arXiv subjects

Bo Liu

Publications and source records attributed to Bo Liu.

At least 559 records · Page 31Linked to original sources

arXiv2020

Strategies for achieving high key rates in satellite-based QKD

Quantum key distribution (QKD) is a pioneering quantum technology on the brink of widespread deployment. Nevertheless, the distribution of secret keys beyond a few 100 kilometers at practical rates remains a major challenge. One approach to circumvent lossy terrestrial transmission of entangled photon pairs is the deployment of optical satellite links. Optimizing these non-static quantum links to yield the highest possible key rate is essential for their successful operation. We therefore developed a high-brightness polarization-entangled photon pair source and a receiver module with a fast steering mirror capable of satellite tracking. We employed this state-of-the-art hardware to distribute photons over a terrestrial free-space link with a distance of 143 km, and extracted secure key rates up to 300 bits per second. Contrary to fiber-based links, the channel loss in satellite downlinks is time-varying and the link time is limited to a few minutes. We therefore propose a model-based optimization of link parameters based on current channel and receiver conditions. This model and our field test will prove helpful in the design and operation of future satellite missions and advance the distribution of secret keys at high rates on a global scale.

quant-ph↗

arXiv2020

The Layer-inserting Growth of Antiferromagnetic Topological Insulator MnBi$_2$Te$_4$ Based on Symmetry and Its X-ray Photoelectron Spectroscopy

The antiferromagnetic topological insulator has attracted lots of attention recently, as its intrinsic magnetism and topological property makes it a potential material to realize the quantum anomalous Hall effect (QAHE) at relative high temperature. Until now, only MnBi$_2$Te$_4$ is predicted and grown successfully. The other MB$_2$T$_4$-family materials predicted (MB$_2$T$_4$:M=transition-metal or rare-earth element, B=Bi or Sb, T=Te, Se, or S) with not only antiferromagnetic topological property but also rich and exotic topological quantum states and dynamically stable (or metastable) structure have not been realized on experiment completely. Here, MnBi$_2$Te$_4$ single crystals have been grown successfully and tested. It shows typical antiferromagnetic character, with Neel temperature of 24.5K and a spin-flop transition at H$\thickapprox$35000 Oe, 1.8K. After obtaining MnBi$_2$Te$_4$ single crystals, we have tried to synthesize the other members of MB$_2$T$_4$-family materials, but things are not going so well. Then it inspires us to discuss the growth mechanism of MnBi$_2$Te$_4$. The growth mode may be the layer-inserting growth mode based on symmetry, which is supported by our X-ray photoelectron spectroscopy (XPS) measurement. The XPS measurement combing with the $Ar^+$ ion sputtering is done to investigate the chemical state of MnBi$_2$Te$_4$. Binding energies (BE) of the MnBi$_2$Te$_4$-related contributions to Mn2p and Te3d spectra agree well with those of inserting material $α$-MnTe. Rising intensity of the Mn2p satellite for divalent Mn (bound to chalcogen) with atomic number of ligand (from MnO to MnBi$_2$Te$_4$) has been observed, thus suggesting classification of MnBi$_2$Te$_4$ as the charge-transfer compound. Understanding the growth mode of MnBi$_2$Te$_4$ can help us to grow the other members of MB$_2$T$_4$-family materials.

cond-mat.str-el↗

arXiv2020

APPLR: Adaptive Planner Parameter Learning from Reinforcement

Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.

cs.RO↗

arXiv2020

Extended Abstract: Motion Planners Learned from Geometric Hallucination

Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-quality demonstrations. This requirement is caused by the fact that among the variety of maneuvers the robot can perform, it is difficult to find the single optimal plan without many trial-and-error or an expert who is already capable of doing so. However, given a plan performed in obstacle-free space, it is relatively easy to find an obstacle geometry, where this plan is optimal. We consider this "dual" problem of classical motion planning and name this process of finding appropriate obstacle geometry as hallucination. In this work, we present two different approaches to hallucinate (1) the most constrained and (2) a minimal obstacle space where a given plan executed during an exploration phase in a completely safe obstacle-free environment remains optimal. We then train an end-to-end motion planner that can produce motions to move through realistic obstacles during deployment. Both methods are tested on a physical mobile robot in real-world cluttered environments.

