Ciro Santilli (@cirosantilli) - New articles

Experimental physics Updated 2025-07-16

 View more

Experiment and theory are like the yin and yang: opposites, but one cannot exist without the other.

 Read the full article

History of physics Updated 2025-07-16

 View more

Good reading list: Abraham Pais Prize for History of Physics.

 Read the full article

Internet forum Updated 2025-07-16

 Read the full article

Invariance of the speed of light Updated 2025-07-16

 View more

This single experimental observation/idea is the basis for all of special relativity.

Special relativity is the direct result of people bending their backs to accommodate for this really weird fact.

 Read the full article

Isomer Updated 2025-07-16

 View more

Isomers were quite confusing for early chemists, before atomic theory was widely accepted, and people where thinking mostly in terms of proportions of equations, related: Section "Isomers suggest that atoms exist".

 Read the full article

Law of physics Updated 2025-07-16

 Read the full article

Jakob Schwichtenberg Updated 2025-07-16

 View more

Personal website: jakobschwichtenberg.com/

PhD at Karlsruhe Institute of Technology in 2019: www-kseta.ttp.kit.edu/fellows/Jakob.Schwichtenberg/ on the strong CP problem.

Books:

No-Nonsense Quantum Field Theory: A Student-Friendly Introduction by Jakob Schwichtenberg (2020)

 Read the full article

Japanese company Updated 2025-07-16

 Read the full article

Jasraj Updated 2025-07-16

 View more

This dude is amazing. He apparently has many devotional songs.

Video 1.

Pandit Jasraj and Ustad Zakir Hussain - 1987 - Haveli Sangeet, Malkauns, and Bhairavi

. Source.

 Read the full article

Mechanics Updated 2025-07-16

 View more

This section is more precisely about classical mechanics.

 Read the full article

Physics conference Updated 2025-07-16

 Read the full article

SQL tree traversal Updated 2025-07-16

 View more

Example: nodejs/sequelize/raw/tree.js

 Read the full article

CIA 2010 covert communication websites / 2012 Internet Census hostprobes Updated 2025-07-16

 View more

Hostprobes quick look on two ranges:

208.254.40:

... similar down

208.254.40.95	1334668500	down	no-response
208.254.40.95	1338270300	down	no-response
208.254.40.95	1338839100	down	no-response
208.254.40.95	1339361100	down	no-response
208.254.40.95	1346391900	down	no-response
208.254.40.96	1335806100	up	unknown
208.254.40.96	1336979700	up	unknown
208.254.40.96	1338840900	up	unknown
208.254.40.96	1339454700	up	unknown
208.254.40.96	1346778900	up	echo-reply (0.34s latency).
208.254.40.96	1346838300	up	echo-reply (0.30s latency).
208.254.40.97	1335840300	up	unknown
208.254.40.97	1338446700	up	unknown
208.254.40.97	1339334100	up	unknown
208.254.40.97	1346658300	up	echo-reply (0.26s latency).

... similar up

208.254.40.126	1335708900	up	unknown
208.254.40.126	1338446700	up	unknown
208.254.40.126	1339330500	up	unknown
208.254.40.126	1346494500	up	echo-reply (0.24s latency).
208.254.40.127	1335840300	up	unknown
208.254.40.127	1337793300	up	unknown
208.254.40.127	1338853500	up	unknown
208.254.40.127	1346454900	up	echo-reply (0.23s latency).

208.254.40.128	1335856500	up	unknown
208.254.40.128	1338200100	down	no-response
208.254.40.128	1338749100	down	no-response
208.254.40.128	1339334100	down	no-response
208.254.40.128	1346607900	down	net-unreach
208.254.40.129	1335699900	up	unknown

... similar down

Suggests exactly 127 - 96 + 1 = 31 IPs.

208.254.42:

... similar down

208.254.42.191	1334522700	down	no-response
208.254.42.191	1335276900	down	no-response
208.254.42.191	1335784500	down	no-response
208.254.42.191	1337845500	down	no-response
208.254.42.191	1338752700	down	no-response
208.254.42.191	1339332300	down	no-response
208.254.42.191	1346499900	down	net-unreach

208.254.42.192	1334668500	up	unknown
208.254.42.192	1336808700	up	unknown
208.254.42.192	1339334100	up	unknown
208.254.42.192	1346766300	up	echo-reply (0.40s latency).
208.254.42.193	1335770100	up	unknown
208.254.42.193	1338444900	up	unknown
208.254.42.193	1339334100	up	unknown

... similar up

208.254.42.221	1346517900	up	echo-reply (0.19s latency).
208.254.42.222	1335708900	up	unknown
208.254.42.222	1335708900	up	unknown
208.254.42.222	1338066900	up	unknown
208.254.42.222	1338747300	up	unknown
208.254.42.222	1346872500	up	echo-reply (0.27s latency).
208.254.42.223	1335773700	up	unknown
208.254.42.223	1336949100	up	unknown
208.254.42.223	1338750900	up	unknown
208.254.42.223	1339334100	up	unknown
208.254.42.223	1346854500	up	echo-reply (0.13s latency).

208.254.42.224	1335665700	down	no-response
208.254.42.224	1336567500	down	no-response
208.254.42.224	1338840900	down	no-response
208.254.42.224	1339425900	down	no-response
208.254.42.224	1346494500	down	time-exceeded

... similar down

Suggests exactly 223 - 192 + 1 = 31 IPs.

