Investigating Transition Matrices on U.S. Residential Mortgage-Backed Securities

The purpose of our research is to expand on the work of Kavvathas (2001) that studies credit rating transition probabilities for corporate bonds. This paper, for the period of 1991-2007 will be focused on rating transition matrices for US residential mortgage-backed securities (RMBS). In particular, we extend their techniques to a different data set and more recent time period by estimating credit rating transition matrices through the cohort method and the time-homogeneous duration method. In addition, we apply an alternative approach to calculate the average transition matrices

HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus

ChatGPT has gained significant interest due to its impressive performance, but people are increasingly concerned about its potential risks, particularly around the detection of AI-generated content (AIGC), which is often difficult for untrained humans to identify. Current datasets utilized for detecting ChatGPT-generated text primarily center around question-answering, yet they tend to disregard tasks that possess semantic-invariant properties, such as summarization, translation, and paraphrasing. Our primary studies demonstrate that detecting model-generated text on semantic-invariant tasks is more difficult. To fill this gap, we introduce a more extensive and comprehensive dataset that considers more types of tasks than previous work, including semantic-invariant tasks. In addition, the model after a large number of task instruction fine-tuning shows a strong powerful performance. Owing to its previous success, we further instruct fine-tuning Tk-instruct and built a more powerful detection system. Experimental results show that our proposed detector outperforms the previous state-of-the-art RoBERTa-based detector

Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval

In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage the capabilities of LLMs for document expansion, i.e. query generation, and effectively transfer expanded knowledge to retrievers using pre-training strategies tailored for passage retrieval. These strategies include contrastive learning and bottlenecked query generation. Furthermore, we incorporate a curriculum learning strategy to reduce the reliance on LLM inferences. Experimental results demonstrate that pre-training with LLM-based document expansion significantly boosts the retrieval performance on large-scale web-search tasks. Our work shows strong zero-shot and out-of-domain retrieval abilities, making it more widely applicable for retrieval when initializing with no human-labeled data.Comment: 10 pages, 3 tables, 4 figures, under revie

Query-as-context Pre-training for Dense Passage Retrieval

Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages from the same document to be relevant, without taking into account the possibility of weakly correlated pairs. Thus, this paper proposes query-as-context pre-training, a simple yet effective pre-training technique to alleviate the issue. Query-as-context pre-training assumes that the query derived from a passage is more likely to be relevant to that passage and forms a passage-query pair. These passage-query pairs are then used in contrastive or generative context-supervised pre-training. The pre-trained models are evaluated on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. Experimental results show that query-as-context pre-training brings considerable gains and meanwhile speeds up training, demonstrating its effectiveness and efficiency. Our code will be available at https://github.com/caskcsg/ir/tree/main/cotmae-qc .Comment: EMNLP 2023 Main Conferenc

PUNR: Pre-training with User Behavior Modeling for News Recommendation

News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing works are mostly focused on improvements in the supervised fine-tuning stage. However, there is still a lack of PLM-based unsupervised pre-training methods optimized for user representations. In this work, we propose an unsupervised pre-training paradigm with two tasks, i.e. user behavior masking and user behavior generation, both towards effective user behavior modeling. Firstly, we introduce the user behavior masking pre-training task to recover the masked user behaviors based on their contextual behaviors. In this way, the model could capture a much stronger and more comprehensive user news reading pattern. Besides, we incorporate a novel auxiliary user behavior generation pre-training task to enhance the user representation vector derived from the user encoder. We use the above pre-trained user modeling encoder to obtain news and user representations in downstream fine-tuning. Evaluations on the real-world news benchmark show significant performance improvements over existing baselines.Comment: Accepted by Findings of EMNLP23. Github Repo: https://github.com/ma787639046/pun

Usefulness of soluble endothelial protein C receptor combined with left ventricular global longitudinal strain for predicting slow coronary flow: A case-control study

