Abstract: The present invention relates to a device for bioassisted conversion of carbon dioxide to organic compounds that can be used a fuels and chemicals. The present invention also relates to a bioassisted process of converting carbon dioxide to organic compounds.

Abstract: A Raman spectroscopic detection device comprising at least one microfluidic sample channel; at least one excitation waveguide for exciting a Raman signal and at least one collection waveguide for collecting a Raman signal. The output of the excitation waveguide and the input of the collection waveguide are positioned directly in the microfluidic sample channel.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: The present disclosure relates to system(s) and method(s) for segregating multimedia frames associated with a character. The system may store sample data corresponding to a set of characters, wherein the sample data may comprise one or more voice samples and one or more visual samples corresponding to each character. The system may receive a multimedia file with a set of multimedia frames. Each multimedia frame may comprise video data and audio data. The system may identify one or more clusters of multimedia frames from the set of multimedia frames. The one or more clusters of multimedia frames may be associated with a target character selected from the set of characters by comparing the multimedia file with the audio and visual data. The system may further comprise steps for generating a target multimedia file, wherein the target multimedia file is generated by combining the one or more clusters of multimedia frames.

Abstract: Various methods and systems are provided for autonomous orchestration of secrets renewal and distribution. A secrets management service (“SMS”) can be utilized to store, renew and distribute secrets in a distributed computing environment. The secrets are initially deployed, after which, SMS can automatically renew the secrets according to a specified rollover policy, and polling agents can fetch updates from SMS. In various embodiments, SMS can autonomously rollover client certificates for authentication of users who access a security critical service, autonomously rollover storage account keys, track delivery of updated secrets to secrets recipients, deliver secrets using a secure blob, and/or facilitate autonomous rollover using secrets staging. In some embodiments, a service is pinned to the path where the service's secrets are stored. In this manner, secrets can be automatically renewed without any manual orchestration and/or the need to redeploy services.

Abstract: A machine learning tool for resolving a compiler error in an application is provided. The application and an associated application metadata file may be stored on a server. The machine learning tool may identify one or more referenced external dependencies causing the compiler error. The machine learning tool may comprise a web crawler configured to locate one or more comparable external dependencies. The web crawler may retrieve an external dependent metadata file for each of the located comparable external dependencies and download the comparable external dependent metadata files. The machine learning tool may be configured to compare the metadata of each comparable external dependent metadata file to the metadata of the application metadata file, assign a confidence level relative to a pre-determined confidence level, for each located comparable external dependency, and download the located comparable external dependencies having a confidence level greater than the pre-determined confidence level.

Abstract: The present invention relates to the development of carbon fiber carbon nanotubes reinforced polymer composites for high strength structural applications. It is very difficult to incorporate higher amount of carbon fiber >60 vol % in any of the polymer matrix. Beyond this loading the mechanical properties of these composite starts deteriorate. Therefore, further improvement in the mechanical properties is not possible. Herein, a novel method is developed to fabricate the hybrid carbon fiber epoxy composites reinforced with multiwalled carbon nanotubes. The flexural strength of the hybrid composites (˜45 vol % CF+CNT) was achieved more than 600 MPa which is more than 35% over pure carbon fiber/epoxy composites (˜50 vol % CF). These high strength hybrid composites can be used in wind mill blades, turbine blades, sport industries, automobile and airframe.

Abstract: A method for managing a software-defined data center is disclosed. The method involves implementing redundant cloud management stacks, processing duplicate application program interface (API) calls in parallel with the redundant cloud management stacks, and performing a data center operation corresponding to the processed duplicate API calls.

Abstract: Systems for dynamically detecting unauthorized activity are provided. A system may receive data from one or more computing devices associated with one or more different channels of communication (e.g., email, telephone, instant messaging, internet browsing, and the like). The received data may be formatted or transformed from an unstructured format to a structured format for further analysis and evaluation. In some arrangements, machine learning may be used to determine whether triggering content was identified in data received from the one or more systems and to evaluate the identified triggering content to determine whether the content, alone or in combination with triggering content from other channels of communication, may indicate an occurrence of unauthorized activity. If so, the identified occurrence may be evaluated to determine whether a false positive has occurred.

Abstract: Systems and methods for creating virtual production logging tool profiles for improved history matching and proactive control of smart wells.

