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The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms. To ensure a balanced representation, two infodemic datasets were created by augmenting previously collected social media textual data. Through word frequency analysis, the thirty-five most frequently occurring infodemic words are identified, shedding light on prevalent discussions surrounding the infodemic. Moreover, topic clustering analysis uncovers thematic structures and provides a deeper understanding of primary topics within each language context. Additionally, sentiment analysis enables comprehension of the emotional tone associated with COVID-19 information on social media platforms in English and Chinese. This research contributes to a better understanding of the COVID-19 infodemic phenomenon and can guide the development of strategies to combat misinformation during public health crises across different languages.

We present analytical results of fundamental properties of one-dimensional (1D) Hubbard model with a repulsive interaction, ranging from fractional excitations to universal thermodynamics, interaction-driven criticality, correlation functions, Contact susceptibilities and quantum cooling. Using the exact solutions of the Bethe Ansatz equations of the Hubbard model, we first rigorously calculate the gapless spin and charge excitations, exhibiting exotic features of fractionalized spinons and holons. Based on the analysis on the fractional charge and spin excitations, the spin-incoherent Luttinger liquid with only the charge propagation mode is elucidated by the asymptotic of the two-point correlation functions with the help of the conformal field theory. Near quadruple critical point, we then further analytically obtain the thermodynamical properties, dimensionless ratios and scaling functions near quantum phase transitions in terms of chemical potential, magnetic field and interaction. In particular, we determine additivity rules of spin and charge susceptibilities, and derive explicit forms of thermodynamics of spin-incoherent Luttinger liquid. Finally, in order to capture deeper insight into the Mott insulator and interaction driven criticality, we further study the double occupancy and its associated Contact and Contact susceptibilities through which an adiabatic cooling scheme upon the quantum criticality is introduced., Comment: New changes, 50 pages +17 figures, new analytical results

The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multi-parameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner's GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing feature in prompt emission to be T_{90}, hardness ratio, and fluence. After building the machine learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class., Comment: 23 pages, 8 tables, 11 figures, accepted for publication by ApJ. Full version of Table 5 is available as ancillary material

Fast radio bursts (FRBs) are one of the most mysterious astronomical transients. Observationally, they can be classified into repeaters and apparently non-repeaters. However, due to the lack of continuous observations, some apparently repeaters may have been incorrectly recognized as non-repeaters. In a series of two papers, we intend to solve such problem with machine learning. In this second paper of the series, we focus on an array of unsupervised machine learning methods. We apply multiple unsupervised machine learning algorithms to the first CHIME/FRB catalog to learn their features and classify FRBs into different clusters without any premise about the FRBs being repeaters or non-repeaters. These clusters reveal the differences between repeaters and non-repeaters. Then, by comparing with the identities of the FRBs in the observed classes, we evaluate the performance of various algorithms and analyze the physical meaning behind the results. Finally, we recommend a list of most credible repeater candidates as targets for future observing campaigns to search for repeated bursts in combination of the results presented in Paper I using supervised machine learning methods., Comment: 15 pages, 15 figures, 2 tables, published on MNRAS. Updated to match the published version

Observationally, the mysterious fast radio bursts (FRBs) are classified as repeating ones and apparently non-repeating ones. While repeating FRBs cannot be classified into the non-repeating group, it is unknown whether the apparently non-repeating FRBs are actually repeating FRBs whose repetitions are yet to be discovered, or whether they belong to another physically distinct type from the repeating ones. In a series of two papers, we attempt to disentangle this mystery with machine learning methods. In this first paper, we focus on an array of supervised machine learning methods. We train the machine learning algorithms with a fraction of the observed FRBs in the first CHIME/FRB catalog, telling them which ones are apparently non-repeating and which ones are repeating. We then let the trained models predict the repetitiveness of the rest of the FRB data with the observed parameters, and we compare the predictions with the observed repetitiveness. We find that the models can predict most FRBs correctly, hinting towards distinct mechanisms behind repeating and non-repeating FRBs. We also find that the two most important distinguishing factors between non-repeating and repeating FRBs are brightness temperature and rest-frame frequency bandwidth. By applying the trained models back to the entire first CHIME catalog, we further identify some potentially repeating FRBs currently reported as non-repeating. We recommend a list of these bursts as targets for future observing campaigns to search for repeated bursts in a combination with the results presented in Paper II using unsupervised machine learning methods., Comment: 14 pages, 5 figures, 4 tables, published on MNRAS. Updated to match the published version

