Abstract: A circuit protection device includes a first temperature sensitive resistor, a second temperature sensitive resistor, an electrically insulating multilayer, a first and second electrode layer, and at least one external electrode. The first temperature sensitive resistor and the second temperature sensitive resistor are electrically connected in parallel, and have a first upper electrically conductive layer and a second lower electrically conductive layer, respectively. The electrically insulating multilayer includes an upper insulating layer, a middle insulating layer, and a lower insulating layer. The upper insulating layer is between the first upper electrically conductive layer and the first electrode layer. The middle layer is laminated between the first temperature sensitive resistor and the second temperature sensitive resistor. The lower insulating layer is between the second lower electrically conductive layer and the second electrode layer.

Abstract: Methods, systems, and media for determining and presenting information related to embedded sound recordings are provided.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Multi-pixel electron microbeam irradiator systems and methods are provided with particular applicability for selectively irradiating predetermined cells or cell locations. A multi-pixel electron microbeam irradiator system can include a plurality of individually addressable electron field emitters sealed in a vacuum. The multi-pixel electron microbeam irradiator system can include an anode comprising one or more electron permeable portions corresponding to the plurality of electron field emitters. Further, the multi-pixel electron microbeam irradiator system can include a controller operable to individually control electron extraction from each of the electron field emitters for selectively irradiating predetermined locations such as cells or cell locations.

Abstract: In a method for sequentially generating segment fields for use in delivering intensity modulated radiotherapy an input continuous intensity map is generated. A segment field is generated directly from the input intensity map. A residual continuous intensity map is generated that is based on the respective photon fluence contributions from the input intensity map and a fractionally intensity map corresponding to the segment field. These steps are repeated for a number of iterations to generate a like number of additional segment fields and residual maps derived therefrom. In each iteration, the residual map generated in the previous iteration is used as the input intensity map.

Abstract: In a method for sequentially generating segment fields for use in delivering intensity modulated radiotherapy an input continuous intensity map is generated. A segment field is generated directly from the input intensity map. A residual continuous intensity map is generated that is based on the respective photon fluence contributions from the input intensity map and a fractionally intensity map corresponding to the segment field. These steps are repeated for a number of iterations to generate a like number of additional segment fields and residual maps derived therefrom. In each iteration, the residual map generated in the previous iteration is used as the input intensity map.