arXiv subjects

Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 289 records · Page 16Linked to original sources

arXiv2021

FAST Globular Cluster Pulsar Survey: Twenty-Four Pulsars Discovered in Fifteen Globular Clusters

We present the discovery of 24 pulsars in 15 Globular Clusters (GCs) using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). These include the first pulsar discoveries in M2, M10, and M14. Most of the new systems are either confirmed or likely members of binary systems. M53C, NGC6517H and I are the only three pulsars confirmed to be isolated. M14A is a black widow pulsar with an orbital period of 5.5 hours and a minimum companion mass of 0.016 \Ms. M14E is an eclipsing binary pulsar with an orbital period of 20.3 hours. With the other 8 discoveries that have been reported elsewhere, in total 32 GC pulsars have been discovered by FAST so far. In addition, We detected M3A twice. This was enough to determine that it is a black widow pulsar with an orbital period of 3.3 hours and a minimum companion mass of 0.0125 \Ms.

astro-ph.HE↗

arXiv2021

Mrk 1239: a Type-2 Counterpart of Narrow-line Seyfert-1?

We present new spectrophotometric and spectropolarimetric observations of Mrk 1239, one of the 8 prototypes that defines type-1 narrow-line Seyfert galaxies (NLS1s). Unlike the other typical NLS1s though, a high degree of polarization ($P\sim$5.6%) and red optical-IR ($g-W_4$ = 12.35) colors suggest that Mrk 1239 is more similar to type-2 active galactic nuclei like NGC 1068. Detailed analysis of spectral energy distribution in the UV-optical-IR yields two components from the nucleus: a direct and transmitted component that is heavily obscured ($E_{B-V} \approx 1.6$), and another indirect and scattered one with mild extinction ($E_{B-V} \sim$ 0.5). Such a two-light-paths scenario is also found in previous reports based on the X-ray data. Comparison of emission lines and the detection of He\,{\footnotesize I}*$λ$10830 BAL at [-3000,-1000] km s$^{-1}$ indicates that the obscuring clouds are at physical scale between the sublimation radius and that of the narrow emission line regions. The potential existence of powerful outflows is found as both the obscurer and scatterer are outflowing. Similar to many other type-2s, jet-like structure in the radio band is found in Mrk 1239, perpendicular to the polarization angle, suggesting polar scattering. We argue that Mrk 1239 is very probably a type-2 counterpart of NLS1s. The identification of 1 out of 8 prototype NLS1s as a type-2 counterpart implies that there can be a substantial amount of analogs of Mrk 1239 misidentified as type-1s in the optical band. Properties of these misidentified objects are going to be explored in our future works.

astro-ph.GA↗

arXiv2021

The FAST Galactic Plane Pulsar Snapshot survey: I. Project design and pulsar discoveries

Discovery of pulsars is one of the main goals for large radio telescopes. The Five-hundred-meter Aperture Spherical radio Telescope (FAST), that incorporates an L-band 19-beam receiver with a system temperature of about 20~K, is the most sensitive radio telescope utilized for discovering pulsars. We designed the {\it snapshot} observation mode for a FAST key science project, the Galactic Plane Pulsar Snapshot (GPPS) survey, in which every four nearby pointings can observe {\it a cover} of a sky patch of 0.1575 square degrees through beam-switching of the L-band 19-beam receiver. The integration time for each pointing is 300 seconds so that the GPPS observations for a cover can be made in 21 minutes. The goal of the GPPS survey is to discover pulsars within the Galactic latitude of $\pm10^{\circ}$ from the Galactic plane, and the highest priority is given to the inner Galaxy within $\pm5^{\circ}$. Up to now, the GPPS survey has discovered 201 pulsars, including currently the faintest pulsars which cannot be detected by other telescopes, pulsars with extremely high dispersion measures (DMs) which challenge the currently widely used models for the Galactic electron density distribution, pulsars coincident with supernova remnants, 40 millisecond pulsars, 16 binary pulsars, some nulling and mode-changing pulsars and rotating radio transients (RRATs). The follow-up observations for confirmation of new pulsars have polarization-signals recorded for polarization profiles of the pulsars. Re-detection of previously known pulsars in the survey data also leads to significant improvements in parameters for 64 pulsars. The GPPS survey discoveries are published and will be updated at http://zmtt.bao.ac.cn/GPPS/ .

astro-ph.HE↗

arXiv2021

LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two-branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the domain shared and private features. Additionally, we introduce a new dataset (SemanticUSL\footnote{The access address of SemanticUSL:\url{https://unmannedlab.github.io/research/SemanticUSL}}) for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. We conducted experiments on real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have differences in channel distributions, reflectivity distributions, diversity of scenes, and sensors setup. Using our approach, we can get a single projection-based LiDAR full-scene semantic segmentation model working on both domains. Our model can keep almost the same performance on the source domain after adaptation and get an 8\%-22\% mIoU performance increase in the target domain.

