ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation

Vision-language pre-training (VLP) methods are blossoming recently, and its crucial goal is to jointly learn visual and textual features via a transformer-based architecture, demonstrating promising improvements on a variety of vision-language tasks. Prior arts usually focus on how to align visual and textual features, but strategies for improving the robustness of model and speeding up model convergence are left insufficiently explored. In this paper, we propose a novel method ViLTA, comprising of two components to further facilitate the model to learn fine-grained representations among image-text pairs. For Masked Language Modeling (MLM), we propose a cross-distillation method to generate soft labels to enhance the robustness of model, which alleviates the problem of treating synonyms of masked words as negative samples in one-hot labels. For Image-Text Matching (ITM), we leverage the current language encoder to synthesize hard negatives based on the context of language input, encouraging the model to learn high-quality representations by increasing the difficulty of the ITM task. By leveraging the above techniques, our ViLTA can achieve better performance on various vision-language tasks. Extensive experiments on benchmark datasets demonstrate that the effectiveness of ViLTA and its promising potential for vision-language pre-training.Comment: 15 pages, 5 figure

Use of Traditional Chinese Medicine and Its Impact on Medical Cost among Urban Ischemic Stroke Inpatients in China: A National Cross-Sectional Study

Background. Traditional Chinese medicine (TCM) has long been widely adopted by the Chinese people and has been covered by China’s basic medical insurance schemes to treat ischemic stroke. Previous research has mainly highlighted the therapy effect of TCM on ischemic stroke patients. Some studies have demonstrated that employing TCM can reduce the medical burden on other diseases. But no research has explored whether using TCM could reduce inpatient medical cost for ischemic stroke in mainland China. The purpose of this study is to investigate the impact of the use of TCM on the total inpatient cost of ischemic stroke and to explore whether TCM has played the role of being complementary to, or an alternative for, conventional medicine to treat ischemic stroke. Methods. We conducted a national cross-sectional analysis based on a 5% random sample from claims data of China Urban Employee Basic Medical Insurance (UEBMI) and Urban Resident Basic Medical Insurance (URBMI) schemes in 2015. Mann–Whitney test was used to compare unadjusted total inpatient cost, conventional medication cost, and nonpharmacy cost estimates. Ordinary least square regression analysis was performed to compare demographics-adjusted total inpatient cost and to examine the association between TCM cost and conventional medication cost. Results. A total of 47321 urban inpatients diagnosed with ischemic stroke were identified in our study, with 92.6% (43843) of the patients using TCM in their inpatient treatment. Total inpatient cost for TCM users was significantly higher than TCM nonusers (USD 1217 versus USD 1036, P<0.001). Conventional medication cost was significantly lower for TCM users (USD 335 versus USD 436, P<0.001). The average cost of TCM per patient among TCM users was USD 289. Among TCM users, conventional medication costs were found to be positively associated with TCM cost after adjusting for confounding factors (Coef. = 0.144, P<0.001). Conclusion. Although the use of TCM reduced the cost of conventional medicine compared with TCM nonusers, TCM imposed an extra financial component on the total inpatient cost on TCM users. Our study suggests that TCM mainly played a complementary role to conventional medicine in ischemic stroke treatment in mainland China

CogVLM: Visual Expert for Pretrained Language Models

We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM

Medical insurance payment schemes and patient medical expenses: a cross-sectional study of lung cancer patients in urban China

