Abstract: Provided are systems and methods for corrective reward optimization for generative sequential labeling. In particular, example aspects of the present disclosure are directed to an effective framework for generative reward optimization of text (or other) data sequences, certain example implementations of which can be referred to as “GROOT”. Example implementations of the proposed framework work by training a generative sequential labeling model to match the decoder output distribution with that of the (possibly black-box) reward function. Using an iterative training regime, the framework can first generate prediction candidates and then correct errors in the candidate. Finally, a loss function can be used that contrasts those candidates based on their reward values (e.g., as measured by a reward function that encodes the specific objectives for a particular setting or application).

Abstract: Customers of a software platform, such as a unified communications as a service platform, are enabled to control their own encryption keys used to encrypt and decrypt data from various communication services in the software platform. A key broker server is employed to map encryption and decryption requests from servers in the platform to key management servers of customers based on user identifiers. Examples of data encrypted may includes conference recordings, webinar recordings, phone call recordings, voicemails, emails, and calendar tokens.

Abstract: Data services for workloads are often provided with a service level agreement specifying various performance guarantees (e.g., latency, availability, scalability, and consistency). Single-master architectures, in which updates t the data set are constrained to a single server, may limit the fulfillment of some performance guarantees. Presented herein are multi-master architectures, in which the server set is partitioned into at least two masters are permitted to update the data set and at least one non-master that is not permitted to update the data set. Non-masters that receive a request to update the data set forward the request to a master server for application to the data set. A master that receives the request applies it to the data set and propagates the update to other master and non-master servers. Conflicting updates may be resolved through a variety of conflict resolution techniques, optionally designating one master server as a conflict resolution server.

Abstract: The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.

Abstract: A method of bridging a first database and a second database. The method includes maintaining a state machine representing a state of a virtual node in the first database, wherein the state of the virtual node conforms to a native protocol for native nodes of the first database, said native protocol of the first database differing from a foreign protocol of the second database. The method further includes receiving an incoming message for the virtual node from one of the native nodes according to the native protocol, and based on the incoming message, accessing the second database. The method further includes updating the state of the virtual node based on the incoming message according to the native protocol, and based on the state of the virtual node as updated, sending an outgoing message to one or more of the native nodes according to the native protocol.

Abstract: A server set for a data set may designate a subset of “master” servers that update the data set in order to reduce data version conflicts involving mutually exclusive updates of the data set. Multi-master configurations may fulfill the performance constraints, and the subset of masters may detect and resolve data version conflicts. However, if multiple masters perform conflict resolution for a particular data version conflict, the resolution may produce inefficiency and redundancy (if the masters reach the same outcome) or additional data version conflicts (if the masters reach different outcomes). Instead, among the masters, a merge master may be identified that applies conflict resolution techniques to data version conflicts and forwards the conflict resolution outcome to the other masters for application to the data set to resolve the data version conflict. The other masters may temporarily store updates in a tentative update set until data version conflicts are resolved.

Abstract: Data services for workloads are often provided with a service level agreement specifying various performance guarantees (e.g., latency, availability, scalability, and consistency). Single-master architectures, in which updates to the data set are constrained to a single server, may limit the fulfillment of some performance guarantees. Presented herein are multi-master architectures, in which the server set is partitioned into at least two masters are permitted to update the data set and at least one non-master that is not permitted to update the data set. Non-masters that receive a request to update the data set forward the request to a master server for application to the data set. A master that receives the request applies it to the data set and propagates the update to other master and non-master servers. Conflicting updates may be resolved through a variety of conflict resolution techniques, optionally designating one master server as a conflict resolution server.

Abstract: Data services are often provided with various performance guarantees. Multi-master architectures, in which multiple master servers are permitted to update a portion of the data set, may facilitate some performance requirements, but may also lead to data version conflicts in which different masters have written different versions of one or more data items. Moreover, conflicts involving different data items may have to be resolved using different conflict resolution techniques. Therefore, various data items of the data set may be associated with a conflict resolution technique selected from a conflict resolution technique set, such as manual conflict resolution; a write order policy, such as last writer wins; a conflict resolution logic; and conflict resolution based on data types. A data version conflict may be resolved by identifying and invoking the selected conflict resolution technique that is associated with the data item, and applying the conflict resolution outcome to the data item.

Abstract: An apparatus and method for loop flattening and reduction in a SIMD pipeline including broadcast, move, and reduction instructions.

