Regulation of antigen presentation in dendritic cells

Ph.DDOCTOR OF PHILOSOPH

A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. Inspired by diagonal approximations and factored approximations such as Kronecker-Factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC) in this work, which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC's approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures

Safe motion planning for autonomous vehicles by quantifying uncertainties of deep learning-enabled environment perception

Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this paper proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception

Negative regulation of EGFR signalling by the human folliculin tumour suppressor protein

Germline mutations in the Folliculin (FLCN) tumour suppressor gene result in fibrofolliculomas, lung cysts and renal cancers, but the precise mechanisms of tumour suppression by FLCN remain elusive. Here we identify Rab7A, a small GTPase important for endocytic trafficking, as a novel FLCN interacting protein and demonstrate that FLCN acts as a Rab7A GTPase-activating protein. FLCN-/- cells display slower trafficking of epidermal growth factor receptors (EGFR) from early to late endosomes and enhanced activation of EGFR signalling upon ligand stimulation. Reintroduction of wild-type FLCN, but not tumour-associated FLCN mutants, suppresses EGFR signalling in a Rab7A-dependent manner. EGFR signalling is elevated in FLCN-/- tumours and the EGFR inhibitor afatinib suppresses the growth of human FLCN-/- cells as tumour xenografts. The functional interaction between FLCN and Rab7A appears conserved across species. Our work highlights a mechanism explaining, at least in part, the tumour suppressor function of FLCN

DDX3X loss is an adverse prognostic marker in diffuse large B-cell lymphoma and is associated with chemoresistance in aggressive non-Hodgkin lymphoma subtypes.

Funder: addenbrooke's charitable trust, cambridge university hospital