arXiv subjects

Hao Liu

Publications and source records attributed to Hao Liu.

At least 613 records · Page 34Linked to original sources

arXiv2019

A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression

Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.

eess.IV↗

arXiv2019

Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing fully supervised methods which rely on numerous annotations by hand, our learner exploits large scale unlabeled videos plus available labeled data to generate auxiliary plausible training annotations. Motivated by the fact that neighbouring facial landmarks are usually correlated and coherent across consecutive frames, our approach automatically reasons about discriminative spatial-temporal relationships among landmarks for stable face tracking. Specifically, we carefully develop an interpretable and efficient network module, which disentangles facial geometry relationship for every static frame and simultaneously enforces the bi-directional cycle-consistency across adjacent frames, thus allowing the modeling of intrinsic spatial-temporal relations from raw face sequences. Extensive experimental results demonstrate that our approach surpasses the performance of most fully supervised state-of-the-arts.

cs.CV↗

arXiv2019

Evolution of hard X-ray photon index in black-hole X-ray binaries: hints for accretion physics

The anti- and positive correlations between X-ray photon index and Eddington-scaled X-ray luminosity were found in decay phase of X-ray binary outbursts and a sample of active galactic nuclei in former works. We further systematically investigate the evolution of X-ray spectral index, along the X-ray flux and Eddington ratio in eight outbursts of four black-hole X-ray binaries, where all selected outbursts have observational data from Rossi X-ray Timing Explorer in both rise and decay phases. In the initial rise phase, the X-ray spectral index is anti-correlated with the flux, and the X-ray spectrum quickly softens when the X-ray flux is approaching the peak value. In the decay phase, the X-ray photon index and the flux follow two different positive correlations, and they become anti-correlated again when the X-ray flux is below a critical value, where the anti-correlation part follow the same trend as that found in the initial rise phase. Compared with other X-ray binaries, GRO J1655-40 has an evident lower critical Eddington ratio for the anti- and positive transition, which suggests that its black-hole mass and distance are not well constrained or its intrinsic physic is different.

astro-ph.HE↗

arXiv2019

Implications on the origin of cosmic rays in light of 10 TV spectral softenings

Precise measurements of the energy spectra of cosmic rays (CRs) show various kinds of features deviating from single power-laws, which give very interesting and important implications on their origin and propagation. Previous measurements from a few balloon and space experiments indicate the existence of spectral softenings around 10 TV for protons (and probably also for Helium nuclei). Very recently, the DArk Matter Particle Explorer (DAMPE) measurement about the proton spectrum clearly reveals such a softening with a high significance. Here we study the implications of these new measurements, as well as the groundbased indirect measurements, on the origin of CRs. We find that a single component of CRs fails to fit the spectral softening and the air shower experiment data simultaneously. In the framework of multiple components, we discuss two possible scenarios, the multiple source population scenario and the background plus nearby source scenario. Both scenarios give reasonable fits to the wide-band data from TeV to 100 PeV energies. Considering the anisotropy observations, the nearby source model is favored.

astro-ph.HE↗

arXiv2019

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.

cs.LG↗

arXiv2019

Triply Robust Off-Policy Evaluation

We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools. Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method. When augmenting doubly robust methods, we call the resulting method Triply Robust. We prove upper bounds on the resulting bias and variance, as well as derive novel minimax bounds based on robust minimax analysis for covariate shift. Our robust regression method is compatible with deep learning, and is thus applicable to complex OPE settings that require powerful function approximators. Finally, we demonstrate superior empirical performance across the standard OPE benchmarks, especially in the case where the logging policy is unknown and must be estimated from data.

cs.LG↗

arXiv2019

Angle-resolved photoemission spectroscopy study of crystal electric field in heavy fermion compound CePt2In7

The three-dimensional electronic structure and Ce 4f electrons of the heavy fermion superconductor CePt2In7 is investigated. Angle-resolved photoemission spectroscopy using variable photon energy establishes the existence of quasi-two and three dimensional Fermi surface topologies. Temperature-dependent 4d-4f on-resonance photoemission spectroscopies reveal that heavy quasiparticle bands begin to form at a temperature well above the characteristic (coherence) temperature T*. T* emergence may be closely related to crystal electric field splitting, particularly the low-lying heavy band formed by crystal electric field splitting.

cond-mat.str-el↗

arXiv2019

General solutions of the leakage in integral transforms and applications to the EB-leakage and detection of the cosmological gravitational wave background

