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In the experiments with natural selection, the number of individuals per species was much higher than in the experiments without natural selection from time step 10,000 (one-way ANOVA for all considered time steps, P = 0.0001; Tukey post hoc test, P < 0.05; Fig 5 ). Moreover, the results for the Selection and High Dispersal and the Selection and Low Dispersal experiments eventually (from time step 14000) converged toward those obtained for the Selection, Enforced Reproductive Isolation and Low Dispersal experiment (around 55 species with several thousand individuals per species, see Table 3 ). This convergence indicates that the three experiments involving natural selection exhibit the same long-term patterns.

Selection, Enforced Reproductive Isolation and Low Dispersal
Fig 5. The number of individuals per species (logarithmic scale) in the different simulation experiments (blue line, Selection, Enforced Reproductive Isolation and Low Dispersal experiment; red line, Selection and Low Dispersal experiment; green line, Selection and High Dispersal experiment; clay line, Selection and Low Dispersal experiment; magenta line, No Selection and High Dispersal experiment).

The higher stability of Selection in Enforced Reproductive Isolation and Low Dispersal compared to the four other experiments is due to the enforced reproductive isolation.

Selection in Enforced Reproductive Isolation and Low Dispersal

https://doi.org/10.1371/journal.pone.0137838.g005

Table 3. Average and standard deviation of the number of species for every experiment.

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Moreover, the species abundance distribution patterns observed in the three runs with natural selection follow a Fisher’s logseries ( Modal Scarf Other Worlds to See by VIDA VIDA sxUmgp
). This pattern was also shown in [ Tote Bag Azania Tote by VIDA VIDA bYjJ5eRvfn
]. Many large species (of more than 10,000 individuals) tend to persist for several thousand time steps showing the stability of these genomic clusters. By contrast, the two experiments without natural selection generate a large number of clusters (around 65,000; Table 3 ) that contain only two or three individuals each. These small clusters tend to persist for only few time steps and have species abundance distribution concentrated in the two first bins (see Fig 6 ), showing that no organization of genotype groups emerged.

Fig 6. Species abundance distribution in different experiments.

(A) Selection, Enforced Reproductive Isolation and Low Dispersal experiment; (B) Selection and Low Dispersal experiment; (C) Selection and High Dispersal experiment; (D)Selection and Low Dispersal experiment; (E) No Selection and High Dispersal experiment.

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Netflix Technology Blog
Learn more about how Netflix designs, builds, and operates our systems and engineering organizations

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Millions of people visit Netflix every day. Many of them are already Netflix members, looking to enjoy their favorite movies and TV shows, and we work hard to ensure they have a great experience. Others are not yet members, and are looking to better understand our service before signing up.

These prospective members arrive from over 190 countries around the world, and each person arrives with a different set of preferences and intentions. Perhaps they want to see what all the buzz is about and learn what Netflix is, or perhaps they already know what Netflix is and can’t wait to sign up and try out the service. Marketing, social, PR, and word of mouth all help to create awareness and convert that into demand. Growth Engineering collects this demand by helping people sign up, while optimizing for key business metrics such as conversion rate, retention, revenue, etc. We do this by building, maintaining, and operating the backend services that support the signup and login flows that work across mobile phones, tablets, computers and connected televisions.

Let’s take a look at what the Netflix sign up experience looks like for two different customers in two different parts of the world, each with different device types and payment methods. Barb is signing up on a set-top-box (STB) device in the United States and prefers to have her billing done through her cable provider. While Riko is signing up on an iPhone 7 in Japan and prefers to use a credit card.

The customer experience is remarkably different in each of these cases, but the goal is the same. We seek to offer the best possible signup experience to our prospective members while at the same time, remaining extremely lean, agile and efficient in our implementation of these disparate experiences.

Offering an amazing signup experience for thousands of devices in over 190 countries is an incredibly challenging and rewarding task.

The SignupFunnel

The signup funnel is where demand is collected. In general, the signup funnel consists of four parts:

In the signup funnel, we have a short time to get to know our users and we want to help them sign up as efficiently and effectively as possible. How do we know if we’re succeeding at meeting these goals? We experiment constantly . We use A/B testing in order to learn and improve how users navigate the signup funnel. This enables Growth Engineering to be a lean team that has a tremendous and measurable impact on the business.

