Like a bilingual baby: The advantage of visually grounding a bilingual language model

Unlike most neural language models, humans learn language in a rich, multi-sensory and, often, multi-lingual environment. Current language models typically fail to fully capture the complexities of multilingual language use. We train an LSTM language model on images and captions in English and Spanish from MS-COCO-ES. We find that the visual grounding improves the model's understanding of semantic similarity both within and across languages and improves perplexity. However, we find no significant advantage of visual grounding for abstract words. Our results provide additional evidence of the advantages of visually grounded language models and point to the need for more naturalistic language data from multilingual speakers and multilingual datasets with perceptual grounding.Comment: Preprint, 7 pages, 2 tables, 1 figur

Upconversion NIR-II fluorophores for mitochondria-targeted cancer imaging and photothermal therapy

Acknowledgements: The work was supported by the National Key R&D Program of China (2020YFA0908800), NSFC (81773674, 81573383), Shenzhen Science and Technology Research Grant (JCYJ20190808152019182), Hubei Province Scientific and Technical Innovation Key Project, National Natural Science Foundation of Hubei Province (2017CFA024, 2017CFB711), the Applied Basic Research Program of Wuhan Municipal Bureau of Science and Technology (2019020701011429), Tibet Autonomous Region Science and Technology Plan Project Key Project (XZ201901-GB-11), the Local Development Funds of Science and Technology Department of Tibet (XZ202001YD0028C), Project First-Class Disciplines Development Supported by Chengdu University of Traditional Chinese Medicine (CZYJC1903), Health Commission of Hubei Province Scientific Research Project (WJ2019M177, WJ2019M178), the China Scholarship Council, and the Fundamental Research Funds for the Central Universities.Peer reviewedPublisher PD

The Genes Coding for the Conversion of Carbazole to Catechol Are Flanked by IS6100 Elements in Sphingomonas sp. Strain XLDN2-5

BACKGROUND: Carbazole is a recalcitrant compound with a dioxin-like structure and possesses mutagenic and toxic activities. Bacteria respond to a xenobiotic by recruiting exogenous genes to establish a pathway to degrade the xenobiotic, which is necessary for their adaptation and survival. Usually, this process is mediated by mobile genetic elements such as plasmids, transposons, and insertion sequences. FINDINGS: The genes encoding the enzymes responsible for the degradation of carbazole to catechol via anthranilate were cloned, sequenced, and characterized from a carbazole-degrading Sphingomonas sp. strain XLDN2-5. The car gene cluster (carRAaBaBbCAc) and fdr gene were accompanied on both sides by two copies of IS6100 elements, and organized as IS6100::ISSsp1-ORF1-carRAaBaBbCAc-ORF8-IS6100-fdr-IS6100. Carbazole was converted by carbazole 1,9a-dioxygenase (CARDO, CarAaAcFdr), meta-cleavage enzyme (CarBaBb), and hydrolase (CarC) to anthranilate and 2-hydroxypenta-2,4-dienoate. The fdr gene encoded a novel ferredoxin reductase whose absence resulted in lower transformation activity of carbazole by CarAa and CarAc. The ant gene cluster (antRAcAdAbAa) which was involved in the conversion of anthranilate to catechol was also sandwiched between two IS6100 elements as IS6100-antRAcAdAbAa-IS6100. Anthranilate 1,2-dioxygenase (ANTDO) was composed of a reductase (AntAa), a ferredoxin (AntAb), and a two-subunit terminal oxygenase (AntAcAd). Reverse transcription-PCR results suggested that carAaBaBbCAc gene cluster, fdr, and antRAcAdAbAa gene cluster were induced when strain XLDN2-5 was exposed to carbazole. Expression of both CARDO and ANTDO in Escherichia coli required the presence of the natural reductases for full enzymatic activity. CONCLUSIONS/SIGNIFICANCE: We predict that IS6100 might play an important role in the establishment of carbazole-degrading pathway, which endows the host to adapt to novel compounds in the environment. The organization of the car and ant genes in strain XLDN2-5 was unique, which showed strong evolutionary trail of gene recruitment mediated by IS6100 and presented a remarkable example of rearrangements and pathway establishments

Face mask integrated with flexible and wearable manganite oxide respiration sensor

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices

The effect of caring ethical climate on employees’ knowledge-hiding behavior: evidence from Chinese construction firms

Abstract This research aims to explore the influencing mechanism of a caring ethical climate on knowledge-hiding behavior in large construction firms according to the reciprocity principle of social exchange theory. This is an empirical study based on the analysis of survey data collected from 413 employees working in large construction firms in China. Hierarchical regression is applied to test the research model. This research finds: (1) Caring ethical climate has a negative influence on knowledge-hiding behavior; (2) Caring ethical climate has a positive influence on psychological contract; (3) Psychological contract has a negative influence on knowledge-hiding behavior; (4) Psychological contract mediates the relationship between caring ethical climate and knowledge-hiding behavior; (5) Task interdependence positively moderates the relationship between psychological contract and knowledge-hiding behavior. Based on the social exchange theory, this study provides significant contributions to the theory and practice of knowledge management in large construction firms by highlighting the influence of a caring ethical climate on knowledge hiding among employees. This paper provides suggestions for reducing knowledge hiding and enhancing knowledge sharing among employees in large construction firms, so as to improve the knowledge management ability of large construction firms and enhance their competitive advantages

Language representations in L2 learners: Toward neural models

We investigated how the language background (L1) of bilinguals influences the representation and use of the second language (L2) through computational models. With the essays part from The International Corpus Network of Asian Learners of English (ICNALE), we compared variables indicating syntactic complexity in their L2 production to predict L1. We then trained neural language models based on BERT to predict the L1 of these English learners. Results showed the systematic influence of L1 syntax properties on English learners' L2 production, which further confirmed integrations of syntactic knowledge across languages in bilingual speakers. Results also found neural models can learn to represent and detect such L1 impacts, while multilingually trained models have no advantage in doing so

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods

Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data

This paper focuses on a joint model to analyze longitudinal proportional and survival data. We utilize a logit transformation on the longitudinal proportional data and employ a partially linear mixed-effect model. With this model, we estimate the unknown function of time using the B-splines technique. Additionally, we introduce a centered Dirichlet process mixture model (CDPMM) to capture the random effects, allowing for a flexible distribution. The survival data are assumed using a Cox proportional hazard model, and the sharing random effects joint model is developed for the two types of data. We develop a Bayesian Lasso (BLasso) approach that combines the Gibbs sampler and the Metropolis–Hastings algorithm. The proposed method allows for the estimation of unknown parameters and the selection of significant covariates simultaneously. We evaluate the performance of our proposed methods through simulation studies and also provide an illustration of our methodologies using an example from the MA.5 research experiment