arXiv subjects

Ping Chen

Publications and source records attributed to Ping Chen.

At least 145 records · Page 8Linked to original sources

arXiv2023

Machine learning assisted phase and size-controlled synthesis of iron oxides

The controllable synthesis of iron oxides particles is a critical issue for materials science, energy storage, biomedical applications, environmental science, and earth science. However, synthesis of iron oxides with desired phase and size are still a time-consuming and trial-and-error process. This study presents solutions for two fundamental challenges in materials synthesis: predicting the outcome of a synthesis from specified reaction parameters and correlating sets of parameters to obtain products with desired outcomes. Four machine learning algorithms, including random forest, logistic regression, support vector machine, and k-nearest neighbor, were trained to predict the phase and particle size of iron oxide based on experimental conditions. Among the models, random forest exhibited the best performance, achieving 96% and 81% accuracy when predicting the phase and size of iron oxides in the test dataset. Premutation feature importance analysis shows that most models (except logistic regression) rely on known features such as precursor concentration, pH, and temperature to predict the phases from synthesis conditions. The robustness of the random forest models was further verified by comparing prediction and experimental results based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models are in good agreement with the experimental results. Additionally, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with desired phase and particle size from previous studies in the dataset.

cond-mat.mtrl-sci↗

arXiv2023

Generalized Category Discovery with Decoupled Prototypical Network

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.

cs.CL↗

arXiv2023

DAA: A Delta Age AdaIN operation for age estimation via binary code transformer

Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets.

cs.CV↗

arXiv2023

SN2017egm: A Helium-rich Superluminous Supernova with Multiple Bumps in the Light Curves

When discovered, SN~2017egm was the closest (redshift $z=0.03$) hydrogen-poor superluminous supernova (SLSN-I) and a rare case that exploded in a massive and metal-rich galaxy. Thus, it has since been extensively observed and studied. We report spectroscopic data showing strong emission at around He~I $λ$10,830 and four He~I absorption lines in the optical. Consequently, we classify SN~2017egm as a member of an emerging population of helium-rich SLSNe-I (i.e., SLSNe-Ib). We also present our late-time photometric observations. By combining them with archival data, we analyze high-cadence ultra-violet, optical, and near-infrared light curves spanning from early pre-peak ($\sim -20\,d$) to late phases ($\sim +300\,d$). We obtain its most complete bolometric light curve, in which multiple bumps are identified. None of the previously proposed models can satisfactorily explain all main light-curve features, while multiple interactions between the ejecta and circumstellar material (CSM) may explain the undulating features. The prominent infrared excess with a blackbody luminosity of $10^7$--$10^8\,L_{sun}$ detected in SN~2017egm could originate from the emission of either an echo of a pre-existing dust shell, or newly-formed dust, offering an additional piece of evidence supporting the ejecta-CSM interaction model. Moreover, our analysis of deep $Chandra$ observations yields the tightest-ever constraint on the X-ray emission of an SLSN-I, amounting to an X-ray-to-optical luminosity ratio $\lesssim 10^{-3}$ at late phases ($\sim100-200\,d$), which could help explore its close environment and central engine.

astro-ph.HE↗

arXiv2023

Measurement operator for quantum nondemolition measurements

We derive a measurement operator corresponding to a quantum nondemolition (QND) measurement of an atomic ensemble. The quantum measurement operator takes the form of a positive operator valued measure (POVM) and is valid for arbitrary interaction times, initial coherent state amplitudes, and final photon measurement outcomes. We analyze the dependence on various parameters and show that the effect of the QND measurement for short interaction times is to apply a Gaussian modulation of the initial state wavefunction. We derive approximate expressions for the POVM in various limits, such as the short interaction time regime and projective measurement limit. Several examples are shown which shows how spin squeezing and Schrodinger cat states can be generated using the measurement.

quant-ph↗

arXiv2023

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts, of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided.

astro-ph.HE↗

arXiv2022

The First Data Release of CNIa0.02 -- A Complete Nearby (Redshift <0.02) Sample of Type Ia Supernova Light Curves

