Abstract: A method for operating a multi-door oven apparatus is provided. In one example, the method comprises moving, by a mechanical device, a first oven door along a first path in a first direction orthogonal to a side surface of an oven, the side surface comprising an entrance to the oven. The method further comprises moving, by the mechanical device, the first oven door along a second path in a second direction parallel to the side surface, wherein moving the first oven door along the second path comprises moving the first oven door past a second oven door.

Abstract: Techniques regarding the composition and/or structure of oven walls are provided. For example, one or more embodiments described herein can comprise an oven with a heat source configured to heat a hollow space within the oven. The oven further can comprise an oven body that can define the hollow space. Also, the oven body can comprise a plurality of connected sides, wherein one or more of the connected sides comprise a plurality of carbon nanotubes.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Techniques regarding the composition and/or structure of oven walls are provided. For example, one or more embodiments described herein can comprise an oven with a heat source configured to heat a hollow space within the oven. The oven further can comprise an oven body that can define the hollow space. Also, the oven body can comprise a plurality of connected sides, wherein one or more of the connected sides comprise a plurality of carbon nanotubes.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: An oven comprising a multi-door operating apparatus is provided. In one example, the multi-door operating apparatus comprises a first panel comprising a first plurality of grooves that traverse a first inside surface of the first panel and a second panel comprising a second plurality of grooves that traverse a second inside surface of the second panel, wherein first inside surface faces the second inside surface, and wherein the first plurality of grooves and the second plurality of grooves extend in a first direction and then turn to extend in a second direction. The multi-door operating apparatus further comprises two or more oven doors configured to move in the first direction and turn to the second direction via projections that extend from the respective oven doors into the first plurality of groves and the second plurality of grooves.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: Techniques for utilizing excess heat generated by an oven to generate electricity are provided. In one example, an oven can comprise a coolant pathway positioned adjacent to a hollow space within the oven, wherein the hollow space can contain heat. The oven can also comprise a chamber in fluid communication with the coolant pathway. The oven can further comprise a turbine in fluid communication with the chamber and an outlet. Moreover, the oven can comprise a generator connected to the turbine, wherein rotation of the turbine can power the generator.

Abstract: Techniques for utilizing excess heat generated by an oven to generate electricity are provided. In one example, an oven can comprise a coolant pathway positioned adjacent to a hollow space within the oven, wherein the hollow space can contain heat. The oven can also comprise a chamber in fluid communication with the coolant pathway. The oven can further comprise a turbine in fluid communication with the chamber and an outlet. Moreover, the oven can comprise a generator connected to the turbine, wherein rotation of the turbine can power the generator.

Abstract: Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Abstract: Techniques for managing an intelligent oven are provided. In one example, an oven system can comprise a memory that stores computer executable components. The oven system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise an oven management component that identifies a parameter of a completed bake based on a second parameter of a new bake and shares the parameter with an input device.

Abstract: Techniques for managing an intelligent oven are provided. In one example, an oven system can comprise a memory that stores computer executable components. The oven system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise an oven management component that identifies a parameter of a completed bake based on a second parameter of a new bake and shares the parameter with an input device.

Abstract: Techniques for managing an intelligent oven are provided. In one example, an oven system can comprise a memory that stores computer executable components. The oven system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise an oven management component that identifies a parameter of a completed bake based on a second parameter of a new bake and shares the parameter with an input device.

Abstract: Techniques for managing an intelligent oven are provided. In one example, an oven system can comprise a memory that stores computer executable components. The oven system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise an oven management component that identifies a parameter of a completed bake based on a second parameter of a new bake and shares the parameter with an input device.

Abstract: Techniques for utilizing excess heat generated by an oven to generate electricity are provided. In one example, an oven can comprise a coolant pathway positioned adjacent to a hollow space within the oven, wherein the hollow space can contain heat. The oven can also comprise a chamber in fluid communication with the coolant pathway. The oven can further comprise a turbine in fluid communication with the chamber and an outlet. Moreover, the oven can comprise a generator connected to the turbine, wherein rotation of the turbine can power the generator.

Abstract: Techniques for utilizing excess heat generated by an oven to generate electricity are provided. In one example, an oven can comprise a coolant pathway positioned adjacent to a hollow space within the oven, wherein the hollow space can contain heat. The oven can also comprise a chamber in fluid communication with the coolant pathway. The oven can further comprise a turbine in fluid communication with the chamber and an outlet. Moreover, the oven can comprise a generator connected to the turbine, wherein rotation of the turbine can power the generator.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.