arXiv subjects

Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 325 records · Page 18Linked to original sources

arXiv2019

Fast Inflow Directly Feeding Black Hole Accretion Disk in Quasars

Quasars are high-luminosity active galactic nuclei believed to be powered by accretion of interstellar matter onto a super-massive black hole (SMBH) therein. Most of the observed energy is released in an accretion disk of inspiralling gas surrounding the SMBH. An enormous amount of fueling material is expected to be transported inwards. However, basic questions remain unanswered as to whether and how the accretion disks are supplied with external gas, since no disk-feeding inflow has hitherto been observed clearly. Here we report the discovery of highly redshifted broad absorption lines arising from neutral hydrogen and helium atoms in a small sample of quasars. Their absorption troughs show a broad range of Doppler velocities from zero extending continuously inward up to as high as $\sim 5,000$ km s$^{-1}$. We thus see through streams of cold gas moving with a radially inward velocity component that spans an immense gradient---a result of gravitational acceleration by the central SMBH. Extensive photo-ionization modeling for the archetypical object SDSS J103516.20+142200.6 indicates the inflowing gas to be dense, thick and moderately ionized, with a characteristic distance to the SMBH of $\sim 1,000$ gravitational radii, possibly overlapping or close to the outer accretion disk. Our results present the first compelling evidence for the long-sought inflow directly feeding quasars' accretion disks with external materials, likely originating from the dusty torus at a parsec scale. Our approach provides a new tool to probe the bulk of the so far elusive fueling inflows in quasars. Their studies may help address some of the fundamental questions concerning accretion physics, the onset and sustainment of quasar activity, and the SMBH growth at centers of most galaxies.

astro-ph.GA↗

arXiv2019

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks (e.g., Recurrent Neural Network) to encode users' historical interactions from left to right into hidden representations for making recommendations. Although these methods achieve satisfactory results, they often assume a rigidly ordered sequence which is not always practical. We argue that such left-to-right unidirectional architectures restrict the power of the historical sequence representations. For this purpose, we introduce a Bidirectional Encoder Representations from Transformers for sequential Recommendation (BERT4Rec). However, jointly conditioning on both left and right context in deep bidirectional model would make the training become trivial since each item can indirectly "see the target item". To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context. Comparing with predicting the next item at each position in a sequence, the Cloze task can produce more samples to train a more powerful bidirectional model. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.

cs.IR↗

arXiv2019

Compositional Network Embedding

Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the principle of compositionally. Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under four different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.

cs.LG↗

arXiv2019

Personalized Re-ranking for Recommendation

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual influence between items, as well as the differences of users' preferences or intents. Therefore, we propose a personalized re-ranking model for recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after any ranking algorithm, by directly using the existing ranking feature vectors. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. We confirm that the performance can be further improved by introducing pre-trained embedding to learn personalized encoding functions for different users. Experimental results on both offline benchmarks and real-world online e-commerce systems demonstrate the significant improvements of the proposed re-ranking model.

cs.IR↗

arXiv2019

SDSS J153636.22+044127.0 and its analogues: shocked outflows, not active binary black holes

The binary emission-line system, particularly the binary broad-line emission system, is considered the most effective indicator of the black hole binary. A plausible example of such a system, SDSS J153636.22+044127.0, was reported as the first known object with two hydrogen Balmer broad-line systems, which are interpreted to be the result of broad-line regions around a pair of black holes (Boroson \& Lauer 2009). Here, we show the follow-up optical and near-infrared spectral observations of SDSS J153636.22+044127.0 and its analogues. In these objects, the broad hydrogen Balmer and Paschen, He I and Mg II lines share the same peculiar emission-line profile (including a blue system, a red system and a double-peaked disk-line component); however, the invariance in the large time interval, the absence of the blue system in He I $λ$10830 profile and the abnormally strong emission of the hydrogen Pa$β$ blue system oppose the binary proposal. We suggest that these unique broad lines arise from the AGN emission-line region and the shock-heated outflowing gases rather than a binary system of two active black holes.

