arXiv subjects

Ping Chen

Publications and source records attributed to Ping Chen.

At least 163 records · Page 9Linked to original sources

arXiv2021

A Linear Relation Between the Color Stretch $s_{BV}$ and the Rising Color Slope $s_0^*(B-V)$ of Type Ia Supernovae

Using data from the Complete Nearby ($z_{host}<0.02$) sample of Type Ia Supernovae (CNIa0.02), we discover a linear relation between two parameters derived from the $B-V$ color curves of Type Ia supernovae: the "color stretch" $s_{BV}$ and the rising color slope $s_0^*(B-V)$ after the peak, and this relation applies to the full range of $s_{BV}$. The $s_{BV}$ parameter is known to be tightly correlated with the peak luminosity, and especially for "fast decliners" (dim Type Ia supernovae), and the luminosity correlation with $s_{BV}$ is markedly better than with the classic light-curve width parameters such as $Δ{m_{15}(B)}$. Thus our new linear relation can be used to infer peak luminosity from $s_0^*$. Unlike $s_{BV}$ (or $Δ{m_{15}}$), the measurement of $s_0^*(B-V)$ does not rely on the well-determined time of light-curve peak or color maximum, making it less demanding on the light-curve coverage than past approaches.

astro-ph.HE↗

arXiv2021

I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling

Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications. The results of current methods are lacking in accuracy and fidelity due to various hand poses and severe occlusions. In this study, we propose an I2UV-HandNet model for accurate hand pose and shape estimation as well as 3D hand super-resolution reconstruction. Specifically, we present the first UV-based 3D hand shape representation. To recover a 3D hand mesh from an RGB image, we design an AffineNet to predict a UV position map from the input in an image-to-image translation fashion. To obtain a higher fidelity shape, we exploit an additional SRNet to transform the low-resolution UV map outputted by AffineNet into a high-resolution one. For the first time, we demonstrate the characterization capability of the UV-based hand shape representation. Our experiments show that the proposed method achieves state-of-the-art performance on several challenging benchmarks.

cs.CV↗

arXiv2021

Annotation of Chinese Predicate Heads and Relevant Elements

A predicate head is a verbal expression that plays a role as the structural center of a sentence. Identifying predicate heads is critical to understanding a sentence. It plays the leading role in organizing the relevant syntactic elements in a sentence, including subject elements, adverbial elements, etc. For some languages, such as English, word morphologies are valuable for identifying predicate heads. However, Chinese offers no morphological information to indicate words` grammatical roles. A Chinese sentence often contains several verbal expressions; identifying the expression that plays the role of the predicate head is not an easy task. Furthermore, Chinese sentences are inattentive to structure and provide no delimitation between words. Therefore, identifying Chinese predicate heads involves significant challenges. In Chinese information extraction, little work has been performed in predicate head recognition. No generally accepted evaluation dataset supports work in this important area. This paper presents the first attempt to develop an annotation guideline for Chinese predicate heads and their relevant syntactic elements. This annotation guideline emphasizes the role of the predicate as the structural center of a sentence. The design of relevant syntactic element annotation also follows this principle. Many considerations are proposed to achieve this goal, e.g., patterns of predicate heads, a flattened annotation structure, and a simpler syntactic unit type. Based on the proposed annotation guideline, more than 1,500 documents were manually annotated. The corpus will be available online for public access. With this guideline and annotated corpus, our goal is to broadly impact and advance the research in the area of Chinese information extraction and to provide the research community with a critical resource that has been lacking for a long time.

