Generalized quantization condition in topological insulator

The topological magnetoelectric effect (TME) is the fundamental quantization effect for topological insulators in units of the fine structure constant $\alpha$. In [Phys. Rev. Lett. 105, 166803(2010)], a topological quantization condition of the TME is given under orthogonal incidence of the optical beam, in which the wave length of the light or the thickness of the TI film must be tuned to some commensurate values. This fine tuning is difficult to realize experimentally. In this article, we give manifestly $SL(2,\mathbb{Z})$ covariant expressions for Kerr and Faraday angles at oblique incidence at a topological insulator thick film. We obtain a generalized quantization condition independent of material details, and propose a more easily realizable optical experiment, in which only the incidence angle is tuned, to directly measure the topological quantization associated with the TME.Comment: 3 figure

Membrane Potential Batch Normalization for Spiking Neural Networks

As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep models, some effective batch normalization (BN) techniques are proposed in SNNs. All these BNs are suggested to be used after the convolution layer as usually doing in CNNs. However, the spiking neuron is much more complex with the spatio-temporal dynamics. The regulated data flow after the BN layer will be disturbed again by the membrane potential updating operation before the firing function, i.e., the nonlinear activation. Therefore, we advocate adding another BN layer before the firing function to normalize the membrane potential again, called MPBN. To eliminate the induced time cost of MPBN, we also propose a training-inference-decoupled re-parameterization technique to fold the trained MPBN into the firing threshold. With the re-parameterization technique, the MPBN will not introduce any extra time burden in the inference. Furthermore, the MPBN can also adopt the element-wised form, while these BNs after the convolution layer can only use the channel-wised form. Experimental results show that the proposed MPBN performs well on both popular non-spiking static and neuromorphic datasets. Our code is open-sourced at \href{https://github.com/yfguo91/MPBN}{MPBN}.Comment: Accepted by ICCV202

Spiking PointNet: Spiking Neural Networks for Point Clouds

Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application potential. However, it still remains underexplored whether SNNs can be generalized to 3D recognition. To this end, we present Spiking PointNet in the paper, the first spiking neural model for efficient deep learning on point clouds. We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic. To solve the problems simultaneously, we present a trained-less but learning-more paradigm for Spiking PointNet with theoretical justifications and in-depth experimental analysis. In specific, our Spiking PointNet is trained with only a single time step but can obtain better performance with multiple time steps inference, compared to the one trained directly with multiple time steps. We conduct various experiments on ModelNet10, ModelNet40 to demonstrate the effectiveness of Spiking PointNet. Notably, our Spiking PointNet even can outperform its ANN counterpart, which is rare in the SNN field thus providing a potential research direction for the following work. Moreover, Spiking PointNet shows impressive speedup and storage saving in the training phase.Comment: Accepted by NeurIP

Multifunctional targeting micelle nanocarriers with both imaging and therapeutic potential for bladder cancer.

BackgroundWe previously developed a bladder cancer-specific ligand (PLZ4) that can specifically bind to both human and dog bladder cancer cells in vitro and in vivo. We have also developed a micelle nanocarrier drug-delivery system. Here, we assessed whether the targeting micelles decorated with PLZ4 on the surface could specifically target dog bladder cancer cells.Materials and methodsMicelle-building monomers (ie, telodendrimers) were synthesized through conjugation of polyethylene glycol with a cholic acid cluster at one end and PLZ4 at the other, which then self-assembled in an aqueous solution to form micelles. Dog bladder cancer cell lines were used for in vitro and in vivo drug delivery studies.ResultsCompared to nontargeting micelles, targeting PLZ4 micelles (23.2 ± 8.1 nm in diameter) loaded with the imaging agent DiD and the chemotherapeutic drug paclitaxel or daunorubicin were more efficient in targeted drug delivery and more effective in cell killing in vitro. PLZ4 facilitated the uptake of micelles together with the cargo load into the target cells. We also developed an orthotopic invasive dog bladder cancer xenograft model in mice. In vivo studies with this model showed the targeting micelles were more efficient in targeted drug delivery than the free dye (14.3×; P < 0.01) and nontargeting micelles (1.5×; P < 0.05).ConclusionTargeting micelles decorated with PLZ4 can selectively target dog bladder cancer cells and potentially be developed as imaging and therapeutic agents in a clinical setting. Preclinical studies of targeting micelles can be performed in dogs with spontaneous bladder cancer before proceeding with studies using human patients

RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks

Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information thus the multiplications of activations and weights can be replaced by additions when implemented on hardware. However, this quantization mechanism will inevitably introduce quantization error, thus causing catastrophic information loss. To address the quantization error problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes. Our method is extremely simple to implement and straightforward to train an SNN. Furthermore, it is shown to consistently outperform previous state-of-the-art methods over different network architectures and datasets.Comment: Accepted by ICCV202

On the Circular Polarisation of Repeating Fast Radio Bursts

Fast spinning (e.g., sub-second) neutron star with ultra-strong magnetic fields (or so-called magnetar) is one of the promising origins of repeating fast radio bursts (FRBs). Here we discuss circularly polarised emissions produced by propagation effects in the magnetosphere of fast spinning magnetars. We argue that the polarisation-limiting region is well beyond the light cylinder, suggesting that wave mode coupling effects are unlikely to produce strong circular polarisation for fast spinning magnetars. Cyclotron absorption could be significant if the secondary plasma density is high. However, high degrees of circular polarisation can only be produced with large asymmetries in electrons and positrons. We draw attention to the non-detection of circular polarisation in current observations of known repeating FRBs. We suggest that the circular polarisation of FRBs could provide key information on their origins and help distinguish different radiation mechanisms.Comment: ApJ accepte

Novel theranostic nanoporphyrins for photodynamic diagnosis and trimodal therapy for bladder cancer

The overall prognosis of bladder cancer has not been improved over the last 30 years and therefore, there is a great medical need to develop novel diagnosis and therapy approaches for bladder cancer. We developed a multifunctional nanoporphyrin platform that was coated with a bladder cancer-specific ligand named PLZ4. PLZ4-nanoporphyrin (PNP) integrates photodynamic diagnosis, image-guided photodynamic therapy, photothermal therapy and targeted chemotherapy in a single procedure. PNPs are spherical, relatively small (around 23 nm), and have the ability to preferably emit fluorescence/heat/reactive oxygen species upon illumination with near infrared light. Doxorubicin (DOX) loaded PNPs possess slower drug release and dramatically longer systemic circulation time compared to free DOX. The fluorescence signal of PNPs efficiently and selectively increased in bladder cancer cells but not normal urothelial cells in vitro and in an orthotopic patient derived bladder cancer xenograft (PDX) models, indicating their great potential for photodynamic diagnosis. Photodynamic therapy with PNPs was significantly more potent than 5-aminolevulinic acid, and eliminated orthotopic PDX bladder cancers after intravesical treatment. Image-guided photodynamic and photothermal therapies synergized with targeted chemotherapy of DOX and significantly prolonged overall survival of mice carrying PDXs. In conclusion, this uniquely engineered targeting PNP selectively targeted tumor cells for photodynamic diagnosis, and served as effective triple-modality (photodynamic/photothermal/chemo) therapeutic agents against bladder cancers. This platform can be easily adapted to individualized medicine in a clinical setting and has tremendous potential to improve the management of bladder cancer in the clinic