astro-ph.GA↗

arXiv2019

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.

astro-ph.HE↗

arXiv2019

The Science and Technology of FAST

This is the Editorial of Special Topic "The Science and Technology of FAST", published by SCPMA (Science China Physics, Mechanics & Astronomy).

astro-ph.HE↗

arXiv2019

The First Pulsar Discovered by FAST

To assist with the commissioning (Jiang et al. 2019) of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we performed a pulsar search, with the primary goal of developing and testing the pulsar data acquisition and processing pipelines. We tested and used three pipelines, two (P1 and P2 hereafter) searched for the periodic signature of pulsars whereas the other one was used to search for bright single pulses (P3 hereafter). A pulsar candidate was discovered in the observation on the 22nd August, 2017, and later confirmed by the Parkes radio telescope on the 10th September, 2017. This pulsar, named PSR J1900-0134, was the first pulsar discovered by FAST. The pulsar has a pulse period of 1.8 s and a dispersion measure (DM) of 188\,pc\,cm$^{-3}$.

astro-ph.IM↗

arXiv2019

Commissioning Progress of the FAST

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) was completed with its main structure installed on September 25, 2016, after which it entered the commissioning phase. This paper aims to introduce the commissioning progress of the FAST over the past two years. To improve its operational reliability and ensure effective observation time, FAST has been equipped with a real-time information system for the active reflector system and hierarchical commissioning scheme for the feed support system, which ultimately achieves safe operation of the two systems. For meeting the high-performance indices, a high-precision measurement system was set up based on the effective control methods that were implemented for the active reflector system and feed support system. Since the commissioning of the FAST, a low-frequency ultra-wideband receiver and 19-beam 1.05-1.45 GHz receiver have been mainly used. Telescope efficiency, pointing accuracy, and system noise temperature were completely tested and ultimately achieved the acceptance indices of the telescope. The FAST has been in the process of national acceptance preparations and has begun to search for pulsars. In the future, it will still strive to improve its capabilities and expand its application prospects.

astro-ph.IM↗

arXiv2019

The Radiation Structure of PSR B2016$+$28 Observed with FAST

With the largest dish Five-hundred-meter Aperture Spherical radio Telescope (FAST), both the mean and single pulses of PSR B2016$+$28, especially including the single-pulse structure, are investigated in detail in this study. The mean pulse profiles at different frequencies can be well fitted in a conal model, and the peak separation of intensity-dependent pulse profiles increases with intensity. The integrated pulses are obviously frequency dependent (pulse width decreases by $\sim\,20\%$ as frequency increases from 300 MHz to 750 MHz), but the structure of single pulses changes slightly (the corresponding correlation scale decreases by only $\sim\,1\%$). This disparity between mean and single pulses provides independent evidence for the existence of the RS-type vacuum inner gap, indicating a strong bond between particles on the pulsar surface. Diffused drifting sub-pulses are analyzed. The results show that the modulation period along pulse series ($P_3$) is positively correlated to the separation between two adjacent sub-pulses ($P_2$). This correlation may hint a rough surface on the pulsar, eventually resulting in the irregular drift of sparks. All the observational results may have significant implications in the dynamics of pulsar magnetosphere and are discussed extensively in this paper.

astro-ph.HE↗

arXiv2019

Study of Three Rotating Radio Transients with FAST

Rotating radio transients (RRATs) are peculiar astronomical objects whose emission mechanism remains under investigation. In this paper, we present observations of three RRATs, J1538+2345, J1854+0306 and J1913+1330, observed with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Specifically, we analyze the mean pulse profiles and temporal flux density evolutions of the RRATs. Owing to the high sensitivity of FAST, the derived burst rates of the three RRATs are higher than those in previous reports. RRAT J1854+0306 exhibited a time-dynamic mean pulse profile, whereas RRAT J1913+1330 showed distinct radiation and nulling segments on its pulse intensity trains. The mean pulse profile variation with frequency is also studied for RRAT J1538+2345 and RRAT J1913+1330, and the profiles at different frequencies could be well fitted with a cone-core model and a conal-beam model, respectively.