cs.CL↗

arXiv2021

The Changing Look Blazar B2 1420+32

Blazars are active galactic nuclei with their relativistic jets pointing toward the observer, with two major sub-classes, the flat spectrum radio quasars and BL Lac objects. We present multi-wavelength photometric and spectroscopic monitoring observations of the blazar, B2 1420+32, focusing on its outbursts in 2018-2020. Multi-epoch spectra show that the blazar exhibited large scale spectral variability in both its continuum and line emission, accompanied by dramatic gamma-ray and optical variability by factors of up to 40 and 15, respectively, on week to month timescales. Over the last decade, the gamma-ray and optical fluxes increased by factors of 1500 and 100, respectively. B2 1420+32 was an FSRQ with broad emission lines in 1995. Following a series of flares starting in 2018, it transitioned between BL Lac and FSRQ states multiple times, with the emergence of a strong Fe pseudo continuum. Two spectra also contain components that can be modeled as single-temperature black bodies of 12,000 and 5,200 K. Such a collection of "changing look" features has never been observed previously in a blazar. We measure gamma-ray-optical and the inter-band optical lags implying emission region separations of less than 800 and 130 gravitational radii respectively. Since most emission line flux variations, except the Fe continuum, are within a factor of 2-3, the transitions between FSRQ and BL Lac classifications are mainly caused by the continuum variability. The large Fe continuum flux increase suggests the occurrence of dust sublimation releasing more Fe ions in the central engine and an energy transfer from the relativistic jet to sub-relativistic emission components.

astro-ph.HE↗

arXiv2021

ASASSN-18am/SN 2018gk : An overluminous Type IIb supernova from a massive progenitor

ASASSN-18am/SN 2018gk is a newly discovered member of the rare group of luminous, hydrogen-rich supernovae (SNe) with a peak absolute magnitude of $M_V \approx -20$ mag that is in between normal core-collapse SNe and superluminous SNe. These SNe show no prominent spectroscopic signatures of ejecta interacting with circumstellar material (CSM), and their powering mechanism is debated. ASASSN-18am declines extremely rapidly for a Type II SN, with a photospheric-phase decline rate of $\sim6.0~\rm mag~(100 d)^{-1}$. Owing to the weakening of HI and the appearance of HeI in its later phases, ASASSN-18am is spectroscopically a Type IIb SN with a partially stripped envelope. However, its photometric and spectroscopic evolution show significant differences from typical SNe IIb. Using a radiative diffusion model, we find that the light curve requires a high synthesised $\rm ^{56}Ni$ mass $M_{\rm Ni} \sim0.4~M_\odot$ and ejecta with high kinetic energy $E_{\rm kin} = (7-10) \times10^{51} $ erg. Introducing a magnetar central engine still requires $M_{\rm Ni} \sim0.3~M_\odot$ and $E_{\rm kin}= 3\times10^{51} $ erg. The high $\rm ^{56}Ni$ mass is consistent with strong iron-group nebular lines in its spectra, which are also similar to several SNe Ic-BL with high $\rm ^{56}Ni$ yields. The earliest spectrum shows "flash ionisation" features, from which we estimate a mass-loss rate of $ \dot{M}\approx 2\times10^{-4}~\rm M_\odot~yr^{-1} $. This wind density is too low to power the luminous light curve by ejecta-CSM interaction. We measure expansion velocities as high as $ 17,000 $ km/s for $H_α$, which is remarkably high compared to other SNe II. We estimate an oxygen core mass of $1.8-3.4$ $M_\odot$ using the [OI] luminosity measured from a nebular-phase spectrum, implying a progenitor with a zero-age main sequence mass of $19-26$ $M_\odot$.

astro-ph.HE↗

arXiv2020

Planck Galactic Cold Clumps in Two Regions: the First Quadrant and the Anti-Center Direction Region

