Abstract: Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

Abstract: Interference on an uplink between user equipment (UE) and a network site can disrupt the communications between the two. An interference mitigation tool can mitigate the effects such interference has on the communications between the UE and the network site by selecting a mitigation action for implementation at the network site. The interference mitigation tool can receive spectrum scan data from the network site to identify the presence of interference in an operating band of the network site. The interference mitigation tool can then determine possible mitigation actions and can select a mitigation action based on a variety of factors or criteria. Additionally, the interference mitigation tool can reverse previously implemented mitigation actions if the interference is no longer impacting the network site. Further, when selecting a mitigation action, the interference mitigation tool can also consider neighboring network sites and how the selected mitigation action would impact them.

Abstract: Aspects of the invention provide an external interference radar that remedies such shortcomings by providing a hybrid approach that provides an initial spectrum analysis to identify known interferences. The signal is also divided into physical resource blocks (PRB) to better identify interferences that occur intermittently. Once these intermittent interferences are illustrated, a signal pattern detection algorithm is applied to the identified signals and may further accurately identify the intermittent interferences. As part of the identification, the radar further provides a report that identifies whether the interference is a permanent or intermittent; a type of the interference and the severity of the interference.

Abstract: Techniques are disclosed for using a computation engine executing a machine learning system to generate, according to constraints, renderings of a building or building information modeling (BIM) data for the building, wherein the constraints include at least one of an architectural style or a building constraint. In one example, an input device is configured to receive an input indicating one or more surfaces for the building and one or more constraints. A machine learning system executed by a computation engine is configured to apply a model, trained using images of buildings labeled with corresponding constraints for the buildings, to the one or more surfaces for the building to generate at least one of a rendering of the one or more surfaces for the building according to the constraints or BIM data for the building according to the constraints. Further, the machine learning system is configured to output the rendering or the BIM data.

Abstract: Aspects of the invention provide an external interference radar that remedies such shortcomings by providing a hybrid approach that provides an initial spectrum analysis to identify known interferences. The signal is also divided into physical resource blocks (PRB) to better identify interferences that occur intermittently. Once these intermittent interferences are illustrated, a signal pattern detection algorithm is applied to the identified signals and may further accurately identify the intermittent interferences. As part of the identification, the radar further provides a report that identifies whether the interference is a permanent or intermittent; a type of the interference and the severity of the interference.

Abstract: Interference on an uplink between user equipment (UE) and a network site can disrupt the communications between the two. An interference mitigation tool can mitigate the effects such interference has on the communications between the UE and the network site by selecting a mitigation action for implementation at the network site. The interference mitigation tool can receive spectrum scan data from the network site to identify the presence of interference in an operating band of the network site. The interference mitigation tool can then determine possible mitigation actions and can select a mitigation action based on a variety of factors or criteria. Additionally, the interference mitigation tool can reverse previously implemented mitigation actions if the interference is no longer impacting the network site. Further, when selecting a mitigation action, the interference mitigation tool can also consider neighboring network sites and how the selected mitigation action would impact them.

Abstract: The disclosure relates to buffer systems having ammonium hydroxide, ammonium bicarbonate, and optionally monoethanolamine, hair color bases, hair color altering compositions, and kits and methods for altering the color of hair. Hair color bases and hair color altering compositions comprise a buffer system having ammonium hydroxide, ammonium bicarbonate, and optionally monoethanolamine.

Abstract: Interference on an uplink between user equipment (UE) and a network site can disrupt the communications between the two. An interference mitigation tool can mitigate the effects such interference has on the communications between the UE and the network site by selecting a mitigation action for implementation at the network site. The interference mitigation tool can receive spectrum scan data from the network site to identify the presence of interference in an operating band of the network site. The interference mitigation tool can then determine possible mitigation actions and can select a mitigation action based on a variety of factors or criteria. Additionally, the interference mitigation tool can reverse previously implemented mitigation actions if the interference is no longer impacting the network site. Further, when selecting a mitigation action, the interference mitigation tool can also consider neighboring network sites and how the selected mitigation action would impact them.

Abstract: Interference on an uplink between user equipment (UE) and a network site can disrupt the communications between the two. An interference mitigation tool can mitigate the effects such interference has on the communications between the UE and the network site by selecting a mitigation action for implementation at the network site. The interference mitigation tool can receive spectrum scan data from the network site to identify the presence of interference in an operating band of the network site. The interference mitigation tool can then determine possible mitigation actions and can select a mitigation action based on a variety of factors or criteria. Additionally, the interference mitigation tool can reverse previously implemented mitigation actions if the interference is no longer impacting the network site. Further, when selecting a mitigation action, the interference mitigation tool can also consider neighboring network sites and how the selected mitigation action would impact them.

Abstract: Techniques are disclosed for using a computation engine executing a machine learning system to generate, according to constraints, renderings of a building or building information modeling (BIM) data for the building, wherein the constraints include at least one of an architectural style or a building constraint. In one example, an input device is configured to receive an input indicating one or more surfaces for the building and one or more constraints. A machine learning system executed by a computation engine is configured to apply a model, trained using images of buildings labeled with corresponding constraints for the buildings, to the one or more surfaces for the building to generate at least one of a rendering of the one or more surfaces for the building according to the constraints or BIM data for the building according to the constraints. Further, the machine learning system is configured to output the rendering or the BIM data.

Abstract: Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

Abstract: A method of monitoring a valve in a turboshaft engine, said valve switching, by closing and/or opening, in response to a control instruction sent at a determined instant, said method comprising calculating a first form of a time signal from the change in a status variable of said turboshaft engine reacting to a switching of said valve, applying a signature test of the switching of the valve to a form of said signal, wherein the method further comprises defining a time interval after sending said control instruction to perform said signature test; acquiring one or more parameters other than the switching of the valve; modelling a signal of said time signal in response to a change in said other parameter(s) to calculate its change; and calculating said second form of the signal is calculated from the first form of the signal by subtracting therefrom the change in the signal calculated from the change in said other parameter(s), over said time interval following a control instruction.

Abstract: A method of monitoring a valve in a turboshaft engine, said valve switching, by closing and/or opening, in response to a control instruction sent at a determined instant, said method comprising calculating a first form of a time signal from the change in a status variable of said turboshaft engine reacting to a switching of said valve, applying a signature test of the switching of the valve to a form of said signal, wherein the method further comprises defining a time interval after sending said control instruction to perform said signature test; acquiring one or more parameters other than the switching of the valve; modelling a signal of said time signal in response to a change in said other parameter(s) to calculate its change; and calculating said second form of the signal is calculated from the first form of the signal by subtracting therefrom the change in the signal calculated from the change in said other parameter(s), over said time interval following a control instruction.