astro-ph.HE↗

arXiv2019

Design of remote control software of near infrared Sky Brightness Monitor in Antarctica

The Near-infrared Sky Brightness Monitor (NIRBM) aims to measure the middle infrared sky background in Antarctica. The NIRBM mainly consists of an InGaAs detector, a chopper, a reflector, a cooler and a black body. The reflector can rotate to scan the sky with a field of view ranging from 0° to 180°. Electromechanical control and weak signal readout functions are accomplished by the same circuit, whose core chip is a STM32F407VG microcontroller. Considering the environment is harsh for humans in Antarctica, a multi-level remote control software system is designed and implemented. A set of EPICS IOCs are developed to control each hardware module independently via serial port communication with the STM32 microcontroller. The tornado web framework and PyEpics are introduced as a combination where PyEpics is used to monitor or change the EPICS Process Variables, functioning as a client for the EPICS framework. Tornado is responsible for the specific operation process of inter-device collaboration, and expose a set of interfaces to users to make calls. Considering the high delay and low bandwidth of the network environment, the tornado back-end is designed as a master-and-agent architecture to improve domestic user experience. The master node is deployed in Antarctic while multiple agent nodes can be deployed domestic. The master and agent nodes communicate with each other through the WebSocket protocol to exchange latest information so that bandwidth is saved. The GUI is implemented in the form of single-page application based on the Vue framework which communicates with tornado through WebSocket and AJAX requests. The web page integrates device control, data curve drawing, alarm display, auto observation and other functions together.

astro-ph.IM↗

arXiv2019

Ranking in Genealogy: Search Results Fusion at Ancestry

Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world's largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census records, court and probate records among many others. Search at Ancestry aims to return relevant records from various record types, allowing our subscribers to build their family trees, research their family history, and make meaningful discoveries about their ancestors from diverse perspectives. In a modern search engine designed for genealogical study, the appropriate ranking of search results to provide highly relevant information represents a daunting challenge. In particular, the disparity in historical records makes it inherently difficult to score records in an equitable fashion. Herein, we provide an overview of our solutions to overcome such record disparity problems in the Ancestry search engine. Specifically, we introduce customized coordinate ascent (customized CA) to speed up ranking within a specific record type. We then propose stochastic search (SS) that linearly combines ranked results federated across contents from various record types. Furthermore, we propose a novel information retrieval metric, normalized cumulative entropy (NCE), to measure the diversity of results. We demonstrate the effectiveness of these two algorithms in terms of relevance (by NDCG) and diversity (by NCE) if applicable in the offline experiments using real customer data at Ancestry.

cs.IR↗

arXiv2019

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the final revenue/profit of the system. Traditional recommendation targets such as rating prediction and top-$k$ recommendation are not directly related to this goal. In this work, we blend the fundamental concepts in online advertising and micro-economics into personalized recommendation for profit maximization. Specifically, we propose value-aware recommendation based on reinforcement learning, which directly optimizes the economic value of candidate items to generate the recommendation list. In particular, we generalize the basic concept of click conversion rate (CVR) in computational advertising into the conversation rate of an arbitrary user action (XVR) in E-commerce, where the user actions can be clicking, adding to cart, adding to wishlist, etc. In this way, each type of user action is mapped to its monetized economic value. Economic values of different user actions are further integrated as the reward of a ranking list, and reinforcement learning is used to optimize the recommendation list for the maximum total value. Experimental results in both offline benchmarks and online commercial systems verified the improved performance of our framework, in terms of both traditional top-$k$ ranking tasks and the economic profits of the system.

cs.IR↗

arXiv2019

Exoplanets in the Antarctic sky. II. 116 Transiting Exoplanet Candidates Found by AST3-II (CHESPA) within the Southern CVZ of TESS

