Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.

Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.

Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.

Abstract: Implementations are described herein for adapting existing source code snippets to new contexts. In various implementations, a command may be detected to incorporate an existing source code snippet into destination source code. An embedding may be generated based on the existing source code snippet, e.g., by processing the existing source code snippet using an encoder. The destination source code may be processed to identify one or more decoder constraints. Subject to the one or more decoder constraints, the embedding may be processed using a decoder to generate a new version of the existing source code snippet that is adapted to the destination source code.

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

Abstract: Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Abstract: Implementations are described herein for automatically identifying, recommending, and/or effecting changes to a legacy source code base by leveraging knowledge gained from prior updates made to other similar legacy code bases. In some implementations, data associated with a first version source code snippet may be applied as input across a machine learning model to generate a new source code embedding in a latent space. Reference embedding(s) may be identified in the latent space based on their distance(s) from the new source code embedding in the latent space. The reference embedding(s) may be associated with individual changes made during the prior code base update(s). Based on the identified one or more reference embeddings, change(s) to be made to the first version source code snippet to create a second version source code snippet may be identified, recommended, and/or effected.

Abstract: A portable launch rig (PLR) may include a support structure including two side supports defining an interior space for lifting and filling a balloon envelope of a balloon. Wheels on each of the side supports enable the PLR to be moved in various directions in order to prepare the PLR for launching the balloon. The side supports are connected by a lateral support beam having a pair of cranes arranged thereon. Each crane has an arm arranged over the interior space that is connected to a spreader beam. The spreader beam includes a lift assembly configured to lift and inflate the balloon envelope within the interior space. The PLR includes a platform and perch for supporting and moving the balloon envelope. A door assembly of the PLR includes a plurality of hangar doors configured to block wind from a respective direction of each hangar door entering the interior space.

Abstract: Systems for determining an individual's current focal distance by measuring parameters associated with binocular vision focusing using one or two contact lenses are provided. In an aspect, a system includes a first contact lens and a second contact lens respectively configured to be worn over first and second eyes of an individual. The first contact lens and the second contact lens respectively include first and second circuits configured to respectively generate first data related to the first eye and second data related to the second eye for determining the current focal distance.

Abstract: In order to install a ground loop for a geothermal heating and/or cooling system in ground material, a drilling machine having a drill bit connected to an end of first tubing may be used to create an borehole in the ground material. After the borehole is created, grout may be pumped into the borehole through the first tubing as the first tubing is removed from the borehole. This allows the borehole to be grouted from the bottom towards the top. Thereafter, second tubing may be inserted into the grout in order to create the ground loop. This eliminates the need for a “tremie” pipe to insert the ground loop and pump grout into the borehole, while still allowing for the borehole to be grouted from the bottom towards the top to reduce the likelihood of voids.

Abstract: A portable launch rig (PLR) may include a support structure including two side supports defining an interior space for lifting and filling a balloon envelope of a balloon. Wheels on each of the side supports enable the PLR to be moved in various directions in order to prepare the PLR for launching the balloon. The side supports are connected by a lateral support beam having a pair of cranes arranged thereon. Each crane has an arm arranged over the interior space that is connected to a spreader beam. The spreader beam includes a lift assembly configured to lift and inflate the balloon envelope within the interior space. The PLR includes a platform and perch for supporting and moving the balloon envelope. A door assembly of the PLR includes a plurality of hangar doors configured to block wind from a respective direction of each hangar door entering the interior space.

Abstract: Systems for determining an individual's current focal distance by measuring parameters associated with binocular vision focusing using one or two contact lenses are provided. In an aspect, a system includes a first contact lens and a second contact lens respectively configured to be worn over first and second eyes of an individual. The first contact lens and the second contact lens respectively include first and second circuits configured to respectively generate first data related to the first eye and second data related to the second eye for determining the current focal distance.

Abstract: Systems for determining an individual's current focal plane by measuring parameters associated with binocular vision focusing using one or two contact lenses are provided. In an aspect, a system includes a first contact lens and a second contact lens respectively configured to be worn over first and second eyes of an individual. The first contact lens and the second contact lens respectively include first and second substrates, and first and second circuits respectively disposed on or within the first and second substrates and configured to respectively generate first data related to a focal trajectory of the first eye and second data related to a focal trajectory of the second eye, wherein the first circuit employs the second contact lens to generate the first data and the second circuit employs the first contact lens to generate the second data.

Abstract: A portable launch rig (PLR) may include a support structure including two side supports defining an interior space for lifting and filling a balloon envelope of a balloon. Wheels on each of the side supports enable the PLR to be moved in various directions in order to prepare the PLR for launching the balloon. The side supports are connected by a lateral support beam having a pair of cranes arranged thereon. Each crane has an arm arranged over the interior space that is connected to a spreader beam. The spreader beam includes a lift assembly configured to lift and inflate the balloon envelope within the interior space. The PLR includes a platform and perch for supporting and moving the balloon envelope. A door assembly of the PLR includes a plurality of hangar doors configured to block wind from a respective direction of each hangar door entering the interior space.

Abstract: Systems for determining an individual's current focal plane by measuring parameters associated with binocular vision focusing using one or two contact lenses are provided. In an aspect, a system includes a first contact lens and a second contact lens respectively configured to be worn over first and second eyes of an individual. The first contact lens and the second contact lens respectively include first and second substrates, and first and second circuits respectively disposed on or within the first and second substrates and configured to respectively generate first data related to a focal trajectory of the first eye and second data related to a focal trajectory of the second eye, wherein the first circuit employs the second contact lens to generate the first data and the second circuit employs the first contact lens to generate the second data.