A method, performed by an electronic device, of predicting an electronic structure of a material includes: receiving input data of a user related to elements constituting the first material; applying the received input data to a trained model for estimating a density of state of the first material; and outputting a first graph indicating the density of state for each energy level of the first material output from the trained model, wherein the trained model is trained to generate the first graph based on pre-input data about a plurality of second materials composed of at least some of the elements constituting the first material and a plurality of second graphs representing the density of state for each energy level of the plurality of second materials.

A method, performed by an electronic device, of predicting an electronic structure of a material includes: receiving input data of a user related to elements constituting the first material; applying the received input data to a trained model for estimating a density of state of the first material; and outputting a first graph indicating the density of state for each energy level of the first material output from the trained model, wherein the trained model is trained to generate the first graph based on pre-input data about a plurality of second materials composed of at least some of the elements constituting the first material and a plurality of second graphs representing the density of state for each energy level of the plurality of second materials.

A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed outside of the first host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that a hidden process exists and corrective action can be taken.

A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed outside of the first host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that a hidden process exists and corrective action can be taken.

A novel and useful quantum computing machine architecture that includes a classic computing core as well as a quantum computing core. A programmable pattern generator executes sequences of instructions that control the quantum core. In accordance with the sequences, a pulse generator functions to generate the control signals that are input to the quantum core to perform quantum operations. A partial readout of the quantum state in the quantum core is generated that is subsequently re-injected back into the quantum core to extend decoherence time. Access gates control movement of quantum particles in the quantum core. Errors are corrected from the partial readout before being re-injected back into the quantum core. Internal and external calibration loops calculate error syndromes and calibrate the control pulses input to the quantum core. Control of the quantum core is provided from an external support unit via the pattern generator or can be retrieved from classic memory where sequences of commands for the quantum core are stored a priori in the memory. A cryostat unit functions to provide several temperatures to the quantum machine including a temperature to cool the quantum computing core to approximately 4 Kelvin.

A novel and useful quantum computing machine architecture that includes a classic computing core as well as a quantum computing core. A programmable pattern generator executes sequences of instructions that control the quantum core. In accordance with the sequences, a pulse generator functions to generate the control signals that are input to the quantum core to perform quantum operations. A partial readout of the quantum state in the quantum core is generated that is subsequently re-injected back into the quantum core to extend decoherence time. Access gates control movement of quantum particles in the quantum core. Errors are corrected from the partial readout before being re-injected back into the quantum core. Internal and external calibration loops calculate error syndromes and calibrate the control pulses input to the quantum core. Control of the quantum core is provided from an external support unit via the pattern generator or can be retrieved from classic memory where sequences of commands for the quantum core are stored a priori in the memory. A cryostat unit functions to provide several temperatures to the quantum machine including a temperature to cool the quantum computing core to approximately 4 Kelvin.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing a robustness assessment operation, the robustness assessment operation assessing robustness of the cognitive computing function, the robustness assessment operation generating a robustness score representing robustness of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

Systems, methods, and computer-readable media for managing compromised sensors in multi-tiered virtualized environments. In some embodiments, a system can receive, from a first capturing agent deployed in a virtualization layer of a first device, data reports generated based on traffic captured by the first capturing agent. The system can also receive, from a second capturing agent deployed in a hardware layer of a second device, data reports generated based on traffic captured by the second capturing agent. Based on the data reports, the system can determine characteristics of the traffic captured by the first capturing agent and the second capturing agent. The system can then compare the characteristics to determine a multi-layer difference in traffic characteristics. Based on the multi-layer difference in traffic characteristics, the system can determine that the first capturing agent or the second capturing agent is in a faulty state.

Systems, methods, and computer-readable media for managing compromised sensors in multi-tiered virtualized environments. In some embodiments, a system can receive, from a first capturing agent deployed in a virtualization layer of a first device, data reports generated based on traffic captured by the first capturing agent. The system can also receive, from a second capturing agent deployed in a hardware layer of a second device, data reports generated based on traffic captured by the second capturing agent. Based on the data reports, the system can determine characteristics of the traffic captured by the first capturing agent and the second capturing agent. The system can then compare the characteristics to determine a multi-layer difference in traffic characteristics. Based on the multi-layer difference in traffic characteristics, the system can determine that the first capturing agent or the second capturing agent is in a faulty state.

The present disclosure relates to a method of reducing a size of an artificial intelligence model by an electronic device, including: inputting an input value for training to a first model; training the first model for performing a specific task based on the input value; inputting the input value to a second model; and training the second model based on an output value of the first model, in which the first model may be an artificial intelligence model larger in size than the second model.