arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 343 records · Page 19Linked to original sources

arXiv2019

MultiPulse {\it Fermi} Gamma-Ray Bursts. I. Evidence of the Transition from Fireball to Poynting-flux-dominated Outflow

The composition of a jet is still an unsolved problem in gamma-ray bursts (GRBs). Several previous studies have suggested that the prompt emission spectrum of GRBs is likely to consist of a few components that may arise from different jet compositions. Here we present a systematic analysis to search for the GRBs that seem to show the transition from a fireball to the Poynting-flux-dominated outflow between well-separated pulses within a single burst, like the GRB 160626B, using the Gamma-ray Burst Monitor data of the \emph{Fermi} satellite. We obtain 43 GRBs with clear multiple pulses and find that 9/43 (21\%) bursts may exhibit such a transition based on the time-integrated spectral analysis. We then select a further four bursts with the data of adequate quality to perform a detailed time-resolved spectral analysis, and we find that in three bursts the thermal-like pulse is a precursor. Furthermore, based on the time-resolved spectra, we constrain the outflow properties for those thermal pulses and find them consistent with the typical properties of the photosphere emission. Also, the later pulses with the softer low-energy spectral index are compatible with the optically thin synchrotron emission model. Our analysis indicates that a good fraction of the multipulse {\it Fermi} bursts may obtain the transition from fireball to the Poynting-flux-dominated outflow.

astro-ph.HE↗

arXiv2019

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

cs.AI↗

arXiv2019

Machine Discovery of Partial Differential Equations from Spatiotemporal Data

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery ($\text{S}^3\text{d}$), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning; which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics.

stat.ML↗

arXiv2019

Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding

Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage. In referring expressions, people usually describe a target entity in terms of its relationship with other contextual entities as well as visual attributes. However, previous weakly supervised REG methods rarely pay attention to the relationship between the entities. In this paper, we propose a knowledge-guided pairwise reconstruction network (KPRN), which models the relationship between the target entity (subject) and contextual entity (object) as well as grounds these two entities. Specifically, we first design a knowledge extraction module to guide the proposal selection of subject and object. The prior knowledge is obtained in a specific form of semantic similarities between each proposal and the subject/object. Second, guided by such knowledge, we design the subject and object attention module to construct the subject-object proposal pairs. The subject attention excludes the unrelated proposals from the candidate proposals. The object attention selects the most suitable proposal as the contextual proposal. Third, we introduce a pairwise attention and an adaptive weighting scheme to learn the correspondence between these proposal pairs and the query. Finally, a pairwise reconstruction module is used to measure the grounding for weakly supervised learning. Extensive experiments on four large-scale datasets show our method outperforms existing state-of-the-art methods by a large margin.

cs.CV↗

arXiv2019

Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

Weakly supervised referring expression grounding aims at localizing the referential object in an image according to the linguistic query, where the mapping between the referential object and query is unknown in the training stage. To address this problem, we propose a novel end-to-end adaptive reconstruction network (ARN). It builds the correspondence between image region proposal and query in an adaptive manner: adaptive grounding and collaborative reconstruction. Specifically, we first extract the subject, location and context features to represent the proposals and the query respectively. Then, we design the adaptive grounding module to compute the matching score between each proposal and query by a hierarchical attention model. Finally, based on attention score and proposal features, we reconstruct the input query with a collaborative loss of language reconstruction loss, adaptive reconstruction loss, and attribute classification loss. This adaptive mechanism helps our model to alleviate the variance of different referring expressions. Experiments on four large-scale datasets show ARN outperforms existing state-of-the-art methods by a large margin. Qualitative results demonstrate that the proposed ARN can better handle the situation where multiple objects of a particular category situated together.

