Abstract: System and methods are provided for control of a therapeutic delivery system. A monitoring device includes a sensor for measuring a biometric parameter of a patient. A feature extractor generates a set of features for the patient, each of the set of features being associated with one of the patient and the therapeutic delivery system. A predictive model predicts a future value for the biometric parameter at a given time according to the set of features. A therapeutic delivery system controller determines a desired dosage for the therapeutic according to the predicted future value for the patient parameter.

Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a call. The system identifies one or more speakers in a call using one or more speaker-identification parameters. The system processes the call to determine one or more speaker-identification parameters, such as a transcript of the call, a facial image of the speaker, a scene image, which is an image of a scene in which the speaker is located during the call, or textual data associated with the call such as names of the speaker or an organization that are retrieved from the scene images or video data of the call. The system analyzes one or more of the speaker-identification parameters and determines the identity of the speaker. The system then identifies the voice segments associated with the identified speaker and marks the voice segments with the identity of the speaker.

Abstract: A method of communication between two communicating entities, a first communicating entity generating a data message including useful data and an authentication header, the method including: generating a message identifier from a given parameter and a date and inserting the identifier into an authentication header; inserting a plurality of authentication data including at least one user identifier and one equipment identifier into the authentication header; determining and inserting a security profile into the authentication header defining the conditions: of encryption of the useful data of at least the transmission of the message by the first communicating entity; of generation of a data signature of the message and of the format of the generated signature, and inserting the useful data into the message to be transmitted.

Abstract: An apparatus comprises a processing platform configured to implement a machine learning system for automated classification of documents comprising text data of at least one database. The machine learning system comprises a clustering module configured to assign each of the documents to one or more of a plurality of clusters corresponding to respective topics identified from the text data in accordance with at least one topic model, and an interface configured to present portions of documents assigned to a particular one of the clusters by the clustering module and to receive feedback regarding applicability of the corresponding topic to each of one or more of the presented portions on a per-portion basis. The topic model is updated based at least in part on the received feedback. The feedback may comprise, for example, selection of a confidence level for applicability of the topic to a given one of the presented portions.

Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a conversation, without requiring any previously recorded voice sample or any other action by the speaker. The system enables unsupervised learning of speakers' fingerprints and using such fingerprints for identifying a speaker in a recording of a conversation. The system identifies one or more speakers, e.g., representatives of an organization, who are in conversation with other speakers, e.g., customers of the organization. The system processes recordings of conversations between a representative and one or more customers to generate multiple voice segments having a human voice, identifies the voice segments that have the same or a similar feature, and determines the voice in the identified voice segments as the voice of the representative.

Abstract: This invention relates to the treatment of macular edema. Macular edema is the main cause of vision loss during diabetic macular edema, wet AMD (Age Related Macular Degeneration), retinal vein occlusion and chronic intraocular inflammation. Currently, beyond photocoagulation by laser irradiation, two types of drugs are used, protein molecules that neutralize VEGF family members and glucocorticoids, with different mechanisms of action, but targeting one single symptom: macular edema. The inventors have now found that macular edema may be treated by increasing the oncotic pressure of the vitreous. According to the inventors' understanding, causing an increase in the oncotic pressure of the vitreous induces a liquid flow from the interstitial water accumulated in the retina tissue to the vitreous compartment, so as to reduce or stop macular edema.

Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a conversation, without requiring any previously recorded voice sample or any other action by the speaker. The system enables unsupervised learning of speakers' fingerprints and using such fingerprints for identifying a speaker in a recording of a conversation. The system identifies one or more speakers, e.g., representatives of an organization, who are in conversation with other speakers, e.g., customers of the organization. The system processes recordings of conversations between a representative and one or more customers to generate multiple voice segments having a human voice, identifies the voice segments that have the same or a similar feature, and determines the voice in the identified voice segments as the voice of the representative.

Abstract: An apparatus comprises a processing platform configured to implement a machine learning system for automated generation of predicted reliability measures and associated early warning indicators for product and part combinations. The machine learning system comprises a data aggregation module configured to extract product and part data from a big data repository, and a reliability predictor configured to generate predicted reliability measures for respective ones of the product and part combinations utilizing a shared model that is determined based at least in part on the extracted product and part data. The machine learning system processes the predicted reliability measures to generate early warning indicators relating to particular ones of the product and part combinations having predicted reliability measures that fail to meet one or more specified criteria.

Abstract: A photometric test system for LED luminaires. The system uses photodetective panels to detect and measure light. By placing an optical absorber layer with low reflectivity and low transmissivity over the photodetective panels, a detection surface which is also an absorber is achieved. This absorber reduces reflection of incident light from the device under test (DUT), and light reflected from the photodetective panels. A pinhole array can be conveniently used for this purpose. This enables the measurement area of the system to be essentially no larger than the emitting area of the DUT. A diffuser positioned between the absorber layer and the photodetective panels increases the accuracy of the system. Simulations and experimental results show that this system can measure total flux with an uncertainty of 4.3%. The demonstrated system is used in 2? geometry. The system measures total flux, color parameters (such as CCT, CRI, chromaticity) and flicker.

Abstract: An apparatus comprises a processing platform configured to implement an analysis and visualization tool utilizing a mixture of multiple reliability measures to characterize each of a plurality of product and part combinations. The analysis and visualization tool comprises a data aggregation module configured to extract product and part data from a big data repository, a reliability measure generator configured to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations, a mixture model module configured to compute a score from the plurality of reliability measures for each of the different product and part combinations, and a visualization module configured to generate at least one visualization as a function of the scores computed for the respective different product and part combinations. The generated visualization may comprise a quadrant plot view visualization displaying the computed scores.

