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The Revere Mobile platformwas built with the grassroots organizer in mind and has become the central dashboard for campaign performance indicators. Revere offers uploading and exporting capability, list segmentation, scheduling, personalization, polling and trivia functionality, as well as easy-to-use reporting.

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, a maritime technology company, provided early testing and support and is currently working to encourage data contributions from the international yachting community. Ongoing participation from Toni Pons S Valencia Wraparound Espadrille Wedge Stone Fabric 2MoGIu
, a provider of marine navigation software, helped kickstart the stream of data from a crowd of mariners.

The crowdsourced bathymetry database now contains more than 117 million points of depth data, which have been used by hydrographers and cartographers to improve chart products and our knowledge of the seafloor. NOAA, working with George Mason University, is using the database depths to assess nautical chart adequacy, determine when areas require updated survey information, and identify chart discrepancies before an incident occurs. The Canadian Hydrographic Service used this dataset to update several charts of the Inside Passage, a network of coastal routes stretching from Seattle, Washington, to Juneau, Alaska.

Data are contributed to the database through a variety of trusted sources (e.g., partner companies, non-profit groups)—referred to as “trusted nodes”—that enable mariners to volunteer seafloor depths measured by their vessels. Contributors have the option to submit their data anonymously or provide additional information (vessel or instrument configuration) that can enrich the dataset. The trusted node compiles the observations and submits them to the crowdsourced bathymetry database, where anyone can access the near real-time data for commercial, scientific, or personal use.

NOAA invites maritime companies to support this crowdsourcing effort in their systems by making it simple for users to participate. For example, Rose Point Navigation Systems further promoted the IHO crowdsourced bathymetry initiative by moving the option to collect and contribute bathymetry data to a more visible section of their program options menu.

By submitting crowdsourced bathymetry data, mariners provide a powerful source of information to supplement current bathymetric coverage. Nautical charts need to be updated as marine sediments shift due to storm events, tides, and other coastal processes that affect busy maritime zones along the coast. Crowdsourced bathymetry data helps cartographers determine whether a charted area needs to be re-surveyed, or if they can make changes based on the information at hand. In some cases, crowdsourced bathymetry data can fill in gaps where bathymetric data is scarce, such as unexplored areas of the Arctic and open ocean and also shallow, complex coastlines that are difficult for traditional survey vessels to access. Crowdsourced bathymetry data is also used to identify dangers to navigation, in which case NOAA can issue a Notice to Mariners about the navigation hazard within 24 hours.

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

At Netflix we engage in what we call consumer science: we test new ideas with real customers, at scale, and we measure for statistically significant differences in how they engage with our product. Are members staying with the service longer? Are they instantly watching more TV shows and movies from us?

As an employee, the results of these tests are more important than your confidence in what the outcome will be, what your title is, or your ability to persuade. I’ve seen even our best product minds bet wrong on such tests on occasion. We absolutely believe we couldn’t build one of the PAROSH Long Dresses Red F5UFXuO
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Job number one for our product-focused engineers is to effectively innovate for Netflix members. The product we built in 2006 would not satisfy our members today. The best product in our market in 2015 will be far better than Netflix is today. It is our fundamental challenge to figure out what a better product can be on behalf of our members, and to build it.

Innovation involves a lot of failure. If we’re never failing, we aren’t trying for something out on the edge from where we are today. In this regard, failure is perfectly acceptable at Netflix. This wouldn’t be the case if we were operating a nuclear power plant or manufacturing cars. The only real failure that’s unacceptable at Netflix is the failure to innovate.

So if you’re going to fail, fail cheaply. And know when you’ve failed, vs. when you’ve gotten it right.

Product development at Netflix starts with a hypothesis, which typically goes something like this:

Algorithm/feature/design X will increase member engagement with our service, and ultimately member retention.

The idea may be a way to increase the relevance of our search results, a new design for device UIs, or a new feature, such as showing members what their Facebook friends are watching from Netflix. This is the crucial first step in our creative process, from which any improvement we can hope to deliver starts. Our intuition and imagination in how better to serve our members fuels our entire product development approach.

The second step is to design a test that will measure the impact of the hypothesis. Sometimes this simply means build it, but often we can build a prototype more quickly that captures the essence of the concept. Maybe the back end isn’t fully scalable; maybe it lacks polish or all of the bells and whistles we’d like to include if we roll it out for everyone.

\begin{aligned} 0 \le \frac{\gamma (\ell )}{\ell } \le 1-A_1, \end{aligned}
\begin{aligned} -L \le \sum _{w \le \ell \le z} \frac{\gamma (\ell )\log \ell }{\ell } - \log \frac{z}{w} \le A_2 \end{aligned}
\begin{aligned} \sum _{j < R/n} \mu ^2(j)h(j)G\left( \frac{\log jn}{\log R} \right)= \mathfrak {S} \log \frac{R}{n} \int _0^1 G\left( \frac{\log (R/n)}{\log R} \left( \frac{\log n}{\log (R/n)} + x\right) \right) dx\\\quad + O(\mathfrak {S}LG_{\max }), \end{aligned}
\begin{aligned} \mathfrak {S} := \prod _\ell \left( 1 - \frac{\gamma (\ell )}{\ell } \right) ^{-1} \left( 1 - \frac{1}{\ell } \right) . \end{aligned}