We report first results from the CHinese Exoplanet Searching Program from Antarctica (CHESPA)---a wide-field high-resolution photometric survey for transiting exoplanets carried out using telescopes of the AST3 (Antarctic Survey Telescopes times 3) project. There are now three telescopes (AST3-I, AST3-II, and CSTAR-II) operating at Dome A---the highest point on the Antarctic Plateau---in a fully automatic and remote mode to exploit the superb observing conditions of the site, and its long and uninterrupted polar nights. The search for transiting exoplanets is one of the key projects for AST3. During the Austral winters of 2016 and 2017 we used the AST3-II telescope to survey a set of target fields near the southern ecliptic pole, falling within the continuous viewing zone of the TESS mission \citep{Ricker10}. The first data release of the 2016 data, including images, catalogs and lightcurves of 26578 bright stars ($7.5\le i \le15$) was presented in \citet{Zhang18}. The best precision, as measured by the RMS of the lightcurves at the optimum magnitude of the survey ($i=10$), is around 2\,mmag. We detect 222 objects with plausible transit signals from these data, 116 of which are plausible transiting exoplanet candidates according to their stellar properties as given by the TESS Input Catalog \citep{Stassun17}, Gaia DR2 \citep{Gaia18} and TESS-HERMES spectroscopy \citep{Sharma18}. With the first data release from TESS expected in late 2018, this candidate list will be a timely for improving the rejection of potential false positives.

astro-ph.EP↗

arXiv2018

Exoplanets in the Antarctic Sky. I. The First Data Release of AST3-II (CHESPA) and New Found Variables within the Southern CVZ of TESS

Located at Dome A, the highest point of the Antarctic plateau, the Chinese Kunlun station is considered to be one of the best ground-based photometric sites because of its extremely cold, dry, and stable atmosphere(Saunders et al. 2009). A target can be monitored from there for over 40 days without diurnal interruption during a polar winter. This makes Kunlun station a perfect site to search for short-period transiting exoplanets. Since 2008, an observatory has been built at Kunlun station and three telescopes are working there. Using these telescopes, the AST3 project has been carried out over the last six years with a search for transiting exoplanets as one of its key programs (CHESPA). In the austral winters of 2016 and 2017, a set of target fields in the Southern CVZ of TESS (Ricker et al. 2009) were monitored by the AST3-II telescope. In this paper, we introduce the CHESPA and present the first data release containing photometry of 26,578 bright stars (m_i < 15). The best photometric precision at the optimum magnitude for the survey is around 2 mmag. To demonstrate the data quality, we also present a catalog of 221 variables with a brightness variation greater than 5 mmag from the 2016 data. Among these variables, 179 are newly identified periodic variables not listed in the AAVSO databasea), and 67 are listed in the Candidate Target List(Stassun et al. 2017). These variables will require careful attention to avoid false-positive signals when searching for transiting exoplanets. Dozens of new transiting exoplanet candidates will be also released in a subsequent paper(Zhang et al. 2018b).

astro-ph.IM↗

arXiv2018

Super Diffusion for Salient Object Detection

One major branch of saliency object detection methods is diffusion-based which construct a graph model on a given image and diffuse seed saliency values to the whole graph by a diffusion matrix. While their performance is sensitive to specific feature spaces and scales used for the diffusion matrix definition, little work has been published to systematically promote the robustness and accuracy of salient object detection under the generic mechanism of diffusion. In this work, we firstly present a novel view of the working mechanism of the diffusion process based on mathematical analysis, which reveals that the diffusion process is actually computing the similarity of nodes with respect to the seeds based on diffusion maps. Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection. A closed-form solution of the optimal parameters for the integration is determined through supervised learning. At the local level, we propose to promote each individual diffusion before the integration. Our mathematical analysis reveals the close relationship between saliency diffusion and spectral clustering. Based on this, we propose to re-synthesize each individual diffusion matrix from the most discriminative eigenvectors and the constant eigenvector (for saliency normalization). The proposed framework is implemented and experimented on prevalently used benchmark datasets, consistently leading to state-of-the-art performance.

cs.CV↗

arXiv2018

DifNet: Semantic Segmentation by Diffusion Networks

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. OurDifNet is trained end-to-end without complex loss functions.

cs.CV↗