Abstract: An apparatus comprises a processing platform configured to implement an expert recommender engine. The expert recommender engine receives information relating to a communication from a user device, and identifies at least one subject matter expert for the communication based on the received information and unstructured text data of a service events database. The expert recommender engine is associated with a clustering module that separates the unstructured text data into topic clusters. The expert recommender engine comprises a collaborative filtering module that receives the topic clusters from the clustering module and utilizes the topic clusters to identify the subject matter expert. The user device is connected with an expert device corresponding to the identified subject matter expert. The expert recommender engine may utilize structured data, social media data and customer satisfaction survey data in combination with the received information and the topic clusters to identify the subject matter expert.

Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a conversation, without requiring any previously recorded voice sample or any other action by the speaker. The system enables unsupervised learning of speakers' fingerprints and using such fingerprints for identifying a speaker in a recording of a conversation. The system identifies one or more speakers, e.g., representatives of an organization, who are in conversation with other speakers, e.g., customers of the organization. The system processes recordings of conversations between a representative and one or more customers to generate multiple voice segments having a human voice, identifies the voice segments that have the same or a similar feature, and determines the voice in the identified voice segments as the voice of the representative.

Abstract: A speaker identification system (“system”) automatically assigns a speaker to voiced segments in a call. The system identifies one or more speakers in a call using one or more speaker-identification parameters. The system processes the call to determine one or more speaker-identification parameters, such as a transcript of the call, a facial image of the speaker, a scene image, which is an image of a scene in which the speaker is located during the call, or textual data associated with the call such as names of the speaker or an organization that are retrieved from the scene images or video data of the call. The system analyzes one or more of the speaker-identification parameters and determines the identity of the speaker. The system then identifies the voice segments associated with the identified speaker and marks the voice segments with the identity of the speaker.

Abstract: An apparatus comprises a processing platform configured to implement an analysis and visualization tool for combined processing of structured and unstructured service event data. The structured service event data comprises service event data stored in one or more structured data fields of a service events database, and the unstructured service event data includes documents comprising unstructured text data of the service events database. The analysis and visualization tool is associated with a clustering module that assigns each of the documents to one or more clusters corresponding to respective topics. The analysis and visualization tool comprises an interface that permits selection of one or more of the structured data fields, and a visualization module configured to generate at least one visualization as a function of the selected one or more structured data fields and particular ones of the cluster topics that relate to the selected one or more structured data fields.

Abstract: A method of communication between two communicating entities, a first communicating entity generating a data message including useful data and an authentication header, the method including: generating a message identifier from a given parameter and a date and inserting the identifier into an authentication header; inserting a plurality of authentication data including at least one user identifier and one equipment identifier into the authentication header; determining and inserting a security profile into the authentication header defining the conditions: of encryption of the useful data of at least the transmission of the message by the first communicating entity; of generation of a data signature of the message and of the format of the generated signature, and inserting the useful data into the message to be transmitted.

Abstract: An apparatus comprises a processing platform configured to implement an analytic engine for evaluation of at least one of a converged infrastructure environment and one or more components of the converged infrastructure environment. The analytic engine comprises an extraction module configured to extract one or more features corresponding to the converged infrastructure environment, a learning and modeling module configured to predict an expected quantitative performance value of at least one of the converged infrastructure environment and the one or more components of the converged infrastructure environment based on the extracted one or more features, and comparison and ranking modules. The comparison module is configured to calculate a difference between an actual quantitative performance value of at least one of the converged infrastructure environment and the one or more components of the converged infrastructure environment and the expected quantitative performance value.

Abstract: Systems and methods for communicating in a wireless network using a first radio access technology (RAT) and an assisting RAT are disclosed. A first connection can be established with an access point using the first RAT, and a second connection can be established with the access point using the assisting RAT. A timing of the first connection can be synchronized based at least in part on a timeline of the assisting RAT. Data can be communicated with the access point over at least the first connection based at least in part on synchronizing the timing, and control data can be communicated with the access point over at least the second connection, wherein the control data is related to the communicating data over at least the first connection.

Abstract: Systems and methods for communicating in a wireless network using a first radio access technology (RAT) and an assisting RAT are disclosed. A first connection can be established with an access point using the first RAT, and a second connection can be established with the access point using the assisting RAT. A timing of the first connection can be synchronized based at least in part on a timeline of the assisting RAT. Communications with the access point over at least the first connection can be based at least in part on the synchronized timing.

Abstract: Systems and methods for communicating in a wireless network using a first radio access technology (RAT) and an assisting RAT are disclosed. A first connection can be established with a user equipment (UE) using the first RAT, and a second connection can be established with the UE using an assisting RAT. A timing of the first connection can be synchronized based at least in part on a timeline of the assisting RAT, and a neighboring access point can be collaborated with in communicating with a UE over at least the first connection.

Abstract: An apparatus comprises a processing platform configured to implement a cluster labeling system for documents comprising unstructured text data. The cluster labeling system comprises a clustering module and a visualization module. The clustering module implements a topic model generator and is configured to assign each of the documents to one or more of a plurality of clusters based at least in part on one or more topics identified from the unstructured text data using at least one topic model provided by the topic model generator. The visualization module comprises multiple view generators configured to generate respective distinct visualizations of a selected one of the clusters. The multiple view generators include at least a bigram view generator configured to provide a visualization of a plurality of term pairs from the selected cluster, and a summarization view generator configured to provide a visualization of representative term sequences from the selected cluster.