Politeness in Historical and Contemporary Chinese

Takes a comparative, diachronic perspective on Chinese politeness and its evolution up to the present day, linking diachronic and synchronic approache

The use of interpreters in the conduct of household surveys: development of U.S. Census Bureau Interpretation Guidelines

Der Beitrag stellt ein laufendes Forschungsprojekt des U. S. Census Bureau vor, dessen Ziel es ist, auf der Basis von best practices der internationalen Umfrageforschung sowie eigener Forschungsergebnisse Richtlinien für Übersetzungen und Übersetzerverhalten in der Umfrageforschung zu formulieren. Diese Richtlinien sollen die Auswahl, den Einsatz und das Training von Übersetzern zum Einsatz in der Feldforschung regeln. Der Beitrag berichtet über Ziele und Aktivitäten im Rahmen dieses Forschungsprojekts, erste Ergebnisse, Umrisse der geplanten Übersetzungs-Richtlinie und weitere Forschungsperspektiven. (ICE

Cross-cultural communication and the telephone survey interview

Genau wie Telefongespräche oder face-to-face-Interviews stellen Telefoninterviews Sprechakte dar, die kultur- und sprachspezifischen Normen unterliegen. Telefoninterviews über sprachliche und kulturelle Grenzen hinweg müssen diese unterschiedlichen Normen des Sprachgebrauchs berücksichtigen. Übersetzte Fragestellungen müssen den Normen der Zielsprache entsprechen, standardisierte Übersetzungen reichen hier nicht aus. Eine zu wörtliche Übersetzung von Fragestellungen kann bei Untersuchungsprojekten, die Sprach- und Kulturgrenzen überschreiten, paradoxerweise die Vergleichbarkeit der Ergebnisse beeinträchtigen. Unterschiedliche Sprachnormen betreffen die Eröffnung eines Gesprächs, Frage- und Antwortsequenzen sowie Themenwechsel. (ICE

Model Stealing Attack against Multi-Exit Networks

Compared to traditional neural networks with a single exit, a multi-exit network has multiple exits that allow for early output from intermediate layers of the model, thus bringing significant improvement in computational efficiency while maintaining similar recognition accuracy. When attempting to steal such valuable models using traditional model stealing attacks, we found that conventional methods can only steal the model's classification function while failing to capture its output strategy. This results in a significant decrease in computational efficiency for the stolen substitute model, thereby losing the advantages of multi-exit networks.In this paper, we propose the first model stealing attack to extract both the model function and output strategy. We employ bayesian changepoint detection to analyze the target model's output strategy and use performance loss and strategy loss to guide the training of the substitute model. Furthermore, we designed a novel output strategy search algorithm that can find the optimal output strategy to maximize the consistency between the victim model and the substitute model's outputs. Through experiments on multiple mainstream multi-exit networks and benchmark datasets, we thoroughly demonstrates the effectiveness of our method

Spatiotemporal evolution and drivers of carbon inequalities in urban agglomeration:An MLD-IDA inequality indicator decomposition

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future

Serum lactate dehydrogenase activities as systems biomarkers for 48 types of human diseases

Most human diseases are systems diseases, and systems biomarkers are better fitted for diagnostic, prognostic, and treatment monitoring purposes. To search for systems biomarker candidates, lactate dehydrogenase (LDH), a housekeeping protein expressed in all living cells, was investigated. To this end, we analyzed the serum LDH activities from 172,933 patients with 48 clinically defined diseases and 9528 healthy individuals. Based on the median values, we found that 46 out of 48 diseases, leading by acute myocardial infarction, had significantly increased (p  0.8) for hepatic encephalopathy and lung fibrosis

SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning

Recent years have witnessed significant success in Self-Supervised Learning (SSL), which facilitates various downstream tasks. However, attackers may steal such SSL models and commercialize them for profit, making it crucial to protect their Intellectual Property (IP). Most existing IP protection solutions are designed for supervised learning models and cannot be used directly since they require that the models' downstream tasks and target labels be known and available during watermark embedding, which is not always possible in the domain of SSL. To address such a problem especially when downstream tasks are diverse and unknown during watermark embedding, we propose a novel black-box watermarking solution, named SSL-WM, for protecting the ownership of SSL models. SSL-WM maps watermarked inputs by the watermarked encoders into an invariant representation space, which causes any downstream classifiers to produce expected behavior, thus allowing the detection of embedded watermarks. We evaluate SSL-WM on numerous tasks, such as Computer Vision (CV) and Natural Language Processing (NLP), using different SSL models, including contrastive-based and generative-based. Experimental results demonstrate that SSL-WM can effectively verify the ownership of stolen SSL models in various downstream tasks. Furthermore, SSL-WM is robust against model fine-tuning and pruning attacks. Lastly, SSL-WM can also evade detection from evaluated watermark detection approaches, demonstrating its promising application in protecting the IP of SSL models

Rust Secreted Protein Ps87 Is Conserved in Diverse Fungal Pathogens and Contains a RXLR-like Motif Sufficient for Translocation into Plant Cells

BACKGROUND: Effector proteins of biotrophic plant pathogenic fungi and oomycetes are delivered into host cells and play important roles in both disease development and disease resistance response. How obligate fungal pathogen effectors enter host cells is poorly understood. The Ps87 gene of Puccinia striiformis encodes a protein that is conserved in diverse fungal pathogens. Ps87 homologs from a clade containing rust fungi are predicted to be secreted. The aim of this study is to test whether Ps87 may act as an effector during Puccinia striiformis infection. METHODOLOGY/PRINCIPAL FINDINGS: Yeast signal sequence trap assay showed that the rust protein Ps87 could be secreted from yeast cells, but a homolog from Magnaporthe oryzae that was not predicted to be secreted, could not. Cell re-entry and protein uptake assays showed that a region of Ps87 containing a conserved RXLR-like motif [K/R]RLTG was confirmed to be capable of delivering oomycete effector Avr1b into soybean leaf cells and carrying GFP into soybean root cells. Mutations in the Ps87 motif (KRLTG) abolished the protein translocation ability. CONCLUSIONS/SIGNIFICANCE: The results suggest that Ps87 and its secreted homologs could utilize similar protein translocation machinery as those of oomycete and other fungal pathogens. Ps87 did not show direct suppression activity on plant defense responses. These results suggest Ps87 may represent an "emerging effector" that has recently acquired the ability to enter plant cells but has not yet acquired the ability to alter host physiology

Organic NIR-II dyes with ultralong circulation persistence for image-guided delivery and therapy

Acknowledgments This work was partially supported by grants from the National Key R&D Program of China (2020YFA0908800), NSFC (82111530209, 81773674, 91959103, 81573383, 21763002), Shenzhen Science and Technology Research Grant (JCYJ20190808152019182), the Applied Basic Research Program of Wuhan Municipal Bureau of Science and Technology (2019020701011429), Hubei Province Scientific and Technical Innovation Key Project (2020BAB058), the Local Development Funds of Science and Technology Department of Tibet (XZ202102YD0033C, XZ202001YD0028C), and the Fundamental Research Funds for the Central Universities.Peer reviewedPublisher PD

SARS-CoV-2 infection causes dopaminergic neuron senescence

COVID-19 patients commonly present with signs of central nervous system and/or peripheral nervous system dysfunction. Here, we show that midbrain dopamine (DA) neurons derived from human pluripotent stem cells (hPSCs) are selectively susceptible and permissive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. SARS-CoV-2 infection of DA neurons triggers an inflammatory and cellular senescence response. High-throughput screening in hPSC-derived DA neurons identified several FDA-approved drugs that can rescue the cellular senescence phenotype by preventing SARS-CoV-2 infection. We also identified the inflammatory and cellular senescence signature and low levels of SARS-CoV-2 transcripts in human substantia nigra tissue of COVID-19 patients. Furthermore, we observed reduced numbers of neuromelanin+ and tyrosine-hydroxylase (TH)+ DA neurons and fibers in a cohort of severe COVID-19 patients. Our findings demonstrate that hPSC-derived DA neurons are susceptible to SARS-CoV-2, identify candidate neuroprotective drugs for COVID-19 patients, and suggest the need for careful, long-term monitoring of neurological problems in COVID-19 patients.</p