Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: The present disclosure describes effective strategies for portfolio management that utilize innovative stock selection and transaction approaches using Deep Reinforcement Learning. Embodiments include creating a candidate set of high momentum stocks determined through momentum scores and using two or more Deep Reinforcement Learning algorithms to generate trading signals for the candidate set. These trading signals are then used to manage the portfolio by adding or removing stocks to/from the portfolio and/or adjusting their weights in the portfolio.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: An apparatus in one embodiment comprises a host device comprising a processor coupled to memory. The host device is configured to communicate over a network with at least one storage system. The host device is further configured to retrieve data corresponding to a plurality of processes for submitting a plurality of input-output operations to the at least one storage system, to identify one or more constraints on the plurality of processes based at least in part on the data; and to control submissions of the plurality of input-output operations to the at least one storage system based at least in part on the one or more constraints. The retrieval of the data, the identification of the one or more constraints and the control of the submissions are performed in a user space of the host device.

Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: Introduced here are approaches to training and then employing computer-implemented models designed to upsample discrete audio signals to higher sampling rates. Assume, for example, that a media production platform obtains a first discrete signal at a relatively low sampling rate. The relatively low sampling frequency may make the first discrete audio signal unsuitable for inclusion in media compilations, so the media production platform may attempt to improve its quality through upsampling. To accomplish this, the media production platform can apply a transform to the first discrete signal to produce a first magnitude spectrogram. Then, the media production platform can apply a computer-implemented model to the first magnitude spectrogram to produce a second magnitude spectrogram. Thereafter, the media production platform can apply an inverse transform to the second magnitude spectrogram to create a second discrete signal that has a higher sampling rate than the first discrete audio signal.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: Disclosed herein are methods, systems, and processes to provide coherency across disjoint caches in clustered environments. It is determined whether a data object is owned by an owner node, where the owner node is one of multiple nodes of a cluster. If the owner node for the data object is identified by the determining, a request is sent to the owner node for the data object. However, if the owner node for the data object is not identified by the determining, selects a node in the cluster is selected as the owner node, and the request for the data object is sent to the owner node.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: A method for implementing geolocation-based polygon analysis to determine network elements of a telecommunications network in a polygon-defined geolocation area, the method includes: receiving data defining the boundaries of a geolocation area; based on the received data, extracting coordinates of the at least one geolocation area; based on the extracted coordinates, determining at least one a polygon; based on geolocation data of network elements, receiving coordinates defining the geolocation of at least one network element; for each network element and for each polygon, comparing the coordinates of the at least one network element with the extracted coordinates defining the boundaries of the geolocation area; based on the comparison, determining for each polygon, that the geolocation of the at least one network element intersects with the geolocation area; based on the determination, for each polygon, outputting the at least one intersecting network element of the polygon.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for facilitating the creation of a master transcription (or simply “transcript”) that more accurately reflects underlying audio by comparing multiple independently generated transcripts. The master transcript may be used to record and/or produce various forms of media content, as further discussed below. Thus, the technology described herein may be used to facilitate editing of text content, audio content, or video content. These computer programs may be supported by a media production platform that is able to generate the interfaces through which individuals (also referred to as “users”) can create, edit, or view media content. For example, a computer program may be embodied as a word processor that allows individuals to edit voice-based audio content by editing a master transcript, and vice versa.

Abstract: Techniques are provided for provisioning zoned storage devices to sequential workloads. One method comprises obtaining a sequentiality classification of at least one workload of an application associated with a storage system comprising a plurality of zoned storage devices; and provisioning at least one of the zoned storage devices for storing the data of the at least one workload in response to the at least one workload being classified as a sequential workload. A sequentiality classification of a workload (e.g., as a sequential workload or a random workload) can be determined by: (i) evaluating the application name and/or application type of an application, (ii) learning input-output workload patterns, such as sequential read/write operations or random read/write operations, and/or (iii) detecting the application access mode to persistent volumes, such as a sequential write access mode.