cs.RO↗

arXiv2020

The NVIDIA PilotNet Experiments

Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway. This system represents a departure from the classical approach for self-driving in which the process is manually decomposed into a series of modules, each performing a different task. In PilotNet, on the other hand, a single deep neural network (DNN) takes pixels as input and produces a desired vehicle trajectory as output; there are no distinct internal modules connected by human-designed interfaces. We believe that handcrafted interfaces ultimately limit performance by restricting information flow through the system and that a learned approach, in combination with other artificial intelligence systems that add redundancy, will lead to better overall performing systems. We continue to conduct research toward that goal. This document describes the PilotNet lane-keeping effort, carried out over the past five years by our NVIDIA PilotNet group in Holmdel, New Jersey. Here we present a snapshot of system status in mid-2020 and highlight some of the work done by the PilotNet group.

cs.CV↗

arXiv2020

Google Landmark Recognition 2020 Competition Third Place Solution

We present our third place solution to the Google Landmark Recognition 2020 competition. It is an ensemble of global features only Sub-center ArcFace models. We introduce dynamic margins for ArcFace loss, a family of tune-able margin functions of class size, designed to deal with the extreme imbalance in GLDv2 dataset. Progressive finetuning and careful postprocessing are also key to the solution. Our two submissions scored 0.6344 and 0.6289 on private leaderboard, both ranking third place out of 736 teams.

cs.CV↗

arXiv2020

Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge

We present our winning solution to the SIIM-ISIC Melanoma Classification Challenge. It is an ensemble of convolutions neural network (CNN) models with different backbones and input sizes, most of which are image-only models while a few of them used image-level and patient-level metadata. The keys to our winning are: (1) stable validation scheme (2) good choice of model target (3) carefully tuned pipeline and (4) ensembling with very diverse models. The winning submission scored 0.9600 AUC on cross validation and 0.9490 AUC on private leaderboard.

cs.CV↗

arXiv2020

Spin-wave focusing induced skyrmion generation

We propose a new method to generate magnetic skyrmions through spin-wave focusing in chiral ferromagnets.A lens is constructed to focus spin waves by a curved interface between two ferromagnetic thin films with different perpendicular magnetic anisotropies. Based on the principle of identical magnonic path length, we derive the lens contour that can be either elliptical or hyperbolical depending on the magnon refractive index. Micromagnetic simulations are performed to verify the theoretical design. It is found that under proper condition magnetic skyrmions emerge near the focus point of the lens where the spin-wave intensity has been significantly enhanced. A close investigation shows that a magnetic droplet first forms and then converts to the skyrmion accompanying with a change of topological charge. Phase diagram about the amplitude and duration-time of the exciting field for skyrmion generation is obtained. Our findings would be helpful for designing novel spintronic devices combining the advantages of skyrmionics and magnonics.

cond-mat.mes-hall↗

arXiv2020

Variance-Reduced Off-Policy Memory-Efficient Policy Search

Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators, which results in poor sample efficiency, and have issues with convergence. A few variance-reduced on-policy policy gradient algorithms have been recently proposed that use methods from stochastic optimization to reduce the variance of the gradient estimate in the REINFORCE algorithm. However, these algorithms are not designed for the off-policy setting and are memory-inefficient, since they need to collect and store a large ``reference'' batch of samples from time to time. To achieve variance-reduced off-policy-stable policy optimization, we propose an algorithm family that is memory-efficient, stochastically variance-reduced, and capable of learning from off-policy samples. Empirical studies validate the effectiveness of the proposed approaches.

cs.LG↗

arXiv2020

A trusted-node-free eight-user metropolitan quantum communication network

Quantum communication is rapidly gaining popularity due to its high security and technological maturity. However, most implementations are limited to just two communicating parties (users). Quantum communication networks aim to connect a multitude of users. Here we present a fully connected quantum communication network on a city wide scale without active switching or trusted nodes. We demonstrate simultaneous and secure connections between all 28 pairings of 8 users. Our novel network topology is easily scalable to many users, allows traffic management features and minimises the infrastructure as well as the user hardware needed.

