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To see where on a map i find is fun, in a geeky investigative sort of way ;)
Not very often we get to get a glimpse into where a particular bird has been, and therefore have a good idea of probability as to where it might be going.
It would seem this particular Caspian Tern was likely heading back south after nesting this year, at same location where it was hatched.or ??....
Please feel free to comment, or share your thoughts and insights.
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14:00:01 up 6 days, 19:19, 0 users, load average: 0.68, 0.66, 0.71 | temp=41.7'C | Start
14:00:09 up 6 days, 19:19, 0 users, load average: 0.78, 0.68, 0.72 | temp=41.7'C | SID plot Finished
Dolly Rae Star – Gong Bath
Sound Plotting event • 20th August 2022 • Stanmer Organics, Stanmer Park, Brighton
Photo credit: Joshua Le Gallienne
Set in the verdant sprawling plots of Stanmer Organics and its space age centrepiece The Earthship, Sound Plotting is a day of site specific sound art, interactive installations, deep listening, sound walks, improvised music and pop up performances. Come and explore the hidden and apparent sonic landscapes of an incredible site and uniquely inspiring artistic and fun environment. Organised by: Lost Property, Sound Art Brighton, The Rose Hill, Safehouse, Yellow Door Collective, Ceremonial Laptop.
14:00:01 up 30 days, 19:53, 0 users, load average: 0.64, 0.52, 0.45 | temp=42.2'C | Start
14:00:06 up 30 days, 19:53, 0 users, load average: 0.75, 0.54, 0.46 | temp=43.3'C | SID plot Finished
02:00:01 up 35 days, 7:53, 0 users, load average: 0.30, 0.38, 0.40 | temp=41.7'C | Start
02:00:06 up 35 days, 7:53, 0 users, load average: 0.36, 0.39, 0.41 | temp=42.2'C | SID plot Finished
14:00:01 up 11 days, 20:31, 0 users, load average: 0.34, 0.67, 0.74 | temp=42.2'C | Start
14:00:09 up 11 days, 20:31, 0 users, load average: 0.47, 0.69, 0.75 | temp=42.8'C | SID plot Finished
Plots dotting the landscape...don't remember exactly where this was taken...somewhere between T.O and Denver.
traceroute is a tool that gives the internet path followed by your packets towards a given destination, under the form of a series of IP addresses. Consider a given set of destinations, and let us call the result of a traceroute towards each of these destinations a measurement round. We are interested in studying how the set of IP addresses we can observe in a measurement round evolves with time. Therefore, we repeat these measurement rounds periodically (every 15 minutes approximately), using a fixed destination set (for more details about these measurements, see this paper).
For each IP address we see at least once during our measurements, we record two things: the number of rounds in which we have observed it, and its number of appearances, i.e. rounds in which the IP is present, but was not present in the round before. For instance, an IP address that we see at rounds 1, 5,6,7 and 10 has been observed in 5 distinct rounds, and has appeared three times.
We then make the above plot: each dot corresponds to an IP address. The coordinate of the dot on the x-axis is the number of rounds the IP address was observed in; its coordinate on the y-axis is its number of appearances. Surprisingly, the plot exhibits a clear geometric shape: we can see a triangle and a circle-like shape in it. These shapes can however be explained.
By definition of the plot, no dot can appear outside of the triangle: no IP address can appear a larger number of times than the number of rounds it was observed in (therefore we cannot have y > x); conversely, no IP address can appear a larger number of times that the number of rounds it was not observed in, since an appearance is defined as a round in which the IP address is not observed, followed by a round in which it is observed (therefore we cannot have y > 4676 - x, 4676 being the total number of measurement rounds). This defines the borders of the triangle.
The circle is in fact a parabola. Consider an IP address that was observed in exactly x distinct rounds during our measurements. If we suppose the rounds this address was observed in were chosen at random among our 4676 measurement rounds, then we can compute the expected number of appearances of this address. A given round corresponds to an appearance with the probability that the address was observed in this round, multiplied by the probability that it was not observed in the previous round, which gives (x / 4676) * (4676 - x / 4676). To obtain the number of expected number of appearances, just multiply this probability by the total number of rounds, giving the equation of the parabola.
