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I shouldn't get lost this time! Using a piece of software called Gartrip, I plotted over 100 way points onto my GPS, directly from the trail marked out on an Ordnance Survey map. These way points formed a route, so that my GPS should be able to point me directly along the path at least according to the maps. In addition, I took my usual map bag and compass.
The Weavers Way is very different from the Peddars Way. It is far from straight, and takes lots of turns. They are marked, but its so easy to miss a little disc.
Did I get lost today? Despite the GPS, I did take a few wrong turns on the marshes, as the trail was so faint - but at least the GPS quickly informed me so I could turn back.
Good Friend Andy and I ploting our groups next route:-) Looking for Fun! My Brother Tommy took this picture!
18:00:02 up 33 days, 23:53, 0 users, load average: 0.30, 0.34, 0.38 | temp=42.2'C | Start
18:00:12 up 33 days, 23:54, 0 users, load average: 0.70, 0.43, 0.41 | temp=42.8'C | SID plot Finished
14:00:01 up 9 days, 20:31, 0 users, load average: 0.29, 0.59, 0.69 | temp=42.8'C | Start
14:00:09 up 9 days, 20:31, 0 users, load average: 0.51, 0.63, 0.70 | temp=43.3'C | SID plot Finished
Here, we aim at measuring the IP-level neighborhood of internet core routers in a rigorous way.
We proceed as follows. First, we send traceroute probes from many monitors distributed in the internet towards a given target router. Then we consider the last but one IP adress of each traceroute measurement as a neighbor of the target.The underlying idea is that, if we use sufficiently many monitors, distributed enough on the internet, then we will discover all the neighbors of the target.
However, because of erroneous information delivered by traceroute, we may also discover fake neighbors.For instance, if there exist two paths m-a-b-c-t and m-a-d-t from monitor m to target t, the discovered path may be m-a-b-t if traceroute probes follow twice the first path then the second one. In this case, we see b as a neighbor of t, which is a mistake.
The appearance of such fake neighbors is due to routing dynamics, which often occurs because of load balancing. See also this video and this one.
Notice however that, if all paths have the same length, the fact that traceroute may mix different routes does not lead to observation of fake neighbors anymore.
We therefore send several probes from each monitor to the selected target and discard monitors from which we observe routes of different length.One question rises: will there be enough monitors left to discover all the neighbors of the target?
For the above plot, we conducted a measurement involving 477 monitors and 8071 random targets. Each traceroute from each monitor to each destination is repeated 10 times. For each destination, we computed the number of monitors whose routes to this destination all have the same length, and we plotted the cumulative distribution of this value. In other words, a point with coordinates (x,y) means that exactly x targets obtained all traceroutes with the same length from y monitors or less.
The plot shows that only 980 targets out of 8 071 (roughly 12%) have less than 350 monitors producing all its traceroute of the same length. Using simulations (not presented here), we have provided strong evidence that such a number is large enough to discover all neighbors of the target, as long as the degree of the target is not too high (typically 50 or less). Thus, our method is able to produce complete and exact neighborhood of most of core routers, and it is robust to routing dynamics.
11:00:02 up 4 days, 16:19, 0 users, load average: 0.52, 0.61, 0.66 | temp=41.2'C | Start
11:00:10 up 4 days, 16:19, 0 users, load average: 0.74, 0.66, 0.68 | temp=42.2'C | SID plot Finished
The Plot 101 Panel, offering advice to aspiring writers, taking place in the foyer of the Ether conference venue.
Panellists, from the left, between the two audience members:
David Witteveen (in front of the whiteboard), Amanda Pillar, Richard Harland (in the hat) and Trudi Canavan.
Continuum 9 Science Fiction Convention.
Melbourne, Victoria, Australia
Dubbed "Plot Realignment Pullover" (Flurbereinigungspulli) by the family! Many different yarns were used for the module plots, all 49 of them having a different pattern. There is no defined front or back.