Why experiment on the signupfunnel?

Every visit to the signup funnel is an opportunity to improve the experience for visitors wanting to learn more about Netflix. We’ve learned from experimentation that different customers have different needs and expectations.

Using a TV remote control to navigate the signup flow can be an onerous and time-consuming task. E.g. by leveraging our partnerships, we are able to offer a signup experience with almost no use of the remote control keypad. This enables us to offer a simple and convenient signup experience with integrated billing. The end result is a lower friction signup flow that has improved user experience and business metrics.

Browsers offer additional conveniences that can be leveraged. In particular, local payment options (e.g. paying using direct debit or local credit cards) and browser autofill enable us to offer an optimized signup experience that lets customers sign up for Netflix and start watching great content in just a few minutes.

As these examples highlight, there are many attributes that can be used to optimize a particular flow. By experimenting with different partnerships, payment methods, and user experiences, we are able to affect the membership base growth rate and ultimately, revenue.

How do we experiment on our signupfunnel?

Growth Engineering owns the business logic and protocols that allow our UI partners to build lightweight and flexible applications for almost any platform (e.g., iOS, Android, Smart TVs, browsers). Our services speak a custom JSON protocol over HTTP. The protocol is stateless and offers a minimal set of primitives and conventions that enable rapid development of features on almost any platform.

Before diving into core concepts, it’s useful to see where Growth Engineering’s services live within the Netflix microservice ecosystem. Typically, these microservices are implemented in Java and are deployed to AWS on EC2 virtual machines.

Growth Engineering owns multiple services that each provide a specific function to the signup funnel. The Orchestration Service is responsible for validating upstream requests, orchestrating calls to downstream services, and composing JSON responses during a signup flow. We assume requests will fail and use libraries like Hystrix to ensure we are latency and fault tolerant. This enables our customers to have an extremely resilient and reliable sign up experience.

The anatomy of a signup — a closerlook

Let’s walk through what it looks like to register for Netflix with a partner-integrated STB device.

Step 1: Request the registration page

The green diamonds and arrows show a successful request path for the registration page.

Step 2: JSON response

The UI can then interpret this response accordingly and render a UI as such:

Step 3: Send form details to the server and create an account

Step 4: JSON Response

As you can see, there is a lot of complexity abstracted away in a simple attempt to register for Netflix. In general, processing a request consists of 3 steps:

The JSON protocol also enables Growth Engineering to be a source of truth for all events pertaining to the signup funnel. This enables us to centrally collect and monitor all the core sign up related business metrics, thus enabling us to be nimble day-to-day.

What’s next?

As the stewards of the business logic for the signup funnel, Growth Engineering has an incredibly important role at Netflix. Our work directly affects the membership growth rate and as a result, directly impacts Netflix revenue. Although Netflix is more than two years into our journey as a fully global entertainment company, we are only just beginning to understand many of the complicated and intricate consumer preferences that will inform the next set of experiments aimed at improving the signup funnel. We are just beginning to unlock user experience improvements in our international markets.

Netflix has over 125 million members worldwide. The number of global broadband households is over 1 billion and the number of daily internet users is over 4 billion. Growth Engineering is key to making Netflix more accessible for people around the world. Gibbon Shorts for Men Libertine Libertine cb1yjVcr
and help us shape the future of global customer acquisition at Netflix.

Like what you read? Give Netflix Technology Blog a round of applause.

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Political corruption may also come in the form of manipulating policies, instituting rules and procedures, influencing the allocation of funding or other resources, or otherwise abusing a position of authority to increase or maintain the individual’s power, status, or wealth.

For example:

William, who has been Mayor of Small Towne for six months, has wanted membership at the Big Man in Towne Golf and Country Club for many years. The owner of ABC Construction wants trouble-free permits for the new apartment complex he intends to build over the next couple of years – something the city council and previous mayor refused to cooperate with.

The owner of the construction company greases the wheels by ensuring William is offered the country club membership he desires, and a relationship from which many deals will flow is cemented. In this example of corruption, William is using his power as Mayor for personal gain.