quant-ph↗

arXiv2020

A Broad Absorption Line Outflow Associated with the Broad Emission Line Region in the Quasar SDSS J075133.35+134548.3

We report the discovery of unusual broad absorption lines (BALs) in the bright quasar SDSS J075133.35+134548.3 at z~1, and present a detailed study of BAL and emission lines using the archived and newly obtained optical and NIR spectroscopic data. The BALs are detected tentatively in Mg II, Al III and reliably in HeI*3889, HeI*10830, which show complexed velocity structures splitting into two components: a high-velocity component (HV), with a blueshifted velocity range of -9300 ~ -3500 km/s, can be reliably detected tentatively in Mg II, Al III and reliably in HeI* 10830, whereas it is undetectable in HeI* 3889; and low-Velocity component (LV), with the velocity of -3500 ~ -1800 km/s, is only detected in HeI*3889 and HeI* 10830. With the BALs from different ions, the HV outflowing gas can be constrained to have a density n_H~10^10.3-10^11.4 cm^-3, a column density N_H~10^21 cm^-2 and an ionization parameter U~10^-1.83 - 10^-1.72; and thus inferring a distance of R_HV~0.5 pc from the central continuum source with a monochromatic luminosity of 7.0*10^45 erg/s at 5100 A. This distance is remarkable similar to that of normal broad emission line region (BLR) estimated according to the luminosity-distance from the reverberation experiments, suggesting the association between the BLR and the HV BAL outflowing gas. Interestingly enough, a blueshifted component is also significantly detected in Mg II and Al III broad emission lines (BELs), and the Al III/Mg II of such a BEL component can be reproduced by the physical parameters inferred from the HV BAL gas. The LV BAL gas very likely have a larger column density and a higher ionization level than the HV BAL gas, and hence a smaller distance than that of the latter. Further spectroscopy with high S/N ratio and broader wavelength coverage is needed to confirm this and shed new light on the possible connection between BALs and BELs.

astro-ph.GA↗

arXiv2020

Anisotropic ultrafast spin/valley dynamics in WTe2 films

WTe2 Weyl semimetal hosts the natural broken inversion symmetry and strong spin orbit coupling, making it promising for exotic spin/valley dynamics within a picosecond timescale. Here, we unveil an anisotropic ultrafast spin/valley dynamics in centimeter-scale, single-crystalline Td-WTe2 films using a femtosecond pump-probe technique at room temperature. We observe a transient (~0.8 ps) intra-valley transition and a subsequent polarization duration (~5 ps) during the whole spin/valley relaxation process. Furthermore, the relaxation exhibits the remarkable anisotropy of approximately six-fold and two-fold symmetries due to the intrinsic anisotropy along the crystalline orientation and the extrinsic matrix element effect, respectively. Our results offer a prospect for the ultrafast manipulation of spin/valleytronics in topological quantum materials for dissipationless high-speed spin/valleytronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors

Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current oriented object detection methods mainly rely on two-stage anchor-based detectors. However, the anchor-based detectors typically suffer from a severe imbalance issue between the positive and negative anchor boxes. To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task. In particular, we first detect the center keypoints of the objects, based on which we then regress the box boundary-aware vectors (BBAVectors) to capture the oriented bounding boxes. The box boundary-aware vectors are distributed in the four quadrants of a Cartesian coordinate system for all arbitrarily oriented objects. To relieve the difficulty of learning the vectors in the corner cases, we further classify the oriented bounding boxes into horizontal and rotational bounding boxes. In the experiment, we show that learning the box boundary-aware vectors is superior to directly predicting the width, height, and angle of an oriented bounding box, as adopted in the baseline method. Besides, the proposed method competes favorably with state-of-the-art methods. Code is available at https://github.com/yijingru/BBAVectors-Oriented-Object-Detection.

