Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript.

Abstract: A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: A method, apparatus and program product automatically generate a grayscale lithography mask file (76) from a three dimensional (3D) model (72) of a desired topography, e.g., as generated by a three dimensional computer aided design (CAD) tool (70).

Abstract: A method, apparatus and program product automatically generate a grayscale lithography mask file (76) from a three dimensional (3D) model (72) of a desired topography, e.g., as generated by a three dimensional computer aided design (CAD) tool (70).

Abstract: A large volume preconcentrator device for concentrating analytes. A housing accepts an analyte vapor flow, and a plurality of collection surfaces are disposed within the housing. A selectively actuatable heater is disposed on each of the plurality of collection surfaces. At least one selectively actuatable damper is disposed within the housing for selectively restricting a collection flow.

Abstract: Embodiments of the invention provide for three-terminal pressure sensors (“3-TPS”), a method of measuring a pressure with a 3-TPS, and a method of manufacturing a 3-TPS. In some embodiments, the 3-TPS includes a semiconducting layer with cavity and a 3-TPS element having at least one piezoresistive layer overlapping at least a portion of the cavity and oriented at an angle selected to provide a desired sensitivity for the 3-TPS. The method of measuring a pressure with a 3-TPS is performed with a 3-TPS that includes an input terminal, first and second output terminals, and a 3-TPS element, the 3-TPS element overlapping at least a portion of a cavity at a predetermined angle. The method comprises providing an input signal to the input terminal of the 3-TPS, determining a difference between two output signals from the respective output terminals of the 3-TPS, and correlating the determined difference to a pressure.

Abstract: A large volume preconcentrator device for concentrating analytes. A housing accepts an analyte vapor flow, and a plurality of collection surfaces are disposed within the housing. A selectively actuatable heater is disposed on each of the plurality of collection surfaces. At least one selectively actuable damper is disposed within the housing for selectively restricting a collection flow.

Abstract: An embodiment of the invention is directed to a capillary electrophoresis apparatus comprising a plurality of separation micro-channels. A sample loading channel communicates with each of the plurality of separation channels. A driver circuit comprising a plurality of electrodes is configured to induce an electric field across each of the plurality of separation channels sufficient to cause analytes in the samples to migrate along each of the channels. The system further comprises a plurality of detectors configured to detect the analytes.

Abstract: An embodiment of the invention is directed to a capillary electrophoresis apparatus comprising a plurality of separation micro-channels. A sample loading channel communicates with each of the plurality of separation channels. A driver circuit comprising a plurality of electrodes is configured to induce an electric field across each of the plurality of separation channels sufficient to cause analytes in the samples to migrate along each of the channels. The system further comprises a plurality of detectors configured to detect the analytes.

Abstract: Embodiments of the invention provide for three-terminal pressure sensors (“3-TPS”), a method of measuring a pressure with a 3-TPS, and a method of manufacturing a 3-TPS. In some embodiments, the 3-TPS includes a semiconducting layer with cavity and a 3-TPS element having at least one piezoresistive layer overlapping at least a portion of the cavity and oriented at an angle selected to provide a desired sensitivity for the 3-TPS. The method of measuring a pressure with a 3-TPS is performed with a 3-TPS that includes an input terminal, first and second output terminals, and a 3-TPS element, the 3-TPS element overlapping at least a portion of a cavity at a predetermined angle. The method comprises providing an input signal to the input terminal of the 3-TPS, determining a difference between two output signals from the respective output terminals of the 3-TPS, and correlating the determined difference to a pressure.

Abstract: The present invention provides a system 10 for measuring and remotely monitoring strain in an element 1 having a strain sensor 20, a telemetry circuit 40 for transmitting strain data to a remote location, and a reader module 60 for transmitting energy to the telemetry circuit and receiving said data.

Abstract: The present invention is a capillary electrophoresis device, comprising a substrate; a first channel in the substrate, and having a buffer arm and a detection arm; a second channel in the substrate intersecting the first channel, and having a sample arm and a waste arm; a buffer reservoir in fluid communication with the buffer arm; a waste reservoir in fluid communication with the waste arm; a sample reservoir in fluid communication with the sample arm; and a detection reservoir in fluid communication with the detection arm. The detection arm and the buffer arm are of substantially equal length.

Abstract: The present invention provides a system 10 for measuring and remotely monitoring strain in an element 1 having a strain sensor 20, a telemetry circuit 40 for transmitting strain data to a remote location, and a reader module 60 for transmitting energy to the telemetry circuit and receiving said data.

Abstract: The present invention is a capillary electrophoresis device, comprising a substrate; a first channel in the substrate, and having a buffer arm and a detection arm; a second channel in the substrate intersecting the first channel, and having a sample arm and a waste arm; a buffer reservoir in fluid communication with the buffer arm; a waste reservoir in fluid communication with the waste arm; a sample reservoir in fluid communication with the sample arm; and a detection reservoir in fluid communication with the detection arm. The detection arm and the buffer arm are of substantially equal length.

Abstract: A radiation detector includes a substrate having a cavity defined therein, an anode surface positioned in the bottom of the cavity and a cathode positioned adjacent the cavity opening. A drift electrode is juxtaposed over the substrate opposite the cavity and defines a region containing a gaseous medium. As ionized charge pairs are established in the gaseous medium due to radiation provided by an external radiation source, electrons drift toward the anode under the influence of a first electric field established between the anode and drift electrode. Thereafter, the electron undergoes avalanche multiplication with the gaseous medium in an avalanche region defined by a second intense electric field established between the anode and cathode. The structure of the present invention provides an electric field gradient geometry which permits optimal design of the avalanche region geometry, and which further minimizes photon feedback from the cathode.