Abstract: A system for predicting the occurrence of an event includes an event detector and a reporting processor. The event detector is configured to: receive data that defines a plurality of social media items; receive a real-time data feed; and predict the occurrence of an event based on a correlation between information in the plurality of social media items and activity associated with the real-time data feed. The reporting processor is configured to determine an event type associated with the event; identify a sentiment of the predicted event based on historical data in the real-time data feed, and generate a recommendation for preventing the occurrence of the event based on at least one of the event type and the sentiment of the predicted event. The recommendation includes a plurality of actions. The reporting processor is coupled to a knowledge graph database that corresponds to an ontology that defines one or more relationships between event types, and response types.

Abstract: Disclosed is an implant and method of making an implant. The implant having a housing that defines a cavity. The housing includes a sensor comprising a base attached to a diaphragm wherein said base may be positioned within said cavity. The sensor may be a capacitive pressure sensor. The diaphragm may be connected to the housing to hermetically seal said housing. The sensor may include electrical contacts positioned on the diaphragm. The attachment between the base and the diaphragm may define a capacitive gap and at least one discontinuity configured to enhance at least one performance parameter of said implant.

Abstract: A device may receive security data identifying assets of an entity, security issues associated with the assets, and objectives associated with the assets and may utilize a data model to generate, based on the security data, asset related data identifying mapped sets of security data. The device may process a first portion of the asset related data, with a first model, to calculate an asset risk likelihood score for an asset of the assets and may process a second portion of the asset related data, with a second model, to calculate an asset criticality score for the asset. The device may process a third portion of the asset related data, with a third model, to calculate an asset control effectiveness score for the asset and may combine the scores to generate a security risk score for the asset. The device may provide the security risk score for display.

Abstract: An anchor assembly for a microelectromechanical systems (MEMS) vibration sensor suspension comprises an anchor body and at least one spring integrally extending from the anchor body. Each spring comprises a first section integrally extending at a first end away from the anchor body to a second end, and first lateral portions of second and third sections extending in opposite lateral directions from the second end. Each of the second and third sections includes a first leg that extends at a first end from the first lateral portion toward the anchor body, a second lateral portion that extends from a second end of the first leg away from the first section, and a second leg that extends from the second lateral portion at a first end away from the anchor body, wherein second ends of the second legs extend farther from the anchor body than the first lateral portions.

Abstract: Disclosed is an implant and method of making an implant. The implant having a housing that defines a cavity. The housing includes a sensor comprising a base attached to a diaphragm wherein said base may be positioned within said cavity. The sensor may be a capacitive pressure sensor. The diaphragm may be connected to the housing to hermetically seal said housing. The sensor may include electrical contacts positioned on the diaphragm. The attachment between the base and the diaphragm may define a capacitive gap and at least one discontinuity configured to enhance at least one performance parameter of said implant.

Abstract: A MEMS diaphragm assembly comprises a first diaphragm, a second diaphragm, and a stationary electrode assembly spaced between the first and second diaphragms and including a plurality of apertures disposed therethrough. Each of a plurality of pillars is disposed through one of the plurality of apertures and connects the first and second diaphragms. At least one of the first and second diaphragms is connected to the stationary electrode assembly at a geometric center of the assembly.

Abstract: A microelectromechanical systems (MEMS) die includes a first diaphragm and a second diaphragm, wherein the first diaphragm and the second diaphragm bound a sealed chamber. A stationary electrode is disposed within the sealed chamber between the first diaphragm and the second diaphragm. A tunnel passes through the first diaphragm and the second diaphragm without passing through the stationary electrode, wherein the tunnel is sealed off from the chamber. The MEMS die further includes a substrate having an opening formed therethrough, wherein the tunnel provides fluid communication from the opening, through the second diaphragm, and through the first diaphragm.

Abstract: A vibration sensor/accelerometer includes, in various implementations, a MEMS die that includes a plate having an aperture, an anchor disposed within the aperture, a plurality of arms (e.g., rigid arms) extending from the anchor, and a plurality of resilient members (e.g., looped or folded springs with a carefully designed spring stiffness), each resilient member connecting the plate to an arm of the plurality of arms. The plate may be made from a solid layer in which the resilient members are etched from the same layer. The MEMS die may also include top and bottom wafers, and travel stoppers extending from the top and bottom wafers as well as through the plate.

Abstract: A MEMS diaphragm assembly comprises a first diaphragm, a second diaphragm, and a stationary electrode assembly spaced between the first and second diaphragms and including a plurality of apertures disposed therethrough. Each of a plurality of pillars is disposed through one of the plurality of apertures and connects the first and second diaphragms. At least one of the first and second diaphragms is connected to the stationary electrode assembly at a geometric center of the assembly.