cs.CV↗

arXiv2021

Calibrating LiDAR and Camera using Semantic Mutual information

We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduce an initial calibration method using 2D MI-based image registration. Finally, we demonstrate the robustness of our method and quantitatively analyze the accuracy on a synthetic dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D benchmark datasets, showing improvement over recent comparable approaches.

cs.CV↗

arXiv2021

Radio Recombination Line Observations at $1.0-1.5$ GHz with FAST

HII regions made of gas ionized by radiations from young massive stars, are widely distributed in the Milky Way. They are tracers for star formation, and their distributions are correlated with the Galactic spiral structure. Radio recombination lines (RRLs) of hydrogen and other atoms allows for the precisest determination of physical parameters such as temperature and density. However, RRLs at around 1.4 GHz from HII regions are weak and their detections are difficult. As a result, only a limited number of detections have been obtained yet. The 19-beam receiver on board of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) can simultaneously cover 23 RRLs for H$nα$, He$nα$, and C$nα$ ($n = 164 - 186$), respectively. This, combined with its unparalleled collecting area, makes FAST the most powerful telescope to detect weak RRLs. In this pilot survey, we use FAST to observe nine HII regions at L band. We allocate 20 minutes pointing time for each source to achieve a sensitivity of around 9 mK in a velocity resolution of 2.0 km/s. In total, 21 RRLs for H$nα$ and C$nα$ at $1.0 - 1.5$ GHz have been simultaneously detected with strong emission signals. Overall, the detection rates for the H167$α$ and C167$α$ RRLs are 100%, while that for the He167$α$ RRL is 33.3%. Using hydrogen and helium RRLs, we measure the electron density, electron temperature, and pressure for three HII regions. This pilot survey demonstrates the capability of FAST in RRL measurements, and a statistically meaningful sample with RRL detection, through which knowledges about Galactic spiral structure and evolution can be obtained, is expected in the future.

astro-ph.GA↗

arXiv2021

Three Pulsars Discovered by FAST in the Globular Cluster NGC 6517 with a Pulsar Candidate Sifting Code Based on Dispersion Measure to Signal-to-Noise Ratio Plots

We report the discovery of three new pulsars in the Globular Cluster (GC) NGC6517, namely NGC 6517 E, F, and G, made with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The spin periods of NGC 6517 E, F, and G are 7.60~ms, 24.89~ms, and 51.59~ms, respectively. Their dispersion measures are 183.29, 183.713, and 185.3~pc~cm$^{-3}$, respectively, all slightly larger than those of the previously known pulsars in this cluster. The spin period derivatives are at the level of 1$\times$10$^{-18}$~s~s$^{-1}$, which suggests these are recycled pulsars. In addition to the discovery of these three new pulsars, we updated the timing solutions of the known isolated pulsars, NGC 6517 A, C, and D. The solutions are consistent with those from Lynch et al. (2011) and with smaller timing residuals. From the timing solution, NGC 6517 A, B (position from Lynch et al. 2011), C, E, and F are very close to each other on the sky and only a few arcseconds from the optical core of NGC 6517. With currently published and unpublished discoveries, NGC6517 now has 9 pulsars, ranking 5$^{th}$ of the GCs with the most pulsars. The discoveries take advantage of the high sensitivity of FAST and a new algorithm used to check and filter possible candidate signals.

astro-ph.HE↗

arXiv2021

OFFSEG: A Semantic Segmentation Framework For Off-Road Driving

Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. These aspects affect the perception of the vehicle from which the information is used for path planning. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues we propose a framework for off-road semantic segmentation called as OFFSEG that involves (i) a pooled class semantic segmentation with four classes (sky, traversable region, non-traversable region and obstacle) using state-of-the-art deep learning architectures (ii) a colour segmentation methodology to segment out specific sub-classes (grass, puddle, dirt, gravel, etc.) from the traversable region for better scene understanding. The evaluation of the framework is carried out on two off-road driving datasets, namely, RELLIS-3D and RUGD. We have also tested proposed framework in IISERB campus frames. The results show that OFFSEG achieves good performance and also provides detailed information on the traversable region.

