Abstract: A method may include applying a protein sequence computation model to generate, based on an input protein sequence, a plurality of proposed protein sequences. A set of possible amino acid residues for each position in at least a portion of an output protein sequence may be identified based on the plurality of proposed protein sequences. A first protein structure having the output protein sequence may be generated by applying a protein structure computation model to select, for each position in at least the portion of the output protein sequence, an amino acid residue from a corresponding set of possible amino acid residues. The protein structure computation model may further determine the conformation of the amino acid residues selected for inclusion in the output protein sequence. In some cases, the first protein structure may be grafted onto a second protein structure to form a third protein structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a large corpus of unstructured data using an extraction neural network to generate a corresponding collection of structured data records. According to one aspect, there is provided a method that includes obtaining a set of input text sequences, and generating a collection of structured data records from the set of input text sequences using an extraction neural network. Each structured data record defines a structured representation of a corresponding input text sequence with reference to a predefined schema of semantic categories. The collection of structured data records can be filtered to identify and remove structured data records that are predicted to be unreliable. The collection of structured data records can then be processed to generate an output that is directed to a selected topic and that aggregates information from across multiple structured data records.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for normalizing a collection of structured data records generated by an extraction neural network to enable large-scale data processing and analysis for query answering. According to one aspect, there is provided a method that includes extracting text strings from the structured data records, embedding the text strings in a latent space, performing an iterative numerical clustering operation in the latent space to cluster the embeddings of the text strings, and then identifying standardized text strings based on a result of the clustering. The structured data records are normalized using the standardized text strings. The normalized collection of structured data records are then used to generate a response to a user query.

Abstract: Provided is a canola variety designated 17GG0168N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 17GG0168N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 17GG0168N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 17GG0168N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 17GG0168N are disclosed.

Abstract: Provided is a canola variety designated 20MC1737N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 20MC1737N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 20MC1737N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 20MC1737N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 20MC1737N are disclosed.

Abstract: Provided is a canola variety designated 20GG1406N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 20GG1406N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 20GG1406N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 20GG1406N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 20GG1406N are disclosed.

Abstract: Provided is a canola variety designated 19GG0375N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 19GG0375N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 19GG0375N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 19GG0375N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 19GG0375N are disclosed.

Abstract: Provided is a canola variety designated 21GG2040N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 21GG2040N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 21GG2040N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described.

Abstract: Provided is a canola variety designated 16GG0316C and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 16GG0316C with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 16GG0316C through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 16GG0316C, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 16GG0316C are disclosed.

Abstract: A convolutional neural network (CNN) system is provided that includes a flexible accelerator configured to convert an input feature map into a set of input sub-feature maps, each having a similar amount of sparsity. The system allows each of the sub-feature maps to be processed independently while taking advantage of the sparsity. In some aspects, the CNN system is configured with an index processor that receives data value indexes and weight indexes and generates data path processor commands for processing by a separate data path processor. In other aspects, unroll circuitry is configured to unroll feature maps to provide index-value compression. The unroll/compression scheme allows an input feature map to be read sequentially (tile-by-tile) so that an accumulate buffer can be implemented with a single read-only path and single write-only path. This can simplify memory control design, eliminating requirements for expensive cache-like structures while also reducing power.

Abstract: A novel canola variety designated 4PRSD48R and seed, plants and plant parts thereof. Methods for producing a canola plant that comprise crossing canola variety 4PRSD48R with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 4PRSD48R through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby. Hybrid canola seed, plant or plant part produced by crossing the canola variety 4PRSD48R or a locus conversion of 4PRSD48R with another canola variety.

Abstract: Provided is a canola variety designated 19MM0844N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 19MM0844N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 19MM0844N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 19MM0844N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 19MM0844N are disclosed.

Abstract: Provided is a canola variety designated 17GM0330N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 17GM0330N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 17GM0330N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 17GM0330N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 17GM0330N are disclosed.

Abstract: Provided is a canola variety designated 21GG1082N and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 21GG1082N with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 21GG1082N through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 21GG1082N, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 21GG1082N are disclosed.

Abstract: Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.

Abstract: Provided is a canola variety designated 4005A678-18 and seed, plants and plant parts thereof produced from a cross of inbred varieties. Methods for producing a canola variety comprise crossing canola variety 4005A678-18 with another canola plant. Methods for producing a canola plant containing in its genetic material one or more traits introgressed into 4005A678-18 through backcross conversion and/or transformation, and to the canola seed, plant and plant part produced thereby are described. Canola variety 4005A678-18, the seed, the plant produced from the seed, plant parts and variants, mutants, and minor modifications of canola variety 4005A678-18 are disclosed.

Abstract: Apparatuses, systems, and techniques to cause information to be invalidated in a second cache location after information is stored in a first cache location. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information to be invalidated in a second cache location after information is stored in a first cache location.

Abstract: Apparatuses, systems, and techniques to prevent information from being read from a second cache location while information is being stored in a first cache location. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to prevent information from being read from a second cache location while information is being stored in a first cache location.

Abstract: A computer-implemented method to generate a unidirectional commitment channel to send one or more tokens from a sender node to a recipient node using a blockchain is disclosed. The method is implemented by a processing resource that performs steps comprising: generating a first commitment transaction representing a commitment component in which a specified set of tokens is sent or committed for transfer from the first node to the second node; generating a return transaction which returns all tokens from the second node to the first node; signing the return transaction and, based on the signing of the return transaction, signing the commitment transaction; creating one or more further return transactions to reflect the transfer of tokens being made off the blockchain.

Abstract: The disclosure relates in some aspects to an apparatus that includes stages of a failure event counting circuit including an Nth stage where N refers to an arbitrary stage of the stages of the failure event counting circuit. The Nth stage may include an Nth fuse trigger circuit configured to receive an event detector signal indicative of a failure event, an Nth electronic fuse configured to disconnect a circuit path between a voltage source and a ground in response to the event detector signal, and an Nth delay circuit coupled to the Nth e-fuse and configured to cause a time delay for activating a subsequent stage of the failure event counting circuit in response to the Nth e-fuse disconnecting. In this aspect, each of the stages of the failure event counting circuit may be configured to use the respective e-fuse to record a discrete failure event.