Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: An illustrative data storage system is configured to efficiently copy virtual machines across different protocols/formats. For example, a storage system virtually copies images of a virtual machine from a first type of presented storage format to a second type of presented storage format. As another example, a storage system virtually copies a source virtual machine image into a block object to form a file of a block-object-based file system configured to present the virtually copied virtual machine image through a file-based protocol to a virtual machine host.

Abstract: An illustrative storage system generates a managed directory group for a file system. For example, the storage system may determine, in a file system, that a first managed directory and a second managed directory satisfy one or more criteria. The storage system may generate, in the file system and based on the determination, a managed directory group configured to provide coordinated storage management functionality to contents of the first managed directory and contents of the second managed directory as a group based on policies of the managed directory group.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: An everything bone pet treat for providing nutrition for a dog is presented. The pet treat has a top, a bottom, an aperture, a filling and one or more toppings. The filling may include one or more ingredients such as wheat flour, corn syrup, glycerin, gelatin, wheat protein lecithin, rosemary extract, potato flour and vegetable glycerin. Toppings are placed upon the top of the pet treat. The toppings include at least one of meat, cheese powder, potato, sweet potato, oatmeal. The meat is preferably bacon, beef, salmon, chicken, etc. The pet treats are highly palatable to pets and therefore very well-suited for the delivery of healthy ingredients.

Abstract: A hot dog bone pet treat for providing nutrition for a dog is presented. The pet treat has a top, a bottom, a filling, a bun and one or more toppings. The filling may include one or more ingredients such as wheat flour, corn syrup, glycerin, gelatin, wheat protein lecithin, rosemary extract, potato flour and vegetable glycerin. Toppings are placed upon the top of the pet treat. The toppings include at least one of meat, cheese powder, potato, sweet potato, oatmeal. The meat is preferably bacon, beef, salmon, chicken, etc. The pet treats are highly palatable to pets and therefore very well-suited to delivery of health ingredients.

Abstract: A hamburger bone pet treat for providing nutrition for a dog is presented. The pet treat has a top, a bottom, a filling, a bun and one or more toppings. The filling may include one or more ingredients such as wheat flour, corn syrup, glycerin, gelatin, wheat protein lecithin, rosemary extract, potato flour and vegetable glycerin. Toppings are placed upon the top of the pet treat. The toppings include at least one of meat, cheese powder, potato, sweet potato, oatmeal. The meat is preferably bacon, beef, salmon, chicken, etc. The pet treats are highly palatable to pets and therefore very well-suited for the delivery of healthy ingredients.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: A system to analyze heart data includes a memory configured to store echocardiographic strain image data. The system also includes a processor operatively coupled to the memory and configured to segment the echocardiographic strain image data. The processor is also configured to analyze the segmented echocardiograph strain image data to generate a point cluster of pixels. The processor is also configured to stitch together points in the point cluster to generate a continuous curve that represents the echocardiographic strain image data. The processor is further configured to analyze the continuous curve to identify a heart condition.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object based on different conversion rates during different network time periods.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: A computer implemented method includes turning off a sensor, receiving fall curve data from the sensor, and comparing the received fall curve data to a set of fall curve signatures to identify the sensor or a sensor fault.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object based on different conversion rates during different network time periods.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.