cs.CV↗

arXiv2021

Ultra-Low Noise L-Band Cryogenic Astronomical Receiver for FAST Telescope

This paper presents an ultra-low noise L-band radio astronomical cryogenic receiver for FAST telescope. The development of key low noise microwave parts of Coupling-LNA and conical quad-ridge OMT and reasonable system integration achieve outstanding performance of receiver.It covers the frequency range of 1.2 GHz to 1.8 GHz. Novel cryogenic Coupling-LNAs with low noise, large return loss, high dynamic range and the function of coupling calibration signals are developed for the proposed receiver.Amplification and coupling function circuits are integrated as a single Coupling-LNA with full noise temperature of 4 K at the physical temperature of 15 K. And its return loss is more than 18 dB, and output 1 dB compression power is +5 dBm. A cryogenic dewar is fabricated to provide 55 K and 15 K cryogenic environment for OMT and Coupling-LNAs, respectively. The receiver's system noise temperature is below 9 K referred to feed aperture plane. Benefiting from optimal design and precise mechanical treatment, good scattering performance of OMT and equalized radiation patterns of horn are achieved with an antenna efficiency above 75%.

astro-ph.IM↗

arXiv2021

Extragalactic HI 21-cm absorption line observations with the Five-hundred-meter Aperture Spherical radio Telescope

We present a pilot study of extragalactic HI 21-cm absorption lines using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We observed 5 continuum sources with HI absorption features firstly identified in the 40% data release of the Arecibo Legacy Fast Arecibo L-Band Feed Array (ALFA) Survey (ALFALFA), including two systems later detected by the Westerbork Synthesis Radio Telescope (WSRT). Most of our observations were carried out during the FAST commissioning phase, and we have tested different observing modes, as well as data reduction methods, to produce the best spectra. Our observations successfully confirmed the existence of HI absorption lines in all these systems, including two sources that were marginally detected by ALFALFA. We fitted the HI profiles with single or double of Gaussian functions, and calculated the HI column densities of each source. The HI absorption profiles obtained by FAST show much higher spectral resolution and higher S/N ratio than the existing data in the literature, thus demonstrating the power of FAST in revealing detailed structures of HI absorption lines. Our pilot observations and tests have enabled us to develop a strategy to search for HI absorption sources using the data from the FAST extragalactic HI survey, which is one of the key projects undertaken at FAST. We expect that over 1,500 extragalactic HI absorbing systems could be detected with survey data, based on sensitivity level we achieved in pilot observations.

astro-ph.GA↗

arXiv2021

NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation

Learning informative representations (aka. embeddings) of users and items is the core of modern recommender systems. Previous works exploit user-item relationships of one-hop neighbors in the user-item interaction graph to improve the quality of representation. Recently, the research of Graph Neural Network (GNN) for recommendation considers the implicit collaborative information of multi-hop neighbors to enrich the representation. However, most works of GNN for recommendation systems do not consider the relational information which implies the expression differences of different neighbors in the neighborhood explicitly. The influence of each neighboring item to the representation of the user's preference can be represented by the correlation between the item and neighboring items of the user. Symmetrically, for a given item, the correlation between one neighboring user and neighboring users can reflect the strength of signal about the item's characteristic. To modeling the implicit correlations of neighbors in graph embedding aggregating, we propose a Neighbor-Aware Graph Attention Network for recommendation task, termed NGAT4Rec. It employs a novel neighbor-aware graph attention layer that assigns different neighbor-aware attention coefficients to different neighbors of a given node by computing the attention among these neighbors pairwisely. Then NGAT4Rec aggregates the embeddings of neighbors according to the corresponding neighbor-aware attention coefficients to generate next layer embedding for every node. Furthermore, we combine more neighbor-aware graph attention layer to gather the influential signals from multi-hop neighbors. We remove feature transformation and nonlinear activation that proved to be useless on collaborative filtering. Extensive experiments on three benchmark datasets show that our model outperforms various state-of-the-art models consistently.

cs.IR↗

arXiv2021

Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Due to the lack of supervision, confident and consistent predictions are usually hard to obtain. Typically, people handle these problems to either adopt an auxiliary task with the well-labeled dataset or incorporate the graphical model with additional requirements on scribble annotations. Instead, this work aims to achieve semantic segmentation by scribble annotations directly without extra information and other limitations. Specifically, we propose holistic operations, including minimizing entropy and a network embedded random walk on neural representation to reduce uncertainty. Given the probabilistic transition matrix of a random walk, we further train the network with self-supervision on its neural eigenspace to impose consistency on predictions between related images. Comprehensive experiments and ablation studies verify the proposed approach, which demonstrates superiority over others; it is even comparable to some full-label supervised ones and works well when scribbles are randomly shrunk or dropped.

cs.CV↗

arXiv2021

Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration

Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications. Numerous systems have been proposed for FSM in the past decade. Although these systems show good performance for small patterns (with no more than four vertices), we found that they have difficulty in mining larger patterns. In this work, we propose a novel two-vertex exploration strategy to accelerate the mining process. Compared with the single-vertex exploration adopted by previous systems, our two-vertex exploration avoids the large memory consumption issue and significantly reduces the memory access overhead. We further enhance the performance through an index-based quick pattern technique that reduces the overhead of isomorphism checks, and a subgraph sampling technique that mitigates the issue of subgraph explosion. The experimental results show that our system achieves significant speedups against the state-of-the-art graph pattern mining systems and supports larger pattern mining tasks that none of the existing systems can handle.

