Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Arenavirus-Host Interactions: Roles of Viral Glycoprotein and Nucleoprotein in Mediating Cell Entry and Host Immune Suppression

University of Minnesota Ph.D. dissertation.June 2018. Major: Veterinary Medicine. Advisors: Yuying Liang, Hinh Ly. 1 computer file (PDF); vii, 109 pages.Several mammalian arenaviruses can infect humans and non-human primates and can cause severe and deadly hemorrhagic fever diseases. There are currently no FDA-approved vaccines available for the prevention of infection by any of these pathogenic arenaviruses and treatment options for the infections are extremely limited. Arenavirus is an enveloped, bi-segmented, single-stranded RNA virus. Its genome encodes four viral proteins: the RNA-dependent RNA polymerase L, the nucleoprotein NP, the glycoprotein complex GPC, and the matrix protein Z. Arenaviral GPC plays critical roles in the first step of virus infection (i.e, cell entry), which involves receptor recognition and virus-host membrane fusion activity. Arenaviral NP is a multifunctional protein, which is involved in the formation of the viral ribonucleoprotein (RNP) complex needed for viral genome replication and transcription, and in host immunosuppression. Arenaviral L protein, together with NP, is responsible for genome replication and transcription. Arenaviral Z protein is the driving force of virion budding from the membrane of the infected cells and is also involved in mediating immune suppression. My study focused on the roles of arenaviral GPC and NP in mediating cell entry and host immune suppression. Arenaviral GPC is synthesized as a single polypeptide and is post-translationally processed by the cellular signal peptidase and S1P cellular protease into stable signal peptide (SSP), the receptor-binding GP1 and transmembrane GP2 subunits. My thesis focused on characterizing (1) the role of the glycoprotein SSP in mediating entry of the Pichinde virus (PICV) into cells in culture and its role in disease pathogenesis in guinea pigs, (2) the role of several conserved amino acids residues in the glycoprotein GP2 C-terminal domain (CTD) of PICV in mediating virus entry in vitro, and (3) the role of NP in mediating immune suppression via the PACT-RIG-I innate immune pathway