Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Technologies for evaluating, scoring, and determining whether to present a caption of an image are provided. The disclosed techniques include receiving an image with associated metadata. Contextual data is identified from the image and the metadata. A generated caption for the image is received from an image caption generator. A first vector representation is generated based on the contextual image data and a second vector representation is generated based on the generated caption. A machine learned model generates a score for the generated caption using the first vector representation and the second vector representation. The score represents a confidence value defining how accurately the caption describes the image. Based on the score, the caption may be presented along with the image on a client device.

Abstract: Methods, systems, and computer programs are presented for detecting near-duplicate profile images of the users in a social network. One method includes operations for identifying an image in a profile of a user of the social network, determining a query feature vector for the image, the query feature vector comprising a set of features, and determining a dominant feature from the features, the dominant feature having a highest value from the values of the features. Further, the method includes operations for determining a bucket in a database of feature vectors based on the dominant feature, determining if the query feature vector is a near duplicate of any feature vector in the determined bucket, and determining if the profile of the user is a duplicate profile or a fake profile based on whether the query feature vector is a near duplicate of any feature vector in the determined bucket.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: Technologies for evaluating, scoring, and determining whether to present a caption of an image are provided. The disclosed techniques include receiving an image with associated metadata. Contextual data is identified from the image and the metadata. A generated caption for the image is received from an image caption generator. A first vector representation is generated based on the contextual image data and a second vector representation is generated based on the generated caption. A machine learned model generates a score for the generated caption using the first vector representation and the second vector representation. The score represents a confidence value defining how accurately the caption describes the image. Based on the score, the caption may be presented along with the image on a client device.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: The invention relates to a photonic probe apparatus and a method for probing tissue to detect and mark biological tissue with cancerous or precancerous states. The apparatus involves a probe for illuminating tissue and collecting light from an illuminated tissue region through the probe, a unit for analyzing collected light to determine whether a threshold measure of probability of a cancerous or precancerous lesion in the probed tissue region in contact with the probe is exceeded, and an integrated tissue marking facility which can be activated to mark the probed tissue region through the probe when the threshold measure is exceeded. The photonic probe apparatus and the method are especially suitable for probing regions in squamous and columnar epithelia to detect and mark regions with cervical cancer or cervical intraepithelial neoplasia (CIN).

Abstract: Systems, methods, and computer program products for managing a consensus group in a distributed computing cluster, by determining that an instance of an authority module executing on a first node, of a consensus group of nodes in the distributed computing cluster, has failed; and adding, by an instance of the authority module on a second node of the consensus group, a new node to the consensus group to replace the first node. The new node is a node in the computing cluster that was not a member of the consensus group at the time the instance of the authority module executing on the first node is determined to have failed.

Abstract: Methods, systems, and computer programs are presented for detecting near-duplicate profile images of the users in a social network. One method includes operations for identifying an image in a profile of a user of the social network, determining a query feature vector for the image, the query feature vector comprising a set of features, and determining a dominant feature from the features, the dominant feature having a highest value from the values of the features. Further, the method includes operations for determining a bucket in a database of feature vectors based on the dominant feature, determining if the query feature vector is a near duplicate of any feature vector in the determined bucket, and determining if the profile of the user is a duplicate profile or a fake profile based on whether the query feature vector is a near duplicate of any feature vector in the determined bucket.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: The invention provides a system (100) for acquiring cervical images which comprises an image acquisition subsystem (120) for acquiring cervical images (122) of a cervical region of a patient during a colposcopy procedure, and a display subsystem (160) for displaying the cervical images on a display (060) by providing image data (162) of the cervical images to the display. According to the invention, the image acquisition subsystem (120) is arranged for, when operating in an interval mode, acquiring the cervical images (122) at predetermined time intervals to obtain a time-series of cervical images showing changes in the cervical region over time, and reporting a progress of said acquiring to the display subsystem. Moreover, the display subsystem (160) is arranged for establishing a progress indicator (400-416) on the display by generating indicator data (164) and providing the indicator data to the display (060), the progress indicator providing visual feedback on the progress of said acquiring to a user.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Techniques presented herein disclose techniques for evaluating application-level data in a storage system for search and analysis. A backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Systems, methods, and computer program products for managing a consensus group in a distributed computing cluster, by determining that an instance of an authority module executing on a first node, of a consensus group of nodes in the distributed computing cluster, has failed; and adding, by an instance of the authority module on a second node of the consensus group, a new node to the consensus group to replace the first node. The new node is a node in the computing cluster that was not a member of the consensus group at the time the instance of the authority module executing on the first node is determined to have failed.