Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields

Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images. Despite its impressive performance, NeRF is plagued by its necessity for numerous calibrated views and its accuracy diminishes significantly in a few-shot setting. To address this challenge, we propose Self-NeRF, a self-evolved NeRF that iteratively refines the radiance fields with very few number of input views, without incorporating additional priors. Basically, we train our model under the supervision of reference and unseen views simultaneously in an iterative procedure. In each iteration, we label unseen views with the predicted colors or warped pixels generated by the model from the preceding iteration. However, these expanded pseudo-views are afflicted by imprecision in color and warping artifacts, which degrades the performance of NeRF. To alleviate this issue, we construct an uncertainty-aware NeRF with specialized embeddings. Some techniques such as cone entropy regularization are further utilized to leverage the pseudo-views in the most efficient manner. Through experiments under various settings, we verified that our Self-NeRF is robust to input with uncertainty and surpasses existing methods when trained on limited training data.Comment: 11 pages, 11 figure

Epidemic spreading on heterogeneous networks with identical infectivity

In this paper, we propose a modified susceptible-infected-recovered (SIR) model, in which each node is assigned with an identical capability of active contacts, $A$, at each time step. In contrast to the previous studies, we find that on scale-free networks, the density of the recovered individuals in the present model shows a threshold behavior. We obtain the analytical results using the mean-field theory and find that the threshold value equals 1/A, indicating that the threshold value is independent of the topology of the underlying network. The simulations agree well with the analytic results. Furthermore, we study the time behavior of the epidemic propagation and find a hierarchical dynamics with three plateaus. Once the highly connected hubs are reached, the infection pervades almost the whole network in a progressive cascade across smaller degree classes. Then, after the previously infected hubs are recovered, the disease can only propagate to the class of smallest degree till the infected individuals are all recovered. The present results could be of practical importance in the setup of dynamic control strategies.Comment: 5 pages, 3 figure

Repetitive transcranial magnetic stimulation regulates neuroinflammation in neuropathic pain

Neuropathic pain (NP) is a frequent condition caused by a lesion in, or disease of, the central or peripheral somatosensory nervous system and is associated with excessive inflammation in the central and peripheral nervous systems. Repetitive transcranial magnetic stimulation (rTMS) is a supplementary treatment for NP. In clinical research, rTMS of 5–10 Hz is widely placed in the primary motor cortex (M1) area, mostly at 80%–90% RMT, and 5–10 treatment sessions could produce an optimal analgesic effect. The degree of pain relief increases greatly when stimulation duration is greater than 10 days. Analgesia induced by rTMS appears to be related to reestablishing the neuroinflammation system. This article discussed the influences of rTMS on the nervous system inflammatory responses, including the brain, spinal cord, dorsal root ganglia (DRG), and peripheral nerve involved in the maintenance and exacerbation of NP. rTMS has shown an anti-inflammation effect by decreasing pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α, and increasing anti-inflammatory cytokines, including IL-10 and BDNF, in cortical and subcortical tissues. In addition, rTMS reduces the expression of glutamate receptors (mGluR5 and NMDAR2B) and microglia and astrocyte markers (Iba1 and GFAP). Furthermore, rTMS decreases nNOS expression in ipsilateral DRGs and peripheral nerve metabolism and regulates neuroinflammation

Real-time monitoring of magnetic drug targeting using fibered confocal fluorescence microscopy

Magnetic drug targeting has been proposed as means of concentrating therapeutic agents at a target site and the success of this approach has been demonstrated in a number of studies. However, the behavior of magnetic carriers in blood vessels and tumor microcirculation still remains unclear. In this work, we utilized polymeric magnetic nanocapsules (m-NCs) for magnetic targeting in tumors and dynamically visualized them within blood vessels and tumor tissues before, during and after magnetic field exposure using fibered confocal fluorescence microscopy (FCFM). Our results suggested that the distribution of m-NCs within tumor vasculature changed dramatically, but in a reversible way, upon application and removal of a magnetic field. The m-NCs were concentrated and stayed as clusters near a blood vessel wall when tumors were exposed to a magnetic field but without rupturing the blood vessel. The obtained FCFM images provided in vivo in situ microvascular observations of m-NCs upon magnetic targeting with high spatial resolution but minimally invasive surgical procedures. This proof-of-concept descriptive study in mice is envisaged to track and quantify nanoparticles in vivo in a non-invasive manner at microscopic resolution

Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity

In this article, we proposed a susceptible-infected model with identical infectivity, in which, at every time step, each node can only contact a constant number of neighbors. We implemented this model on scale-free networks, and found that the infected population grows in an exponential form with the time scale proportional to the spreading rate. Further more, by numerical simulation, we demonstrated that the targeted immunization of the present model is much less efficient than that of the standard susceptible-infected model. Finally, we investigated a fast spreading strategy when only local information is available. Different from the extensively studied path finding strategy, the strategy preferring small-degree nodes is more efficient than that preferring large-degree nodes. Our results indicate the existence of an essential relationship between network traffic and network epidemic on scale-free networks.Comment: 5 figures and 7 page