Sixty five Planck Galactic cold clumps (PGCCs) from the first quadrant (IQuad) and thirty nine of PGCCs from the Anti-Center direction region (ACent) were observed in $^{12}$CO, $^{13}$CO and C$^{18}$O J=1-0 lines using the PMO 13.7-m telescope. All the targets were detected with all the three lines, except for 12 IQuad and 8 ACent PGCCs without C$^{18}$O detection. Seventy six and 49 velocity components were obtained in IQuad and ACent respectively. One-hundred and forty-six cores were extracted from 76 IQuad clumps and 100 cores from 49 ACent clumps. The average T$_{\mathrm{ex}}$ of IQuad cores and ACent cores are 12.4 K and 12.1 K, respectively. The average line width of $^{13}$CO of IQuad cores and ACent cores are 1.55 km s$^{-1}$ and 1.77 km s$^{-1}$, respectively. Among the detected cores, 24 in IQuad and 13 in ACent have asymmetric line profiles. The small blue excesses, $\sim$0.03 in IQuad and 0.01 in ACent, indicate that the star formation is not active in these PGCC cores. Power-law fittings of core mass function to the high mass end give indexes of -0.57 in IQuad and -1.02 in ACent which are flatter than the slope of initial mass function given by \citeauthor{1955ApJ...121..161S}. The large turnover masses with value of 28 M$_{\odot}$ for IQuad cores and 77 M$_{\odot}$ for ACent cores suggest low star formation efficiencies in PGCCs. The correlation between virial mass and gas mass indicates that most of PGCC cores in both regions are not likely pressure-confined.

astro-ph.GA↗

arXiv2020

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces.

cs.CR↗

arXiv2020

Risk Modelling on Liquidations with Lévy Processes

It has been decades since the academic world of ruin theory defined the insolvency of an insurance company as the time when its surplus falls below zero. This simplification, however, needs careful adaptions to imitate the real-world liquidation process. Inspired by Broadie et al. (2007) and Li et al. (2020), this paper uses a three-barrier model to describe the financial stress towards bankruptcy of an insurance company. The financial status of the insurer is divided into solvent, insolvent and liquidated three states, where the insurer's surplus process at the state of solvent and insolvent is modelled by two spectrally negative Lévy processes, which have been taken as good candidates to model insurance risks. We provide a rigorous definition of the time of liquidation ruin in this three-barrier model. By adopting the techniques of excursions in the fluctuation theory, we study the joint distribution of the time of liquidation, the surplus at liquidation and the historical high of the surplus until liquidation, which generalizes the known results on the classical expected discounted penalty function in Gerber and Shiu (1998). The results have semi-explicit expressions in terms of the scale functions and the Lévy triplets associated with the two underlying Lévy processes. The special case when the two underlying Lévy processes coincide with each other is also studied, where our results are expressed compactly via only the scale functions. The corresponding results have good consistency with the existing literatures on Parisian ruin with (or without) a lower barrier in Landriault et al. (2014), Baurdoux et al. (2016) and Frostig and Keren-Pinhasik (2019). Besides, numerical examples are provided to illustrate the underlying features of liquidation ruin.

q-fin.RM↗

arXiv2020

SN 2016gsd: An unusually luminous and linear type II supernova with high velocities

We present observations of the unusually luminous Type II supernova (SN) 2016gsd. With a peak absolute magnitude of V = $-$19.95 $\pm$ 0.08, this object is one of the brightest Type II SNe, and lies in the gap of magnitudes between the majority of Type II SNe and the superluminous SNe. Its light curve shows little evidence of the expected drop from the optically thick phase to the radioactively powered tail. The velocities derived from the absorption in H$α$ are also unusually high with the blue edge tracing the fastest moving gas initially at 20000 km s$^{-1}$, and then declining approximately linearly to 15000 km s$^{-1}$ over $\sim$100 d. The dwarf host galaxy of the SN indicates a low-metallicity progenitor which may also contribute to the weakness of the metal lines in its spectra. We examine SN 2016gsd with reference to similarly luminous, linear Type II SNe such as SNe 1979C and 1998S, and discuss the interpretation of its observational characteristics. We compare the observations with a model produced by the JEKYLL code and find that a massive star with a depleted and inflated hydrogen envelope struggles to reproduce the high luminosity and extreme linearity of SN 2016gsd. Instead, we suggest that the influence of interaction between the SN ejecta and circumstellar material can explain the majority of the observed properties of the SN. The high velocities and strong H$α$ absorption present throughout the evolution of the SN may imply a circumstellar medium configured in an asymmetric geometry.