This is stated in [ Sam Edelman Paris 2 Embroidered Loafer Mule Light Blue Fp9Cw
, Lemma 3.5] as a direct consequence of [ 6 , Lemma 4]. \square

Our main claim of this section is expressed in the following lemma.

\begin{aligned} G^{(m)}_d(\mathbf {t_i}) := F\left( t_1, \ldots , t_{m-1}, \frac{\log (R/d)}{\log R} \left( \frac{\log d}{\log R/d}+t_m\right) , t_{m+1}, \ldots , t_k\right) \end{aligned}
\begin{aligned} F_{\max } := \sup _{(x_1, \ldots , x_k) \in [0, 1]^k} |F(x_1, \ldots , x_k)| + \sum _{i = 1}^k \left| \frac{\partial F}{\partial x_i}(x_1, \ldots , x_k) \right| . \end{aligned}

Before proving Lemma 6.2 , we first prove the following claim, which expresses the y^{(m, q)} variables in terms of the function F .

\begin{aligned} y_{r_1, \ldots , r_k}^{(m, q)} = \frac{\phi (W)}{W} \prod _{i \ne m} \frac{\phi (r_i)}{r_i}\sum _{d \mid q} \mu (d) \log \frac{R}{d} \int _0^1 H_d(t_m) dt_m + O\left( \frac{F_{\max }\phi (W)\log R}{WD_0} \right) , \end{aligned}
\begin{aligned}H_d(t_m) = F\left( \frac{\log r_1}{\log R}, \ldots , \frac{\log r_{m-1}}{\log R}, \frac{\log (R/d)}{\log R} \left( \frac{\log d}{\log R/d}+t_m\right) , \frac{\log r_{m+1}}{\log R}, \ldots , \frac{\log r_k}{\log R} \right) . \end{aligned}
\begin{aligned}y_{r_1, \ldots , r_k}^{(m, q)} = \sum _{d \mid q} \mu (d)d \sum _{d \mid a_m} \frac{y_{r_1, \ldots , r_{m-1}, a_m, r_{m+1}, \ldots , r_k}}{\phi (a_m)} + O\left( \frac{y_{\max }\phi (W)\log R}{WD_0} \right) \\\\\quad = \sum _{d \mid q} \frac{\mu (d)d}{\phi (d)} \sum _{\begin{array}{c} a_m' < R/d \\ (a_m', dW\prod _{i\ne m} r_i) = 1 \end{array}} F\left( \frac{\log r_1}{\log R}, \ldots , \frac{\log r_{m-1}}{\log R}, \frac{\log (a_m' \cdot d)}{\log R}, \frac{\log r_{m+1}}{\log R}, \ldots , \frac{\log a_k}{\log R} \right) \\\\\qquad \cdot \frac{\mu (a_m')^2}{\phi (a_m')} + O\left( \frac{y_{\max }\phi (W)\log R}{WD_0}\right) . \end{aligned}
\begin{aligned} \gamma (\ell ) = {\left\{ \begin{array}{ll} 0 \quad \text { if } \ell \mid dW\prod _{i \ne m}r_i \\ 1 \quad \text { otherwise}\\ \end{array}\right. }. \end{aligned}
\begin{aligned} L\ll 1+ \sum _{\ell \mid Wd\prod _{i \ne m} r_i} \frac{\log \ell }{\ell } + \left( \log {z/w}-\sum _{w \le \ell \le z} \frac{\log \ell }{\ell } \right) \\\ll 1 + \sum _{\ell < \log R} \frac{\log \ell }{\ell } + \sum _{\begin{array}{c} \ell > \log R \\ \ell \mid Wd\prod _{i\ne m} r_i \end{array}} \frac{\log \log R}{\log R} \ll \log \log N \end{aligned}
\begin{aligned} \mathfrak {S} = \frac{\phi \left( dW\prod _{i\ne m} r_i\right) }{dW \prod _{i\ne m} r_i} = \frac{\phi (d)\phi (W)\prod _{i\ne m} \phi (r_i)}{dW\prod _{i\ne m} r_i}. \end{aligned}
\begin{aligned} y_{r_1, \ldots , r_k}^{(m, q)}= \frac{\phi (W)}{W} \prod _{i \ne m} \frac{\phi (r_i)}{r_i}\sum _{d \mid q} \mu (d) \log \frac{R}{d} \int _0^1 H_d(t_m) dt_m \\\quad + \sum _{d \mid q} \frac{d}{\phi (d)} O\left( \frac{F_{\max }\log \log N\phi (d)\phi (W) \prod _{i\ne m} \phi (r_i)}{dW\prod _{i\ne m} r_i} \right) \\\quad + O\left( \frac{F_{\max }\phi (W)\log R}{WD_0} \right) . \end{aligned}

We are now ready to prove Lemma 6.2 .

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