The epistemic use of yào in Mandarin Chinese and its theoretical implications

The epistemic use of the Mandarin Chinese modal yào comes with typologically interesting properties. In this paper, the distribution and meaning of the epistemic use of yào will be described first. This use of yào is restricted to certain explicit strict comparative constructions, but forbidden in many other degree and non-degree constructions. Second, epistemic yào cannot appear above or below negation. Third, epistemic yào has a quantificational force stronger than that of existential modals, yet weaker than that of strong necessity modals. In the theoretical component of the paper, I argue that epistemic yào is a modifier for strict comparative morphemes, a syntactic/semantic function that sets it apart from many other epistemic modals that take propositions as direct argument. The weak necessity quantificational force of epistemic yào is encoded in its semantics by making recourse to alternative modal bases. Epistemic yào's inability to form scopal relation with negation arises from two factors: (i) its status as a strict comparative morpheme modifier, and (ii) competition between lexical items with identical semantics. Through investigating the epistemic use of yào, some hitherto unnoticed interesting modal properties in natural language are brought to the forefront, and new intra- and inter-linguistic variations in the distribution and meaning of modals are revealed

Concealed Questions from a Cross-linguistic Perspective

The paper draws on empirical data of concealed questions from Banda Acehnese and revives the proposal that CQs are questions in disguise, contra many recent analyses. To show that the proposal works for English CQs, we examine five prominent empirical patterns with them: identity interpretation, Heim’s Ambiguity, quantified and indefinite CQs, coordination, and Greenberg’s Contrast. They do not pose any real challenge to the question-in-disguise analysis. Towards the end of the paper we discuss a couple of remaining issues

Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion

The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (ɛ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of Nc. For the ɛ−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions. Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics schem

Exhaustifying Focus Intervention Effects: A Crosslinguistic Study

BLS 39: General Session and Special Session on Space and Directionalit

Weak Generic Sentences: Partitioning and Comparison

BLS 38: General Session and Thematic Session on Language Contac

A BAC-NOMA Design for 6 G umMTC With Hybrid SIC: Convex Optimization or Learning-Based?

This paper presents a new backscattering communication (BackCom)-assisted non-orthogonal multiple access (BAC-NOMA) transmission scheme for device-to-device (D2D) communications. This scheme facilitates energy and spectrum cooperation between BackCom devices and cellular downlink users in 6 G ultra-massive machine -type communications (umMTC) scenarios. Given its quasi-uplink nature, the hybrid successive interference cancellation (SIC) is applied to further improve performance. The data rate of BackCom devices with high quality of service (QoS) requirements is maximized by jointly optimizing backscatter coefficients and the beamforming vector. The use of hybrid SIC and BackCom yields two non-concave sub-problems involving transcendental functions. To address this problem, this paper designs and compares convex optimization-based and unsupervised deep learning-based algorithms. In the convex optimization, the closed-form backscatter coefficients of the first sub-problem are obtained, and then semi-definite relaxation (SDR) is utilized to design the beamforming vector. On the other hand, the second sub-problem is approximated by using a combination of sequential convex approximation (SCA) and SDR. For unsupervised deep learning-based optimization, a loss function is properly designed to satisfy constraints. Computer simulations show the following instructive results: i) the superiority of the hybrid SIC strategy; ii) the distinct sensitivities and efficacies of these two algorithms in response to varying parameters; iii) the superior robustness of the unsupervised deep learning-based optimization

Existence of solutions for fractional boundary value problem with nonlinear derivative dependence

We investigate the existence of solutions for fractional boundary value problem including both left and right fractional derivatives by using variational methods and iterative technique

Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) methods for reconstructing distance-redshift relation from the Pantheon+ sample of type Ia supernovae (SNe Ia) in a cosmology-independent way. The A219 GRB data set at low redshift are used to calibrate the Amati relation ($E_{\rm p}$-$E_{\rm iso}$) relation by the ML methods selected with the best performance %and the calibrated results are extrapolated to the high redshift data to construct the Hubble diagram at high redshift. We constrain cosmological models via the Markov Chain Monte Carlo numerical method with the GRBs at high redshift and the latest observational Hubble data (OHD). By the K-Nearest Neighbors (KNN) methods, we obtain $\Omega_{\rm m}$ = $0.29^{+0.09}_{-0.06}$, $h$ = $0.66^{+0.04}_{-0.07}$ , $w_0$ = $-0.83^{+0.66}_{-0.31}$, $w_a$ = $-0.91^{+0.87}_{-0.46}$ at 1-$\sigma$ confidence level for the Chevallier-Polarski-Linder (CPL) model in a flat space, which favor the dark energy with a possible evolution ($w_a\neq0$) at 1-$\sigma$. These results are consistent with those obtained from GRBs calibrated via the Gaussian Process.Comment: 10 pages, 3 tables, 8 figures. arXiv admin note: text overlap with arXiv:2307.1646