The CNIa0.02 project aims to collect a complete, nearby sample of Type Ia supernovae (SNe Ia) light curves, and the SNe are volume-limited with host-galaxy redshifts z_host < 0.02. The main scientific goal is to infer the distributions of key properties (e.g., the luminosity function) of local SNe Ia in a complete and unbiased fashion in order to study SN explosion physics. We spectroscopically classify any SN candidate detected by the All-Sky Automated Survey for Supernovae (ASAS-SN) that reaches peak brightness < 16.5 mag. Since ASAS-SN scans the full sky and does not target specific galaxies, our target selection is effectively unbiased by host-galaxy properties. We perform multi-band photometric observations starting from the time of discovery. In the first data release (DR1), we present the optical light curves obtained for 247 SNe from our project (including 148 SNe in the complete sample), and we derive parameters such as the peak fluxes, dm15 and s_BV.

astro-ph.HE↗

arXiv2022

The prevalence and influence of circumstellar material around hydrogen-rich supernova progenitors

Narrow transient emission lines (flash-ionization features) in early supernova (SN) spectra trace the presence of circumstellar material (CSM) around the massive progenitor stars of core-collapse SNe. The lines disappear within days after the SN explosion, suggesting that this material is spatially confined, and originates from enhanced mass loss shortly (months to a few years) prior to explosion. We performed a systematic survey of H-rich (Type II) SNe discovered within less than two days from explosion during the first phase of the Zwicky Transient Facility (ZTF) survey (2018-2020), finding thirty events for which a first spectrum was obtained within $< 2$ days from explosion. The measured fraction of events showing flash ionisation features ($>36\%$ at $95\%$ confidence level) confirms that elevated mass loss in massive stars prior to SN explosion is common. We find that SNe II showing flash ionisation features are not significantly brighter, nor bluer, nor more slowly rising than those without. This implies that CSM interaction does not contribute significantly to their early continuum emission, and that the CSM is likely optically thin. We measured the persistence duration of flash ionisation emission and find that most SNe show flash features for $\approx 5 $ days. Rarer events, with persistence timescales $>10$ days, are brighter and rise longer, suggesting these may be intermediate between regular SNe II and strongly-interacting SNe IIn.

astro-ph.HE↗

arXiv2022

UV-Based 3D Hand-Object Reconstruction with Grasp Optimization

We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art.

cs.CV↗

arXiv2022

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning

Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same granularity. In this paper, we investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. It is also a challenging task since supervised training on coarse-grained categories tends to focus on inter-class distance (distance between coarse-grained classes) but ignore intra-class distance (distance between fine-grained sub-classes) which is essential for separating fine-grained categories. Considering most current methods cannot transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner. Extensive experiments on public datasets show both effectiveness and efficiency of our model over compared methods. Code and data are available at https://github.com/Lackel/Hierarchical_Weighted_SCL.

cs.CL↗

arXiv2022

On the surplus management of funds with assets and liabilities in presence of solvency requirements

In this paper we consider a company whose assets and liabilities evolve according to a correlated bivariate geometric Brownian motion, such as in Gerber and Shiu (2003). We determine what dividend strategy maximises the expected present value of dividends until ruin in two cases: (i) when shareholders won't cover surplus shortfalls and a solvency constraint (as in Paulsen, 2003) is consequently imposed, and (ii) when shareholders are always to fund any capital deficiency with capital (asset) injections. In the latter case, ruin will never occur and the objective is to maximise the difference between dividends and capital injections. Developing and using appropriate verification lemmas, we show that the optimal dividend strategy is, in both cases, of barrier type. Both value functions are derived in closed form. Furthermore, the barrier is defined on the ratio of assets to liabilities, which mimics some of the dividend strategies that can be observed in practice by insurance companies. Existence and uniqueness of the optimal strategies are shown. Results are illustrated.

math.OC↗

arXiv2022

Progenitor, environment, and modelling of the interacting transient, AT 2016jbu (Gaia16cfr)