cs.CV↗

arXiv2019

Determination of Electron Band Structure using Temporal Interferometry

We propose an all-optical method to directly reconstruct the band structure of semiconductors. Our scheme is based on the temporal Young's interferometer realized by high harmonic generation (HHG) with a few-cycle laser pulse. As a time-energy domain interferometric device, temporal interferometer encodes the band structure into the fringe in the energy domain. The relation between the band structure and the emitted harmonic frequencies is established. This enables us to retrieve the band structure from the HHG spectrum with a single-shot measurement. Our scheme paves the way to study matters under ambient conditions and to track the ultrafast modification of band structures.

physics.optics↗

arXiv2019

Dynamic Prediction of Competing Risk Events using Landmark Sub-distribution Hazard Model with Multiple Longitudinal Biomarker

The cause-specific cumulative incidence function (CIF) quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted CIF by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each landmark time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted CIF. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.

q-bio.QM↗

arXiv2019

A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to form a classifier or regressor, to predict the local and overall coordination environments, respectively. Using Li3FeO3.5 as a model system, it was demonstrated that the prediction accuracy of the DNN classifier is higher than 98%, and the predictions of the DNN regressor also showed notable agreement with the ground truth. Therefore, despite its simplicity, this DNN architecture can be expected to be generally capable of predicting the structural properties of various systems. Fine tuning of the hyperparameters, bias-variance tradeoff, and strategies to enrich the versatility of the model were also discussed.

physics.comp-ph↗

arXiv2019

Reciprocal-space-trajectory perspective on high harmonic generation in solids

We revisit the mechanism of high harmonic generation (HHG) from solids by comparing HHG in laser fields with different ellipticities but constant maximum amplitude. It is shown that the cutoff of HHG is strongly extended in a circularly polarized field. Moreover, the harmonic yield with large ellipticity is comparable to or even higher than that in the linearly polarized field. To understand the underlying physics, we develop a reciprocal-space-trajectory method, which explains HHG in solids by a trajectory ensemble from different ionization times and different initial states in the reciprocal space. We show that the cutoff extension is related to an additional pre-acceleration step prior to ionization, which has been overlooked in solids. By analyzing the trajectories and the time-frequency spectrogram, we show that the HHG in solids cannot be interpreted in terms of the classical re-collision picture alone. Instead, the radiation should be described by the electronhole interband polarization, which leads to the unusual ellipticity dependence. We propose a new four-step model to understand the mechanism of HHG in solids.

physics.optics↗

arXiv2019

Self-similarity and power-laws in GRB 190114C

Following Fermi and NOT observations, Ruffini et al. (2019b) soon identified GRB 190114C as BdHN I at z=0.424, it has been observed since, with unprecedented accuracy, [...] all the way to the successful optical observation of our predicted supernova (SN). This GRB is a twin of GRB 130427A. Here we take advantage of the GBM data and identify in it three different Episodes. Episode 1 represents the precursor which includes the SN breakout and the creation of the new neutron star ($ν$NS), the hypercritical accretion of the SN ejecta onto the NS binary companion, exceeding the NS critical mass at $t_{rf}=1.9$s. Episode 2 starting at $t_{rf}=1.9$s includes three major events: the formation of the BH, the onset of the GeV emission and the onset of the ultra-relativistic prompt emission (UPE), which extends all the way up to $t_{rf}=3.99$s. Episode 3 which occurs at times following $t_{rf}=3.99$s reveals the presence of a cavity carved out in the SN ejecta by the BH formation. We perform an in depth time-resolved spectral analysis on the entire UPE with the corresponding determination of the spectra best fit by a cut-off power-law and a black body (CPL+BB) model, and then we repeat the spectral analysis in 5 successive time iterations in increasingly shorter time bins: we find a similarity in the spectra in each stage of the iteration revealing clearly a self-similar structure. We find a power-law dependence of the BB temperature with index $-1.56\pm0.38$, a dependence with index $-1.20\pm0.26$ for the gamma-ray luminosity confirming a similar dependence with index $-1.20\pm0.36$ which we find as well in the GeV luminosity, both expressed in the rest-frame. We thus discover in the realm of relativistic astrophysics the existence of a self-similar physical process and power-law dependencies, extensively described in the micro-physical world by the classical works of Heisenberg-Landau-Wilson.

