MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table

Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exceptionally fast training by reducing the complexity of multilayer perceptron (MLP) networks. However, storing features in dense grids demands a substantial amount of memory space, resulting in a notable memory bottleneck within computer system. Consequently, it leads to a significant increase in training times without prior hyper-parameter tuning. To address this issue, in this work, we are the first to propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality. Specifically, we first design a mixed-feature hash encoding to adaptively mix part of multi-level feature grids and map it to a single hash table. Following that, in order to obtain the correct index of a grid point, we further develop an index transformation method that transforms indices of an arbitrary level grid to those of a canonical grid. Extensive experiments benchmarking with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality

An Improved Global Harmony Search Algorithm for the Identification of Nonlinear Discrete-Time Systems Based on Volterra Filter Modeling

This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.Comment: Published as a conference paper at NeurIPS 202

Addressless: A New Internet Server Model to Prevent Network Scanning

Eliminating unnecessary exposure is a principle of server security. The huge IPv6 address space enhances security by making scanning infeasible, however, with recent advances of IPv6 scanning technologies, network scanning is again threatening server security. In this paper, we propose a new model named addressless server, which separates the server into an entrance module and a main service module, and assigns an IPv6 prefix instead of an IPv6 address to the main service module. The entrance module generates a legitimate IPv6 address under this prefix by encrypting the client address, so that the client can access the main server on a destination address that is different in each connection. In this way, the model provides isolation to the main server, prevents network scanning, and minimizes exposure. Moreover it provides a novel framework that supports flexible load balancing, high-availability, and other desirable features. The model is simple and does not require any modification to the client or the network. We implement a prototype and experiments show that our model can prevent the main server from being scanned at a slight performance cost

The intestinal γδ T cells: functions in the gut and in the distant organs

Located in the frontline against the largest population of microbiota, the intestinal mucosa of mammals has evolved to become an effective immune system. γδ T cells, a unique T cell subpopulation, are rare in circulation blood and lymphoid tissues, but rich in the intestinal mucosa, particularly in the epithelium. Via rapid production of cytokines and growth factors, intestinal γδ T cells are key contributors to epithelial homeostasis and immune surveillance of infection. Intriguingly, recent studies have revealed that the intestinal γδ T cells may play novel exciting functions ranging from epithelial plasticity and remodeling in response to carbohydrate diets to the recovery of ischemic stroke. In this review article, we update regulatory molecules newly defined in lymphopoiesis of the intestinal γδ T cells and their novel functions locally in the intestinal mucosa, such as epithelial remodeling, and distantly in pathological setting, e.g., ischemic brain injury repair, psychosocial stress responses, and fracture repair. The challenges and potential revenues in intestinal γδ T cell studies are discussed