Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.

Abstract: Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: An electrical component includes an inkjet-printed graphene electrode. Graphene oxide flakes are deposited on a substrate in a graphene oxide ink using an inkjet printer. The deposited graphene oxide is thermally reduced to graphene. The electrical properties of the electrode are comparable to those of electrodes made using activated carbon, carbon nanotubes or graphene made by other methods. The electrical properties of the graphene electrodes may be tailored by adding nanoparticles of other materials to the ink to serve as conductivity enhancers, spacers, or to confer pseudocapacitance. Inkjet-printing can be used to make graphene electrodes of a desired thickness in preselected patterns. Inkjet printing can be used to make highly-transparent graphene electrodes. Inkjet-printed graphene electrodes may be used to fabricate double-layer capacitors that store energy by nanoscale charge separation at the electrode-electrolyte interface (i.e., “supercapacitors”).

Abstract: A multi-layer device is provided for connecting to an electrical unit enclosed within the multi-layer device. A first wafer has a first outer terminal and a second outer terminal with etch pits. A first insulator has a first surface bonded to the first wafer and a first inner terminal located on an opposing second surface. A second wafer has a first surface bonded to the second surface of the first insulating layer and includes a pillar electrically connected to the first wafer. A second insulator has a first surface bonded to a second surface of the second wafer and a second inner terminal located on the first surface of the second insulator. The first outer terminal is electrically connected to the first inner terminal, and the second outer terminal is electrically connected to the second inner terminal. The first and second outer terminals are adapted for connecting to an electrical unit.

Abstract: Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.

Abstract: A multi-layer device is provided for connecting to an electrical unit enclosed within the multi-layer device. A first wafer has a first outer terminal and a second outer terminal with etch pits. A first insulator has a first surface bonded to the first wafer and a first inner terminal located on an opposing second surface. A second wafer has a first surface bonded to the second surface of the first insulating layer and includes a pillar electrically connected to the first wafer. A second insulator has a first surface bonded to a second surface of the second wafer and a second inner terminal located on the first surface of the second insulator. The first outer terminal is electrically connected to the first inner terminal, and the second outer terminal is electrically connected to the second inner terminal. The first and second outer terminals are adapted for connecting to an electrical unit.