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Table 2

In step 1, the time cost is mainly determined by spatial filtering, resulting in time. As for the initialization of a single neuron given a seed pixel, it is only ( ). Considering the fact that the number of neurons is typically much smaller than the number of pixels in this data, the complexity for step one remains . In step 2, the complexity of estimating is and estimating scales linearly with the number of pixels . For each pixel, the computational complexity for estimating is . Thus, the computational complexity in updating the background component is . In step 3, the computational complexities of solving problems (P-S) and (P-T) have been discussed in previous literature ( Pnevmatikakis et al., 2016 ) and they scale linearly with pixel number and time , thatis, . For the interventions, the one with the largest computational cost is picking undetected neurons from the residual, which is the same as the initialization step. Therefore, the computational cost for step 4 is . To summarize, the complexity for running CNMF-E is , thatis, the method scales linearly with both the number of pixels and the total recording time.

Our MATLAB implementation supports running CNMF-E in three different modes that are optimized for different datasets: single-mode, patch-mode and multi-batch-mode.

Single-mode is a naive implementation that loads data into memory and fits the model. It is fast for processing small datasets (<1 GB).

For larger datasets, many computers have insufficient RAM for loading all data into memory and storing intermediate results. Patch-mode CNMF-E divides the whole FOV into multiple small patches and maps data to the hard drive ( Giovannucci et al., 2017b ). The data within each patch are loaded only when we process that patch. This significantly reduces the memory consumption. More importantly, this mode allows running CNMF-E in parallel on multi-core CPUs, yielding a speed-up roughly proportional to the number of available cores.

Multi-batch mode builds on patch-mode and is optimized for even larger datasets, especially data collected over multiple sessions/days. This mode segments data into multiple batches temporally and assumes that the neuron footprints are shared across all batches. We process each batch using patch mode and perform partial weighted updates on given the traces obtained in each batch.

All modes also include a logging system for keeping track of manual interventions and intermediate operations.

The Python implementation is similar; see Giovannucci et al., 2017b ) for full details.

To provide a sense of the running time of the different steps of the algorithm, we timed the code on the simulation data shown in Figure 4 . This dataset is 253 × 316 pixels × 2000 frames. The analyses were performed on a desktop with Intel Xeon CPU E5-2650 v4 @2.20 GHz and 128 GB RAM running Ubuntu 16.04. We used a parallel implementation for performing the CNMF-E analysis, with patch size 64 × 64 pixels, using up to 12 cores. PCA/ICA took 211 seconds to converge, using 250 PCs and 220 ICs. CNMF-E spent 55 s for initialization, 1 s for merging and deleting components, 110 s for the first round of the background estimation and 40 s in the following updates, 8 s for picking neurons from the residual, and 10 s per iteration for updating spatial ( A ) and temporal ( C ) components, resulting in a total of 258 s.

Note:

There are different default maps for Sales Cloud depending on the version you have running (for example, Sales Cloud Release 9, Release 8 Bundle 8, or Release 8 Bundle 6).

To start using mappings, refer to the typical workflow described in the following table:

Click in the Navigator at any time to display the Mappings page. From the Mappings page you can select, delete, or create a mapping of the attributes in DaaS to the attributes in your application.

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What You Can Do from the Mappings Page

On the Mappings page, you see a list of all mappings in your system. The following mappings are provided. They should be sufficient for most search export and match export jobs:

SalesCloudCompanyExport and SalesCloudContactExport for search export mappings to Oracle Sales Cloud

MarketingCloudCompanyExport , MarketingCoudContactExport , and MarketingCloudContactCompanyExport for search export mappings to Oracle Eloqua Marketing Cloud

SalesCloudCompanyMatch and SalesCloudContactMatch for match export mappings to Oracle Sales Cloud

MarketingCloudCompanyMatch , MarketingCloudContactMatch , and MarketingCloudContactCompanyMatch for match export mappings to Oracle Eloqua Marketing Cloud

CompanyExport and ContactExport for export mappings to other Oracle Cloud applications

You cannot delete these provided mappings. However, you can create a new map based on a default map and edit the attributes that way.

The following table highlights what you can do from the Mappings page:

What You See on the Mappings Page

The following table describes the details shown on the Mappings page:

Topics:

Creating Mappings for Oracle Sales Cloud

Creating Mappings for Oracle Eloqua Marketing Cloud

Creating Mappings for Other Oracle Cloud Applications

To create a new mapping for use with Oracle Sales Cloud:

Note:

Match Export mappings require that certain attributes exist. If you delete mandatory attributes, then your import job will fail. See About Matching Records and Analyzing Rejected Match Export Jobs .

If you get error message DCS-1059, make sure your mapping file has Column Data Attribute = External ID mapped to Column Target Attribute = Party ID, or, make sure that your input file contains a column header called External ID (or any name) with values that uniquely identifies the account record in Oracle Sales Cloud.

When prompted for the mapping, select the one that you created.

Adding New Companies to Oracle Sales Cloud

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