For an orthogonal integral transform with complete dataset, any two components are linearly independent; however, when some data points are missing, there is going to be leakage from one component to another, which is referred to as the "leakage in integral transforms" in this work. A special case of this kind of leakage is the EB-leakage in detection of the cosmological gravitational wave background (CGWB). I first give the general solutions for all integral transforms, prove that they are the best solutions, and then apply them to the case of EB-leakage and detection of the CGWB. In the upcoming decade, most likely, new cosmic microwave background (CMB) data are from ground/balloon experiments, so they provide only partial sky coverage. Even in a fullsky mission, due to the Galactic foreground, part of the sky is still unusable. Within this context, the EB-leakage becomes inevitable. I show how to use the general solutions to achieve the minimal error bars of the EB-leakage, and use it to find out the maximum ability to detect the CGWB through CMB. The results show that, when focusing on the tensor-to-scalar ratio $r$ (at a pivot scale of 0.05 Mpc$^{-1}$), $1\%$ sky coverage ($f_{sky}=1\%$) is enough for a $5σ$-detection of $r\ge 10^{-2}$, but is barely enough for $r=10^{-3}$. If the target is to detect $r\sim 10^{-4}$ or $10^{-5}$, then $f_{sky}\ge 10\%$ is strongly recommended to enable a $5σ$-detection and to reserve some room for other errors.

astro-ph.CO↗

arXiv2019

Variational Inference with Tail-adaptive f-Divergence

Variational inference with α-divergences has been widely used in modern probabilistic machine learning. Compared to Kullback-Leibler (KL) divergence, a major advantage of using α-divergences (with positive α values) is their mass-covering property. However, estimating and optimizing α-divergences require to use importance sampling, which could have extremely large or infinite variances due to heavy tails of importance weights. In this paper, we propose a new class of tail-adaptive f-divergences that adaptively change the convex function f with the tail of the importance weights, in a way that theoretically guarantees finite moments, while simultaneously achieving mass-covering properties. We test our methods on Bayesian neural networks, as well as deep reinforcement learning in which our method is applied to improve a recent soft actor-critic (SAC) algorithm. Our results show that our approach yields significant advantages compared with existing methods based on classical KL and α-divergences.

cs.LG↗

arXiv2019

Methods for pixel domain correction of EB leakage

In observation of the cosmic microwave background (CMB) polarization, "$EB$~leakage" refers to the artificial $B$-mode signal coming from the leakage of $E$-mode signal when part of the sky is unavailable or excluded. Correction of such leakage is one of the preconditions for detecting primordial gravitational waves via the CMB $B$-mode signal. In this work, we design two independent methods for correcting the $EB$~leakage directly in the pixel domain using standard definitions of the $E$- and $B$-modes. The two methods give consistent results, and both are fast and easy to implement. Tests on a CMB simulation containing zero initial $B$-mode show an efficient suppression of the $EB$ leakage. When combined with the MASTER method to reconstruct the full-sky $B$-mode spectrum in simulations with a relatively simple mask, the error from EB-leakage is suppressed further by more than one order of magnitude at the recombination bump, and up to three orders of magnitude at higher multipoles, compared to a "pure" MASTER scheme under the same conditions. Meanwhile, although the final power spectrum estimation benefits from apodization, the pixel domain correction itself is done without apodization, and thus the methods offer more freedom in choosing an apodization based on specific requirements.

astro-ph.CO↗

arXiv2019

Fast Algorithms for Surface Reconstruction from Point Cloud

We consider constructing a surface from a given set of point cloud data. We explore two fast algorithms to minimize the weighted minimum surface energy in [Zhao, Osher, Merriman and Kang, Comp.Vision and Image Under., 80(3):295-319, 2000]. An approach using Semi-Implicit Method (SIM) improves the computational efficiency through relaxation on the time-step constraint. An approach based on Augmented Lagrangian Method (ALM) reduces the run-time via an Alternating Direction Method of Multipliers-type algorithm, where each sub-problem is solved efficiently. We analyze the effects of the parameters on the level-set evolution and explore the connection between these two approaches. We present numerical examples to validate our algorithms in terms of their accuracy and efficiency.

math.NA↗

arXiv2019

Dense Scale Network for Crowd Counting

Crowd counting has been widely studied by computer vision community in recent years. Due to the large scale variation, it remains to be a challenging task. Previous methods adopt either multi-column CNN or single-column CNN with multiple branches to deal with this problem. However, restricted by the number of columns or branches, these methods can only capture a few different scales and have limited capability. In this paper, we propose a simple but effective network called DSNet for crowd counting, which can be easily trained in an end-to-end fashion. The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales. The dilation rates in dilation layers are carefully selected to prevent the block from gridding artifacts. To further enlarge the range of scales covered by the network, we cascade three blocks and link them with dense residual connections. We also introduce a novel multi-scale density level consistency loss for performance improvement. To evaluate our method, we compare it with state-of-the-art algorithms on four crowd counting datasets (ShanghaiTech, UCF-QNRF, UCF_CC_50 and UCSD). Experimental results demonstrate that DSNet can achieve the best performance and make significant improvements on all the four datasets (30% on the UCF-QNRF and UCF_CC_50, and 20% on the others).

cs.CV↗

arXiv2019

Statistical properties of polarized CMB foreground maps

Foreground removal techniques for CMB analyses make specific assumptions about the properties of foregrounds in temperature and in polarization. By investigating the statistics of foreground components more understanding about the degree to which these assumptions are valid can be obtained. In this work we investigate $E$- and $B$-mode maps of the two strongest polarized foregrounds, synchrotron and thermal dust emission, with regards to their similarity with Gaussian processes, their spectral variations and cross-correlations. We perform tests in patches of $\sim3.7^\circ$ size collectively covering the full sky and find most of them to conform with their Gaussian expectation according to the statistics in use. Correlations exhibit distinct differences in $E$- and $B$-mode signals which point towards necessities in foreground removal methods. We discuss potential consequences and possible further directions.