Abstract: A microelectromechanical systems (MEMS) die includes a first diaphragm and a second diaphragm, wherein the first diaphragm and the second diaphragm bound a sealed chamber. A stationary electrode is disposed within the sealed chamber between the first diaphragm and the second diaphragm. A tunnel passes through the first diaphragm and the second diaphragm without passing through the stationary electrode, wherein the tunnel is sealed off from the chamber. The MEMS die further includes a substrate having an opening formed therethrough, wherein the tunnel provides fluid communication from the opening, through the second diaphragm, and through the first diaphragm.

Abstract: Disclosed is an implant and method of making an implant. The implant having a housing that defines a cavity. The housing includes a sensor comprising a base attached to a diaphragm wherein said base may be positioned within said cavity. The sensor may be a capacitive pressure sensor. The diaphragm may be connected to the housing to hermetically seal said housing. The sensor may include electrical contacts positioned on the diaphragm. The attachment between the base and the diaphragm may define a capacitive gap and at least one discontinuity configured to enhance at least one performance parameter of said implant.

Abstract: A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.

Abstract: A device may receive security data identifying assets of an entity, security issues associated with the assets, and objectives associated with the assets and may utilize a data model to generate, based on the security data, asset related data identifying mapped sets of security data. The device may process a first portion of the asset related data, with a first model, to calculate an asset risk likelihood score for an asset of the assets and may process a second portion of the asset related data, with a second model, to calculate an asset criticality score for the asset. The device may process a third portion of the asset related data, with a third model, to calculate an asset control effectiveness score for the asset and may combine the scores to generate a security risk score for the asset. The device may provide the security risk score for display.

Abstract: In some implementations, a collaborative knowledge system may receive a first set and a second set of privatized embeddings. The first set of privatized embeddings may be generated by a local model based on a first set of private documents associated with a first knowledge domain. The second set of privatized embeddings may be generated by a local model based on a second set of private documents associated with a second, different knowledge domain. The collaborative knowledge system may train, based on the first and second sets of privatized embeddings, a centralized model. The collaborative knowledge system may receive a query associated with the first knowledge domain or the second knowledge domain. The collaborative knowledge system may generate a response to the query based on processing the query with the centralized model. The collaborative knowledge system may provide the response to the query to a user device.

Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: A device may obtain configuration data for a server-side model. The device may generate synthetic usage data based on the configuration data. The device may train the server-side model based on the synthetic usage data. The device may determine update data based on the trained server-side model. The device may provide, to a client device, a message that includes the update data. The message may be configured to cause the client device to train a client-side model based on the update data and historical user data associated with the client device. The device may receive, from the client device, the client-side model data and may select content data based on the client-side model data and the trained server-side model. The device may provide the content data to the client device to permit the client device to provide content that is associated with the content data.

Abstract: A device may obtain configuration data for a server-side model. The device may generate synthetic usage data based on the configuration data. The device may train the server-side model based on the synthetic usage data. The device may determine update data based on the trained server-side model. The device may provide, to a client device, a message that includes the update data. The message may be configured to cause the client device to train a client-side model based on the update data and historical user data associated with the client device. The device may receive, from the client device, the client-side model data and may select content data based on the client-side model data and the trained server-side model. The device may provide the content data to the client device to permit the client device to provide content that is associated with the content data.

Abstract: In some implementations, a device may receive a first document from a first client device. The device may parse the first document into first sentences. The device may receive a second document from a second client device. The device may parse the second document into second sentences. The device may select a first sentence of the first document and a second sentence of the second document. The device may convert the first sentence into a first vector and the second sentence into a second vector. The device may determine a similarity score associated with the first vector and the second vector. The device may determine, based on the similarity score, that a first word set in the first sentence and a second word set in the second sentence correspond to a reference annotation. The device may perform an action associated with the reference annotation.

Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: An image retrieval system may receive an image query that includes image data. The image retrieval system may determine an image descriptor based on the image data. The image retrieval system may obtain a text descriptor associated with the image descriptor in the descriptor repository. The image retrieval system may generate a document query comprising a search parameter, the search parameter including the text descriptor. The image retrieval system may identify, in a document database, text documents based on the document query. The text documents may be associated with document identifiers. The image retrieval system may obtain, from the file mapping repository, image identifiers associated with the document identifiers. The image query result from the image retrieval system may reference images associated with the image identifiers.