arXiv subjects

Hao Tian

Publications and source records attributed to Hao Tian.

At least 181 records · Page 10Linked to original sources

arXiv2020

Monolithic piezoelectric control of soliton microcombs

High-speed laser frequency actuation is critical in all applications employing lasers and frequency combs, and is prerequisite for phase locking, frequency stabilization and stability transfer among multiple optical carriers. Soliton microcombs have emerged as chip-scale, broadband and low-power-consumption frequency comb sources.Yet, integrated microcombs relying on thermal heaters for on-chip actuation all exhibit only kilohertz actuation bandwidth. Consequently, high-speed actuation and locking of microcombs have been attained only with off-chip bulk modulators. Here, we present high-speed microcomb actuation using integrated components. By monolithically integrating piezoelectric AlN actuators on ultralow-loss Si3N4 photonic circuits, we demonstrate voltage-controlled soliton tuning, modulation and stabilization. The integrated AlN actuators feature bi-directional tuning with high linearity and low hysteresis, operate with 300 nW power and exhibit flat actuation response up to megahertz frequency, significantly exceeding bulk piezo tuning bandwidth. We use this novel capability to demonstrate a microcomb engine for parallel FMCW LiDAR, via synchronously tuning the laser and microresonator. By applying a triangular sweep at the modulation rate matching the frequency spacing of HBAR modes, we exploit the resonant build-up of bulk acoustic energy to significantly lower the required driving to a CMOS voltage of only 7 Volts. Our approach endows soliton microcombs with integrated, ultralow-power-consumption, and fast actuation, significantly expanding the repertoire of technological applications.

physics.app-ph↗

arXiv2019

Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion

By integrating dynamics models into model-free reinforcement learning (RL) methods, model-based value expansion (MVE) algorithms have shown a significant advantage in sample efficiency as well as value estimation. However, these methods suffer from higher function approximation errors than model-free methods in stochastic environments due to a lack of modeling the environmental randomness. As a result, their performance lags behind the best model-free algorithms in some challenging scenarios. In this paper, we propose a novel Hybrid-RL method that builds on MVE, namely the Risk Averse Value Expansion (RAVE). With imaginative rollouts generated by an ensemble of probabilistic dynamics models, we further introduce the aversion of risks by seeking the lower confidence bound of the estimation. Experiments on a range of challenging environments show that by modeling the uncertainty completely, RAVE substantially enhances the robustness of previous model-based methods, and yields state-of-the-art performance. With this technique, our solution gets the first place in NeurIPS 2019: Learn to Move.

cs.LG↗

arXiv2019

Learning to Recommend via Meta Parameter Partition

In this paper we propose to solve an important problem in recommendation -- user cold start, based on meta leaning method. Previous meta learning approaches finetune all parameters for each new user, which is both computing and storage expensive. In contrast, we divide model parameters into fixed and adaptive parts and develop a two-stage meta learning algorithm to learn them separately. The fixed part, capturing user invariant features, is shared by all users and is learned during offline meta learning stage. The adaptive part, capturing user specific features, is learned during online meta learning stage. By decoupling user invariant parameters from user dependent parameters, the proposed approach is more efficient and storage cheaper than previous methods. It also has potential to deal with catastrophic forgetting while continually adapting for streaming coming users. Experiments on production data demonstrates that the proposed method converges faster and to a better performance than baseline methods. Meta-training without online meta model finetuning increases the AUC from 72.24% to 74.72% (2.48% absolute improvement). Online meta training achieves a further gain of 2.46\% absolute improvement comparing with offline meta training.

cs.IR↗

arXiv2019

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations. In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE.

cs.CL↗

arXiv2019

Tracing Kinematic and Chemical Properties of Sagittarius Stream by K-Giants, M-Giants, and BHB stars

