Abstract: Methods and apparatuses relating to processing neural networks are described. In one embodiment, an apparatus to process a neural network includes a plurality of fully connected layer chips coupled by an interconnect; a plurality of convolutional layer chips each coupled by an interconnect to a respective fully connected layer chip of the plurality of fully connected layer chips and each of the plurality of fully connected layer chips and the plurality of convolutional layer chips including an interconnect to couple each of a forward propagation compute intensive tile, a back propagation compute intensive tile, and a weight gradient compute intensive tile of a column of compute intensive tiles between a first memory intensive tile and a second memory intensive tile.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Abstract: Ad segments on a Web page are filled with ads that are served by a service provider operating between a user computer and publisher on one end and multiple ad serving entities on the other. The service provider implements a bidding process for the ad segment. The winning ad serving entity (DSP, ATD, advertiser, etc.) has its ad delivered to the user browser by the service provider where it is displayed in the Web page. Rules are provided that define conditions for accepting bids below a goal e-CPM. Bids are filtered out if they would reduce an average e-CPM below the goal e-CPM. Additionally, bids may be filtered out based on a minimum floor e-CPM.

Abstract: Ad segments on a Web page are filled with ads that are served by a service provider operating between a user computer and publisher on one end and multiple ad serving entities on the other. The service provider implements a bidding process for the ad segment. The winning ad serving entity (DSP, ATD, advertiser, etc.) has its ad delivered to the user browser by the service provider where it is displayed in the Web page. The floor e-CPM for the ad segment is determined by the publisher using data related to the user (demographic, geographic, socio-economic, etc.), information on the ad segment (e.g., location on page), and relationships the publisher (e.g., a DSP having a relationship with the publisher may be offered a lower e-CPM). The service provider provides tools and highly granular information for the publisher in order to create an appropriate floor e-CPM for the ad segment.

Abstract: Various types of Intelligent Ad Auction and Response Latency Timeout compliance techniques are described which may be implemented at Advertising Service Provider Systems and ad servers for intelligently handling ad requests in online and mobile electronic data networks.

Abstract: Various aspects are disclosed for enabling an advertising server to improve profitability by selectively and dynamically discriminating between the various ad server resources which are to be allocated towards servicing different incoming impressions. The ad server may be configured or designed to dynamically evaluate the parameters of each incoming impressions in order to assess the likelihood of that incoming impression being successfully filled by demand partners and/or advertising networks. At various times when conditions warrant, the ad server may selectively and dynamically choose to throttle or drop the servicing of selected incoming ad requests/impressions in order to minimize losses which may occur as a result of servicing ad requests/impressions which result in defaults.

Abstract: Various aspects are disclosed for enabling an advertising server to improve profitability by selectively and dynamically discriminating between the various ad server resources which are to be allocated towards servicing different incoming impressions. The ad server may be configured or designed to dynamically evaluate the parameters of each incoming impressions in order to assess the likelihood of that incoming impression being successfully filled by demand partners and/or advertising networks. At various times when conditions warrant, the ad server may selectively and dynamically choose to throttle or drop the servicing of selected incoming ad requests/impressions in order to minimize losses which may occur as a result of servicing ad requests/impressions which result in defaults.

Abstract: Ad segments on a Web page are filled with ads that are served by a service provider operating between a user computer and publisher on one end and multiple ad serving entities on the other. The service provider implements a bidding process for the ad segment. The winning ad serving entity (DSP, ATD, advertiser, etc.) has its ad delivered to the user browser by the service provider where it is displayed in the Web page. Rules are provided that define conditions for accepting bids below a goal e-CPM. Bids are filtered out if they would reduce an average e-CPM below the goal e-CPM. Additionally, bids may be filtered out based on a minimum floor e-CPM.

Abstract: Ad segments on a Web page are filled with ads that are served by a service provider operating between a user computer and publisher on one end and multiple ad serving entities on the other. The service provider implements a bidding process for the ad segment. The winning ad serving entity (DSP, ATD, advertiser, etc.) has its ad delivered to the user browser by the service provider where it is displayed in the Web page. Rules are provided that define conditions for accepting bids below a goal e-CPM. Bids are filtered out if they would reduce an average e-CPM below the goal e-CPM. Additionally, bids may be filtered out based on a minimum floor e-CPM.

Abstract: Various types of Intelligent Ad Auction and Response Latency Timeout compliance techniques are described which may be implemented at Advertising Service Provider Systems and ad servers for intelligently handling ad requests in online and mobile electronic data networks.

Abstract: Systems, methods, apparatus and software can combine information about host access to virtualization functionality and virtualization functionality access to storage, use this information in decisions pertaining to high availability of virtualization in an SAN. Upon detection of the partitioning of a SAN fabric, accessibility information is gathered. That information is analyzed to uncover potential failover scenarios, orchestrate such failovers, and in some cases select best case solutions from among several possible solutions based on access prioritization criteria (e.g., defined priority, maximum access, maximum I/O, etc.).