CIA 2010 covert communication websites CGI comms variant Updated 2025-07-16
Later on, we've also come across some stylistic hits in IP ranges with apparent slight variations of the CGI comms pattern:
Since these are so rare, it is still a bit hard to classify them for sure, but they are of great interest no doubt, as as we start to notice these patterns more tend to come if it is a thing.
C. elegans body system Updated 2025-07-16
Browse S3 bucket on web browser Updated 2025-07-16
They can't even make this basic stuff just work!
Adversarial machine learning Updated 2025-07-16
activatedgeek/LeNet-5 run on GPU Updated 2025-07-16
By default, the setup runs on CPU only, not GPU, as could be seen by running htop. But by the magic of PyTorch, modifying the program to run on the GPU is trivial:and leads to a faster runtime, with less
cat << EOF | patch
diff --git a/run.py b/run.py
index 104d363..20072d1 100644
--- a/run.py
+++ b/run.py
@@ -24,7 +24,8 @@ data_test = MNIST('./data/mnist',
data_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8)
data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)
-net = LeNet5()
+device = 'cuda'
+net = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=2e-3)
@@ -43,6 +44,8 @@ def train(epoch):
net.train()
loss_list, batch_list = [], []
for i, (images, labels) in enumerate(data_train_loader):
+ labels = labels.to(device)
+ images = images.to(device)
optimizer.zero_grad()
output = net(images)
@@ -71,6 +74,8 @@ def test():
total_correct = 0
avg_loss = 0.0
for i, (images, labels) in enumerate(data_test_loader):
+ labels = labels.to(device)
+ images = images.to(device)
output = net(images)
avg_loss += criterion(output, labels).sum()
pred = output.detach().max(1)[1]
@@ -84,7 +89,7 @@ def train_and_test(epoch):
train(epoch)
test()
- dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True)
+ dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True).to(device)
torch.onnx.export(net, dummy_input, "lenet.onnx")
onnx_model = onnx.load("lenet.onnx")
EOFuser as now we are spending more time on the GPU than CPU:real 1m27.829s
user 4m37.266s
sys 0m27.562s activatedgeek/LeNet-5 use ONNX for inference Updated 2025-07-16
Note that:
- the images must be drawn with white on black. If you use black on white, it the accuracy becomes terrible. This is a good very example of brittleness in AI systems!
- images must be converted to 32x32 for
lenet.onnx, as that is what training was done on. The training step converted the 28x28 images to 32x32 as the first thing it does before training even starts
We can try the code adapted from thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/ at lenet/infer.py:and it works pretty well! The program outputs:as desired.
cd lenet
cp ~/git/LeNet-5/lenet.onnx .
wget -O 9.png https://raw.githubusercontent.com/cirosantilli/media/master/Digit_9_hand_drawn_by_Ciro_Santilli_on_GIMP_with_mouse_white_on_black.png
./infer.py 9.png9We can also try with images directly from Extract MNIST images.and the accuracy is great as expected.
infer_mnist.py lenet.onnx mnist_png/out/testing/1/*.png MineDojo Updated 2025-07-16
University of Oxford student newspaper Updated 2025-07-16
They actually have two The Oxford Student and Cherwell. As brilliantly highlighted in this first of April piece:
Related:
- www.thestudentroom.co.uk/showthread.php?t=1167619 "OxStu vs. Cherwell"
Oxshag Updated 2025-07-16
College puffer jacket Updated 2025-07-16
E.g.: thecollegestore.co.uk/products/ladies-oxford-college-puffer-jacket?variant=40590030864549 Black with 5 rows, on left chest "colege name", logo, "Oxford", and right chest optional initials (or sometimes other identifiers/nicknames) to help distinguish from all the other people's identical clothes.
This has a whitelabel version: www.workweargiant.co.uk/product/result-urban-holkham-down-feel-jacket/, the name appears to be "Holkham Down Feel Jacket".
If you look 20 and wear one of those, it's almost an ID, you can get anywhere that does not require a key card, porters won't look at you twice!
- www.oxfordstudent.com/2019/03/25/in-opposition-to-stash/ In opposition to stash by Morgan Jones (2019), basically because university is your last chance to wear what you want on many professions.
Oxford slang Updated 2025-07-16
Bibliography:
Varsity (Cambridge) Updated 2025-07-16
Ubuntu feature request Updated 2025-07-16
Ubuntu HOWTO Updated 2025-07-16
Large language model Updated 2025-07-16
Trinity term Updated 2025-07-16
Like the U.S.' summer term.
E-learning system prior to Canvas: weblearn.ox.ac.uk/portal. Appears fully custom and closed source?
Hilary term Updated 2025-07-16
Like the U.S.' spring term.
Michaelmas term Updated 2025-07-16
TensorFlow quantum Updated 2025-07-16
Version of TensorFlow with a Cirq backend that can run in either quantum computers or classical computer simulations, with the goal of potentially speeding up deep learning applications on a quantum computer some day.
SymPy special function Updated 2025-07-16
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