Abstract: The present disclosure provides a device for controlling the shape of an aerosol particle condensation growth flow field through an electromagnetic field. The device includes an aerosol growth device and a power supply. The aerosol growth device includes a porous medium, magnetic rubber and an electromagnet group. The magnetic rubber is sleeved in an inner cavity of the electromagnet group, and the porous medium is sleeved in an inner cavity of the magnetic rubber. The magnetic rubber is clung or clings to the porous medium, and the power supply is connected with the electromagnet group. The present disclosure also provides a method for controlling the shape of the aerosol particle condensation growth flow field through the electromagnetic field.

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 training and using machine learning to determine mappings between matching nodes of graphs representing predecessor source code snippets and graphs representing successor source code snippets. In various implementations, first and second graphs may be obtained, wherein the first graph represents a predecessor source code snippet and the second graph represents a successor source code snippet. The first graph and the second graph may be applied as inputs across a trained machine learning model to generate node similarity measures between individual nodes of the first graph and nodes of the second graph. Based on the node similarity measures, a mapping may be determined across the first and second graphs between pairs of matching nodes.

Abstract: Provided are hybrid tRNA/pre-miRNA molecules, e.g., comprising a single tRNA and one, two or more pre-miRNA molecules, useful for the production and therapeutic delivery of an inserted RNA sequence, e.g., one or more miRNAs. Also provided are liposomes and nanoparticles that include the hybrid tRNA/pre-miRNA molecules. Methods of treating cancer by administration of the hybrid tRNA/pre-miRNA molecules are also provided.

Abstract: Implementations are described herein for learning and utilizing mappings between source code changes and regions of latent space associated with code change intents that motivated those source code changes. In various implementations, data indicative of a change made to source code snippet may be applied as input across a machine learning model to generate a new source code change embedding in a latent space. Reference source code change embedding(s) may be identified in the latent space based on distance(s) between the reference source code change embedding(s) and the new source code change embedding in the latent space. Based on the identified reference embedding(s), code change intent(s) may be identified. Association(s) may be created between the source code snippet and the code change intent(s).

Abstract: Provided are hybrid tRNA/pre-microRNA and tRNA molecules and their use in methods of preventing and treating hepatocellular carcinoma (HCC). In some embodiments, provided are polynucleotides that include a tRNA operably linked to one or more pre-microRNA (pre-miRNA), where the tRNA and/or pre-miRNA are operably linked to one or more inserted RNA molecules that inhibit the growth or proliferation of a hepatocellular carcinoma (HCC) cell.

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. In various implementations, one or more components of one or more generative adversarial networks, such as a generator machine learning model, may be trained to generate “synthetically-naturalistic” source code that can be used as a translation of source code in an unfamiliar language. In some implementations, a discriminator machine learning model may be employed to aid in training the generator machine learning model, e.g., by being trained to discriminate between human-generated (“genuine”) and machine-generated (“synthetic”) 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 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.