arXiv subjects

Xi Chen

Publications and source records attributed to Xi Chen.

At least 1,081 records · Page 60Linked to original sources

arXiv2018

Single-layer graphdiyne on Pt(111): Improved catalysis confined under two-dimensional overlayer

In recent years, two-dimensional confined catalysis, i.e. the enhanced catalytic reactions in confined spaces between metal surface and two-dimensional overlayer, makes a hit and opens up a new way to enhance the performance of catalysts. In this work, graphdiyne overlayer was proposed as a more excellent material than graphene or hexagonal boron nitride for two-dimensional confined catalysis. Density functional theory calculations revealed the superiority of graphdiyne overlayer originated from the steric hindrance effect which increases the catalytic ability and lowers the reaction barriers. Moreover, with the big triangle holes as natural gas tunnels, graphdiyne possesses higher efficiency for the transit of gaseous reactants and products than graphene or hexagonal boron nitride. The results in this work would benefit future development two-dimensional confined catalysis.

cond-mat.mtrl-sci↗

arXiv2018

A Primary Exploration to Quasi-Two-Dimensional Rare-Earth Ferromagnetic Particles: Holmium-Doped MoS2 Sheet as Room-Temperature Magnetic Semiconductor

Recently, two-dimensional materials and nanoparticles with robust ferromagnetism are even of great interest to explore basic physics in nanoscale spintronics. More importantly, room-temperature magnetic semiconducting materials with high Curie temperature is essential for developing next-generation spintronic and quantum computing devices. Here, we develop a theoretical model on the basis of density functional theory calculations and the Ruderman-Kittel-Kasuya-Yoshida theory to predict the thermal stability of two-dimensional magnetic materials. Compared with other rare-earth (dysprosium (Dy) and erbium (Er)) and 3d (copper (Cu)) impurities, holmium-doped (Ho-doped) single-layer 1H-MoS2 is proposed as promising semiconductor with robust magnetism. The calculations at the level of hybrid HSE06 functional predict a Curie temperature much higher than room temperature. Ho-doped MoS2 sheet possesses fully spin-polarized valence and conduction bands, which is a prerequisite for flexible spintronic applications.

cond-mat.mtrl-sci↗

arXiv2018

Exploration of Graph Computing in Power System State Estimation

With the increased complexity of power systems due to the integration of smart grid technologies and renewable energy resources, more frequent changes have been introduced to system status, and the traditional serial mode of state estimation algorithm cannot well meet the restrict time-constrained requirement for the future dynamic power grid, even with advanced computer hardware. To guarantee the grid reliability and minimize the impacts caused by system status fluctuations, a fast, even SCADA-rate, state estimator is urgently needed. In this paper, a graph based power system modeling is firstly explored and a graph computing based state estimation is proposed to speed up its performance. The power system is represented by a graph, which is a collection of vertices and edges, and the measurements are attributes of vertices and edges. Each vertex can independently implement local computation, like formulations of the node-based H matrix, gain matrix and righthand-side (RHS) vector, only with the information on its connected edges and neighboring vertices. Then, by taking advantages of graph database, these node-based data are conveniently collected and stored in the compressed sparse row (CSR) format avoiding the complexity and heaviness introduced by the sparse matrices. With communications and synchronization, centralized computation of solving the weighted least square (WLS) state estimation is completed with hierarchical parallel computing. The proposed strategy is implemented on a graph database platform. The testing results of IEEE 14-bus, IEEE 118-bus systems and a provincial system in China verify the accuracy and high-performance of the proposed methodology.

eess.SY↗

arXiv2018

Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor skills.

cs.LG↗

arXiv2018

A Simple Neural Attentive Meta-Learner

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.