cs.CV↗

arXiv2020

Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature

Recently, many zero-shot learning (ZSL) methods focused on learning discriminative object features in an embedding feature space, however, the distributions of the unseen-class features learned by these methods are prone to be partly overlapped, resulting in inaccurate object recognition. Addressing this problem, we propose a novel adversarial network to synthesize compact semantic visual features for ZSL, consisting of a residual generator, a prototype predictor, and a discriminator. The residual generator is to generate the visual feature residual, which is integrated with a visual prototype predicted via the prototype predictor for synthesizing the visual feature. The discriminator is to distinguish the synthetic visual features from the real ones extracted from an existing categorization CNN. Since the generated residuals are generally numerically much smaller than the distances among all the prototypes, the distributions of the unseen-class features synthesized by the proposed network are less overlapped. In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features. Extensive experimental results on six benchmark datasets demonstrate that our method could achieve a significantly better performance than existing state-of-the-art methods by 1.2-13.2% in most cases.

cs.CV↗

arXiv2020

Energy recovery operation for continuous-wave X-ray free-electron lasers

A superconducting linear accelerator operating in continuous-wave mode could produce X-ray free electron lasers (XFEL) at megahertz repetition rate, with the capability that delivering wide spectral range coherent radiation to multi end stations. In this Letter, the energy recovery Linac (ERL) mode is proposed to flexibly control the electron beam energy for a continuous-wave superconducting Linac. Theoretical investigations and multi-dimensional numerical simulations are applied to the Linac case of Shanghai high-repetition-rate XFEL and extreme light facility. The results show that, with ERL operation in the last 25 cryo-modules, the strict requirements on RF power system could be significantly relaxed. And if one exhaust the RF power, the maximum electron beam energy can be enhanced from 8.74 GeV to 11.41GeV in ERL mode. The optimization of the ERL operation, the multi-energy electron beam transport and the XFEL performance improvements are presented.

physics.acc-ph↗

arXiv2020

Meta-Learning with Network Pruning

Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks. To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network. A uniform concentration analysis reveals the benefit of network capacity constraint for reducing generalization gap of the proposed meta-learner. We have implemented our approach on top of Reptile assembled with two network pruning routines: Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT). Extensive experimental results on benchmark datasets with different over-parameterized deep networks demonstrate that our method not only effectively alleviates meta-overfitting but also in many cases improves the overall generalization performance when applied to few-shot classification tasks.

cs.LG↗

arXiv2020

Ion specificity modulated inhomogeneous interfacial flow inhibits bubble coalescence in electrolyte solutions

Inhibition of bubble coalescence in electrolyte solutions enables the formation of oceanic whitecaps and affects the heat and mass transfer in many bubble related engineering processes. For different electrolytes, the ability to inhibit bubble coalescence correlates to the ion specificity at the air water interface at an abnormal cation-anion pair relationship, rather than the typically expected cation or anion series that was widely reported in atmospheric, bio- and chemical processes. Here we show that the inhomogeneous interfacial flow, at a different electrolyte concentration from the solution because of the surface specificity of both cation and anion, contributes to the bubble coalescence inhibition behavior in electrolyte solutions. The interfacial flow, achieved with the mobile air-water interface, contributes to the continuous change of electrolyte concentration within the liquid film formed between two colliding bubbles, thereby resulting in a concentration gradient of electrolytes between the thin film and the bulk solution. The electrolyte concentration gradient, hence surface tension gradient, becomes significant to resist film thinning when the film thickness reaches tens of nanometers. The retarded film thinning between two bubbles and delayed bubble coalescence were experimentally captured by high-speed interferometry and quantitatively explained by the proposed electrolyte transportation model. This finding clearly highlights the coupled effect of interfacial flow and ion specificity, and shows important implications for improved understanding of ocean waves, for future development of colloidal science in high concentration electrolyte solutions that is critical for biology, and benefits many applied fields like water splitting and mineral extraction.

cond-mat.soft↗

arXiv2020

APPLD: Adaptive Planner Parameter Learning from Demonstration

Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.

cs.RO↗