Judicial corruption refers to misuse, dishonesty, or exploitation by judges, who hold a position of power over a great many people and issues. This may occur through the offering and acceptance of bribes, whether for payment of money, or giving of favors. Judicial corruption may show as bias in the hearing and judgment of arguments during hearings or trial , with decisions clearly favoring one party over the other.

Ben is on trial for charges of rape , for an incident that occurred while he was at a large frat party nearly a year ago. Ben’s father, Arthur, is a retired attorney, and a wealthy and influential man in the community. Arthur had a covert chat with the Coline trousers Yellow amp; Orange Frankie Morello NXpJBK
while they were both at the golf course, making the point that the whole incident and criminal charges has ruined his son’s life. Arthur bemoaned the fact that, if convicted by the jury, Ben would have to register as a DENIM Denim trousers The Great ESoD65Z
, and his prospects for college were already shot.

After the jury found Ben guilty of the rape charges, the judge became responsible for sentencing him. Much to the family’s and jury’s shock, the judge sentenced Ben to only a few months in the county jail, even though the Cashmere Silk Scarf Blossom by VIDA VIDA OQP8aDqs
in the state clearly demanded a minimum of three years in prison. It soon became known that Arthur had been a financial and influential supporter of the judge for years. In this example of corruption, the judge’s actions were influenced by his relationship with the perpetrator’s father, which had been quite lucrative for him in the past.

Police corruption has historically been thought of as the acceptance of bribes, but it actually may be accomplished through deeper, and less obvious, means. Specifically, police corruption may involve selectively pursuing, or failing to pursue, a criminal investigation or arrest, for the purpose of financial gain, career advancement, or other personal gain. It may also involve defying procedures or a set code of conduct in order to ensure a suspect is convicted. This may be done by falsifying evidence , or lying about procedures used to obtain evidence.

Our paper is organized as follows. We first review how image segmentation can be thought of as image classification as well as the mathematical structure of conv-nets. We show that conv-nets can accurately segment the cytoplasms of bacterial cells and mammalian cell nuclei from fluorescent microscopy images. We show that integrating phase microscopy images with fluorescent microscopy images of a nuclear marker enables the accurate segmentation of the cytoplasm of mammalian cells. We show that by integrating conv-nets into an image analysis pipeline, we can quantify the growth of thousands of bacterial cells and track individual mammalian nuclei with almost no manual correction. We also show that incorporation of cytoplasmic segmentation masks provides a more accurate quantification of fluorescent protein localization kinase translocation reporters (KTRs) [ 6 ]. A quantitative comparison demonstrates that conv-nets are superior to other methods, both in terms of accuracy and curation time. We show how this approach can be used to both segment and classify different mammalian cell types in a co-culture using just the phase image and a nuclear marker. We highlight particular features of our work—image normalization, segmentation refinement with active contours, and receptive field size—which were critical for conv-nets to perform robustly on live-cell imaging data using relatively small training data sets. We also explore how much recent deep learning advances—namely dropout, batch normalization, and multi-resolution networks—impact segmentation performance [ 37 , Cashmere Silk Scarf Daisy m by VIDA VIDA HutYMjG
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]. All of the software and training data described here, as well as a Docker container, is available at https://simtk.org/projects/deepcell .

Image segmentation of single cells in microscopy images can be converted into an image classification problem [ 38 ]. Consider a manually annotated image where each pixel has been identified as either a cell boundary, cellular interior, or background (non-cell) pixel, as depicted in Fig 1a . By sampling a small region around each pixel and assigning the resulting image that pixel’s respective class, we can construct a training data set that contains representative images of each class. After the manually annotated image is reconstructed in this way, the image segmentation task is effectively reduced to finding a classifier that can distinguish between the three classes in the training data set and is robust enough to classify new images. Should such a classifier exist, any new microscope image could be segmented by decomposing it into small overlapping images, applying the classifier to each image, and then reassembling the classification prediction into a new image. This image can then be subjected to standard computer vision techniques to produce a segmentation mask—an image where the pixels for each cell’s interior have been assigned a unique integer label. Conv-nets can function as exactly such a classifier for data acquired from live-cell imaging experiments because they have both substantial representational power to encode the nonlinear relationship between images and classes, yet are general enough to provide robust predictions for new images [ 32 , 33 ].

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