BackgroundAs the main cause of cancer death, lung cancer imposes seriously health and economic burdens on individuals, families, and the health system. In China, there is no national study analyzing the hospitalization expenditures of different payment methods by lung cancer inpatients. Based on the 2010-2016 database of insured urban resident lung cancer inpatients from the China Medical Insurance Research Association (CHIRA), this paper aims to investigate the characteristics and cost of hospitalized lung cancer patient, to examine the differences in hospital expenses and patient out-of-pocket (OOP) expenses under four medical insurance payment methods: fee-for-service (FFS), per-diem payments, capitation payments (CAP) and case-based payments, and to explore the medical insurance payment method that can be conducive to controlling the cost of lung cancer.MethodThis is a 2010-2016, 7-year cross-sectional study. CHIRA data are not available to researchers after 2016. The Medical Insurance Database of CHIRA was screened using the international disease classification system to yield 28,200 inpatients diagnosed with lung cancer (ICD-10: C34, C34.0, C34.1, C34.2, C34.3, C34.8, C34.9). The study includes descriptive analysis and regression analysis based on generalized linear models (GLM).ResultsThe average patient age was 63.4 years and the average length of hospital stay (ALOS) was 14.2 day; 60.7% of patients were from tertiary hospitals; and 45% were insured by FFS. The per-diem payment had the lowest hospital expenses (RMB7496.00/US$1176.87), while CAP had the lowest OOP expenses (RMB1328.18/US$208.52). Compared with FFS hospital expenses, per-diem was 21.3% lower (95% CI = -0.265, -0.215) and case-based payment was 8.4% lower (95% CI = -0.151, -0.024). Compared with the FFS, OOP expenses, per-diem payments were 9.2% lower (95% CI = -0.130, -0.063) and CAP was 15.1% lower (95% CI = -0.151, -0.024).ConclusionFor lung cancer patients, per-diem payment generated the lowest hospital expenses, while CAP meant patients bore the lowest OOP costs. Policy makers are suggested to give priority to case-based payments to achieve a tripartite balance among medical insurers, hospitals, and insured members. We also recommend future studies comparing the disparities of various diseases for the cause of different medical insurance schemes

A bibliometric and knowledge-map analysis of the glymphatic system from 2012 to 2022

ObjectiveTo explore the development context, research hotspots and frontiers in the glymphatic system (GS) field from 2012 to 2022 by bibliometric analysis.MethodsThe Web of Science Core Collection (WoSCC) database was searched for articles published between 2012 and 2022. Microsoft Excel was used to manage the data. VOSviewer, CiteSpace, GraphPad Prism, the Web of Science, and an online analysis platform for bibliometrics (http://bibliometric.com/) were used to analyze the countries, institutions, journals, and collaboration networks among authors and the types of articles, developmental directions, references, and top keywords of published articles.ResultsA total of 412 articles were retrieved, including 39 countries/regions, 223 research institutes and 171 academic journals. The subject classifications related to the GS were Neuroscience, Clinical Neuroscience and Radiology/Nuclear Medicine/Medical Imaging. The United States has maintained its dominant and most influential position in GS research. Among research institutions and journals, the Univ Rochester and Journal of Cerebral Blood Flow and Metabolism had the highest number of academic articles, respectively. Nedergaard M had the most published article, and Iliff JJ had the most co-citations. The top two keywords with the highest frequency were “glymphatic system” and “cerebrospinal fluid.”ConclusionThis research provides valuable information for the study of the GS. The bibliometric analysis of this area will encourage potential collaborations among researchers, defining its frontiers and directions for development

Sedimentary ancient DNA reveals past ecosystem and biodiversity changes on the Tibetan Plateau: Overview and prospects

Alpine ecosystems on the Tibetan Plateau are being threatened by ongoing climate warming and intensified human activities. Ecological time-series obtained from sedimentary ancient DNA (sedaDNA) are essential for understanding past ecosystem and biodiversity dynamics on the Tibetan Plateau and their responses to climate change at a high taxonomic resolution. Hitherto only few but promising studies have been published on this topic. The potential and limitations of using sedaDNA on the Tibetan Plateau are not fully understood. Here, we (i) provide updated knowledge of and a brief introduction to the suitable archives, region-specific taphonomy, state-of-the-art methodologies, and research questions of sedaDNA on the Tibetan Plateau; (ii) review published and ongoing sedaDNA studies from the Tibetan Plateau; and (iii) give some recommendations for future sedaDNA study designs. Based on the current knowledge of taphonomy, we infer that deep glacial lakes with freshwater and high clay sediment input, such as those from the southern and southeastern Tibetan Plateau, may have a high potential for sedaDNA studies. Metabarcoding (for microorganisms and plants), metagenomics (for ecosystems), and hybridization capture (for prehistoric humans) are three primary sedaDNA approaches which have been successfully applied on the Tibetan Plateau, but their power is still limited by several technical issues, such as PCR bias and incompleteness of taxonomic reference databases. Setting up high-quality and open-access regional taxonomic reference databases for the Tibetan Plateau should be given priority in the future. To conclude, the archival, taphonomic, and methodological conditions of the Tibetan Plateau are favorable for performing sedaDNA studies. More research should be encouraged to address questions about long-term ecological dynamics at ecosystem scale and to bring the paleoecology of the Tibetan Plateau into a new era

Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states

Fast charging of the lithium-ion battery (LIB) is an enabling technology for the popularity of electric vehicles. However, high-rate charging regardless of the physical limits can induce irreversible degradation or even hazardous safety issues to the LIB system. Motivated by this, this paper proposes a machine learning-based fast charging strategy with multi-physical awareness within a battery-to-cloud framework. In particular, a reduced-order electrochemical-thermal model is built in the cloud to perceive the microscopic states of LIB, leveraging which the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm is exploited for the first time to train a fast charging strategy. Hardware-in-Loop tests and experiments with practical LIBs are carried out for validation. Results suggest that the battery-to-cloud architecture can mitigate the risk of a heavy computing burden in the real-time controller. The proposed strategy can effectively mitigate the unfavorable over-temperature and lithium deposition, which benefits the safety and longevity during fast charging. Given a similar charging speed, the proposed machine learning approach extends the LIB cycle life by about 75% compared to the commonly-used empirical protocol. Meanwhile, the proposed strategy is proven superior to the state-of-the-art rule-based and the model-based strategies in terms of charging rapidity, charging safety and computational complexity. Moreover, the trained low-complexity strategy is highly adaptive to the ambient temperature and initial charging state, which promises robust performance in practical applications

Effects of emotion words activation and satiation on facial expression perception: evidence from behavioral and ERP investigations

ObjectiveThe present study investigated the impact of emotion concepts obtained from external environmental experiences on the perception of facial expressions by manipulating the activation and satiation of emotion words, which was based on the argument between basic emotion theory and constructed emotion theory.MethodsExperiment 1 explored the effects of emotion activation on happy, disgusted, emotion-label words and emotion-laden words in a facial expression judgment task through behavioral experimentation. Experiment 2 explored the effect of semantic satiation on emotion-label words and emotion-laden words using the event-related potential technique.ResultsExperiment 1 found that facial expression perception was influenced by both types of emotion words and showed a significant emotional consistency effect. Experiment 2 found that N170 exhibited a more negative amplitude in the consistent condition compared to the inconsistent condition in the right hemisphere. More importantly, in the later stage of facial expression processing, emotion-label words and emotion-laden words both obstructed the perception of disgusted facial expressions and elicited more negative N400 amplitude in the emotion consistency condition, showing a reversed N400 effect.ConclusionThese results suggested that emotion concepts in the form of language influenced the perception of facial expressions, but there were differences between happy and disgusted faces. Disgusted faces were more dependent on emotion concept information and showed different performances in semantic activation and satiation conditions

The role of sulfur emission from the petroleum industry on ultrafine particle number concentration in Singapore

Ultrafine particles, defined as particles with a diameter (dp) smaller than 100 nm, serve as an important component of cloud condensation nuclei, in addition to impacting human health. The dominant sources of ultrafine particles include traffic emissions and nucleation. Singapore is a tropical city that hosts petrochemical industries. To identify the sources of ultrafine particles, a year-long observation of the number size distribution was conducted in Singapore in 2018 and 2019. The concentrations of CO, CO2, CH4, and SO2 were also monitored. The particle number concentration during the southwest monsoon season was high, while that during the northeast monsoon period was relatively low. The CO concentration increased during the morning traffic rush hours, which was associated with relatively minor enhancements in ultrafine particle number concentration. The events for a high number concentration of the Aitken mode particles (dp 50 nm) correlated with the enhancements in CO concentration (ΔCO) for CH4-dominant air masses, suggesting that incomplete combustion processes, such as traffic emission, are important for the size range. Conversely, the number concentration of the Aitken mode particles (dp < 50 nm) increased for SO2-dominant air masses, suggesting the importance of industrial plume.National Research Foundation (NRF)Published versionThis work was supported by the Singapore National Research Foundation (NRF) under its Singapore National Research Fellowship scheme (National Research Fellow Award, NRF2012NRFNRFF001-031) and the National Natural Science Foundation of China (42175121 and 4215061048)