cs.DB↗

arXiv2021

Graph Attention Collaborative Similarity Embedding for Recommender System

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our framework consists of two parts: the first part is to learn explicit graph collaborative filtering information such as user-item association through embedding propagation with attention mechanism, and the second part is to learn implicit graph collaborative information such as user-user similarities and item-item similarities through auxiliary loss. We design a new loss function that combines BPR loss with adaptive margin and similarity loss for the similarities learning. Extensive experiments on three benchmarks show that our model is consistently better than the latest state-of-the-art models.

cs.IR↗

arXiv2021

Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning

Stochastic Gradient Descent (SGD) is the key learning algorithm for many machine learning tasks. Because of its computational costs, there is a growing interest in accelerating SGD on HPC resources like GPU clusters. However, the performance of parallel SGD is still bottlenecked by the high communication costs even with a fast connection among the machines. A simple approach to alleviating this problem, used in many existing efforts, is to perform communication every few iterations, using a constant averaging period. In this paper, we show that the optimal averaging period in terms of convergence and communication cost is not a constant, but instead varies over the course of the execution. Specifically, we observe that reducing the variance of model parameters among the computing nodes is critical to the convergence of periodic parameter averaging SGD. Given a fixed communication budget, we show that it is more beneficial to synchronize more frequently in early iterations to reduce the initial large variance and synchronize less frequently in the later phase of the training process. We propose a practical algorithm, named ADaptive Periodic parameter averaging SGD (ADPSGD), to achieve a smaller overall variance of model parameters, and thus better convergence compared with the Constant Periodic parameter averaging SGD (CPSGD). We evaluate our method with several image classification benchmarks and show that our ADPSGD indeed achieves smaller training losses and higher test accuracies with smaller communication compared with CPSGD. Compared with gradient-quantization SGD, we show that our algorithm achieves faster convergence with only half of the communication. Compared with full-communication SGD, our ADPSGD achieves 1:14x to 1:27x speedups with a 100Gbps connection among computing nodes, and the speedups increase to 1:46x ~ 1:95x with a 10Gbps connection.

cs.LG↗

arXiv2021

Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks

Training Graph Convolutional Networks (GCNs) is expensive as it needs to aggregate data recursively from neighboring nodes. To reduce the computation overhead, previous works have proposed various neighbor sampling methods that estimate the aggregation result based on a small number of sampled neighbors. Although these methods have successfully accelerated the training, they mainly focus on the single-machine setting. As real-world graphs are large, training GCNs in distributed systems is desirable. However, we found that the existing neighbor sampling methods do not work well in a distributed setting. Specifically, a naive implementation may incur a huge amount of communication of feature vectors among different machines. To address this problem, we propose a communication-efficient neighbor sampling method in this work. Our main idea is to assign higher sampling probabilities to the local nodes so that remote nodes are accessed less frequently. We present an algorithm that determines the local sampling probabilities and makes sure our skewed neighbor sampling does not affect much the convergence of the training. Our experiments with node classification benchmarks show that our method significantly reduces the communication overhead for distributed GCN training with little accuracy loss.

cs.LG↗

arXiv2020

FAST: Its Scientific Achievements and Prospects

FAST is the largest single-dish radio telescope in the world. The characteristics of FAST are presented and analyzed in the context of the parameter space to show how FAST science achievements are affected. We summarize the scientific achievements of FAST and discuss its future science based on the new parts of the parameter space that can be explored by FAST.

astro-ph.IM↗

arXiv2020

Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Many approaches have been proposed. Typically, they handle this problem to either introduce a well-labeled dataset from another related task, turn to iterative refinement and post-processing with the graphical model, or manipulate the scribble label. This work aims to achieve semantic segmentation supervised by scribble label directly without auxiliary information and other intermediate manipulation. Specifically, we impose diffusion on neural representation by random walk and consistency on neural eigenspace by self-supervision, which forces the neural network to produce dense and consistent predictions over the whole dataset. The random walk embedded in the network will compute a probabilistic transition matrix, with which the neural representation diffused to be uniform. Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts. In addition to comparing the common scribble dataset, we also conduct experiments on the modified datasets that randomly shrink and even drop the scribbles on image objects. The results demonstrate the superiority of the proposed method and are even comparable to some full-label supervised ones. The code and datasets are available at https://github.com/panzhiyi/RW-SS.

cs.CV↗