We present the bolometric lightcurve, identification and analysis of the progenitor candidate, and preliminary modelling of AT2016jbu (Gaia16cfr). We find a progenitor consistent with a $\sim$22--25~$M_{\odot}$ yellow hypergiant surrounded by a dusty circumstellar shell, in agreement with what has been previously reported. We see evidence for significant photometric variability in the progenitor, as well as strong H$α$ emission consistent with pre-existing circumstellar material. The age of the environment as well as the resolved stellar population surrounding AT2016jbu, support a progenitor age of $>$10 Myr, consistent with a progenitor mass of $\sim$22~$M_{\odot}$. A joint analysis of the velocity evolution of AT2016jbu, and the photospheric radius inferred from the bolometric lightcurve shows the transient is consistent with two successive outbursts/explosions. The first outburst ejected material with velocity $\sim$650$kms^{-1}$, while the second, more energetic event, ejected material at $\sim$4500$kms^{-1}$. Whether the latter is the core-collapse of the progenitor remains uncertain. We place a limit on the ejected $^{56}$Ni mass of $<$0.016$M_{\odot}$. Using the BPASS code, we explore a wide range of possible progenitor systems, and find that the majority of these are in binaries, some of which are undergoing mass transfer or common envelope evolution immediately prior to explosion. Finally, we use the SNEC code to demonstrate that the low-energy explosion within some of these binary systems, together with sufficient CSM, can reproduce the overall morphology of the lightcurve of AT2016jbu.

astro-ph.HE↗

arXiv2022

Photometric and spectroscopic evolution of the interacting transient AT 2016jbu (Gaia16cfr)

We present the results from a high cadence, multi-wavelength observation campaign of AT 2016jbu (aka Gaia16cfr), an interacting transient. This dataset complements the current literature by adding higher cadence as well as extended coverage of the lightcurve evolution and late-time spectroscopic evolution. Photometric coverage reveals that AT 2016jbu underwent significant photometric variability followed by two luminous events, the latter of which reached an absolute magnitude of M$_V\sim$-18.5 mag. This is similar to the transient SN 2009ip whose nature is still debated. Spectra are dominated by narrow emission lines and show a blue continuum during the peak of the second event. AT 2016jbu shows signatures of a complex, non-homogeneous circumstellar material (CSM). We see slowly evolving asymmetric hydrogen line profiles, with velocities of 500km$s^{-1}$ seen in narrow emission features from a slow moving CSM, and up to 10,000km$s^{-1}$ seen in broad absorption from some high velocity material. Late-time spectra ($\sim$+1 year) show a lack of forbidden emission lines expected from a core-collapse supernova and are dominated by strong emission from H, He i and Ca ii. Strong asymmetric emission features, a bumpy lightcurve, and continually evolving spectra suggest an inhibit nebular phase. We compare the evolution of H$α$ among SN 2009ip-like transients and find possible evidence for orientation angle effects. The light-curve evolution of AT 2016jbu suggests similar, but not identical, circumstellar environments to other SN 2009ip-like transients.

astro-ph.SR↗

arXiv2022

Deterministic preparation of supersinglets with collective spin projections

We introduce a procedure to generate supersinglets, the multipartite generalization of angular momentum singlet states. A supersinglet is defined as a total spin zero state consisting of $ N $ spin-$ j $ particles. They are highly entangled and have zero spin variance in any direction, and as such are potentially useful for quantum metrology. Our scheme is based on projective measurements that measure the collective spin of the whole spin ensemble. A local unitary rotation is applied conditionally on the measurement outcome, such as to maximize the probability of obtaining spin zero on the subsequent measurement. The sequence is repeated in the $ z $- and $ x $-basis until convergence is obtained towards the supersinglet state. Our sequence works regardless of the initial state, and no postselection is required. Due to the use of strong projective measurements, very fast convergence towards zero spin variance is obtained. We discuss an example implementation using quantum nondemolition measurements in atomic ensembles, and perform numerical simulations to demonstrate the procedure.

