A Copula-Based Method for Estimating Shear Strength Parameters of Rock Mass

The shear strength parameters (i.e., the internal friction coefficient f and cohesion c) are very important in rock engineering, especially for the stability analysis and reinforcement design of slopes and underground caverns. In this paper, a probabilistic method, Copula-based method, is proposed for estimating the shear strength parameters of rock mass. The optimal Copula functions between rock mass quality Q and f, Q and c for the marbles are established based on the correlation analyses of the results of 12 sets of in situ tests in the exploration adits of Jinping I-Stage Hydropower Station. Although the Copula functions are derived from the in situ tests for the marbles, they can be extended to be applied to other types of rock mass with similar geological and mechanical properties. For another 9 sets of in situ tests as an extensional application, by comparison with the results from Hoek-Brown criterion, the estimated values of f and c from the Copula-based method achieve better accuracy. Therefore, the proposed Copula-based method is an effective tool in estimating rock strength parameters

QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation

Recent years have witnessed the success of question answering (QA), especially its potential to be a foundation paradigm for tackling diverse NLP tasks. However, obtaining sufficient data to build an effective and stable QA system still remains an open problem. For this problem, we introduce an iterative bootstrapping framework for QA data augmentation (named QASnowball), which can iteratively generate large-scale high-quality QA data based on a seed set of supervised examples. Specifically, QASnowball consists of three modules, an answer extractor to extract core phrases in unlabeled documents as candidate answers, a question generator to generate questions based on documents and candidate answers, and a QA data filter to filter out high-quality QA data. Moreover, QASnowball can be self-enhanced by reseeding the seed set to fine-tune itself in different iterations, leading to continual improvements in the generation quality. We conduct experiments in the high-resource English scenario and the medium-resource Chinese scenario, and the experimental results show that the data generated by QASnowball can facilitate QA models: (1) training models on the generated data achieves comparable results to using supervised data, and (2) pre-training on the generated data and fine-tuning on supervised data can achieve better performance. Our code and generated data will be released to advance further work

Adversarial Language Games for Advanced Natural Language Intelligence

We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker must hide its intention and subtly induce the defender, while a competitive defender must be cautious with its utterances and infer the intention of the attacker. Such language abilities can facilitate many important downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments and empirical studies on several baseline attack and defense strategies show promising and interesting results. Based on the analysis on the game and experiments, we discuss multiple promising directions for future research.Comment: Accepted by AAAI 202

White Matter Injury After Intracerebral Hemorrhage

Spontaneous intracerebral hemorrhage (ICH) accounts for 15% of all stroke cases. ICH is a devastating form of stroke associated with high morbidity, mortality, and disability. Preclinical studies have explored the mechanisms of neuronal death and gray matter damage after ICH. However, few studies have examined the development of white matter injury (WMI) following ICH. Research on WMI indicates that its pathophysiological presentation involves axonal damage, demyelination, and mature oligodendrocyte loss. However, the detailed relationship and mechanism between WMI and ICH remain unclear. Studies of other acute brain insults have indicated that WMI is strongly correlated with cognitive deficits, neurological deficits, and depression. The degree of WMI determines the short- and long-term prognosis of patients with ICH. This review demonstrates the structure and functions of the white matter in the healthy brain and discusses the pathophysiological mechanism of WMI following ICH. Our review reveals that the development of WMI after ICH is complex; therefore, comprehensive treatment is essential. Understanding the relationship between WMI and other brain cells may reveal therapeutic targets for the treatment of ICH

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

A Nonlinear Method for Characterizing Discrete Defects in Thick Multilayer Composites

Discrete defects in thick composites are difficult to detect for the small size and the structure noise that appears in multilayer composites. In this paper, a nonlinear method, called recurrence analysis, has been used for characterizing discrete defects in thick section Carbon Fiber Reinforced Polymer (CFRP) with complex lay-up. A 10 mm thick CFRP specimen with nearly zero porosity was selected, and blind holes with different diameters were artificially constructed in the specimen. The second half of the backscattered signal was analyzed by recurrence analysis for areas with or without a defect. The recurrence plot (RP) visualized the chaotic behavior of the ultrasonic pulse, and the statistical results of recurrence quantification analysis (RQA) characterized the instability of the signal and the effect of defects. The results show that the RQA variable differences are related to the size of blind holes, which give a probable detection of discrete geometric changes in thick multilayer composites

Prenatal Diagnosis of Congenital Cataract: Sonographic Features and Perinatal Outcome in 41 Cases

Purpose To describe the prenatal ultrasonographic characteristics and perinatal outcomes of congenital cataract. Materials and Methods We analyzed congenital cataract diagnosed prenatally at four referral centers between August 2004 and February 2019. The diagnosis was confirmed by postnatal ophthalmologic evaluation of liveborn infants or autopsy for terminated cases. Maternal demographics, genetic testing results, prenatal ultrasound images, and perinatal outcomes were abstracted. Results Total of 41 cases of congenital cataract diagnosed prenatally among 788751 women undergoing anatomic survey. Based on the sonographic characteristics, 16/41 (39.0%) had a dense echogenic structure, 15/41 (36.6%) had a hyperechogenic spot and 10/41 (24.4%) had the double ring sign. 17/41 (41.5%) were isolated, and 24/41 (58.5%) had associated intraocular and extraocular findings. Microphthalmia, cardiac abnormalities, and central nervous system abnormalities were the most common associated abnormalities. Regarding potential etiology, 6 cases had a known family history of congenital cataract, 4 cases had confirmed congenital rubella infection, and 2 cases had aneuploidy. 31/41 (75.6%) elected termination and 10/41 (24.4%) elected to continue their pregnancy. Among the 10 cases, one case died, one case was lost to follow-up, and the remaining 8 cases were referred for ophthalmologist follow-up and postnatal surgery. Conclusion Once fetal cataracts are detected, a detailed fetal anatomy survey to rule out associated abnormalities and a workup to identify the potential etiology are recommended. Prenatal diagnosis of congenital cataracts provides vital information for counseling and subsequent management

Tracking Intramolecular Vibrational Redistribution in Polyatomic Small-Molecule Liquids by Ultrafast Time–Frequency-Resolved CARS

Selective excitation of C–H stretching vibrational modes, detection of intramolecular vibrational energy redistribution (IVR), and vibrational modes coupling in the electronic ground state of benzene are performed by using femtosecond time- and frequency-resolved coherent anti-Stokes Raman scattering (CARS) spectroscopy. Both of the parent modes in the Raman-active bands are coherently excited by an ultrafast stimulated Raman pump, giving initial excitations of 3056 cm^–1 (A_1g) and 3074 cm^–1 (E_2g) and subsequent IVR from the parent modes to daughter modes of 1181 and 992 cm^–1, and the coherent vibrational coupling of the relevant modes is tracked. The directionality and selectivity of IVR and coherent coupling among all of the relevant vibrational modes are discussed in the view of molecular symmetry