You have just applied a formatting style to a cell, use Ctrl + Y to quickly apply the same formatting to another cell.
I've used Microsoft Excel for 20 years and these 10 shortcuts will make you an Excel Guru in 5 minutes & maximize your productivity 100x:
🧵 Don't use Excel without knowing these 10 shortcuts:
You have just applied a formatting style to a cell, use Ctrl + Y to quickly apply the same formatting to another cell.
If you have a cell that contains a formula, you can use Ctrl + ~ to switch between the formula view and the value view of the cell. This allows you to see the underlying formula used to calculate the cell value.
The AutoSum shortcut is useful for quickly calculating the sum of a range of cells without having to manually type in the formula
When tracking the progress of a project, add the date each time a task is completed. Use Ctrl + ; to quickly insert the current date.
This shortcut is useful for quickly creating a hyperlink to a website, file, or another location in your spreadsheet.
This shortcut opens the Paste Special dialog box, allowing you to select the options for pasting the copied data, such as formatting, formulas, values, or comments.
This is useful for quickly copying data or formulas from the top cell to the cells below.
If you have data with headers in the top row, and you want to keep the headers visible while scrolling, use Alt + W + F + F to freeze the top row, so the headers remain visible while scrolling through the data.
If you have multiple columns, and some of the columns contain text or numbers that are too wide to display in full, then use Alt + H + O + I to quickly adjust the width of the columns to display the full content of the cells.
This shortcut adds a filter to the selected cells, allowing you to sort and filter the data based on certain information, such as sales data for a time period or region.
Use filter options to display only the information you need.
If you have a word or value you need to replace with something else, use Ctrl + H to enter the text or value you want to find, and then enter the text or value you want to replace it with, to replace all instances of it.
This shortcut is useful for adding comments to cells to provide additional information or context about the data.
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How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
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This is a pretty valiant attempt to defend the "Feminist Glaciology" article, which says conventional wisdom is wrong, and this is a solid piece of scholarship. I'll beg to differ, because I think Jeffery, here, is confusing scholarship with "saying things that seem right".
The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.
Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)
There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.
At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?
Imagine for a moment the most obscurantist, jargon-filled, po-mo article the politically correct academy might produce. Pure SJW nonsense. Got it? Chances are you're imagining something like the infamous "Feminist Glaciology" article from a few years back.https://t.co/NRaWNREBvR pic.twitter.com/qtSFBYY80S
— Jeffrey Sachs (@JeffreyASachs) October 13, 2018
The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.
Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)
There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.
At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?
