Spontaneous emission from individual atoms in vapor lasts nanoseconds, if not microseconds, and beatings in this emission involve only directly excited energy sublevels. In contrast, the superfluorescent emissions burst on a much-reduced timescale an
d their beatings involve both directly and indirectly excited energy sublevels. In this work, picosecond and femtosecond superfluorescent beatings are observed from a dense cesium atomic vapor. Cesium atoms are excited by 60-femtosecond long, 800 nm laser pulses via two-photon processes into their coherent superpositions of the ground 6S and excited 8S states. As a part of the transient four wave mixing process, the yoked superfluorescent blue light at lower transitions of 6S - 7P are recorded and studied. Delayed buildup time of this blue light is measured as a function of the input laser beam power using a high-resolution 2 ps streak camera. The power dependent buildup delay time is consistently doubled as the vapor temperature is lowered to cut the number of atoms by half. At low power and density, a beating with a period of 100 picoseconds representing the ground state splitting is observed. The autocorrelation measurements of the generated blue light exhibit a beating with a quasi-period of 230 fs corresponding to the splitting of the 7P level primarily at lower input laser power. Understanding and, eventually, controlling the intriguing nature of superfluorescent beatings may permit a rapid quantum operation free from the rather slow spontaneous emission processes from atoms and molecules.
We prove the positive energy conjecture for a class of asymptotically Horowitz-Myers metrics on $mathbb{R}^{2}timesmathbb{T}^{n-2}$. This generalizes the previous results of Barzegar-Chru{s}ciel-H{o}rzinger-Maliborski-Nguyen as well as the authors.
Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or ``tricks, such as data augmentation
, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, different training schedules, different backbone architectures and even different input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing seventeen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, given the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results show that such kind of mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to work on few-shot learning but also remove the effects of the nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://github.com/RL-VIG/LibFewShot.
Teleoperation of robots enables remote intervention in distant and dangerous tasks without putting the operator in harms way. However, remote operation faces fundamental challenges due to limits in communication delay and bandwidth. The proposed work
improves the performances of teleoperation architecture based on Fractal Impedance Controller (FIC), by integrating the most recent manipulation architecture in the haptic teleoperation pipeline. The updated controller takes advantage of the inverse kinematics optimisation in the manipulation, and hence improves dynamic interactions during fine manipulation without renouncing the robustness of the FIC controller. Additionally, the proposed method allows an online trade-off between the manipulation controller and the teleoperated behaviour, allowing a safe superimposition of these two behaviours. The validated experimental results show that the proposed method is robust to reduced communication bandwidth and delays. Moreover, we demonstrated that the remote teleoperated robot remains stable and safe to interact with, even when the communication with the master side is abruptly interrupted.
Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is li
mited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.
Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms have been shown to be a promising technique to achieve this goal. Howeve
r, acquiring high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor. In the present work, we apply synthetic images, which resemble the experimental image data in terms of geometrical and visual features, to train state-of-art deep learning-based Mask R-CNN algorithms to segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material, within optical intensity-based images from spectromicroscopy. The performance evaluation demonstrates that even though the deep learning model is trained on pure synthetically generated structures, it can segment real optical intensity-based spectromicroscopy images of complex V2O5 nanowire structures in overlapped particle networks, thus providing reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy (SEM) images, which are fundamentally different from the training dataset known to the model. The proposed methodology of using a purely synthetic dataset to train the deep learning model can be extended to any optical intensity-based images of variable particle morphology, extent of agglomeration, material class, and beyond.
Legged robots are becoming increasingly powerful and popular in recent years for their potential to bring the mobility of autonomous agents to the next level. This work presents a deep reinforcement learning approach that learns a robust Lidar-based
perceptual locomotion policy in a partially observable environment using Proximal Policy Optimisation. Visual perception is critical to actively overcome challenging terrains, and to do so, we propose a novel learning strategy: Dynamic Reward Strategy (DRS), which serves as effective heuristics to learn a versatile gait using a neural network architecture without the need to access the history data. Moreover, in a modified version of the OpenAI gym environment, the proposed work is evaluated with scores over 90% success rate in all tested challenging terrains.
Based on first-principles calculations and symmetry analysis, we predict atomically thin ($1-N$ layers) 2H group-VIB TMDs $MX_2$ ($M$ = Mo, W; $X$ = S, Se, Te) are large-gap higher-order topological crystalline insulators protected by $C_3$ rotation
symmetry. We explicitly demonstrate the nontrivial topological indices and existence of the hallmark corner states with quantized fractional charge for these familiar TMDs with large bulk optical band gaps ($1.64-1.95$ eV for the monolayers), which would facilitate the experimental detection by STM. We find that the well-defined corner states exist in the triangular finite-size flakes with armchair edges of the atomically thin ($1-N$ layers) 2H group-VIB TMDs, and the corresponding quantized fractional charge is the number of layers $N$ divided by 3 modulo integers, which will simply double including spin degree of freedom.
The powerful learning ability of deep neural networks enables reinforcement learning (RL) agents to learn competent control policies directly from high-dimensional and continuous environments. In theory, to achieve stable performance, neural networks
assume i.i.d. inputs, which unfortunately does no hold in the general RL paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of catastrophic interference and the collapse in performance as later training is likely to overwrite and interfer with previously learned policies. In this paper, we introduce the concept of context into single-task RL and develop a novel scheme, termed as Context Division and Knowledge Distillation (CDaKD) driven RL, to divide all states experienced during training into a series of contexts. Its motivation is to mitigate the challenge of aforementioned catastrophic interference in deep RL, thereby improving the stability and plasticity of RL models. At the heart of CDaKD is a value function, parameterized by a neural network feature extractor shared across all contexts, and a set of output heads, each specializing on an individual context. In CDaKD, we exploit online clustering to achieve context division, and interference is further alleviated by a knowledge distillation regularization term on the output layers for learned contexts. In addition, to effectively obtain the context division in high-dimensional state spaces (e.g., image inputs), we perform clustering in the lower-dimensional representation space of a randomly initialized convolutional encoder, which is fixed throughout training. Our results show that, with various replay memory capacities, CDaKD can consistently improve the performance of existing RL algorithms on classic OpenAI Gym tasks and the more complex high-dimensional Atari tasks, incurring only moderate computational overhead.
Autonomous surgical execution relieves tedious routines and surgeons fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the si
mulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.