astro-ph.HE↗

arXiv2019

GRB 190114C: An Upgraded Legend

Gamma-ray burst (GRB) 190114C first resembles the legendary GRB 130427A: Both are strong sources of GeV emission, exhibiting consistent GeV spectral evolution, and almost identical in detail for the morphology of light-curves in X-ray, gamma-ray and GeV bands, inferring a standard system with different scales. GRB 190114C is richer than GRB 130427A: a large percentage of $\sim 30\%$ energy is thermal presenting in the gamma-ray prompt emission, making it as one of the most thermal-prominent GRBs; Moreover, GRB 190114C extends the horizon of GRB research, that for the first time the ultra-high energy TeV emission ($> 300$~GeV) is detected in a GRB as reported by the MAGIC team. Furthermore, GRB 190114C urges us to revisit the traditional theoretical framework, since most of the GRB's energy may emit in the GeV and TeV range, not in the conventional MeV range. Since GRB 190114C confidently supports that MeV and GeV emissions have the same origin, it helps to establish a new comprehensive acceleration and radiation mechanism. Overall, GRB 190114C refreshes our GRB knowledge and challenges our current GRB theoretical interpretation.

astro-ph.HE↗

arXiv2019

Two Predictions of supernova: GRB 130427A / SN 2013cq and GRB 180728A / SN 2018fip

On 2018 July 28, GRB 180728A triggered \textit{Swift} satellites and, soon after the determination of the redshift, we identified this source as a type II binary-driven hypernova (BdHN II) in our model. Consequently, we predicted the appearance time of its associated supernova (SN), which was later confirmed as SN 2018fip. A BdHN II originates in a binary composed of a carbon-oxygen core (CO$_{\rm core}$) undergoing SN, and the SN ejecta hypercritically accrete onto a companion neutron star (NS). From the time of the SN shock breakout to the time when the hypercritical accretion starts, we infer the binary separation $\simeq 3 \times 10^{10}$ cm. The accretion explains the prompt emission of isotropic energy $\simeq 3 \times 10^{51}$ erg, lasting $\sim 10$ s, and the accompanying observed blackbody emission from a thermal convective instability bubble. The new neutron star ($ν$NS) originating from the SN powers the late afterglow from which a $ν$NS initial spin of $2.5$ ms is inferred. We compare GRB 180728A with GRB 130427A, a type I binary-driven hypernova (BdHN I) with isotropic energy $> 10^{54}$ erg. For GRB 130427A we have inferred an initially closer binary separation of $\simeq 10^{10}$ cm, implying a higher accretion rate leading to the collapse of the NS companion with consequent black hole formation, and a faster, $1$ ms spinning $ν$NS. In both cases, the optical spectra of the SNe are similar, and not correlated to the energy of the gamma-ray burst. We present three-dimensional smoothed-particle-hydrodynamic simulations and visualisations of the BdHNe I and II.

astro-ph.HE↗

arXiv2019

Image Classification base on PCA of Multi-view Deep Representation

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance.

cs.CV↗

arXiv2019

Precision Higgs Physics at CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

hep-ex↗

arXiv2019

Molecular rotation movie filmed with high-harmonic generation

Direct imaging of molecular dynamics is a long-standing goal in physics and chemistry. As an emerging tool, high-harmonic spectroscopy (HHS) enables accessing molecular dynamics on femtosecond to attosecond time scales. However, decoding information from the harmonic signals is usually painstaking due to the coherent nature of high-harmonic generation (HHG). Here we show that this obstacle can be effectively overcome by exploiting machine learning in HHS. Combining the machine learning with an angle-resolved HHS method, we demonstrate that the rich dynamics of molecular rotational wave packet is fully reconstructed from the angular distributions of HHG measured at various time delays of the probe pulse. The experimental retrievals are in good agreement with the numerical simulations. These findings provide a comprehensive picture of molecular rotation in space and time which will facilitate the development of related researches on molecular dynamics imaging.