Abstract: Distributed transactions are performed over a collection of servers operating as replicas of a data set, where a successful transaction involves meeting a quorum count of replicas that locally commit the transaction. However, performance constraints of data sets and consuming applications may vary (e.g., sensitivity to latency, scalability, and/or consistency), and the performance characteristics of the server set may be partly determined by the transactional commitment and quorum selection. The distributed transaction may be applied by designating the replicas as a set of followers and a leader that initiates the transaction and receives acknowledgments of local commits by each follower. On condition of the acknowledgments meeting a quorum count for the data set according to the performance characteristics of the application, the leader locally commits the transaction and delivers a result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a tuple of respective input sequences to generate an output. In one aspect, one of the systems includes a neural network comprising a plurality of encoder neural networks and a head neural network, each encoder neural network configured to: receive a respective input sequence from the tuple; process the respective input sequence using one or more encoder network layers to generate an encoded representation comprising a sequence of tokens; and process each of some or all of the tokens in the sequence of tokens using a projection layer to generate a lower-dimensional representation, and the head neural network configured to: receive lower-dimensional representations of a respective proper subset of the sequence of tokens generated by the encoder neural network; and process the lower-dimensional representations to generate the output.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: An application on a device may interact with a document service that provides access to a document service. The interaction may occur in a variety of contexts, such as a device context (e.g., the document service and the application provided on the same device), a local context (e.g., via a LAN), and a remote context (e.g., over the Internet). It may be advantageous to adapt the interaction to the current context, while also providing a consistent application interface that alleviates the application from context-specific implementations, and also achieving this adaptation in a performant manner. These considerations may be achieved in a balanced manner by providing a set of runtimes, where each runtime mediates the application/document service interaction between the application and the document service in a contextually adapted manner. The device may automatically detect the context of the interaction and select a contextually suitable runtime to service the application.

Abstract: Doxorubicin is extracted from blood using anionic material, such as a resin comprising sulfonated polystyrene divinylbenzene beads, and polyethersulfone membrane, or both. After exposing the resin and/or membrane to blood in order to remove doxorubicin therefrom, the doxorubicin maybe extracted from the resin and/or membrane by exposing the material to an extraction solution, sonicating the extraction solution to enhance release of the doxorubicin, and repeating the exposure and sonication in order to remove substantially all of doxorubicin from the resin.

Abstract: An apparatus and method for loop flattening and reduction in a SIMD pipeline including broadcast, move, and reduction instructions. One embodiment of a processor comprises: a decoder to decode a broadcast instruction to generate a decoded broadcast instruction identifying a plurality of operations, the broadcast instruction including an opcode and first and second source operands, and having a split value associated therewith; and execution circuitry to execute the operations of the decoded broadcast instruction to copy a first data element specified by the first source operand to each of a first set of contiguous data element locations in a destination register and to copy a second data element specified by the second source operand to a second set of contiguous data element locations in the destination register, wherein the first and second sets of contiguous data element locations are determined in accordance with the split value.

Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.

Abstract: Doxorubicin is extracted from blood using anionic material, such as a resin comprising sulfonated polystyrene divinylbenzene beads, and polyethersulfone membrane, or both. After exposing the resin and/or membrane to blood in order to remove doxorubicin therefrom, the doxorubicin maybe extracted from the resin and/or membrane by exposing the material to an extraction solution, sonicating the extraction solution to enhance release of the doxorubicin, and repeating the exposure and sonication in order to remove substantially all of doxorubicin from the resin.

Abstract: Data sets such as databases are often distributed over a number of servers, where each server stores a subset of records and an index that enables the server to locate the records in response to queries. However, tight coupling of indexing and storage may limit load-balancing, fault recovery, and distribution. Instead, the set of servers may be partitioned into a set of storage servers that store the records and a set of index servers of the index over the records. In a set that is decoupled in this manner, load-balancing may involve provisioning and locating index servers independently of the provisioning and locating of the storage servers in view of the particular sensitivities and tolerances of various applications. Additionally, the index servers may also utilize a data layout that is selected and adapted independent of the data layout of the storage servers and/or the schema of the data stored thereby.

Abstract: Systems and methods provide distantly supervised wrapper induction for semi-structured documents, including automatically generating and annotating training documents for the wrapper. Training of the wrapper may occur in two phases using the training documents. An example method includes identifying a training set of semi-structured web pages having a subject entity that exists in a knowledge base and, for each training page, identifying target objects, identifying predicates in the knowledge base that connect the subject entity to a target objects identified in the training page, and annotating the training page. Annotating a training page includes generating a feature set for a mention of the target object, generating predicate-target object pairs for the mention, and labeling each predicate-target object pair with a corresponding example type and weight. The annotated training pages are used to train the wrapper to extract new subject entities and new facts from the set of semi-structured web pages.

Abstract: Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.