We characterize the kinematic and chemical properties of $\sim$3,000 Sagittarius (Sgr) stream stars, including K-giants, M-giants, and BHBs, select from SEGUE-2, LAMOST, and SDSS separately in Integrals-of-Motion space. The orbit of Sgr stream is quite clear from the velocity vector in $X$-$Z$ plane. Stars traced by K-giants and M-giants present the apogalacticon of trailing steam is $\sim$ 100 kpc. The metallicity distributions of Sgr K-, M-giants, and BHBs present that the M-giants are on average the most metal-rich population, followed by K-giants and BHBs. All of the K-, M-giants, and BHBs indicate that the trailing arm is on average more metal-rich than leading arm, and the K-giants show that the Sgr debris is the most metal-poor part. The $α$-abundance of Sgr stars exhibits a similar trend with the Galactic halo stars at lower metallicity ([Fe/H] $<\sim$ $-$1.0 dex), and then evolve down to lower [$α$/Fe] than disk stars at higher metallicity, which is close to the evolution pattern of $α$-element of Milky Way dwarf galaxies. We find $V_Y$ and metallicity of K-giants have gradients along the direction of line-of-sight from the Galactic center in $X$-$Z$ plane, and the K-giants show that $V_Y$ increases with metallicity at [Fe/H] $>\sim-$1.5 dex. After dividing the Sgr stream into bright and faint stream according to their locations in equatorial coordinate, the K-giants and BHBs show that the bright and faint stream present different $V_Y$ and metallicities, the bright stream is on average higher in $V_Y$ and metallicity than the faint stream.

astro-ph.GA↗

arXiv2019

Hybrid Integrated Photonics Using Bulk Acoustic Resonators

Microwave frequency acousto-optic modulation is realized by exciting high overtone bulk acoustic wave resonances (HBAR resonances) in the photonic stack. These confined mechanical stress waves transmit exhibit vertically transmitting, high quality factor (Q) acoustic Fabry Perot resonances that extend into the Gigahertz domain, and offer stress-optical interaction with the optical modes of the microresonator. Although HBAR are ubiquitously used in modern communication, and often exploited in superconducting circuits, this is the first time they have been incorporated on a photonic circuit based chip. The electro-acousto-optical interaction observed within the optical modes exhibits high actuation linearity, low actuation power and negligible crosstalk. Using the electro-acousto-optic interaction, fast optical resonance tuning is achieved with sub-nanosecond transduction time. By removing the silicon backreflection, broadband acoustic modulation at 4.1 and 8.7 GHz is realized with a 3 dB bandwidth of 250 MHz each. The novel hybrid HBAR nanophotonic platform demonstrated here, allowing on chip integration of micron-scale acoustic and photonic resonators, can find immediate applications in tunable microwave photonics, high bandwidth soliton microcomb stabilization, compact opto-electronic oscillators, and in microwave to optical conversion schemes. Moreover the hybrid platform allows implementation of momentum biasing, which allows realization of on chip non-reciprocal devices such as isolators or circulators and topological photonic bandstructures.

physics.app-ph↗

arXiv2019

Sequential Evaluation and Generation Framework for Combinatorial Recommender System

In the combinatorial recommender systems, multiple items are fed to the user at one time in the result page, where the correlations among the items have impact on the user behavior. In this work, we model the combinatorial recommendation as the problem of generating a sequence(ordered list) of items from a candidate set, with the target of maximizing the expected overall utility(e.g. total clicks) of the sequence. Toward solving this problem, we propose the Evaluation-Generation framework. On the one hand of this framework, an evaluation model is trained to evaluate the expected overall utility, by fully considering the user, item information and the correlations among the co-exposed items. On the other hand, generation policies based on heuristic searching or reinforcement learning are devised to generate potential high-quality sequences, from which the evaluation model select one to expose. We propose effective model architectures and learning metrics under this framework. We also offer series of offline tests to thoroughly investigate the performance of the proposed framework, as supplements to the online experiments. Our results show obvious increase in performance compared with the previous solutions.

cs.IR↗

arXiv2019

Identifying Galactic Halo Substructure in 6D Phase-space Using $\sim$13,000 LAMOST K Giants

