Abstract: The present disclosure describes a method for conditioning and preprocessing raw cosmic ray data to extract features and generate a binary string representing the inherent randomness in the data. Measurable characteristics of cosmic ray particles, such as arrival times and amplitudes, are processed to create random binary sequences. These binary strings are then used to train a generative adversarial network (GAN) framework. In the GAN framework, the generator creates random sequences resembling the target distribution, which matches the entropy of the conditioned cosmic data. The discriminator evaluates the generated sequences by comparing them to the conditioned cosmic data and assigns a randomness score that characterizes the quality of the generated sequences. This adversarial process ensures the generation of high-quality random sequences that are statistically indistinguishable from the conditioned input data.

Abstract: The present disclosure describes a method for conditioning and preprocessing raw cosmic ray data to extract features and generate a binary string representing the inherent randomness in the data. Measurable characteristics of cosmic ray particles, such as arrival times and amplitudes, are processed to create random binary sequences. These binary strings are then used to train a generative adversarial network (GAN) framework. In the GAN framework, the generator creates random sequences resembling the target distribution, which matches the entropy of the conditioned cosmic data. The discriminator evaluates the generated sequences by comparing them to the conditioned cosmic data and assigns a randomness score that characterizes the quality of the generated sequences. This adversarial process ensures the generation of high-quality random sequences that are statistically indistinguishable from the conditioned input data.

Abstract: The present disclosure describes a method for conditioning and preprocessing raw cosmic ray data to extract features and generate a binary string representing the inherent randomness in the data. Measurable characteristics of cosmic ray particles, such as arrival times and amplitudes, are processed to create random binary sequences. These binary strings are then used to train a generative adversarial network (GAN) framework. In the GAN framework, the generator creates random sequences resembling the target distribution, which matches the entropy of the conditioned cosmic data. The discriminator evaluates the generated sequences by comparing them to the conditioned cosmic data and assigns a randomness score that characterizes the quality of the generated sequences. This adversarial process ensures the generation of high-quality random sequences that are statistically indistinguishable from the conditioned input data.

Abstract: The present disclosure describes a method for conditioning and preprocessing raw cosmic ray data to extract features and generate a binary string representing the inherent randomness in the data. Measurable characteristics of cosmic ray particles, such as arrival times and amplitudes, are processed to create random binary sequences. These binary strings are then used to train a generative adversarial network (GAN) framework. In the GAN framework, the generator creates random sequences resembling the target distribution, which matches the entropy of the conditioned cosmic data. The discriminator evaluates the generated sequences by comparing them to the conditioned cosmic data and assigns a randomness score that characterizes the quality of the generated sequences. This adversarial process ensures the generation of high-quality random sequences that are statistically indistinguishable from the conditioned input data.

Abstract: A verification system using additional factors such as biometrics can provide a tenant system with the ability to verify the identity of an end user. The enrollment and verification can be performed without sharing identity knowledge between the tenant and the verification ensuring the privacy of the end user. The enrollment and verification can also be performed in an auditable way while maintaining anonymity.

Abstract: A verification system using additional factors such as biometrics can provide a tenant system with the ability to verify the identity of an end user. The enrollment and verification can be performed without sharing identity knowledge between the tenant and the verification ensuring the privacy of the end user. The enrollment and verification can also be performed in an auditable way while maintaining anonymity.

Abstract: A verification system using additional factors such as biometrics can provide a tenant system with the ability to verify the identity of an end user. The enrollment and verification can be performed without sharing identity knowledge between the tenant and the verification ensuring the privacy of the end user. The enrollment and verification can also be performed in an auditable way while maintaining anonymity.