Derive the biased reading of A-not-A questions in Mandarin

Among many forms of A-not-A questions in Mandarin Chinese, the shi-not-shi question is found to be unique in that it obligatorily gives rise to a biased reading toward its prejacent, so-called positive bias. The previous pragmatic approach by Ye (2020) establishes a link between focus in polar questions and question bias to explain this biased reading. However, the current study finds that two other A-not-A questions formed by epistemic modals, hui-not-hui and keneng-not-keneng which are not focus markers, obligatorily produce positively biased readings as well. I propose that biased A-not-A questions are a type of high-negation questions with A-not-A residing outside of TP. shi, hui and keneng should all be analyzed as epistemic modals which are the overt realization of Goodhue’s (2019) epistemic operator scoped by the negator. The positively biased reading is derived from the resulting unbalanced partition based on general pragmatic principles. This analysis from the semantic aspect provides new evidence for the argument that the first A has reality only in PF. Furthermore, the Mandarin Chinese data lends evidence to Goodhue’s (2019) argument that there exists a doxastic speech operator between NegP and TP in high negation questions. The paper also provides explanations for previously remaining questions on bias cancelation by the stress marker daodi and factive predicates like zhidao (“know”)

Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state representation method to model the prediction problem as a Markov decision process (MDP) and use reinforcement learning (RL) to solve the problem. Additionally, we built a dataset called SmallCity based on the crowdsourcing method to conduct the research. As far as we know, this is the first time the IRL approach has been introduced to the urban safety perception and planning field to help experts quantitatively analyze perceptual features. Our results showed that IRL has promising prospects in this field. We will later open-source the crowdsourcing data collection site and the model proposed in this paper.Comment: ACML2022 Camera-ready Versio

Clustering of Diverse Multiplex Networks

This dissertation introduces the DIverse MultiPLEx Generalized Dot Product Graph (DIMPLE-GDPG) network model where all layers of the network have the same collection of nodes and follow the Generalized Dot Product Graph (GDPG) model. In addition, all layers can be partitioned into groups such that the layers in the same group are embedded in the same ambient subspace but otherwise all matrices of connection probabilities can be different. In common particular cases, where layers of the network follow the Stochastic Block Model (SBM) and Degree Corrected Block Model (DCBM), this setting implies that the groups of layers have common community structures but all matrices of block connection probabilities can be different. For DCBM, each group can also equip with nodes\u27 specific weights. We refer to this two versions as the DIMPLE model and the DIMPLE-DECOR model. While the DIMPLE-GDPG model generalizes the COmmon Subspace Independent Edge (COSIE) random graph model, the DIMPLE model generalizes a multitude of papers that study multilayer networks with the same community structures in all layers (which include the tensor block model, the checker-board model as well as the Mixture Multilayer Stochastic Block Model (MMLSBM) as particular cases). This dissertation introduces novel algorithms for the recovery of similar groups of layers, for the estimation of the ambient subspaces in the groups of layers in the DIMPLE-GDPG setting, and for the within-layer clustering in the case of the DIMPLE model. We also consider applications of the DIMPLE models to real-life data, and its comparison with the MMLSBM. And the DIMPLE model with its SBM-imposed structures provided better descriptions of the organization of layers than the ones obtained on the basis of the MMLSBM setting

Mineralization of 4-chlorophenol and analysis of bacterial community in microbial fuel cells

Abstract4-Chlorophenol (4-CP) was co-metabolically degraded and mineralized with the presence of glucose in microbial fuel cells (MFCs), achieving a degradation rate of 0.58 ± 0.036mg/L-h (7.2 ± 0.5mg/g VSS-h) with an electricity generation of 5.4 ± 0.4W/m3 at an initial 4-CP concentration of 25mg/L. Compared to the open circuit controls, current generation accelerated the removal of 4-CP. Coulombic efficiency decreased from 30.3 ± 2.9% at an initial 4- CP concentration of 5mg/L to 6.3 ± 0.9% at 40mg/L. 4-CP was degraded via the formation of phenol, which was further mineralized. Dominant bacteria most similar to both the exoelectrogenic and electrotrophic uncultured Desulfovibrio, the exoelectrogenic and recalcitrant degrader of uncultured Desulfobulbus, and the exoelectrogenic uncultured Microbacterium were identified in the biofilms. These results demonstrate that 4-CP mineralization using MFCs may be a promising process for remediation of water contaminated with 4-CP as well as for power generation

Phylogenomic assessment of the role of hybridization and introgression in trait evolution

