All-Optical Spiking Neuron Based On Passive Micro-Resonator

Neuromorphic photonics that aims to process and store information simultaneously like human brains has emerged as a promising alternative for the next generation intelligent computing systems. The implementation of hardware emulating the basic functionality of neurons and synapses is the fundamental work in this field. However, previously proposed optical neurons implemented with SOA-MZIs, modulators, lasers or phase change materials are all dependent on active devices and quite difficult for integration. Meanwhile, although the nonlinearity in nanocavities has long been of interest, the previous theories are intended for specific situations, e.g., self-pulsation in microrings, and there is still a lack of systematic studies in the excitability behavior of the nanocavities including the silicon photonic crystal cavities. Here, we report for the first time a universal coupled mode theory model for all side-coupled passive microresonators. Attributed to the nonlinear excitability, the passive microresonator can function as a new type of all-optical spiking neuron. We demonstrate the microresonator-based neuron can exhibit the three most important characteristics of spiking neurons: excitability threshold, refractory period and cascadability behavior, paving the way to realize all-optical spiking neural networks.Comment: 8 pages, 7 figure

Motion-enhanced Holography

Holographic displays, which enable pixel-level depth control and aberration correction, are considered the key technology for the next-generation virtual reality (VR) and augmented reality (AR) applications. However, traditional holographic systems suffer from limited spatial bandwidth product (SBP), which makes them impossible to reproduce \textit{realistic} 3D displays. Time-multiplexed holography creates different speckle patterns over time and then averages them to achieve a speckle-free 3D display. However, this approach requires spatial light modulators (SLMs) with ultra-fast refresh rates, and current algorithms cannot update holograms at such speeds. To overcome the aforementioned challenge, we proposed a novel architecture, motion-enhanced holography, that achieves \textit{realistic} 3D holographic displays without artifacts by continuously shifting a special hologram. We introduced an iterative algorithm to synthesize motion-enhanced holograms and demonstrated that our method achieved a 10 dB improvement in the peak signal-to-noise ratio (PSNR) of 3D focal stacks in numerical simulations compared to traditional holographic systems. Furthermore, we validated this idea in optical experiments utilizing a high-speed and high-precision programmable three-axis displacement stage to display full-color and high-quality 3D focal stacks.Comment: 10 pages, 5 figure

C. elegans fatty acid two-hydroxylase regulates intestinal homeostasis by affecting heptadecenoic acid production

Background/Aims: The hydroxylation of fatty acids at the C-2 position is the first step of fatty acid α-oxidation and generates sphingolipids containing 2-hydroxy fatty acyl moieties. Fatty acid 2-hydroxylation is catalyzed by Fatty acid 2-hydroxylase (FA2H) enzyme. However, the precise roles of FA2H and fatty acid 2-hydroxylation in whole cell homeostasis still remain unclear. Methods: Here we utilize Caenorhabditis elegans as the model and systemically investigate the physiological functions of FATH-1/C25A1.5, the highly conserved worm homolog for mammalian FA2H enzyme. Immunostaining, dye-staining and translational fusion reporters were used to visualize FATH-1 protein and a variety of subcellular structures. The “click chemistry” method was employed to label 2-OH fatty acid in vivo. Global and tissue-specific RNAi knockdown experiments were performed to inactivate FATH-1 function. Lipid analysis of the fath-1 deficient mutants was achieved by mass spectrometry. Results: C. elegans FATH-1 is expressed at most developmental stages and in most tissues. Loss of fath-1 expression results in severe growth retardation and shortened lifespan. FATH-1 function is crucially required in the intestine but not the epidermis with stereospecificity. The “click chemistry” labeling technique showed that the FATH-1 metabolites are mainly enriched in membrane structures preferable to the apical side of the intestinal cells. At the subcellular level, we found that loss of fath-1 expression inhibits lipid droplets formation, as well as selectively disrupts peroxisomes and apical endosomes. Lipid analysis of the fath-1 deficient animals revealed a significant reduction in the content of heptadecenoic acid, while other major FAs remain unaffected. Feeding of exogenous heptadecenoic acid (C17: 1), but not oleic acid (C18: 1), rescues the global and subcellular defects of fath-1 knockdown worms. Conclusion: Our study revealed that FATH-1 and its catalytic products are highly specific in the context of chirality, C-chain length, spatial distribution, as well as the types of cellular organelles they affect. Such an unexpected degree of specificity for the synthesis and functions of hydroxylated FAs helps to regulate protein transport and fat metabolism, therefore maintaining the cellular homeostasis of the intestinal cells. These findings may help our understanding of FA2H functions across species, and offer potential therapeutical targets for treating FA2H-related diseases

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., $7.5$). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., $512\times512$) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page and codes: https://ml.cs.tsinghua.edu.cn/prolificdreamer/Comment: NeurIPS 2023 (Spotlight

Miniaturized Computational Photonic Molecule Spectrometer

Miniaturized spectrometry system is playing an essential role for materials analysis in the development of in-situ or portable sensing platforms across research and industry. However, there unavoidably exists trade-offs between the resolution and operation bandwidth as the device scale down. Here, we report an extreme miniaturized computational photonic molecule (PM) spectrometer utilizing the diverse spectral characteristics and mode-hybridization effect of split eigenfrequencies and super-modes, which effectively eliminates the inherent periodicity and expands operation bandwidth with ultra-high spectral resolution. These results of dynamic control of the frequency, amplitude, and phase of photons in the photonic multi-atomic systems, pave the way to the development of benchtop sensing platforms for applications previously unfeasible due to resolution-bandwidth-footprint limitations, such as in gas sensing or nanoscale biomedical sensing

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems