Manual Over the Edge (Its Not Random)

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Lord Huron - The Night We Met

I really like the OpenVino architecture, which lets you use different OpenVino plugins depending on your target. This will certainly help in the future. With regards to findinging the right OpenVino package for your Raspberry, I recommend visiting the Intel download center.

Intelligence at the Edge Part 4: Edge Node Security | Analog Devices

This is a more complicated process than the one in the Google environment. Which makes sense, as the more options you have, the more complicated the setup is. Once you have installed the OpenVino environment, you can play with a really cool demo that chains three models. In the picture below, you can see how they work. The first model detects the car. Then a second model detects the license plate. The third model reads the plate. To port our frozen model on the Raspberry, we have to use the Model Optimizer. The model optimizer is not on the Raspberry, which is why you need OpenVino on your desktop.

The model optimizer is a very powerful converter, and comes with many different options. You have generic parameters and also specific parameters for the different ML frameworks. Once you have the two files. You should see something like this:. I tried to understand how to connect a pair of cameras and to use the depth module. I also tried to use net. I was unsuccessful. Both SoCs have very impressive specifications and plenty of processing power. It handles million pixels per second. The chip uses over 20 hardware accelerators to perform optical flow and stereo depth.

On top of that, 16 programmable bit vector processors enable you to run multiple concurrent vision application pipelines. Unfortunately, Google gives far less information about the edge-tpu. They do share that the graphic processing unit of the edge-tpu has 4 shaders and that it handles a staggering 1. In addition, Google claims the video processing unit is able to process 4kp60 in H. On the paper the Intel SoC seems more powerful that the edge-tpu. The performance-per-watt of both devices seems to be really good. The two pictures below show the power consumption of the two devices at rest.

Google kindly notifies us during the setup that the accelerator might become very hot. Most likely the Pi Cam specifications and the USB-2 limitation make it hard to put the devices under enough stress. To load the devices I made a very short python program with one loop making the inference always with the same image.

The idea was to lower the incidence of the usb2 bus but a quick wireshark session showed that the image moves every time we make an inference.

I measured 70ms per inference on a mobilenet v1 for the Myriad, 60ms for edge-tpu on a mobilenet v2, and ms on the Raspberry alone. The mobilenet v2 3. Therefore, on the Raspberry Pi, the two devices would be in the same ballpark, with a small lead for the Myriad. It is important also to keep in mind that python is not the best option for performance. Considering the specifications of the two devices, the performance-per-dollar ends up being quite similar.

The design factors are also very similar, making it easy to integrate the usb keys in any projects. The V2 covers only one. The processing power of both devices allows developers to handle a broad scope of use cases. The many pre-compiled networks make it easy to get quick and good results. Still, fully quantizing your own networks remains an advanced task. The conversion requires an advanced understanding of your network and how the operations work. This implies that the Myriad will produce more accuracy.

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Intel and Google have clearly taken two very different approaches. I really like how all components fit together. Intel, on the other hand, offers Intel intermediary representation and Openvino plugins that developers can use to optimize their networks to run on all kind of hardware. For each network model we simulate networks and then simulate Susceptible-Infected-Recovered SIR epidemics per simulated network. Simulated networks are compared with the empirical network in terms of network features and simulated epidemic features.

Create a new random edge in networkx

We use three kinds of empirical contact networks which we further describe below. All networks are assumed symmetric.

The high school data [ 29 ] describes the cumulative time in close proximity between pairs of people over one day in a U. Networks derived from this data provide examples of contact networks relevant to infections where close proximity, but not necessarily physical contact, is important.

Such SIR-type infections include corona virus, influenza, norovirus, rhinovirus, varicella and measles. Pertussis i. In total, in this data there are people, comprised of students, 73 teachers, 55 staff, and 5 other individuals. One person has no contacts. By specifying a lower bound c for contact duration we can define a network in which a tie represents total contact of at least c time units. For this study we consider networks given by 75, 60 and 6 minute durations referred to as HS75, HS60 and HS6 respectively , which give a range of densities. Clearly the network ties from larger c are a subset of the ties from smaller c.

We use these three networks to illustrate the effects of increasing network density and connectivity. Details of the networks are provided in Table 1. To confine comparisons of simulated epidemics to similarly sized networks, isolates have been removed from HS75 and HS60 networks. Mean node degree increases from 2. We also show maximal clique sizes.

A maximal clique is a clique that cannot be made larger by including an adjacent node i. Maximal clique sizes range from 1 to 4 nodes for HS75, to between 1 and 25 nodes for HS6. Loosely, HS75 is a sparse network with small cliques while HS6 is dense with high mean numbers of contacts and large cliques.

The relationships network of Bearman et al. If a student reported having a special romantic relationship in the last 18 months, they were asked to describe their three most recent relationships including any current ones and up to three individuals with whom they had a non-romantic sexual relationship in the previous 18 months. A network tie represents a romantic, or a non-romantic sexual, relationship between students of the high school or between a student of the high school and another of the feeder middle school.

Our version of the network, shown in Fig 2 , was manually recoded starting from Fig 2 of Bearman et al. This contact network is relevant for infections where transmission involves intimate or sexual contact. A network tie indicates a romantic or non-romantic sexual relationship was reported by one of the incident nodes.

Gender is denoted by node colour blue-male, pink-female. This version of the network was manually re-coded starting from Fig 2 of Bearman et al. In the relationships network there are nodes and edges, including one male-male edge and one female-female edge. If one ignored those two same-sex edges the network would be bipartite, although we illustrate more general models here.

Notably, the network has only one triangle, one 9-star and three overlapping 4-cycles. In particular, clustering is not a significant feature of the network. For this study we use the same empirical PWID network described previously [ 24 ] and shown in Fig 3. A tie represents two people have engaged in injecting behaviour at the same place and time within three months prior to interview. The population of interest are in three urban neighbourhoods in Melbourne, Australia.

A number of personal details are known for each respondent. This contact network is relevant for infections involving blood-to-blood contact. Hepatitis B is an example of an infection that can be approximated as SIR-type. Waves are indicated by shade, from wave 0 black to wave 2 light gray. Importantly, although the data collection used network-based methods, a complete network census was not performed.

See Rolls et al. For the high school networks we consider several configuration-type models and an exponential random graph model ERGM. For each network model we generate a random sample of networks. Some of the subgraphs have been proposed elsewhere [ 12 , 15 ], others help to mitigate the restriction that modelled triangles cannot share edges, and others are motivated by hypotheses on human tie formation.

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Fig 4 shows the subgraphs, with node colour used to denote the various node roles. The number of node roles is shown in column three. For each subgraph, node roles are distinguished by node colour. The number of node roles for each subgraph is shown in column 3. The standard configuration model [ 2 , 3 ] and the edge-triangle model [ 12 , 15 ] have been proposed previously.

Our first variation of the edge-triangle model adds the subgraphs of four nodes square, diamond, 4-clique are described in [ 15 ] which also provides the relevant generating functions. The node role sequence is a 6-tuple for each node.