astro-ph.HE↗

arXiv2020

Materials for hydrogen-based energy storage: Past, recent progress and future outlook

Magnesium hydride owns the largest share of publications on solid materials for hydrogen storage. The Magnesium group of international experts contributing to IEA Task 32 Hydrogen Based Energy Storage recently published two review papers presenting the activities of the group focused on magnesium hydride based materials and on Mg based compounds for hydrogen and energy storage. This review article not only overviews the latest activities on both fundamental aspects of Mg-based hydrides and their applications, but also presents a historic overview on the topic and outlines projected future developments. Particular attention is paid to the theoretical and experimental studies of Mg-H system at extreme pressures, kinetics and thermodynamics of the systems based on MgH2,nanostructuring, new Mg-based compounds and novel composites, and catalysis in the Mg based H storage systems. Finally, thermal energy storage and upscaled H storage systems accommodating MgH2 are presented.

cond-mat.mtrl-sci↗

arXiv2020

The Rise and Fall of ASASSN-18pg: Following a TDE from Early To Late Times

We present nearly 500 days of observations of the tidal disruption event ASASSN-18pg, spanning from 54 days before peak light to 441 days after peak light. Our dataset includes X-ray, UV, and optical photometry, optical spectroscopy, radio observations, and the first published spectropolarimetric observations of a TDE. ASASSN-18pg was discovered on 2018 July 11 by the All-Sky Automated Survey for Supernovae (ASAS-SN) at a distance of $d=78.6$ Mpc, and with a peak UV magnitude of $m\simeq14$ it is both one of the nearest and brightest TDEs discovered to-date. The photometric data allow us to track both the rise to peak and the long-term evolution of the TDE. ASASSN-18pg peaked at a luminosity of $L\simeq2.2\times10^{44}$ erg s$^{-1}$, and its late-time evolution is shallower than a flux $\propto t^{-5/3}$ power-law model, similar to what has been seen in other TDEs. ASASSN-18pg exhibited Balmer lines and spectroscopic features consistent with Bowen fluorescence prior to peak which remained detectable for roughly 225 days after peak. Analysis of the two-component H$α$ profile indicates that, if they are the result of reprocessing of emission from the accretion disk, the different spectroscopic lines may be coming from regions between $\sim10$ and $\sim60$ light-days from the black hole. No X-ray emission is detected from the TDE and there is no evidence of a jet or strong outflow detected in the radio. Our spectropolarimetric observations give no strong evidence for significant asphericity in the emission region, with the emission region having an axis ratio of at least $\sim0.65$.

astro-ph.HE↗

arXiv2020

Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text

Named entity recognition is a challenging task in Natural Language Processing, especially for informal and noisy social media text. Chinese word boundaries are also entity boundaries, therefore, named entity recognition for Chinese text can benefit from word boundary detection, outputted by Chinese word segmentation. Yet Chinese word segmentation poses its own difficulty because it is influenced by several factors, e.g., segmentation criteria, employed algorithm, etc. Dealt improperly, it may generate a cascading failure to the quality of named entity recognition followed. In this paper we integrate a boundary assembling method with the state-of-the-art deep neural network model, and incorporate the updated word boundary information into a conditional random field model for named entity recognition. Our method shows a 2% absolute improvement over previous state-of-the-art results.