Abstract: An apparatus comprises at least one processing device comprising a processor coupled to a memory, with the at least one processing device being configured to provide at least a portion of an input-output (IO) stack for processing of IO operations in a host device for delivery to a storage system over selected ones of a plurality of paths through a network. The IO stack comprises at least a multi-path device overlying one or more logical storage devices. The at least one processing device is further configured to perform a check at each of one or more points in the IO stack to confirm that a given IO operation directed to a given device of the IO stack is received from an expected overlying device of the IO stack. The IO stack illustratively comprises an encryption device overlying the multi-path device, supporting end-to-end encryption for one or more logical storage devices.

Abstract: Methods, apparatus, and processor-readable storage media for block-level classification of unstructured data are provided herein. An example apparatus includes a host device comprising a processor coupled to memory, the host device being configured to communicate over a network with a storage system, and further being configured to: obtain a pointer to a page cache associated with an input-output operation for at least one page of unstructured data of a file; obtain an index node object of the file based at least in part on the pointer to the page cache; derive at least one characteristic of the file based at least in part on the obtained index node object; and provide an indication of the at least one characteristic to the storage system. The storage system determines whether to apply one or more functions to the unstructured data based on the indication.

Abstract: An apparatus comprises at least one processing device configured to implement a multi-path layer in a host device, wherein the multi-path layer controls delivery of input-output (IO) operations from the host device to a storage system over selected ones of a plurality of paths through a network. The multi-path layer is configured, for each of at least a subset of the IO operations, to store at least a process identifier, a user identifier and an access type for the IO operation. The multi-path layer is further configured to perform analytics on the stored process identifiers, user identifiers and access types to detect an access pattern, and responsive to the detected access pattern having one or more designated characteristics associated with malware, to generate an alert. The alert may be generated by inserting security alert indicators into respective ones of the IO operations, for extraction therefrom by the storage system.

Abstract: An apparatus comprises a processing device configured to control delivery of input-output operations from a host device to a storage system over selected ones of a plurality of paths through a network. The processing device is further configured to identify whether operational information of the host device corresponding to a given write input-output operation comprises one or more index nodes, and to analyze the one or more index nodes responsive to a positive identification. The processing device is also configured to determine whether one or more portions of data corresponding to the given write input-output operation comprise file data based on the analysis of the one or more index nodes, to encrypt at least part of the file data responsive to an affirmative determination, and to deliver the given write input-output operation comprising the encrypted file data to the storage system.

Abstract: Host computers running applications that store data on a block-based storage system such as a SAN provide hints that differentiate IO data based on which application generated the IO. The hints may include tags that are associated with IO commands sent to the block-based storage system. Each host application is associated with a unique identifier that is placed in the tag. Application name-to-identifier mappings may be sent from the hosts to the block-based storage system. Per-identifier/application deduplication statistics are maintained by the block-based storage system and shared with other block-based storage system. Deduplication is disabled or de-emphasized for IO data generated by applications with statistically low deduplication ratios.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for manipulating noisy audio signals to produce clean audio signals that are sufficiently high quality so as to be largely, if not entirely, indistinguishable from “rich” recordings generated by recording studios. When a noisy audio signal is obtained by a media production platform, the noisy audio signal can be manipulated to sound as if recording occurred with sophisticated equipment in a soundproof environment. Manipulation can be performed by a model that, when applied to the noisy audio signal, can manipulate its characteristics so as to emulate the characteristics of clean audio signals that are learned through training.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for manipulating noisy audio signals to produce clean audio signals that are sufficiently high quality so as to be largely, if not entirely, indistinguishable from “rich” recordings generated by recording studios. When a noisy audio signal is obtained by a media production platform, the noisy audio signal can be manipulated to sound as if recording occurred with sophisticated equipment in a soundproof environment. Manipulation can be performed by a model that, when applied to the noisy audio signal, can manipulate its characteristics so as to emulate the characteristics of clean audio signals that are learned through training.