Trait evolution among a set of species—a central theme in evolutionary biology—has long been understood and analyzed with respect to a species tree. However, the field of phylogenomics, which has been propelled by advances in sequencing technologies, has ushered in the era of species/gene tree incongruence and, consequently, a more nuanced understanding of trait evolution. For a trait whose states are incongruent with the branching patterns in the species tree, the same state could have arisen independently in different species (homoplasy) or followed the branching patterns of gene trees, incongruent with the species tree (hemiplasy). Another evolutionary process whose extent and significance are better revealed by phylogenomic studies is gene flow between different species. In this work, we present a phylogenomic method for assessing the role of hybridization and introgression in the evolution of polymorphic or monomorphic binary traits. We apply the method to simulated evolutionary scenarios to demonstrate the interplay between the parameters of the evolutionary history and the role of introgression in a binary trait’s evolution (which we call xenoplasy). Very importantly, we demonstrate, including on a biological data set, that inferring a species tree and using it for trait evolution analysis in the presence of gene flow could lead to misleading hypotheses about trait evolution

The similar and different evolutionary trends of MATE family occurred between rice and Arabidopsis thaliana

Expression profiles of Arabidopsis MATE genes under various stress. (TIFF 5235 kb

Genome-scale identification of Soybean BURP domain-containing genes and their expression under stress treatments

<p>Abstract</p> <p>Background</p> <p>Multiple proteins containing BURP domain have been identified in many different plant species, but not in any other organisms. To date, the molecular function of the BURP domain is still unknown, and no systematic analysis and expression profiling of the gene family in soybean (<it>Glycine max</it>) has been reported.</p> <p>Results</p> <p>In this study, multiple bioinformatics approaches were employed to identify all the members of BURP family genes in soybean. A total of 23 BURP gene types were identified. These genes had diverse structures and were distributed on chromosome 1, 2, 4, 6, 7, 8, 11, 12, 13, 14, and 18. Phylogenetic analysis suggested that these BURP family genes could be classified into 5 subfamilies, and one of which defines a new subfamily, BURPV. Quantitative real-time PCR (qRT-PCR) analysis of transcript levels showed that 15 of the 23 genes had no expression specificity; 7 of them were specifically expressed in some of the tissues; and one of them was not expressed in any of the tissues or organs studied. The results of stress treatments showed that 17 of the 23 identified BURP family genes responded to at least one of the three stress treatments; 6 of them were not influenced by stress treatments even though a stress related <it>cis</it>-element was identified in the promoter region. No stress related <it>cis</it>-elements were found in promoter region of any BURPV member. However, qRT-PCR results indicated that all members from BURPV responded to at least one of the three stress treatments. More significantly, the members from the RD22-like subfamily showed no tissue-specific expression and they all responded to each of the three stress treatments.</p> <p>Conclusions</p> <p>We have identified and classified all the BURP domain-containing genes in soybean. Their expression patterns in different tissues and under different stress treatments were detected using qRT-PCR. 15 out of 23 BURP genes in soybean had no tissue-specific expression, while 17 out of them were stress-responsive. The data provided an insight into the evolution of the gene family and suggested that many BURP family genes may be important for plants responding to stress conditions.</p

A latent spatial piecewise exponential model for interval-censored disease surveillance data with time-varying covariates and misclassification

Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specificity of the tests used to generate the disease surveillance data. Other challenges in modeling such data include interval censoring, relating disease spread to distance between units, and incorporating time-varying covariates, which are the unobserved disease statuses. We propose a latent spatial piecewise exponential model (PEX) with misclassification of events to address the challenges in modeling such disease surveillance data. Specifically, a piecewise exponential model is used to describe the latent disease process, with spatial distance and timevarying covariates incorporated for disease spread. The observed surveillance data with imperfect diagnostic tests are then modeled using a binary misclassification process given the latent disease statuses from the PEX model. Model parameters are estimated through a Bayesian approach utilizing non-informative priors. A simulation study is performed to evaluate the model performance and the results are compared with a candidate model where no misclassification is considered. For further illustration, we discuss an application of this model to a porcine reproductive and respiratory syndrome virus (PRRSV) surveillance data collected from commercial swine farms

Effects of Water Content and Particle Size on Yield and Reactivity of Lignite Chars Derived from Pyrolysis and Gasification

Water inside coal particles could potentially enhance the interior char–steam reactions during pyrolysis and gasification. This study aims to examine the effects of water contents on the char conversion during the pyrolysis and gasification of Shengli lignite. The ex-situ reactivities of chars were further analyzed by a thermo gravimetric analyzer (TGA). Under the pyrolysis condition, the increase in water contents has monotonically decreased the char yields only when the coal particles were small (\u3c75 μm). In contrast, the water in only large coal particles (0.9–2.0 mm) has clearly favored the increase in char conversion during the gasification condition where 50% steam in argon was used as external reaction atmosphere. The waved reactivity curves for the subsequent char–air reactions were resulted from the nature of heterogeneity of char structure. Compared to the large particles, the less interior char–steam reactions for the small particles have created more differential char structure which showed two different stages when reacting with air at the low temperature in TGA