We construct a large halo K-giant sample by combining the positions, distances, radial velocities, and metallicities of over 13,000 LAMOST DR5 halo K giants with the Gaia DR2 proper motions, which covers a Galactocentric distance range of 5-120 kpc. Using a position-velocity clustering estimator (the 6Distance), we statistically quantify the presence of position-velocity substructure at high significance: K giants have more close pairs in position-velocity space than a smooth stellar halo. We find that the amount of substructure in the halo increases with increasing distance and metallicity. With a percolation algorithm named friends-of-friends (FoF) to identify groups, we identify members belonging to Sagittarius (Sgr) Streams, Monoceros Ring, Virgo overdensity, Hercules-Aquila Cloud, Orphan Streams and other unknown substructures and find that the Sgr streams account for a large part of grouped stars beyond 20 kpc and enhance the increase of substructure with distance and metallicity. For the first time, we identify spectroscopic members of Monoceros Ring in the south and north Galactic hemisphere, which presents a rotation of about 185 km s^{-1} and mean metallicity is -0.66 dex.

astro-ph.GA↗

arXiv2019

ERNIE: Enhanced Representation through Knowledge Integration

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test.

cs.CL↗

arXiv2019

PORT: A piezoelectric optical resonance tuner

This abstract presents an aluminum nitride (AlN) piezoelectric actuator for tuning optical resonance modes of silicon nitride photonic resonators. The AlN actuator is fabricated on top of a thick silicon dioxide cladding that encapsulates the nitride resonator and waveguide. The PORT is defined by undercutting the cladding layer with a lateral silicon etch. It tunes the optical wavelength by 20pm on applying 60 V to the top electrode with a 0.5nA current draw. The thick oxide cladding preserves the resonator's loaded quality factor Q optical of 64,000 across the entire tuning range. The first bending mode is at 1.1MHz enabling a tuning speed of <1 μs.

physics.app-ph↗

arXiv2019

Detecting the Sagittarius stream with LAMOST DR4 M giants \& Gaia DR2

We use LAMOST DR4 M giants combined with Gaia DR2 proper motions and ALLWISE photometry to obtain an extremely pure sample of Sagittarius (Sgr) stream stars. Using TiO5 and CaH spectral indices as an indicator, we selected out a large sample of M giant stars from M dwarf stars in LAMOST DR4 spectra. Considering the position, distance, proper motion and the angular momentum distribution, we obtained 164 pure Sgr stream stars. We find that the trailing arm has higher energy than the leading arm in same angular momentum. The trailing arm we detected extends to a heliocentric distance of $\sim 130$ kpc at $\tildeΛ_{\odot}\sim 170^{\circ}$, which is consistent with the feature found in RR Lyrae in \citet{sgr2017}. Both of these detections of Sgr, in M giants and in RR Lyrae, imply that the Sgr stream may contain multiple stellar populations with a broad metallicity range.

astro-ph.GA↗

arXiv2019

Artificial Intelligence for Prosthetics - challenge solutions

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.

cs.LG↗

arXiv2019

An intuitive 3D map of the Galactic warp's precession traced by classical Cepheids

The Milky Way's neutral hydrogen (HI) disk is warped and flared. However, a dearth of accurate HI-based distances has thus far prevented the development of an accurate Galactic disk model. Moreover, the extent to which our Galaxy's stellar and gas disk morphologies are mutually consistent is also unclear. Classical Cepheids, primary distance indicators with distance accuracies of 3-5%, offer a unique opportunity to develop an intuitive and accurate three-dimensional picture. Here, we establish a robust Galactic disk model based on 1339 classical Cepheids. We provide strong evidence that the warp's line of nodes is not oriented in the Galactic Center-Sun direction. Instead, it subtends a mean angle of 17.5 \pm 1 (formal) \pm 3 (systematic) and exhibits a leading spiral pattern. Our Galaxy thus follows Briggs' rule for spiral galaxies, which suggests that the origin of the warp is associated with torques forced by the massive inner disk. The stellar disk traced by Cepheids follows the gas disk in terms of their amplitudes; the stellar disk extends to at least 20 kpc. This morphology provides a crucial, updated map for studies of the kinematics and archaeology of the Galactic disk.

astro-ph.GA↗

arXiv2019

Radial velocity measurements from LAMOST medium-resolution spectroscopic observations: A pointing towards the Kepler field

