Source-independent quantum random number generation

Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics. In general, a physical generator contains two parts---a randomness source and its readout. The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness. However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output. To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted. In our randomness analysis, we make no assumptions about the dimension of the source. For instance, multiphoton emissions are allowed in optical implementations. Our analysis takes into account the finite-key effect with the composable security definition. In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit. In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over $5\times 10^3$ bit/s.Comment: 11 pages, 7 figure

Precision Crystal Calorimetry in High Energy Physics

Crystal Calorimetry is widely used in high energy physics because of its precision. Recent development in crystal technology identified two key issues to reach and maintain crystal precision: light response uniformity and calibration in situ. Crystal radiation damage is understood. While the damage in alkali halides is found to be caused by the oxygen/hydroxyl contamination, it is the structure defects, such as oxygen vacancies, cause damage in oxides.Comment: 8 pages with 13 eps Figures, RevTe

Few-shot Image Generation via Masked Discrimination

Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained on only a few images. The discriminator can easily remember all the training samples and guide the generator to replicate them, leading to severe diversity degradation. Several methods have been proposed to relieve overfitting by adapting GANs pre-trained on large source domains to target domains with limited real samples. In this work, we present a novel approach to realize few-shot GAN adaptation via masked discrimination. Random masks are applied to features extracted by the discriminator from input images. We aim to encourage the discriminator to judge more diverse images which share partially common features with training samples as realistic images. Correspondingly, the generator is guided to generate more diverse images instead of replicating training samples. In addition, we employ cross-domain consistency loss for the discriminator to keep relative distances between samples in its feature space. The discriminator cross-domain consistency loss serves as another optimization target in addition to adversarial loss and guides adapted GANs to preserve more information learned from source domains for higher image quality. The effectiveness of our approach is demonstrated both qualitatively and quantitatively with higher quality and greater diversity on a series of few-shot image generation tasks than prior methods