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# Deep Learning at RoCS | ||
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This repository contains files relating to Deep Learning at the Rosseland Centre for Solar Physics. | ||
This repository contains files relating to Deep Learning at the Rosseland Centre for Solar Physics. The webpage is available at https://ita-solar.github.io/dlrocs. To compile the webpage locally, install the dependencies with `pip install -r requirements.txt` and run `mkdocs serve`. | ||
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The embedded documents are hosted on Google Drive. Go to https://drive.google.com/drive/folders/1o0BzZKXZLRAwpsFoxtLy6pzh6PyokKpl?usp=sharing to view and modify the documents. |
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# Deep Learning RoCS 2024 | ||
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This page has general information. | ||
The **Deep Learning Workshop at the Rosseland Centre for Solar Physics (RoCS)** will take place as a in-person meeting in the week 16-20 September 2024, in Oslo, Norway. | ||
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During this week, we will have a series of talks, discussions, and hands-on sessions on the application of deep learning techniques to solar physics. The workshop will cover a wide range of topics, including but not limited to: signal denoising, PSF (spatial/spectral) deconvolution, magnetic field extrapolations, feature detection/tracking, radiative transfer/inversions, uncertainty estimation, explainability, etc. | ||
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The goal of the workshop is to bring together researchers working on deep learning techniques in solar physics to share their knowledge, discuss the latest developments in the field, and foster collaborations. The workshop will be an opportunity for participants to present their work, exchange ideas, and learn from each other. | ||
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<div style="position: relative; display: inline-block;"> | ||
<img src="https://www.discover-the-world.com/app/uploads/2021/10/norway-oslo-cityscape-from-ekebergparken-istk-1800x0-c-default.jpg" alt="Oslo Cityscape" style="width: 100%;"> | ||
<div style="position: absolute; bottom: 8px; left: 8px; color: white; background-color: rgba(0, 0, 0, 0.5); padding: 5px; font-size: 12px;"> | ||
Photo credit: Discover the World | ||
</div> | ||
</div> |
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# Pratical information | ||
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Some info here. | ||
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## Accommodation: | ||
The accommodation of the participants is the Scandic Victoria Hotel, Rosenkrantzgate 13, 0121 Oslo, Norway. The hotel is located in the city center of Oslo, close to the National Theatre. The National Theatre station is a train station well connected to the airport, and a metro station too, which conects to the University where the venue is located. | ||
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<iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d2000.1811813699874!2d10.73434347774496!3d59.91254037490197!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x46416e87820ed083%3A0xa9af7b88b9a6ff76!2sScandic%20Victoria!5e0!3m2!1sen!2sno!4v1725658228311!5m2!1sen!2sno" width="600" height="450" style="border:0;" allowfullscreen"></iframe> | ||
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## Venue: | ||
The Deep Learning workshop will be held at the Rosseland Centre for Solar Physics, located at the | ||
Institute of Theoretical Astrophysics (ITA), University of Oslo, Norway. | ||
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<iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d1998.6049576856876!2d10.7157174020996!3d59.93869706167309!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x46416de78d5b8f73%3A0x599b474329d7c3f3!2sInstitute%20of%20Theoretical%20Astrophysics!5e0!3m2!1sen!2sno!4v1725657988280!5m2!1sen!2sno" width="600" height="450" style="border:0;" allowfullscreen></iframe> | ||
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## Transportation: | ||
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### General information: | ||