cs.AI↗

arXiv2018

Improved Selection Criteria for HII Regions, based on IRAS Sources

We present new criteria for selecting HII regions from the Infrared Astronomical Satellite (IRAS) Point Source catalogue (PSC), based on an HII region catalogue derived manually from the all-sky Wide-field Infrared Survey Explorer (WISE). The criteria are used to augment the number of HII region candidates in the Milky Way. The criteria are defined by the linear decision boundary of two samples: IRAS point sources associated with known HII regions, which serve as the HII region sample, and IRAS point sources at high Galactic latitudes, which serve as the non-HII region sample. A machine learning classifier, specifically a support vector machine (SVM), is used to determine the decision boundary. We investigate all combinations of four IRAS bands and suggest that the optimal criterion is log(F$_{\rm 60}$/F$_{\rm 12}$)$\ge$(-0.19$\times$log(F$_{\rm 100}$/F$_{\rm 25}$)+ 1.52), with detections at 60 and 100 micron. This selects 3041 HII region candidates from the IRAS PSC. We find that IRAS HII region candidates show evidence of evolution on the two-colour diagram. Merging the WISE HII catalogue with IRAS HII region candidates, we estimate a lower limit of approximately 10200 for the number of HII regions in the Milky Way.

astro-ph.GA↗

arXiv2018

Web-Scale Responsive Visual Search at Bing

In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.

cs.CV↗

arXiv2018

Automatic Conflict Detection in Police Body-Worn Audio

Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions. Moreover, standard techniques to compute them fall short when applied to such diversified and noisy contexts. We develop a pipeline catered to this task combining adaptive noise removal, non-speech filtering and new measures of conflict based on the repetition and intensity of phrases in speech. We demonstrate the effectiveness of our approach on body-worn audio data collected by the Los Angeles Police Department.

eess.AS↗

arXiv2018

Distribution-free Junta Testing

We study the problem of testing whether an unknown $n$-variable Boolean function is a $k$-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over $\{0,1\}^n$. Our first main result is that distribution-free $k$-junta testing can be performed, with one-sided error, by an adaptive algorithm that uses $\tilde{O}(k^2)/ε$ queries (independent of $n$). Complementing this, our second main result is a lower bound showing that any non-adaptive distribution-free $k$-junta testing algorithm must make $Ω(2^{k/3})$ queries even to test to accuracy $ε=1/3$. These bounds establish that while the optimal query complexity of non-adaptive $k$-junta testing is $2^{Θ(k)}$, for adaptive testing it is $\text{poly}(k)$, and thus show that adaptivity provides an exponential improvement in the distribution-free query complexity of testing juntas.

cs.CC↗

arXiv2018

Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques

It is an important task to reliably detect and track multiple moving objects for video surveillance and monitoring. However, when occlusion occurs in nonlinear motion scenarios, many existing methods often fail to continuously track multiple moving objects of interest. In this paper we propose an effective approach for detection and tracking of multiple moving objects with occlusion. Moving targets are initially detected using a simple yet efficient block matching technique, providing rough location information for multiple object tracking. More accurate location information is then estimated for each moving object by a nonlinear tracking algorithm. Considering the ambiguity caused by the occlusion among multiple moving objects, we apply an unscented Kalman filtering (UKF) technique for reliable object detection and tracking. Different from conventional Kalman filtering (KF), which cannot achieve the optimal estimation in nonlinear tracking scenarios, UKF can be used to track both linear and nonlinear motions due to the unscented transform. Further, it estimates the velocity information for each object to assist to the object detection algorithm, effectively delineating multiple moving objects of occlusion. The experimental results demonstrate that the proposed method can correctly detect and track multiple moving objects with nonlinear motion patterns and occlusions.

cs.CV↗

arXiv2018

Parameter Space Noise for Exploration

Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.

cs.LG↗

arXiv2018

Hierarchical Spatial Transformer Network

Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time. Emergence of spatial transformer network makes dream come true. Spatial transformer network and its variants can handle global displacement well, but lack the ability to deal with local spatial variance. Hence how to achieve a better manner of deformation in the neural network has become a pressing matter of the moment. To address this issue, we analyze the advantages and disadvantages of approximation theory and optical flow theory, then we combine them to propose a novel way to achieve image deformation and implement it with a hierarchical convolutional neural network. This new approach solves for a linear deformation along with an optical flow field to model image deformation. In the experiments of cluttered MNIST handwritten digits classification and image plane alignment, our method outperforms baseline methods by a large margin.