astro-ph.CO↗

arXiv2019

Predicting Solar Flares Using a Long Short-Term Memory Network

We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma classes, namely >=M5.0 class, >=M class, and >=C class, and build three LSTM models separately, each corresponding to a gamma class. Each LSTM model is used to make predictions of its corresponding gamma-class flares. The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. Each data sample has 40 features including 25 magnetic parameters obtained from the Space-weather HMI Active Region Patches (SHARP) and related data products as well as 15 flare history parameters. We survey the flare events that occurred from 2010 May to 2018 May, using the GOES X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select flares with identified ARs in the NCEI flare catalogs. These flare events are used to build the labels (positive vs. negative) of the data samples. Experimental results show that (i) using only 14-22 most important features including both flare history and magnetic parameters can achieve better performance than using all the 40 features together; (ii) our LSTM network outperforms related machine learning methods in predicting the labels of the data samples. To our knowledge, this is the first time that LSTMs have been used for solar flare prediction.

astro-ph.SR↗

arXiv2019

Noise residuals for GW150914 using maximum likelihood and numerical relativity templates

We reexamine the results presented in a recent work by Nielsen et al. [1], in which the properties of the noise residuals in the 40\,ms chirp domain of GW150914 were investigated. This paper confirmed the presence of strong (i.e., about 0.80) correlations between residual noise in the Hanford and Livingston detectors in the chirp domain as previously seen by us [2] when using a numerical relativity template given in [3]. It was also shown in [1] that a so-called maximum likelihood template can reduce these statistically significant cross-correlations. Here, we demonstrate that the reduction of correlation and statistical significance is due to (i) the use of a peculiar template which is qualitatively different from the properties of GW150914 originally published by LIGO, (ii) a suspicious MCMC chain, (iii) uncertainties in the matching of the maximum likelihood (ML) template to the data in the Fourier domain, and (iv) a biased estimation of the significance that gives counter-intuitive results. We show that rematching the maximum likelihood template to the data in the 0.2\,s domain containing the GW150914 signal restores these correlations at the level of $60\%$ of those found in [1]. With necessary corrections, the probability given in [1] will decrease by more than one order of magnitude. Since the ML template is itself problematic, results associated with this template are illustrative rather than final.

astro-ph.IM↗

arXiv2019

More than 3-mm-long carrier diffusion and strong absorption over the full solar spectrum in copper oxide and selenium composite film

Usually, single-crystal material has a longer carrier diffusion length due to fewer defects. Here, we report a nano/amorphous copper oxide and selenium (COS) composite film prepared by radio frequency magnetron co-sputtering of CuO and Se and post annealing. The photo-generated carriers in the COS film can diffuse very far (3.147 mm). Additionally, the COS film can strongly absorb light over the entire solar spectrum region (250-2500 nm) (average absorptance is ~80%). These results indicate that the COS composite is an excellent material and can be used in solar cells and other photo-electric devices.

physics.app-ph↗

arXiv2019

Blind correction of the EB-leakage in the pixel domain

We study the problem of EB-leakage that is associated with incomplete polarized CMB sky. In the blind case that assumes no additional information about the statistical properties and amplitudes of the signal from the missing sky region, we prove that the recycling method (Liu et al.~2018) gives the unique best estimate of the EB-leakage. Compared to the previous method, this method reduces the uncertainties in the BB power spectrum due to EB-leakage by more than one order of magnitude in the most interesting domain of multipoles, where $\ell$ is between $80$ and $200$. This work also provides a useful guideline for observational design of future CMB experiments.

astro-ph.CO↗

arXiv2019

From top-hat masking to smooth transitions: P-filter and its application to polarized microwave sky maps

In CMB science, the simplest idea to remove a contaminated sky region is to multiply the sky map with a mask that is 0 for the contaminated region and 1 elsewhere, which is also called a top-hat masking. Although it is easy to use, such top-hat masking is known to suffer from various leakage problems. Therefore, we want to extend the top-hat masking to a series of semi-analytic functions called the P-filters. Most importantly, the P-filters can seamlessly realize the core idea of masking in CMB science, and, meanwhile, guarantee continuity up to the first derivative everywhere. The P-filters can significantly reduce many leakage problems without additional cost, including the leakages due to low-, high-, and band-pass filtering, and the E-to-E, B-to-B, B-to-E, and E-to-B leakages. The workings of the P-filter are illustrated by using the WMAP and Planck polarization sky maps. By comparison to the corresponding WMAP/Planck masks, we show that the P-filter performs much better than top-hat masking, and meanwhile, has the potential to supersede the principal idea of masking in CMB science. Compared to mask apodization, the P-filter is ``outward'', that tends to make proper use of the region that was marked as 0; whereas apodization is ``inward'', that always kills more signal in the region marked as 1.

astro-ph.CO↗