The public transportation system in Oslo is very efficient and well connected. The main means of transportation are the metro, the tram, the bus, and the train. It also includes ferries and boats that connect the city with the islands in the fjord (in case you want to visit them). The metro is the most used means of transportation in the city and it is the easiest way to get around. Download the Ruter (https://ruter.no/) app to your from the app store to buy the tickets. You will have to associate a debit/credit card to pay for the tickets. The tickets are valid for all means of transportation in the city. For most of you the "7-day ticket" is the best option. The hotel and the venue are inside the "Zone 1", so please choose the "Zone 1" option when buying the tickets. | ||
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### From the airport to the hotel: | ||
The easiest way to get from the airport to the hotel is by train. The train station is located in the airport and the train takes you to the National Theatre station, which is located close to the hotel. The train ride takes around 45 minutes. Use the Ruter app to check the time table and the next train. Google maps also works but the timing is not as accurate. Usually regional trains with the tag "RE10", "RE11" or "R12" are doing this route. To buy a ticket from the Ruter app, you will have to add "Zone 1 to 4N" to your ticket. The train station is located in the airport, just follow the signs. Same idea for the return trip. | ||
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**Important note**: If your flight arrives late at night, regional trains might not be available. There is another option which is the "Flytoget" train. This train is more expensive but it runs until 00:50. If you arrive after this time, there is a bus service called "Flybussen" that runs even later. The bus stop is located outside the airport, just follow the signs. The bus takes you to the city center and you can take a taxi from there to the hotel. In any case, please share your flight details with us so we can help you with the transportation. | ||
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### From the hotel to the venue: | ||
The easiest way to get from the hotel to the venue is by metro. The metro station is located close to the hotel and the venue is located in the campus at the metro stop called Blindern. The metro ride takes around 15 minutes. Look for the sign "T-bane" to find the metro station. If you buy single tickets, remember to choose the "Zone 1" option. Take lines 4 or 5 in the direction of "Vestli" or "Sognsvann" and get off at the stop called "Blindern". | ||
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![Map of the metro system in Oslo](imgs/metro.png) |
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# Programme | ||
<!-- # Programme --> | ||
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<!-- The last version of the programme can be found here: --> | ||
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<!-- <iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vSX2gWNptsTDTVwK52gmw3upZAdg9R8DJZiXxeSVKxIl3Syn8CjqC2_WRi61K2s_1AHQXYAqf5I0_3u/pubhtml?gid=0&single=true&widget=true&headers=false" width="100%" height="100%" style="border:0; position:absolute; top:0; left:0; bottom:0; right:0;" allowfullscreen></iframe> --> | ||
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<!-- <iframe src="https://docs.google.com/document/d/1WTD64wFU8kiLUhPD-0amli0EFL8TRWiHVmEkwB8xsgw/pub?embedded=true" width=600 height=450 style="border:0; position:absolute; top:0; left:0; bottom:0; right:0;" allowfullscreen></iframe> --> | ||
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<!-- <iframe src="https://docs.google.com/document/d/1WTD64wFU8kiLUhPD-0amli0EFL8TRWiHVmEkwB8xsgw/pub?embedded=true" width="100%" height="100%" style="border:0;" allowfullscreen></iframe> --> | ||
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<!-- <style> | ||
body, html { | ||
margin: 0; | ||
padding: 0; | ||
width: 100%; | ||
height: 100%; | ||
overflow: hidden /* ; Hide overflow to prevent double scrollbars */ | ||
} | ||
.iframe-container { | ||
position: relative; | ||
width: 100%; | ||
height: 100vh; /* Full viewport height */ | ||
display: flex; | ||
justify-content: center; | ||
align-items: center; | ||
} | ||
.iframe-container iframe { | ||
width: 100%; | ||
height: 100%; | ||
border: none; /* Remove borders to avoid double scrollbars */ | ||
} | ||
</style> | ||
<div class="iframe-container"> | ||
<iframe src="https://docs.google.com/document/d/1WTD64wFU8kiLUhPD-0amli0EFL8TRWiHVmEkwB8xsgw/pub?embedded=true" allowfullscreen></iframe> | ||
</div> --> | ||
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<!DOCTYPE html> | ||
<html lang="en"> | ||
<head> | ||
<meta charset="UTF-8"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
<title>Embedded Document</title> | ||
<style> | ||
body, html { | ||
margin: 0; | ||
padding: 0; | ||