quant-ph↗

arXiv2022

Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a reinforcement learning-based semantics-preserving (i.e.functionality-preserving) attack against black-box GNNs (GraphNeural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semanticNopsand their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these "how to select" decisions. To evaluate the attack, we have trained two kinds of GNNs with five types(i.e., Backdoor, Trojan-Downloader, Trojan-Ransom, Adware, and Worm) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than three baseline attacks, namely the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.

cs.CR↗

arXiv2022

Investigating the Nature of the Luminous Ambiguous Nuclear Transient ASASSN-17jz

We present observations of the extremely luminous but ambiguous nuclear transient (ANT) ASASSN-17jz, spanning roughly 1200 days of the object's evolution. ASASSN-17jz was discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) in the galaxy SDSS J171955.84+414049.4 on UT 2017 July 27 at a redshift of $z=0.1641$. The transient peaked at an absolute $B$-band magnitude of $M_{B,{\rm peak}}=-22.81$, corresponding to a bolometric luminosity of $L_{\rm bol,peak}=8.3\times10^{44}$~erg~s$^{-1}$, and exhibited late-time ultraviolet emission that was still ongoing in our latest observations. Integrating the full light curve gives a total emitted energy of $E_{\rm tot}=(1.36\pm0.08)\times10^{52}$~erg, with $(0.80\pm0.02)\times10^{52}$~erg of this emitted within 200 days of peak light. This late-time ultraviolet emission is accompanied by increasing X-ray emission that becomes softer as it brightens. ASASSN-17jz exhibited a large number of spectral emission lines most commonly seen in active galactic nuclei (AGNs) with little evidence of evolution. It also showed transient Balmer features which became fainter and broader over time, and are still being detected $>1000$ days after peak brightness. We consider various physical scenarios for the origin of the transient, including supernovae (SNe), tidal disruption events (TDEs), AGN outbursts, and ANTs. We find that the most likely explanation is that ASASSN-17jz was an SN~IIn occurring in or near the disk of an existing AGN, and that the late-time emission is caused by the AGN transitioning to a more active state.

astro-ph.HE↗

arXiv2022

A Boundary Regression Model for Nested Named Entity Recognition

Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for a word or a NE candidate in a sentence. In shallow structures, categorized features are weighted to support the prediction. Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations. This approach unfolds a dense space saturated with high-order abstract semantic information, where the prediction is based on distributed feature representations. In this paper, positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework, which simultaneously predicts the classification score of a NE candidate and refine its spatial location in a sentence. It has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sntence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability. Experiments demonstrate state-of-the-art performance for nested NE recognition\footnote{Our codes to implement the BR model are available at: \url{https://github.com/wuyuefei3/BR}.}.

cs.CL↗

arXiv2022

To TDE or not to TDE: The luminous transient ASASSN-18jd with TDE-like and AGN-like qualities

We present the discovery of ASASSN-18jd (AT 2018bcb), a luminous optical/UV/X-ray transient located in the nucleus of the galaxy 2MASX J22434289--1659083 at $z=0.1192$. Over the year after discovery, Swift UVOT photometry shows the UV SED of the transient to be well modeled by a slowly shrinking blackbody with temperature $T \sim 2.5 \times 10^{4} \rm ~K$, a maximum observed luminosity of $L_\text{max} = 4.5^{+0.6}_{-0.3} \times 10^{44} \rm ~erg ~s^{-1}$, and a radiated energy of $E = 9.6^{+1.1}_{-0.6} \times 10^{51} \rm ~erg$. X-ray data from Swift XRT and XMM-Newton show a transient, variable X-ray flux with blackbody and power-law components that fade by nearly an order of magnitude over the following year. Optical spectra show strong, roughly constant broad Balmer emission as well as transient features attributable to He II, N III-V, O III, and coronal Fe. While ASASSN-18jd shares similarities with Tidal Disruption Events (TDEs), it is also similar to the newly-discovered nuclear transients seen in quiescent galaxies and faint Active Galactic Nuclei (AGNs).

astro-ph.HE↗