physics.optics↗

arXiv2019

On the $α$-Intensity Correlation in Gamma-Ray Bursts: Subphotospheric Heating with Varying Entropy

The emission mechanism during the prompt phase in gamma-ray bursts (GRBs) can be investigated through correlations between spectral properties. Here, we revisit the correlation relating the instantaneous flux, $F$, and the photon index below the spectral break, $α$, in individual emission pulses, by studying the 38 most prominent pulses in the Fermi/GBM GRB catalogue. First, we search for signatures of the bias in the determination of $α$ due to the limited spectral coverage (window effect) expected in the synchrotron case. The absence of such a characteristic signature argues against the simplest synchrotron models. We instead find that the observed correlation between $F$ and $α$ can, in general, be described by the relation $F(t) \propto {\rm e}^{k\,α(t)}$, for which the median $k = 3$. We suggest that this correlation is a manifestation of subphotospheric heating in a flow with a varying entropy. Around the peak of the light curve, a large entropy causes the photosphere to approach the saturation radius, leading to an intense emission with a narrow spectrum. As the entropy decreases the photosphere secedes from the saturation radius, and weaker emission with a broader spectrum is expected. This simple scenario naturally leads to a correlated variation of the intensity and spectral shape, covering the observed range.

astro-ph.HE↗

arXiv2019

MATHUSLA: A Detector Proposal to Explore the Lifetime Frontier at the HL-LHC

The observation of long-lived particles at the LHC would reveal physics beyond the Standard Model, could account for the many open issues in our understanding of our universe, and conceivably point to a more complete theory of the fundamental interactions. Such long-lived particle signatures are fundamentally motivated and can appear in virtually every theoretical construct that address the Hierarchy Problem, Dark Matter, Neutrino Masses and the Baryon Asymmetry of the Universe. We describe in this document a large detector, MATHUSLA, located on the surface above an HL-LHC $pp$ interaction point, that could observe long-lived particles with lifetimes up to the Big Bang Nucleosynthesis limit of 0.1 s. We also note that its large detector area allows MATHUSLA to make important contributions to cosmic ray physics. Because of the potential for making a major breakthrough in our conceptual understanding of the universe, long-lived particle searches should have the highest level of priority.

hep-ex↗

arXiv2018

Constraining the Type of Central Engine of GRBs with Swift Data

The central engine of gamma-ray bursts (GRBs) is poorly constrained. There exist two main candidates: a fast-rotating black hole and a rapidly spinning magnetar. Furthermore, the X-ray plateaus are widely accepted by the energy injection into the external shock. In this paper, we systematically analyze the \emph{Swift}/XRT light curves of 101 GRBs having plateau phases and known redshifts (before May 2017). Since a maximum energy budget ($\sim2\times10^{52}$ erg) exists for magnetars but not for a black hole, this provides a good clue to identify the type of GRB central engine. We calculate the isotropic kinetic energy $E_{\rm K,iso}$ and the isotropic X-ray energy release $E_{\rm X,iso}$ for individual GRB. We identify three categories based on how likely a black hole harbor at central engine: 'Gold' (9 out of 101, both $E_{\rm X,iso}$ and $E_{\rm K,iso}$ exceed the energy budget), 'Silver' (69 out of 101, $E_{\rm X,iso}$ less than the limit but $E_{\rm K,iso}$ is greater than the limit), and 'Bronze' (23 out of 101, the energies are not above the limit). We then derive and test the black hole parameters with the Blandford Znajek mechanism, and find that the observations of the black hole candidate ('Gold'+'Silver') samples are consistent with the expectations of the black hole model. Furthermore, we also test the magnetar candidate ('Bronze') sample with the magnetar model, and find that the magnetar surface magnetic led ($B_{p}$) and initial spin period ($P_{0}$) are consistent with the expectations of the magnetar model. Our analysis indicates that, if the magnetar wind is isotropic, a magnetar central engine is possible for 20\% of the analyzed GRBs. For most GRBs a black hole is most likely operating.

astro-ph.HE↗