width: 100%; | ||
height: 100%; | ||
overflow: hidden; /* Prevent scrolling on the body */ | ||
/* font-family: Arial, sans-serif; Set a clean, default font */ | ||
} | ||
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.iframe-container { | ||
position: absolute; | ||
width: 100%; | ||
height: 100vh; /* Full viewport height */ | ||
display: flex; | ||
justify-content: center; | ||
align-items: center; | ||
background-color: #f9f9f9; /* Light background for better contrast */ | ||
} | ||
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.iframe-wrapper { | ||
width: 100%; | ||
height: 100%; | ||
box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Subtle shadow for depth */ | ||
} | ||
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.iframe-wrapper iframe { | ||
width: 100%; | ||
height: 100%; | ||
border: none; /* Remove borders to avoid double scrollbars */ | ||
border-radius: inherit; /* Inherit parent's border radius */ | ||
} | ||
</style> | ||
</head> | ||
<body> | ||
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<div class="iframe-container"> | ||
<div class="iframe-wrapper"> | ||
<iframe src="https://docs.google.com/document/d/1WTD64wFU8kiLUhPD-0amli0EFL8TRWiHVmEkwB8xsgw/pub?embedded=true" allowfullscreen title="Embedded Document"></iframe> | ||
</div> | ||
</div> | ||
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</body> | ||
</html> | ||
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Programme info here. |
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anyio==4.4.0 | ||
argon2-cffi==23.1.0 | ||
argon2-cffi-bindings==21.2.0 | ||
arrow==1.3.0 | ||
asttokens==2.4.1 | ||
async-lru==2.0.4 | ||
attrs==24.2.0 | ||
babel==2.16.0 | ||
beautifulsoup4==4.12.3 | ||
bleach==6.1.0 | ||
certifi==2024.7.4 | ||
cffi==1.17.0 | ||
charset-normalizer==3.3.2 | ||
click==8.1.7 | ||
colorama==0.4.6 | ||
comm==0.2.2 | ||
contourpy==1.2.1 | ||
cycler==0.12.1 | ||
debugpy==1.8.5 | ||
decorator==5.1.1 | ||
defusedxml==0.7.1 | ||
executing==2.0.1 | ||
fastjsonschema==2.20.0 | ||
filelock==3.13.1 | ||
fonttools==4.53.1 | ||
fqdn==1.5.1 | ||
fsspec==2024.2.0 | ||
ghp-import==2.1.0 | ||
gitdb==4.0.11 | ||
GitPython==3.1.43 | ||
h11==0.14.0 | ||
httpcore==1.0.5 | ||
httpx==0.27.0 | ||
idna==3.7 | ||
ipykernel==6.29.5 | ||
ipython==8.26.0 | ||
isoduration==20.11.0 | ||
jedi==0.19.1 | ||
Jinja2==3.1.4 | ||
joblib==1.4.2 | ||
json5==0.9.25 | ||
jsonpointer==3.0.0 | ||
jsonschema==4.23.0 | ||
jsonschema-specifications==2023.12.1 | ||
jupyter-events==0.10.0 | ||
jupyter-lsp==2.2.5 | ||
jupyter-server-mathjax==0.2.6 | ||
jupyter_client==8.6.2 | ||
jupyter_core==5.7.2 | ||
jupyter_server==2.14.2 | ||
jupyter_server_terminals==0.5.3 | ||
jupyterlab==4.2.4 | ||
jupyterlab_git==0.50.1 | ||
jupyterlab_pygments==0.3.0 | ||
jupyterlab_server==2.27.3 | ||
kiwisolver==1.4.5 | ||
Markdown==3.7 | ||
MarkupSafe==2.1.5 | ||
matplotlib==3.9.2 | ||
matplotlib-inline==0.1.7 | ||
mergedeep==1.3.4 | ||
mistune==3.0.2 | ||
mkdocs==1.6.1 | ||
mkdocs-get-deps==0.2.0 | ||
mkdocs-material==9.5.34 | ||
mkdocs-material-extensions==1.3.1 | ||
mpmath==1.3.0 | ||
nbclient==0.10.0 | ||
nbconvert==7.16.4 | ||
nbdime==4.0.1 | ||
nbformat==5.10.4 | ||
nest-asyncio==1.6.0 | ||
networkx==3.2.1 | ||
notebook_shim==0.2.4 | ||
numpy==2.1.0 | ||
overrides==7.7.0 | ||
packaging==24.1 | ||
paginate==0.5.7 | ||
pandas==2.2.2 | ||
pandocfilters==1.5.1 | ||
parso==0.8.4 | ||
pathspec==0.12.1 | ||
pexpect==4.9.0 | ||
pillow==10.4.0 | ||
platformdirs==4.2.2 | ||
plotly==5.23.0 | ||
prometheus_client==0.20.0 | ||
prompt_toolkit==3.0.47 | ||
psutil==6.0.0 | ||
ptyprocess==0.7.0 | ||
pure_eval==0.2.3 | ||
pycparser==2.22 | ||
Pygments==2.18.0 | ||
pymdown-extensions==10.9 | ||
pyparsing==3.1.2 | ||
python-dateutil==2.9.0.post0 | ||
python-json-logger==2.0.7 | ||
pytz==2024.1 | ||
PyYAML==6.0.2 | ||
pyyaml_env_tag==0.1 | ||
pyzmq==26.1.1 | ||
referencing==0.35.1 | ||
regex==2024.7.24 | ||
requests==2.32.3 | ||
rfc3339-validator==0.1.4 | ||
rfc3986-validator==0.1.1 | ||
rpds-py==0.20.0 | ||
scikit-learn==1.5.1 | ||
scipy==1.14.0 | ||
seaborn==0.13.2 | ||
Send2Trash==1.8.3 | ||
setuptools==73.0.1 | ||
six==1.16.0 | ||
smmap==5.0.1 | ||
sniffio==1.3.1 | ||
soupsieve==2.6 | ||
stack-data==0.6.3 | ||
sympy==1.12 | ||
tenacity==9.0.0 | ||
terminado==0.18.1 | ||
threadpoolctl==3.5.0 | ||
tinycss2==1.3.0 | ||
torch==2.4.0+cpu | ||
tornado==6.4.1 | ||
traitlets==5.14.3 | ||
types-python-dateutil==2.9.0.20240316 | ||
typing_extensions==4.9.0 | ||
tzdata==2024.1 | ||
uri-template==1.3.0 | ||
urllib3==2.2.2 | ||
watchdog==5.0.2 | ||
wcwidth==0.2.13 | ||
webcolors==24.8.0 | ||
webencodings==0.5.1 | ||
websocket-client==1.8.0 |