We investigate the exciton complexes photoluminescence, dynamics and photon statistics in the concurrent strong weak coupling regime in our unique site controlled singular inverted pyramidal InGaAs/GaAs quantum dots photonic crystal cavities platform
. Different from a clear boundary between strong and weak QD cavity coupling, we demonstrate the strong and weak coupling can coexist dynamically, as a form of intermediate regime mediated by phonon scattering. The detuning dependent microphotoluminescence spectrum reveals concurrence of exciton cavity polariton mode avoided crossing, as a signature of Rabi doublet of the strong coupled system, the blue shifting of coupled exciton cavity mode energy near zero detuning ascribed to the formation of collective states mediated by phonon assisted coupling, and their partial out of synchronization linewidth narrowing linked to their mixed behavior. By detailing the optical features of strongly confined exciton-photon complexes and the quantum statistics of coupled cavity photons, we reveal the dynamics and antibunching/bunching photon statistical signatures of the concurrent strong weak intermediate coupled system at near zero-detuning. This study suggests our device has potential for new and subtle cavity quantum electrodynamical phenomena, cavity enhanced indistinguishable single photon generation, and cluster state generation via the exciton-photon complexes for quantum networks.
PolarLight is a gas pixel X-ray polarimeter mounted on a CubeSat, which was launched into a Sun-synchronous orbit in October 2018. We build a mass model of the whole CubeSat with the Geant4 toolkit to simulate the background induced by the cosmic X-r
ay background (CXB) and high energy charged particles in the orbit. The simulated energy spectra and morphologies of event images both suggest that the measured background with PolarLight is dominated by high energy electrons, with a minor contribution from protons and the CXB. The simulation reveals that, in the energy range of 2-8 keV, there are roughly 28% of the background events are caused by energy deposit from a secondary electron with an energy of a few keV, in a physical process identical to the detection of X-rays. Thus, this fraction of background cannot be discriminated from X-ray events. The background distribution is uneven on the detector plane, with an enhancement near the edges. The edge effect is because high energy electrons tend to produce long tracks, which are discarded by the readout electronics unless they have partial energy deposits near the edges. The internal background rate is expected to be around 6 x 10^-3 counts/s/cm2 in 2-8 keV if an effective particle discrimination algorithm can be applied. This indicates that the internal background should be negligible for future focusing X-ray polarimeters with a focal size in the order of mm.
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate
are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts; and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds.
Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or
surfels, without any knowledge of the scene priors. In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, i.e. Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction with a commodity RGB-D camera. Our PLIVox encodes scene priors considering both the local geometry and uncertainty parameterized by a deep neural network. With such deep priors, we are able to perform online implicit 3D reconstruction achieving state-of-the-art camera trajectory estimation accuracy and mapping quality, while achieving better storage efficiency compared with previous online 3D reconstruction approaches. Our implementation is available at https://www.github.com/huangjh-pub/di-fusion.
In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models. We propose a new highly efficient and accurate approach ba
sed on duality diagram similarity (DDS) between deep neural networks (DNNs). DDS is a generic framework to represent and compare data of different feature dimensions. We validate our approach on the Taskonomy dataset by measuring the correspondence between actual transfer learning performance rankings on 17 taskonomy tasks and predicted rankings. Computing DDS based ranking for $17times17$ transfers requires less than 2 minutes and shows a high correlation ($0.86$) with actual transfer learning rankings, outperforming state-of-the-art methods by a large margin ($10%$) on the Taskonomy benchmark. We also demonstrate the robustness of our model selection approach to a new task, namely Pascal VOC semantic segmentation. Additionally, we show that our method can be applied to select the best layer locations within a DNN for transfer learning on 2D, 3D and semantic tasks on NYUv2 and Pascal VOC datasets.
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynami
c object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving. At the core of our system lies a multi-level probabilistic association mechanism and a heterogeneous Conditional Random Field (CRF) clustering approach combining semantic, spatial and motion information to jointly infer cluster segmentations online for every frame. The poses of camera and dynamic objects are instantly solved through a sliding-window optimization. Our system is evaluated on Oxford Multimotion and KITTI dataset both quantitatively and qualitatively, reaching comparable results to state-of-the-art solutions on both odometry and dynamic trajectory recovery.
Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of
parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights sharing techniques. We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same kernel weights across layers allows to reduce the model size tremendously, more precisely, the network is compressed in memory by a factor of L, where L is the desired depth of the network, disregarding the fully connected layer for prediction. The number of parameters in DACNN barely increases as the network grows deeper, which allows us to build deep DACNNs without any concern about memory costs. We also introduce a partial shared weights network (DACNN-mix) as well as an easy-plug-in module, coined regulators, to boost the performance of our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and SVHN. Our results show that we can save massive amounts of memory with our model, while maintaining a high accuracy performance.
Vertically stacked van der Waals heterostructures constitute a promising platform for providing tailored band alignment with enhanced excitonic systems. Here we report observations of neutral and charged interlayer excitons in trilayer WSe2-MoSe2-WSe
2 van der Waals heterostructures and their dynamics. The addition of a WSe2 layer in the trilayer leads to significantly higher photoluminescence quantum yields and tunable spectral resonance compared to its bilayer heterostructures at cryogenic temperatures. The observed enhancement in the photoluminescence quantum yield is due to significantly larger electron-hole overlap and higher light absorbance in the trilayer heterostructure, supported via first-principle pseudopotential calculations based on spin-polarized density functional theory. We further uncover the temperature- and power-dependence, as well as time-resolved photoluminescence of the trilayer heterostructure interlayer neutral excitons and trions. Our study elucidates the prospects of manipulating light emission from interlayer excitons and designing atomic heterostructures from first-principles for optoelectronics.