Although the one dimensional (1D) repulsive Fermi-Hubbard model has been intensively studied over many decades, a rigorous understanding of many aspects of the model is still lacking. In this work, based on the solutions to the thermodynamic Bethe ansatz equations, we provide a rigorous study on the following: (1) We calculate the fractional excitations of the system in various phases, from which we identify the parameter regime featuring the spin incoherent Luttinger liquid (SILL). We investigate the universal properties and the asymprotic of correlation functions of the SILL. (2) We study the interaction-driven phase transition and the associated criticality, and build up an essential connection between the Contact susceptibilities and the variations of density, magnetization and entropy with respect to the interaction strength. As an application of these concepts, which hold true for higher dimensional systems, we propose a quantum cooling scheme based on the interaction-driven refrigeration cycle., Comment: revised version 4 figures 6 page, supplementary material was added

Using exact Bethe ansatz solution, we rigorously study excitation spectra of the spin-1/2 Fermi gas (called Yang-Gaudin model) with an attractive interaction. Elementary excitations of this model involve particle-hole excitations, hole excitations and adding particles in the Fermi seas of pairs and unpaired fermions. The gapped magnon excitations in spin sector show a ferromagnetic coupling to the Fermi sea of the single fermions. By numerically and analytically solving the Bethe ansatz equations and the thermodynamic Bethe ansatz equations of this model, we obtain excitation energies for various polarizations in the phase of the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO)-like state. For a small momentum (long-wavelength limit) and in the strong interaction regime, we analytically obtained their linear dispersions with curvature corrections, effective masses as well as velocities in particle-hole excitations of pairs and unpaired fermions. Such a type of particle-hole excitations display a novel separation of collective motions of bosonic modes within paired and unpaired fermions. Finally, we also discuss magnon excitations in spin sector and the application of the Bragg spectroscopy for testing such separated charge excitation modes of pairs and single fermions., Comment: 27 pages, 12 figures, pedagogical study of collective excitations in the 1D attractive Fermi gas, added new references and corrected few typos

The multi-messenger joint observations of GW170817 and GRB170817A shed new light on the study of short-duration gamma-ray bursts (SGRBs). Not only did it substantiate the assumption that SGRBs originate from binary neutron star (BNS) mergers, but it also confirms that the jet generated by this type of merger must be structured, hence the observed energy of an SGRB depends on the viewing angle from the observer. However, the precise structure of the jet is still subject to debate. Moreover, whether a single unified jet model can be applied to all SGRBs is not known. Another uncertainty is the delay timescale of BNS mergers with respect to star formation history of the Universe. In this paper, we conduct a global test of both delay and jet models of BNS mergers across a wide parameter space with simulated SGRBs. We compare the simulated peak flux, redshift and luminosity distributions with the observed ones and test the goodness-of-fit for a set of models and parameter combinations. Our simulations suggest that GW170817/GRB 170817A and all SGRBs can be understood within the framework of a universal structured jet viewed at different viewing angles. Furthermore, models invoking a jet plus cocoon structure with a lognormal delay timescale is most favored. Some other combinations (e.g. a Gaussian delay with a power-law jet model) are also acceptable. However, the Gaussian delay with Gaussian jet model and the entire set of power-law delay models are disfavored., Comment: 9 pages, 5 figures, Published in MNRAS. Updated to correct typos