The fact that the parabola can clearly be observed means that a large number of IP addresses seem to behave randomly in our observations: they appear the same number of times as they would if they were observed at random times. Dots above the parabola correspond to addresses that tend to blink on and off more than expected; finally, a large number of dots below the parabola mean that many IP addresses tend to be more stable than expected: they appear fewer times than expected, meaning that when they appear they tend to stay there a large number of consecutive rounds before disappearing.
So now if I could just dig under that fence I'd be free!!! Meerkats are perhaps THE most photogenic animals. But somehow they always look like they are plotting something :)
My car (in black) is sitting on the side street between the plot and my neighbours (shown on Vorplan as WEG)
If I put a mail box up I could get mail. Not really but the numbers plot numbers are a nice touch. I better stop decorating and get to weeding.
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Het resultaat is niet geheel zoals ik hoopte, maar misschien toch wel aardig om te zien. Plottijd: +- 45 min.
Network robustness is a very important question in many contexts: in communication networks, equipment failures may disrupt the network and prevent users from communicating; in distribution networks (such as power or water distribution), breakdowns can prevent service to customers; also, diseases can spread in contact networks, and vaccinating people (thus in a sense removing them from the network of the disease's spread) can prevent the infection from reaching a large number of persons.
Many papers have studied this question by considering the size of the largest connected component (i.e. the largest set of nodes such that there exists a path between any two nodes) as a criteria for evaluating the robustness of a network: the larger the size of this component, the larger the number of users who can communicate (or the number of people a disease can infect), and hence the more robust the network.
Most real-world networks have heterogeneous degree distributions (i.e. they have a large number of nodes with small degrees, a small number of nodes with a very high degree, and all intermediate cases), hence studying the robustness of power-law random networks seems relevant. This plot shows the resilience of such a network in face of failures and malevolent attacks. Failures are thought to be random events, and are modeled by random removing of nodes. Attacks aim at disrupting quickly a network, and are modeled by the removal of nodes by decreasing order of their degree. In both cases, the plot shows the fraction of nodes remaining in the largest connected component, as a function of the number of removed nodes.
The behaviors of the network in both cases are very different: while the plot for failures decreases very smoothly and reaches 0 only when almost all nodes have been removed, the plot for attacks decreases very sharply and reaches 0 when only a small fraction of the nodes has been removed. This seems to imply that networks with heterogeneous degree distributions are very resilient to failures, but very fragile against attacks. This would be due to the high degree nodes, which hold the network together. In case of failures, very few of these nodes are removed, and the network resists, but in case of attacks the removal of these nodes disrupts the network very quickly.
The third plot, which shows the effect of almost random attacks, can mitigate this conclusion. These attacks simply consist in removing randomly nodes that have degree at least 2. We can observe that, though it is not as efficient as a classical attack, this strategy succeeds in disrupting the network far more quickly than random failures. This shows that the weakness of networks with heterogeneous degree distributions in face of attacks is also caused by their large number of nodes of degree one.
See this survey for more information.
08:00:01 up 4 days, 14:31, 0 users, load average: 0.79, 0.82, 0.74 | temp=42.2'C | Start
08:00:09 up 4 days, 14:31, 0 users, load average: 0.89, 0.84, 0.75 | temp=42.2'C | SID plot Finished
Dubbed "Plot Realignment Pullover" by the family! Many different yarns were used for the module plots, all 49 of them having a different pattern. Has no defined front or back.
Possibly as a result of the fine numberwang baby grow we, as masters of the mathematical arts, bought our son.
10:00:01 up 4 days, 15:19, 0 users, load average: 0.50, 0.59, 0.67 | temp=41.2'C | Start
10:00:09 up 4 days, 15:19, 0 users, load average: 0.81, 0.65, 0.69 | temp=41.2'C | SID plot Finished
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