Abstract: Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.

Abstract: A method for providing autofill suggestions in a development environment includes obtaining, from a user interface executing on a user device, a user input representing source code generated within a development environment. The source code is created using a particular programming language and a programming code base. The method further includes determining, using a machine learning model, at least one autofill suggestion based on the user input, the autofill suggestion continuing the source code represented by the user input. The method further includes determining, using a rule-based semantic checker configured for the particular programming language, whether the autofill suggestion is semantically correct based on the development environment and the programming code base. The method also includes, when the autofill suggestion is semantically correct, transmitting the autofill suggestion for display on the user interface of the user device.

Abstract: A method for providing autofill suggestions in a development environment includes obtaining, from a user interface executing on a user device, a user input representing source code generated within a development environment. The source code created using a particular programming language and a programming code base. The method further includes determining, using a machine learning model, at least one autofill suggestion based on the user input, the autofill suggestion continuing the source code represented by the user input. The method further includes determining, using a rule-based semantic checker configured for the particular programming language, whether the autofill suggestion is semantically correct based on the development environment and the programming code base. The method also includes, when the autofill suggestion is semantically correct, transmitting the autofill suggestion for display on the user interface of the user device.

Abstract: Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.

Abstract: A method for providing autofill suggestions in a development environment includes obtaining, from a user interface executing on a user device, a user input representing source code generated within a development environment. The source code created using a particular programming language and a programming code base. The method further includes determining, using a machine learning model, at least one autofill suggestion based on the user input, the autofill suggestion continuing the source code represented by the user input. The method further includes determining, using a rule-based semantic checker configured for the particular programming language, whether the autofill suggestion is semantically correct based on the development environment and the programming code base. The method also includes, when the autofill suggestion is semantically correct, transmitting the autofill suggestion for display on the user interface of the user device.

Abstract: A photovoltaic device and method utilizing a light harvesting device and a photovoltaic cell; wherein the light harvesting device includes an organic semiconductor photoactive layer capable of multiple exciton generation with a luminescent material dispersed therein; wherein the bandgap of the luminescent material is selected such that the triplet excitons, formed as a result from the multiple exciton generation in the organic semiconductor, can be transferred from the organic semiconductor into the luminescent material non-radiatively via Dexter Energy Transfer; a photovoltaic cell disposed in an emissive light path of the luminescent material and having a first photoactive layer, wherein the bandgap of the luminescent material matches or is higher than the bandgap of the first photoactive layer.

Abstract: A photovoltaic device comprising a light harvesting device and a photovoltaic cell; wherein the light harvesting device comprises an organic semiconductor photoactive layer capable of multiple exciton generation with a luminescent material dispersed therein; wherein the bandgap of the luminescent material is selected such that the triplet excitons, formed as a result from the multiple exciton generation in the organic semiconductor, can be transferred from the organic semiconductor into the luminescent material non-radiatively via Dexter Energy Transfer; a photovoltaic cell disposed in an emissive light path of the luminescent material and having a first photoactive layer, wherein the bandgap of the luminescent material matches or is higher than the bandgap of the first photoactive layer.