HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de

Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.INDEX TERMS Deep learning, machine learning, unsupervised learning, generative adversarial networks.

Hardware Trojans (HTs) implemented by adversaries serve as backdoors to subvert or augment the normal operation of infected devices, which may lead to functionality changes, sensitive information leakages, or Denial of Service attacks. To tackle such threats, this paper proposes a novel verification technique for hardware trust, namely VeriTrust, which facilitates to detect HTs inserted at design stage. Based on the observation that HTs are usually activated by dedicated trigger inputs that are not sensitized with verification test cases, VeriTrust automatically identifies such potential HT trigger inputs by examining verification corners. The key difference between VeriTrust and existing HT detection techniques is that VeriTrust is insensitive to the implementation style of HTs. Experimental results show that VeriTrust is able to detect all HTs evaluated in this paper (constructed based on various HT design methodologies shown in the literature) at the cost of moderate extra verification time, which is not possible with existing solutions.

Spontaneous canine cancers are valuable but relatively understudied and underutilized models. To enhance their usage, we reanalyze whole exome and genome sequencing data published for 684 cases of >7 common tumor types and >35 breeds, with rigorous quality control and breed validation. Our results indicate that canine tumor alteration landscape is tumor type-dependent, but likely breed-independent. Each tumor type harbors major pathway alterations also found in its human counterpart (e.g., PI3K in mammary tumor and p53 in osteosarcoma). Mammary tumor and glioma have lower tumor mutational burden (TMB) (median < 0.5 mutations per Mb), whereas oral melanoma, osteosarcoma and hemangiosarcoma have higher TMB (median ≥ 1 mutations per Mb). Across tumor types and breeds, TMB is associated with mutation of TP53 but not PIK3CA, the most mutated genes. Golden Retrievers harbor a TMB-associated and osteosarcoma-enriched mutation signature. Here, we provide a snapshot of canine mutations across major tumor types and breeds.