cs.CL↗

arXiv2020

Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance

The study of model bias and variance with respect to decision boundaries is critically important in supervised classification. There is generally a tradeoff between the two, as fine-tuning of the decision boundary of a classification model to accommodate more boundary training samples (i.e., higher model complexity) may improve training accuracy (i.e., lower bias) but hurt generalization against unseen data (i.e., higher variance). By focusing on just classification boundary fine-tuning and model complexity, it is difficult to reduce both bias and variance. To overcome this dilemma, we take a different perspective and investigate a new approach to handle inaccuracy and uncertainty in the training data labels, which are inevitable in many applications where labels are conceptual and labeling is performed by human annotators. The process of classification can be undermined by uncertainty in the labels of the training data; extending a boundary to accommodate an inaccurately labeled point will increase both bias and variance. Our novel method can reduce both bias and variance by estimating the pointwise label uncertainty of the training set and accordingly adjusting the training sample weights such that those samples with high uncertainty are weighted down and those with low uncertainty are weighted up. In this way, uncertain samples have a smaller contribution to the objective function of the model's learning algorithm and exert less pull on the decision boundary. In a real-world physical activity recognition case study, the data presents many labeling challenges, and we show that this new approach improves model performance and reduces model variance.

cs.LG↗

arXiv2020

SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis

\textit{SummerTime} seeks to summarize globally time series signals and provides a fixed-length, robust summarization of the variable-length time series. Many classical machine learning methods for classification and regression depend on data instances with a fixed number of features. As a result, those methods cannot be directly applied to variable-length time series data. One common approach is to perform classification over a sliding window on the data and aggregate the decisions made at local sections of the time series in some way, through majority voting for classification or averaging for regression. The downside to this approach is that minority local information is lost in the voting process and averaging assumes that each time series measurement is equal in significance. Also, since time series can be of varying length, the quality of votes and averages could vary greatly in cases where there is a close voting tie or bimodal distribution of regression domain. Summarization conducted by the \textit{SummerTime} method will be a fixed-length feature vector which can be used in-place of the time series dataset for use with classical machine learning methods. We use Gaussian Mixture models (GMM) over small same-length disjoint windows in the time series to group local data into clusters. The time series' rate of membership for each cluster will be a feature in the summarization. The model is naturally capable of converging to an appropriate cluster count. We compare our results to state-of-the-art studies in physical activity classification and show high-quality improvement by classifying with only the summarization. Finally, we show that regression using the summarization can augment energy expenditure estimation, producing more robust and precise results.

cs.LG↗

arXiv2020

The Most Rapidly Declining Type I Supernova 2019bkc/ATLAS19dqr

We report observations of the hydrogen-deficient supernova (SN) 2019bkc/ATLAS19dqr. With B- and r-band decline between peak and 10 days post peak of Delta m_10(B)=5.24+/-0.07 mag and Delta m_10(r)=3.85+/-0.10$ mag, respectively, SN 2019bkc is the most rapidly declining SN I discovered so far. While its closest matches are the rapidly declining SN 2005ek and SN 2010X, the light curves and spectra of SN 2019bkc show some unprecedented characteristics. SN 2019bkc appears "hostless," with no identifiable host galaxy near its location, although it may be associated with the galaxy cluster MKW1 at z = 0.02. We evaluate a number of existing models of fast-evolving SNe, and we find that none of them can satisfactorily explain all aspects of SN 2019bkc observations.

astro-ph.HE↗

arXiv2020

The Highly Luminous Type Ibn Supernova ASASSN-14ms

We present photometric and spectroscopic follow-up observations of the highly luminous Type Ibn supernova ASASSN-14ms, which was discovered on UT 2014-12-26.61 at $m_V \sim 16.5$. With a peak absolute $V$-band magnitude brighter than $-20.5$, a peak bolometric luminosity of $1.7 \times 10^{44}$ ergs s$^{-1}$, and a total radiated energy of $2.1 \times 10^{50}$ ergs, ASASSN-14ms is one of the most luminous Type Ibn supernovae yet discovered. In simple models, the most likely power source for this event is a combination of the radioactive decay of $^{56}$Ni and $^{56}$Co at late times and the interaction of supernova ejecta with the progenitor's circumstellar medium at early times, although we cannot rule out the possibility of a magnetar-powered light curve. The presence of a dense circumstellar medium is indicated by the intermediate-width He I features in the spectra. The faint ($m_g \sim 21.6$) host galaxy SDSS J130408.52+521846.4 has an oxygen abundance below $12+\log(O/H) \lesssim 8.3$, a stellar mass of $M_* \sim 2.6 \times 10^8 M_{\odot}$, and a star formation rate of $\textrm{SFR} \sim 0.02$ $M_{\odot}$ yr$^{-1}$.

