Abstract: Described herein are methods and systems for the classification of seizure in a subject. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor seizures or related symptoms that the subject is experienced or is experiencing. The processing module may also generate indications or assessments for seizure at an individual level.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bandwidth extension. One of the methods includes obtaining a low-resolution version of an input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period. Generating the high-resolution version includes generating a representation of the low-resolution version of the input; processing the representation of the low-resolution version of the input through a conditioning neural network to generate a conditioning input; and processing the conditioning input using a generative neural network to generate the high-resolution version of the input.

Abstract: A data management system (DMS) may facilitate the storage tiering of snapshots on cloud environments. For example, the DMS may transmit snapshot signaling to a first cloud environment that instructs the first cloud environment to capture a first snapshot of a computing object and to store the first snapshot in a first type of cloud storage at the first cloud environment. The DMS may determine that the first snapshot has been stored in the first type of cloud storage for a duration of time that satisfies an archival threshold. Based on the archival threshold being satisfied, the DMS may transmit archival signaling that instructs the first cloud environment to store the first snapshot to a second type of cloud storage. The second type of cloud storage may be associated with a longer access latency than the first type of cloud storage.

Abstract: Methods, systems, and devices for data management are described. The initiation or forthcoming initiation of a data protection operation for a computing system may be identified. Short-term information of the computing system, including information stored in the volatile memory of the computing system, network traffic associated with the computing system, or both, may be obtained based on the initiation or forthcoming initiation of the data protection operation. Long-term information of the computing system, including information stored in the non-volatile memory of the computing system, may be obtained based on the data protection operation being initiated. Both the short-term information and the long-term information may be stored for further analysis.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bandwidth extension. One of the methods includes obtaining a low-resolution version of an input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period. Generating the high-resolution version includes generating a representation of the low-resolution version of the input; processing the representation of the low-resolution version of the input through a conditioning neural network to generate a conditioning input; and processing the conditioning input using a generative neural network to generate the high/resolution version of the input.

Abstract: A data management system (DMS) may facilitate the storage tiering of snapshots on cloud environments. For example, the DMS may transmit snapshot signaling to a first cloud environment that instructs the first cloud environment to capture a first snapshot of a computing object and to store the first snapshot in a first type of cloud storage at the first cloud environment. The DMS may determine that the first snapshot has been stored in the first type of cloud storage for a duration of time that satisfies an archival threshold. Based on the archival threshold being satisfied, the DMS may transmit archival signaling that instructs the first cloud environment to store the first snapshot to a second type of cloud storage. The second type of cloud storage may be associated with a longer access latency than the first type of cloud storage.

Abstract: Techniques are described herein for using operational transforms to perform operations on parallel copies of a document model. A method includes: determining that a first operation is to be performed on a second parallel copy; and in response: determining that a revision of a first parallel copy matches a revision of the second parallel copy; and in response: performing the first operation on the second parallel copy to obtain a calculation result including a first list of commands; applying the first list of commands to the second parallel copy; performing an operational transform on at least one command in the first list of commands based on queued user edits to the first parallel copy, the queued user edits including a second list of commands, to obtain a transformed list of commands; and applying the transformed list of commands to the first parallel copy.

Abstract: Techniques are described herein for using operational transforms to perform operations on parallel copies of a document model. A method includes: determining that a first operation is to be performed on a second parallel copy; and in response: determining that a revision of a first parallel copy matches a revision of the second parallel copy; and in response: performing the first operation on the second parallel copy to obtain a calculation result including a first list of commands; applying the first list of commands to the second parallel copy; performing an operational transform on at least one command in the first list of commands based on queued user edits to the first parallel copy, the queued user edits including a second list of commands, to obtain a transformed list of commands; and applying the transformed list of commands to the first parallel copy.

Abstract: A data management system (DMS) may facilitate the storage tiering of snapshots on cloud environments. For example, the DMS may transmit snapshot signaling to a first cloud environment that instructs the first cloud environment to capture a first snapshot of a computing object and to store the first snapshot in a first type of cloud storage at the first cloud environment. The DMS may determine that the first snapshot has been stored in the first type of cloud storage for a duration of time that satisfies an archival threshold. Based on the archival threshold being satisfied, the DMS may transmit archival signaling that instructs the first cloud environment to store the first snapshot to a second type of cloud storage. The second type of cloud storage may be associated with a longer access latency than the first type of cloud storage.

Abstract: Described herein are systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.

Abstract: Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated “offline” relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bandwidth extension. One of the methods includes obtaining a low-resolution version of an input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period. Generating the high-resolution version includes generating a representation of the low-resolution version of the input; processing the representation of the low-resolution version of the input through a conditioning neural network to generate a conditioning input; and processing the conditioning input using a generative neural network to generate the high/resolution version of the input.

Abstract: Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated “offline” relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.