The project is just off Shoolagiri. Shoolagiri is part of Bangalore -- Chennai Industrial Corridor. The 6-lane work of the Bangalore -- Chennai Highway is already underway. This is the beginning of the technological future in this corridor.
Features
Adjoining a Natural Lake
Avenue and Fruit Bearing Trees
Eco Cottage and Clubhouse
Security
Few minutes' drive from Bangalore Electronic City
Neighbourhood
TIDEL IT SEZ
Maria Convent
Bangalore -- Chennai Industrial Corridor
Proposed 5000 Acre SIPCOT Phase III
Bio- Technology SEZ
For more details, visit our website: www.graterbangalore.com/lakedew.html
The modularity is widely used to evaluate the quality of a partition of a graph in communities. Each community contributes to the global modularity according to the formula below, where m is the number of links of the graph, e is the number of links inside a given community C and a is the number of links with at least one extremity inside C:
We extracted communities from a co-authoring network obtained from [1] and we used the Louvain method to compute the communities. On the plot, each level 1 dot corresponds to one community and gives its own contribution to the modularity versus its size. There is a strong correlation between the size and the contribution of a community which is typical of the resolution limit problem: small communities must be merged in order to maximize the modularity. See [3] for formal details on this assertion.
This is strengthened by the other levels communities. Level 2 communities correspond to all communities obtained when decomposing level 1 communities, i.e., subcommunities of the network. Level 3 are sub-subcommunities, and so on. The more we decompose the network, the smaller the communities are and the smaller their contribution to the global modularity.
Maximizing the modularity is interesting but if one wants to understand the structure of a network, the maximal modularity partition hides a lot of information which can only be unraveled by looking at the complete hierarchy of the community structure.
[1] Arxiv dataset
[2] Louvain method for community extraction
[3] S. Fortunato and M. Barthélemy, Resolution limit in community detection, PNAS January 2, 2007 vol. 104 no. 1, 36-41
14:00:01 up 4 days, 20:31, 0 users, load average: 0.55, 0.73, 0.76 | temp=42.8'C | Start
14:00:09 up 4 days, 20:31, 0 users, load average: 1.01, 0.82, 0.79 | temp=43.3'C | SID plot Finished
Plot de Pvc para suelo técnico en exterior.
Adobe Ilustrator.
Polygroup renovando sus fichas técnicas.
During WW2, the cliffs had observation towers to spot incoming enemy aircraft. The details were given to the people in the plotting room who would try and predict where the craft would go. This was then passed on to the anti-aircraft guns who would fire in the predicted direction.
The little door leads to an escape hatch! It's blocked up though.
Dubbed "Plot Realignment Pullover" by the family! Many different yarns were used for the module plots, all 49 of them having a different pattern. Box-type pattern, there is no front or back.
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
19:00:01 up 2 days, 56 min, 0 users, load average: 0.83, 0.78, 0.76 | temp=41.7'C | Start
19:00:09 up 2 days, 56 min, 0 users, load average: 1.08, 0.83, 0.78 | temp=41.7'C | SID plot Finished
Many real-world networks can be represented as large graphs. Computational manipulation of such large graphs is common, but current tools for graph visualization are limited to datasets of a few thousand nodes.
These graphs contain sets of highly connected nodes that we call “communitiesâ€Â. Furthermore, these communities often have their own parts which are more connected than the rest that can be viewed as “sub-communitiesâ€Â. We used the Louvain method to extract communities and sub-communities from a sample network obtained from Arxiv dataset. We also used GUESS which is a graph exploration tool that contains an interpreted language (Gython) combined with a graphical front-end.
Using extracted hierarchical clustering dendrogram from Louvain method, we developed a tool which visualizes different hierarchical partitions of graph. Also, it allows us to manually merge and unmerge nodes into and from a community.
The plot shows the five levels of the decomposition, the smallest graph being the one between the communitiues whose decomposition maximizes the modularity according to Louvain method.