cs.CV↗

arXiv2018

Electrostatically induced quantum point contact in bilayer graphene

We report the fabrication of electrostatically defined nanostructures in encapsulated bilayer graphene, with leakage resistances below depletion gates as high as $R \sim 10~$G$Ω$. This exceeds previously reported values of $R =~$10 - 100 k$Ω$.\cite{Zou2010,Yan2010,Zhu2016a} We attribute this improvement to the use of a graphite back gate. We realize two split gate devices which define an electronic channel on the scale of the Fermi-wavelength. A channel gate covering the gap between the split gates varies the charge carrier density in the channel. We observe device-dependent conductance quantization of $ΔG = 2~e^2/h$ and $ΔG = 4~e^2/h$. In quantizing magnetic fields normal to the sample plane, we recover the four- fold Landau level degeneracy of bilayer graphene. Unexpected mode crossings appear at the crossover between zero magnetic field and the quantum Hall regime.

cond-mat.mes-hall↗

arXiv2018

Inverse engineering for fast transport and spin control of spin-orbit-coupled Bose-Einstein condensates in moving harmonic traps

We investigate fast transport and spin manipulation of tunable spin-orbit-coupled Bose-Einstein condensates in a moving harmonic trap. Motivated by the concept of "shortcuts to adiabaticity", we design inversely the time-dependent trap position and spin-orbit coupling strength. By choosing appropriate boundary conditions we obtain fast transport and spin flip simultaneously. The non-adiabatic transport and relevant spin dynamics are illustrated with numerical examples, and compared with the adiabatic transport with constant spin-orbit-coupling strength and velocity. Moreover, the influence of nonlinearity induced by interatomic interaction is discussed in terms of the Gross-Pitaevskii approach, showing the robustness of the proposed protocols. With the state-of-the-art experiments, such inverse engineering technique paves the way for coherent control of spin-orbit-coupled Bose-Einstein condensates in harmonic traps.

quant-ph↗

arXiv2017

PixelSNAIL: An Improved Autoregressive Generative Model

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \times 32$ ImageNet (3.80 bits per dim). Our implementation is available at https://github.com/neocxi/pixelsnail-public

cs.LG↗

arXiv2017

A spectral line survey of IRC +10216 between 13.3 and 18.5 GHz

A spectral line survey of IRC +10216 between 13.3 and 18.5 GHz is carried out using the Shanghai Tian Ma 65 m Radio Telescope (TMRT-65m) with a sensitivity of < 7 mK. Thirty-five spectral lines of 12 different molecules and radicals are detected in total. Except for SiS, the detected molecules are all carbon-chain molecules, including HC3N, HC5N, HC7N, HC9N, C6H, C6H-, C8H, SiC2, SiC4, c-C3H2 and l-C5H. The presence of rich carbon-bearing molecules is consistent with the identity of IRC +10216 as a carbon-rich AGB star. The excitation temperatures and column densities of the observed species are derived by assuming a local thermodynamic equilibrium and homogeneous conditions.

astro-ph.SR↗

arXiv2017

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal with high-dimensional continuous state spaces through complex heuristics, often relying on optimism in the face of uncertainty or intrinsic motivation. In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks. States are mapped to hash codes, which allows to count their occurrences with a hash table. These counts are then used to compute a reward bonus according to the classic count-based exploration theory. We find that simple hash functions can achieve surprisingly good results on many challenging tasks. Furthermore, we show that a domain-dependent learned hash code may further improve these results. Detailed analysis reveals important aspects of a good hash function: 1) having appropriate granularity and 2) encoding information relevant to solving the MDP. This exploration strategy achieves near state-of-the-art performance on both continuous control tasks and Atari 2600 games, hence providing a simple yet powerful baseline for solving MDPs that require considerable exploration.

cs.AI↗

arXiv2017

An efficient non-linear Feshbach engine

We investigate a thermodynamic cycle using a Bose-Einstein condensate with nonlinear interactions as the working medium. Exploiting Feshbach resonances to change the interaction strength of the BEC allows us to produce work by expanding and compressing the gas. To ensure a large power output from this engine these strokes must be performed on a short timescale, however such non-adiabatic strokes can create irreversible work which degrades the engine's efficiency. To combat this, we design a shortcut to adiabaticity which can achieve an adiabatic-like evolution within a finite time, therefore significantly reducing the out-of-equilibrium excitations in the BEC. We investigate the effect of the shortcut to adiabaticity on the efficiency and power output of the engine and show that the tunable nonlinearity strength, modulated by Feshbach resonances, serves as a useful tool to enhance the system's performance.

quant-ph↗