astro-ph.HE↗

arXiv2020

ASASSN-18tb: A Most Unusual Type Ia Supernova Observed by TESS and SALT

We present photometric and spectroscopic observations of the unusual Type Ia supernova ASASSN-18tb, including a series of SALT spectra obtained over the course of nearly six months and the first observations of a supernova by the Transiting Exoplanet Survey Satellite (TESS). We confirm a previous observation by Kollmeier et al. (2019) showing that ASASSN-18tb is the first relatively normal Type Ia supernova to exhibit clear broad ($\sim1000$ km s$^{-1}$) H$α$ emission in its nebular phase spectra. We find that this event is best explained as a sub-Chandrasekhar mass explosion with $M_{Ni} \approx 0.3\; \rm{M}_\odot$. Despite the strong H$α$ signature at late times, we find that the early rise of the supernova shows no evidence for deviations from a single-component power-law and is best fit with a moderately shallow power-law of index $1.69\pm0.04$. We find that the H$α$ luminosity remains approximately constant after its initial detection at phase +37 d, and that the H$α$ velocity evolution does not trace that of the Fe~III$~\lambda4660$ emission. These suggest that the H$α$ emission arises from circumstellar medium (CSM) rather than swept up material from a non-degenerate companion. However, ASASSN-18tb is strikingly different from other known CSM-interacting Type Ia supernovae in a number of significant ways. Those objects typically show an H$α$ luminosity two orders of magnitude higher than what is seen in ASASSN-18tb, pushing them away from the empirical light-curve relations that define "normal" Type Ia supernovae. Conversely, ASASSN-18tb exhibits a fairly typical light curve and luminosity for an underluminous or transitional SN Ia, with $M_R \approx -18.1$ mag. Moreover, ASASSN-18tb is the only SN Ia showing H$α$ from CSM interaction to be discovered in an early-type galaxy.

astro-ph.HE↗

arXiv2019

Variable H$α$ Emission in the Nebular Spectra of the Low-Luminosity Type Ia SN2018cqj/ATLAS18qtd

We present optical photometry and spectroscopy of the Type Ia supernova SN2018cqj/ATLAS18qtd. The supernova exploded in an isolated region at $\sim 65$~kpc from the S0 galaxy IC~550 at $z=0.0165$ ($D\approx 74$~Mpc) and has a redshift consistent with a physical association to this galaxy. Multicolor photometry show that SN2018cqj/ATLAS18qtd is a low-luminosity ($M_{B_{max}}\approx -17.9$ mag), fast-declining Type Ia with color stretch $s_{BV} \approx 0.6$ and $B$-band decline rate $Δm_{15}(B) \approx 1.77$ mag. Two nebular-phase spectra obtained as part of the 100IAS survey at +193 and +307 days after peak show the clear detection of a narrow H$α$ line in emission that is resolved in the first spectrum with $\rm FWHM \approx 1200$ km s$^{-1}$ and $L_{Hα} \approx 3.8\times 10^{37}$ erg s$^{-1}$. The detection of a resolved H$α$ line with a declining luminosity is broadly consistent with recent models where hydrogen is stripped from the non-degenerate companion in a single-degenerate progenitor system. However, the amount of hydrogen consistent with the luminosities of the H$α$ line would be $\sim 10^{-3}$ M$_{\odot}$, significantly less than theoretical model predictions in the classical single-degenerate progenitor systems. SN2018cqj/ATLAS18qtd is the second low-luminosity, fast-declining Type Ia SN after SN2018fhw/ASASSN-18tb that shows narrow H$α$ in emission in its nebular-phase spectra.

astro-ph.HE↗