Abstract: Embodiments of the present invention provide a system for executing, securing, and non-repudiation of pooled conditional smart contracts over a distributed blockchain network. In particular, the system may receive an instrument request from a beneficiary entity, where the instrument request includes an instrument amount. The system can then identify a lead contribution amount that a lead entity is willing to provide to meet a portion of the instrument amount. A set of supporting entities can be identified as willing to provide supporting contribution amounts to meet the remainder of the instrument amount. A conditional contract can be sent to each supporting entity that, when signed, authorizes the system to transfer contribution amounts, which may be in the form of cryptocurrency, from blockchain addresses of the lead and supporting entities to a blockchain address of the beneficiary entity. Once the instrument amount has been secured, the system executes the transactions.

Abstract: A machine learning tool for resolving a compiler error in an application is provided. The application and an associated application metadata file may be stored on a server. The machine learning tool may identify one or more referenced external dependencies causing the compiler error. The machine learning tool may comprise a web crawler configured to locate one or more comparable external dependencies. The web crawler may retrieve an external dependent metadata file for each of the located comparable external dependencies and download the comparable external dependent metadata files. The machine learning tool may be configured to compare the metadata of each comparable external dependent metadata file to the metadata of the application metadata file, assign a confidence level relative to a pre-determined confidence level, for each located comparable external dependency, and download the located comparable external dependencies having a confidence level greater than the pre-determined confidence level.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: a gateway stack that includes a master, a backup, and at least one slave amongst nodes of the GS based on an election pursuant to a gateway stack protocol. The gateway stack provides gateway services for a Network Virtualization over Layer 3 (NVO3) network in a fail-safe manner by utilizing all of the nodes in the gateway stack. A data interface between the gateway stack and a switch is aggregated to evenly distribute inbound packets amongst the nodes of the gateway stack.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.

Abstract: Various embodiments determine relevance of place data by determining whether a place record is relevant based on a set of features associated with the place record. For a given place record, a set of features may be generated based on values of one or more attributes included in the given place record. A given place record may be processed by at least one machine learning model, such as a classifier, which receives as input a set of features of the given place record and outputs a prediction score indicating the certainty or probability that the given place record is associated with, or belongs to, a particular class. The certainty/probability of association between a given place record and a particular class can assist some embodiments in determining (e.g., predicting) whether the given place record is relevant or non-relevant for an intended use, such as a software application for a ride service.

Abstract: Methods and apparatus of modeling work vehicle data and predicting machine failures based on the same are disclosed. An example apparatus includes a text miner to text mine first alert data to identify a first machine failure within the first alert data; a failure alert sequence identifier to identify a first alert sequence associated with the first machine failure; a conditional probability determiner to determine a conditional probability of the first alert sequence leading to failure based on i) a number of work machines in which the first alert sequence ended in failure and ii) a number of work machines in which the first alert sequence did not end in failure; and a collator to update a model by correlating the first alert sequence with the first machine failure and first probable parts used to repair the first machine failure.

Abstract: Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Abstract: Various embodiments determine accuracy of place data by determining a context for a place record (that is included in the place data) and determining accuracy of the place record based on a set of criteria associated with the context determined for the place record. For some embodiments, the set of criteria is used in place of, or in conjunction with, another set of fixed criteria for determining accuracy of the place record. Context for the given place record may be determined based on a set of features for the given place record, and the set of features may be generated (e.g., derived or extracted) based on values of one or more attributes (e.g., record fields or fields) included in the given place record. For some embodiments, a quality of a place record is determined based on at least the determination of an accuracy of the place record.

Abstract: Systems and methods for improving attribute data for a point of interest (POI) are provided. A networked system accesses trip data associated with the POI. The networked system generates, using a processor-implemented clustering algorithm, a first spatial cluster and a second spatial cluster using coordinates corresponding to the POI indicated in the trip data. A centroid for the first spatial cluster and a centroid for the second spatial cluster are identified by the networked system. The networked system determines that a difference in distance between the centroid for the first spatial cluster and the centroid for the second spatial cluster meets or transgresses a centroid distance threshold. In response to the determining, a database is updated to indicate a new attribute for the POI, the new attribute corresponds to an attribute associated with either the first spatial cluster or the second spatial cluster.