Radial velocity is one of key measurements in understanding the fundamental properties of stars, stellar clusters and the Galaxy. A plate of stars in the Kepler field were observed in May of 2018 with the medium-resolution spectrographs of LAMOST, aiming to test the performance of this new system which is the upgraded equipment of LAMOST after the first five-year regular survey.We present our analysis on the radial velocity measurements (RVs) derived from these data. The results show that slight and significant systematic errors exist among the RVs obtained from the spectra collected by different spectrographs and exposures, respectively. After correcting the systematic errors with different techniques, the precision of RVs reaches ~1.3, ~1.0, ~0.5 and ~0.3 km/s at S/Nr = 10, 20, 50, and 100, respectively. Comparing with the RVs of the standard stars of the APOGEE survey, our RVs are calibrated with a zero-point shift of ~7 km/s. The results indicate that the LAMOST medium-resolution spectroscopic system may provide RVs in a reasonable accuracy and precision for the selected targets.

astro-ph.SR↗

arXiv2018

Rotating halo traced by the K giant stars from LAMOST and Gaia

With the help of Gaia DR2, we are able to obtain the full 6-D phase space information for stars from LAMOST DR5. With high precision of position, velocity, and metallicity, the rotation of the local stellar halo is presented using the K giant stars with [Fe/H]$<-1$ dex within 4 kpc from the Sun. By fitting the rotational velocity distribution with three Gaussian components, stellar halo, disk, and a counter-rotating hot population, we find that the local halo progradely rotates with $V_T=+27^{+4}_{-5}$ km s$^{-1}$ providing the local standard of rest velocity of $V_{LSR}=232$ km s$^{-1}$. Meanwhile, we obtain the dispersion of rotational velocity is $σ_{T}=72^{+4}_{-4}$ km s$^{-1}$. Although the rotational velocity strongly depends on the choice of $V_{LSR}$, the trend of prograde rotation is substantial even when $V_{LSR}$ is set at as low as 220 km s$^{-1}$. Moreover, we derive the rotation for subsamples with different metallicities and find that the rotational velocity is essentially not correlated with [Fe/H]. This may hint a secular evolution origin of the prograde rotation. It shows that the metallicity of the progradely rotating halo is peaked within -1.9$<$[Fe/H]$<$-1.6 without considering the selection effect. We also find a small fraction of counter-rotating stars with larger dispersion and lower metallicity. Finally, the disk component rotates with $V_T=+182^{+6}_{-6}$ km s$^{-1}$ and $σ_T=45^{+3}_{-3}$ km s$^{-1}$, which is quite consistent with the metal-weak thick disk population.

astro-ph.GA↗

arXiv2018

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test platform. We built neural networks as our policy to map sensor readings to control signals on the UAV. Our experiment scenarios cover both simulation and reality. We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation, at the same time it ensures safety and keeps training cost acceptable.

cs.RO↗

arXiv2018

Transferring Grasp Configurations using Active Learning and Local Replanning

We present a new approach to transfer grasp configurations from prior example objects to novel objects. We assume the novel and example objects have the same topology and similar shapes. We perform 3D segmentation on these objects using geometric and semantic shape characteristics. We compute a grasp space for each part of the example object using active learning. We build bijective contact mapping between these model parts and compute the corresponding grasps for novel objects. Finally, we assemble the individual parts and use local replanning to adjust grasp configurations while maintaining its stability and physical constraints. Our approach is general, can handle all kind of objects represented using mesh or point cloud and a variety of robotic hands.

cs.RO↗

arXiv2018

A spectral collocation method for nonlocal diffusion equations

Nonlocal diffusion model provides an appropriate description of the diffusion process of solute in the complex medium, which cannot be described properly by classical theory of PDE. However, the operators in the nonlocal diffusion models are nonlocal, so the resulting numerical methods generate dense or full stiffness matrices. This imposes significant computational and memory challenge for a nonlocal diffusion model. In this paper, we develop a spectral collocation method for the nonlocal diffusion model and provide a rigorous error analysis which theoretically justifies the spectral rate of convergence provided that the kernel functions and the source functions are sufficiently smooth. Compared to finite difference methods and finite element methods, because of the high order convergence rates, the numerical cost of spectral collocation methods will be greatly decreased. Numerical results confirm the exponential rate of convergence.

math.NA↗