Background: Slow coronary flow (SCF) is an angiographic entity characterized by delayed coronary opacification without an evident obstructive lesion in the epicardial coronary artery. However, patients with SCF have decreased left ventricular (LV) global longitudinal strain (GLS). SCF is associated with inflammation, and soluble endothelial protein C receptor (sEPCR) is a potential biomarker of inflammation. Therefore, under evaluation herein, was the relationship between SCF and sEPCR and the predictive value of sEPCR and LV GLS for SCF was investigated. Methods: Twenty-eight patients with SCF and 34 controls were enrolled. SCF was diagnosed by the thrombolysis in myocardial infarction frame count (TFC). The plasma level of sEPCR was quantified using enzyme-linked immunosorbent assay. LV GLS was measured by two-dimensional speckle-tracking echocardiography. Results: Plasma sEPCR was significantly higher in patients with SCF than in controls and was positively correlated with the mean TFC (r = 0.67, p < 0.001) and number of involved vessels (r = 0.61, p < 0.001). LV GLS was decreased in patients with SCF compared to that in controls. sEPCR level (OR = 3.14, 95% CI 1.55–6.36, p = 0.001) and LV GLS (OR = 1.44, 95% CI 1.02–2.04, p = 0.04) were independent predictors of SCF. sEPCR predicted SCF (area under curve [AUC]: 0.83); however, sEPCR > 9.63 ng/mL combined with LV GLS > −14.36% demonstrated better predictive power (AUC: 0.89; sensitivity: 75%; specificity: 91%). Conclusions: Patients with SCF have increased plasma sEPCR and decreased LV GLS. sEPCR may be a useful potential biomarker for SCF, and sEPCR combined with LV GLS can better predict SCF

Experimental and Numerical Analysis of Rock Burst Tendency and Crack Development Characteristics of Tianhu Granite

Rock burst is a serious nonlinear dynamic geological hazard in underground engineering construction. In this paper, a true triaxial unloading rock burst experiment and numerical simulation are carried out on Tianhu granite to investigate the rock burst tendency and crack development characteristics of surrounding rock after excavation. The experiment and numerical simulation process monitored the rock burst stress path to determine the rock burst stress. According to the evolution law of the frequency and amplitude of rock burst acoustic emission monitoring, the shape characteristics of rock burst fragments are analyzed. The rock burst numerical simulation analysis is carried out by the PFC software, and the temporal and spatial evolution law of cracks is obtained. The research results show that the laboratory experiment and numerical simulation of Tianhu granite have rock burst strengths of 163.4 MPa and 161 MPa, respectively, and the average rock burst stress ratio is 8.38, that is, the Tianhu granite has a low rock burst tendency. During the rock burst, the development of tensile cracks will produce flaky debris, and the development of shear cracks will produce lumpy debris. Rock burst will happen when the crack growth rate to be exceeded the unloading crack growth rate; therefore, it can be used as a precursor signal for the occurrence of rock burst

Evaluation of left ventricular function in patients with coronary slow flow: A systematic review and meta-analysis

Background: Coronary slow flow (CSF) is an angiographic finding defined as delayed distal vessel perfusion without severe stenosis of the epicardial coronary arteries. However, definite alterations in left ventricular (LV) function in patients with CSF remains inconsistent. This study aimed to clarify the changes in LV function in patients with CSF and explore the factors that may influence LV function. Methods: PubMed, Embase, and Cochrane Library databases were systematically searched. Standardized mean differences and 95% confidence intervals (CI) for the LV function parameters were calculated. Subgroup analysis, meta-regression analysis, and correlation analysis were performed to explore the factors influencing LV function. Results: Twenty-two studies (1101 patients with CSF) were included after searching three databases. In patients with CSF, LV ejection function in patients with CSF was marginally lower (61.8%; 95% CI: 61.0%, 62.7%), global longitudinal strain was decreased (–18.2%; 95% CI: –16.7%, –19.7%). Furthermore, left atrial diameter, left atrial volume index, and E/e′ were significantly increased, while E/A and e’ were significantly decreased. The mean thrombolysis in myocardial infarction frame count (TFC) was linearly associated with LV function; the larger the mean TFC, the greater the impairment of LV function. Conclusions: Left ventricular systolic and diastolic functions were impaired in patients with CSF, and this impairment was aggravated with increasing mean TFC