Direct and inverse problems on free vibration analysis of cracked non-uniform beams carrying spring-mass systems by finite element method

This paper presents an analytical approach to investigate the free vibration analysis of cracked non-uniform beam carrying spring-mass systems by finite element method and illustrates a valid and reliable damage identification method which using hybrid neural genetic technique. Firstly, based on the finite element method, the dynamic characteristics of non-uniform cracked beam carrying spring-mass systems are obtained. Then, the first five frequencies are used as input parameters by combining genetic algorithm with neural network to identify the damage. Finally, Numerical simulations of direct and inverse problems of non-uniform cracked beams carrying a spring-mass system are carried out

PUnifiedNER: a Prompting-based Unified NER System for Diverse Datasets

Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new model to be trained and stored. In this work, we present a ``versatile'' model -- the Prompting-based Unified NER system (PUnifiedNER) -- that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible. By using prompt learning, PUnifiedNER is a novel approach that is able to jointly train across multiple corpora, implementing intelligent on-demand entity recognition. Experimental results show that PUnifiedNER leads to significant prediction benefits compared to dataset-specific models with impressively reduced model deployment costs. Furthermore, the performance of PUnifiedNER can achieve competitive or even better performance than state-of-the-art domain-specific methods for some datasets. We also perform comprehensive pilot and ablation studies to support in-depth analysis of each component in PUnifiedNER.Comment: Accepted to AAAI 202

SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning

Contrastive learning methods achieve state-of-the-art results in unsupervised sentence representation learning. Although playing essential roles in contrastive learning, data augmentation methods applied on sentences have not been fully explored. Current SOTA method SimCSE utilizes a simple dropout mechanism as continuous augmentation which outperforms discrete augmentations such as cropping, word deletion and synonym replacement. To understand the underlying rationales, we revisit existing approaches and attempt to hypothesize the desiderata of reasonable data augmentation methods: balance of semantic consistency and expression diversity. Based on the hypothesis, we propose three simple yet effective discrete sentence augmentation methods, i.e., punctuation insertion, affirmative auxiliary and double negation. The punctuation marks, auxiliaries and negative words act as minimal noises in lexical level to produce diverse sentence expressions. Unlike traditional augmentation methods which randomly modify the sentence, our augmentation rules are well designed for generating semantically consistent and grammatically correct sentences. We conduct extensive experiments on both English and Chinese semantic textual similarity datasets. The results show the robustness and effectiveness of the proposed methods

Deeply Coupled Cross-Modal Prompt Learning

Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models to downstream tasks has gained significant attention recently. Existing prompt-tuning methods in cross-modal learning, however, either solely focus on language branch, or learn vision-language interaction in a shallow mechanism. In this context, we propose a Deeply coupled Cross-modal Prompt learning (DCP) method based on CLIP. DCP flexibly accommodates the interplay between vision and language with a Cross-Modal Prompt Attention (CMPA) mechanism, which enables the mutual exchange of respective representation through a well-connected multi-head attention module progressively and strongly. We then conduct comprehensive few-shot learning experiments on 11 image classification datasets and analyze the robustness to domain shift as well. Thorough experimental analysis evidently demonstrates the superb few-shot generalization and compelling domain adaption capacity of a well-executed DCP. The code can be found at https://github.com/GingL/CMPA.Comment: Accepted by ACL 2023 finding

What Makes Pre-trained Language Models Better Zero/Few-shot Learners?

In this paper, we propose a theoretical framework to explain the efficacy of prompt learning in zero/few-shot scenarios. First, we prove that conventional pre-training and fine-tuning paradigm fails in few-shot scenarios due to overfitting the unrepresentative labelled data. We then detail the assumption that prompt learning is more effective because it empowers pre-trained language model that is built upon massive text corpora, as well as domain-related human knowledge to participate more in prediction and thereby reduces the impact of limited label information provided by the small training set. We further hypothesize that language discrepancy can measure the quality of prompting. Comprehensive experiments are performed to verify our assumptions. More remarkably, inspired by the theoretical framework, we propose an annotation-agnostic template selection method based on perplexity, which enables us to ``forecast'' the prompting performance in advance. This approach is especially encouraging because existing work still relies on development set to post-hoc evaluate templates. Experiments show that this method leads to significant prediction benefits compared to state-of-the-art zero-shot methods

What Large Language Models Bring to Text-rich VQA?

Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this work, we focus on investigating the advantages and bottlenecks of LLM-based approaches in addressing this problem. To address the above concern, we separate the vision and language modules, where we leverage external OCR models to recognize texts in the image and Large Language Models (LLMs) to answer the question given texts. The whole framework is training-free benefiting from the in-context ability of LLMs. This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets. Besides, based on the ablation study, we find that LLM brings stronger comprehension ability and may introduce helpful knowledge for the VQA problem. The bottleneck for LLM to address text-rich VQA problems may primarily lie in visual part. We also combine the OCR module with MLLMs and pleasantly find that the combination of OCR module with MLLM also works. It's worth noting that not all MLLMs can comprehend the OCR information, which provides insights into how to train an MLLM that preserves the abilities of LLM

History of Suicidal Behavior and Clozapine Prescribing Among People With Schizophrenia in China: A Cohort Study

BACKGROUND: Clozapine is an off-label drug used in most countries to prevent suicide in individuals with schizophrenia. However, few studies have reported real-world prescription practices. This study aimed to explore the association between a history of suicidal behavior and clozapine prescribing during eight weeks of hospitalization for individuals with early-stage schizophrenia. METHODS: This observational cohort study used routine health data collected from a mental health hospital in Beijing, China. The study included 1057 inpatients who had schizophrenia onset within 3 years. History of suicidal behavior was coded from reviewing medical notes according to the Columbia Suicide Severity Rating Scale. Information on antipsychotic use during hospitalization was extracted from the prescription records. Time to clozapine use was analyzed using Cox regression models adjusted for sociodemographic and clinical covariates. RESULTS: The prevalence rates of self-harm, suicidal behavior, and suicide attempt were 12.3%, 7.5%, and 5.4%, respectively. A history of self-harm history was positively associated with clozapine uses upon admission (4.1% vs. 0.8%, exact p = 0.009). Among those who had not used clozapine and had no clozapine contraindication, A history of suicidal behavior increased the possibility of switch to clozapine within 56 days after admission (Hazard Ratio[95% CI], 6.09[2.08-17.83]) or during hospitalization (4.18[1.62-10.78]). CONCLUSION: The use of clozapine for early-stage schizophrenia was more frequent among those with suicidal behavior than among those without suicidal behavior in China, although the drug instructions do not label its use for suicide risk

East Meets West: An International Dialogue on Mediation and Med-Arb in the United States and China

This Second Beijing Arbitration Commission (BAC)/Straus Institute for Dispute Resolution International Videoconference, following up on last year\u27s successful inaugural program, will provide different perspectives on the current BAC initiative and evolving attitudes toward mediation and med-arb. Topics include: (1) the development and current state of business mediation in the U.S.; (2) the challenges and opportunities confronting China in developing stand-alone business mediation; (3) reflections on the skills necessary for mediators; (4) common pitfalls in mediation; (5) perspectives on med-arb (as opposed to stand-alone mediation); and (6) how to most effectively use mediation in conjunction with arbitration procedures

Serum Neuroactive Metabolites of the Tryptophan Pathway in Patients With Acute Phase of Affective Disorders

BACKGROUND: Many studies showed disrupted tryptophan metabolism in patients with affective disorders. The aims of this study were to explore the differences in the metabolites of tryptophan pathway (TP) and the relationships between TP metabolites and clinical symptoms, therapeutic effect in patients with bipolar disorder with acute manic episode (BD-M), depressive episode (BD-D) and major depressive disorder (MDD). METHODS: Patients with BD-M (n=52) and BD-D (n=39), MDD (n=48) and healthy controls (HCs, n=49) were enrolled. The serum neuroactive metabolites levels of the TP were measured by liquid chromatography-tandem mass spectrometry. Hamilton Depression Scale-17 item (HAMD-17) and Young Mania Rating Scale (YMRS) were used to evaluate depressive and manic symptoms at baseline and after 8 weeks of antidepressants, mood stabilizers, some also received antipsychotic medication. RESULTS: The levels of tryptophan (TRP) and kynurenic acid (KYNA) were significantly lower and the ratios of tryptophan/kynurenine (TRP/KYN), 5-hydroxytryptamine/tryptophan (5-HT/TRP), quinolinic acid/kynurenic acid (QUIN/KYNA) were higher in BD-M, BD-D, MDD vs. HC. The levels of QUIN and the ratios of QUIN/KYNA were higher in BD-M than in BD-D, MDD, and HCs. The 5-hydroxyindoleacetic acid (5-HIAA) levels of patients with MDD were significantly higher than those in BD-M and BD-D. Binary logistic regression analysis showed the lower peripheral KYNA, the higher the QUIN level, and the higher the risk of BD-M; the lower peripheral KYNA and the higher KYN/TRP and 5-HT/TRP, the higher the risk of BD-D; and the lower the peripheral KYNA level and the higher the KYN/TRP and 5-HT/TRP, the higher the risk of MDD. Correlation analysis, showing a significant association between tryptophan metabolites and improvement of clinical symptoms, especially depression symptoms. CONCLUSIONS: Patients with affective disorders had abnormal tryptophan metabolism, which involved in 5-HT and kynurenine pathway (KP) sub-pathway. Tryptophan metabolites might be potential biomarkers for affective disorders and some metabolites have been associated with remission of depressive symptoms