Let's have a look at the file 68: outcome: no clear hits like on 208. One wonders why.

It does appears that long sequences of ranges are a sort of fingerprint. The question is how unique it would be.

First:

n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq
t=$n-up-uniq.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq tmp"
time sqlite3 $t 'create table t (i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'

This reduces us to 2 million IP rows from the total possible 16 million IPs.

OK now just counting hits on fixed windows has way too many results:

sqlite3 208-up-uniq.sqlite "\
SELECT * FROM (
  SELECT min(i), COUNT(*) OVER (
    ORDER BY i RANGE BETWEEN 15 PRECEDING AND 15 FOLLOWING
  ) as c FROM t
) WHERE c > 20 and c < 30
"

Let's try instead consecutive ranges of length exactly 31 instead then:

sqlite3 208-up-uniq.sqlite <<EOF
SELECT f, t - f as c FROM (
  SELECT min(i) as f, max(i) as t
  FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t)
  GROUP BY grp
  ORDER BY i
) where c = 31
EOF

271. Hmm. A bit more than we'd like...

Another route is to also count the ups:

n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq-cnt
t=$n-up-uniq-cnt.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq-cnt tmp"
time sqlite3 $t 'create table t (cnt integer, i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select cnt as integer, str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'

Let's see how many consecutives with counts:

sqlite3 208-up-uniq-cnt.sqlite <<EOF
SELECT f, t - f as c FROM (
  SELECT min(i) as f, max(i) as t
  FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t WHERE cnt >= 3)
  GROUP BY grp
  ORDER BY i
) where c > 28 and c < 32
EOF

Let's check on 66:

grep -e '66.45.179' -e '66.45.179' 66

not representative at all... e.g. several convfirmed hits are down:

66.45.179.215   1335305700      down    no-response
66.45.179.215   1337579100      down    no-response
66.45.179.215   1338765300      down    no-response
66.45.179.215   1340271900      down    no-response
66.45.179.215   1346813100      down    no-response

 Read the full article

Galactic algorithm Updated 2025-07-16

 Read the full article

Hadron Updated 2025-07-16

 Read the full article

Monophyly Updated 2025-07-16

 View more

Basically the same as clade.

 Read the full article

runwayml/stable-diffusion Updated 2025-07-16

 View more

github.com/runwayml/stable-diffusion

Conda install is a bit annoying, but gets the job done. The generation quality is very good.

Someone should package this better for end user "just works after Conda install" image generation, it is currently much more of a library setup.

Tested on Amazon EC2 on a g5.xlarge machine, which has an Nvidia A10G, using the AWS Deep Learning Base GPU AMI (Ubuntu 20.04) image.

First install Conda as per Section "Install Conda on Ubuntu", and then just follow the instructions from the README, notably the Reference sampling script section.

git clone https://github.com/runwayml/stable-diffusion
cd stable-diffusion/
git checkout 08ab4d326c96854026c4eb3454cd3b02109ee982
conda env create -f environment.yaml
conda activate ldm
mkdir -p models/ldm/stable-diffusion-v1/
wget -O models/ldm/stable-diffusion-v1/model.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms

This took about 2 minutes and generated 6 images under outputs/txt2img-samples/samples, includining an image outputs/txt2img-samples/grid-0000.png which is a grid montage containing all the six images in one:

https://raw.githubusercontent.com/cirosantilli/media/master/Runwayml_stable-diffusion_a-photograph-of-an-astronaut-riding-a-horse.png

TODO how to change the number of images?

A quick attempt at removing their useless safety features (watermark and NSFW text filter) is:

diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index 59c16a1..0b8ef25 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -87,10 +87,10 @@ def load_replacement(x):
 def check_safety(x_image):
     safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
     x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-    assert x_checked_image.shape[0] == len(has_nsfw_concept)
-    for i in range(len(has_nsfw_concept)):
-        if has_nsfw_concept[i]:
-            x_checked_image[i] = load_replacement(x_checked_image[i])
+    #assert x_checked_image.shape[0] == len(has_nsfw_concept)
+    #for i in range(len(has_nsfw_concept)):
+    #    if has_nsfw_concept[i]:
+    #        x_checked_image[i] = load_replacement(x_checked_image[i])
     return x_checked_image, has_nsfw_concept


@@ -314,7 +314,7 @@ def main():
                             for x_sample in x_checked_image_torch:
                                 x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                                 img = Image.fromarray(x_sample.astype(np.uint8))
-                                img = put_watermark(img, wm_encoder)
+                                # img = put_watermark(img, wm_encoder)
                                 img.save(os.path.join(sample_path, f"{base_count:05}.png"))
                                 base_count += 1

but that produced 4 black images and only two unfiltered ones. Also likely the lack of sexual training data makes its porn suck, and not in the good way.

 Read the full article

Double bond Updated 2025-07-16

 Read the full article

Inner source Updated 2025-07-16

 View more

If you are going to do closed source, at least do it like this.

Basically the opposite of need to know for software.

 Read the full article

Jordan-Holder Theorem Updated 2025-07-16

 View more

Uniqueness results for the composition series of a group.

 Read the full article

Ciro Santilli @cirosantilli 37