the arrangement of atoms in the reactants. In other words, if a reaction yields stereoisomers, the formation of each stereoisomer is tied directly to a specific stereochemical configuration of the reactants.
Block sort is a sorting algorithm that divides data into fixed-size blocks, sorts those blocks independently, and then merges the results. It often aims to leverage data locality and cache efficiency, making it useful in specific scenarios where traditional sorting algorithms might be less efficient. ### Overview of Block Sort: 1. **Divide into Blocks**: The input data is partitioned into smaller blocks of a certain size.
Subset of ImageNet. About 167.62 GB in size according to www.kaggle.com/competitions/imagenet-object-localization-challenge/data.
Contains 1,281,167 images and exactly 1k categories which is why this dataset is also known as ImageNet1k: datascience.stackexchange.com/questions/47458/what-is-the-difference-between-imagenet-and-imagenet1k-how-to-download-it
www.kaggle.com/competitions/imagenet-object-localization-challenge/overview clarifies a bit further how the categories are inter-related according to WordNet relationships:
The 1000 object categories contain both internal nodes and leaf nodes of ImageNet, but do not overlap with each other.
image-net.org/challenges/LSVRC/2012/browse-synsets.php lists all 1k labels with their WordNet IDs.There is a bug on that page however towards the middle:and there is one missing label if we ignore that dummy
n02119789: kit fox, Vulpes macrotis
n02100735: English setter
n02096294: Australian terriern03255030: dumbbell
href="ht:
n02102040: English springer, English springer spanielhref= line. A thinkg of beauty!Also the lines are not sorted by synset, if we do then the first three lines are:
n01440764: tench, Tinca tinca
n01443537: goldfish, Carassius auratus
n01484850: great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariasgist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57 has lines of type:therefore numbered on the exact same order as image-net.org/challenges/LSVRC/2012/browse-synsets.php
n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_huskygist.github.com/yrevar/942d3a0ac09ec9e5eb3a lists all 1k labels as a plaintext file with their benchmark IDs.therefore numbered on sorted order of image-net.org/challenges/LSVRC/2012/browse-synsets.php
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',The official line numbering in-benchmark-data can be seen at
LOC_synset_mapping.txt, e.g. www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txtn01440764 tench, Tinca tinca
n01443537 goldfish, Carassius auratus
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariasThe official page: www.image-net.org/challenges/LSVRC/index.php points to a download link on Kaggle: www.kaggle.com/competitions/imagenet-object-localization-challenge/data Kaggle says that the size is 167.62 GB!
To download from Kaggle, create an API token on kaggle.com, which downloads a The download speed is wildly server/limited and take A LOT of hours. Also, the tool does not seem able to pick up where you stopped last time.
kaggle.json file then:mkdir -p ~/.kaggle
mv ~/down/kaggle.json ~/.kaggle
python3 -m pip install kaggle
kaggle competitions download -c imagenet-object-localization-challengeAnother download location appears to be: huggingface.co/datasets/imagenet-1k on Hugging Face, but you have to login due to their license terms. Once you login you have a very basic data explorer available: huggingface.co/datasets/imagenet-1k/viewer/default/train.
This section is about companies that primarily specialize in machine learning.
The term "machine learning company" is perhaps not great as it could be argued that any of the Big tech are leaders and sometimes, especially in the case of Google, has a main product that is arguably a form of machine learning.
1959 by Voice of America.
Related:
- twitter.com/yoheinakajima/status/1759107727463518702 "smallest RAG test possible of an indirect relationship on a knowledge graph"
- www.quora.com/Do-knowledge-graphs-bases-have-a-place-in-the-pursuit-of-artificial-general-intelligence-AGI-or-can-their-features-be-better-represented-in-a-learning-based-system "Do knowledge graphs / bases have a place in the pursuit of artificial general intelligence (AGI), or can their features be better represented in a learning-based system?"
www.irishtimes.com/life-and-style/people/mackenzie-scott-how-the-former-mrs-bezos-became-a-philanthropist-like-no-other-1.4850049 MacKenzie Scott: How the former Mrs Bezos became a philanthropist like no other (2020) has some good mentions:
But as Scott's fame for giving away money has grown, so too has the deluge of appeals for gifts from strangers and old friends alike. That clamour may have driven Scott's already discreet operation further underground, with recent philanthropic announcements akin to sudden lightning bolts for unsuspecting recipients.
Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact