Research on the Language Protection Modes of Cross-Border Ethnic Groups in China and Russia—Taking the Oroqen nationality in China and the Evenki nationality in Russia as Examples

Cross-border ethnic groups are a group of people living separately in two or more modern countries due to a long history of development. The Oroqen nationality and the Evenki nationality, as homologous ethnic groups living across the border of China and Russia, both belong to the Manchu-Tungusic group of Altaic languages, which are very similar and have great significance for the survival of the two ethnic cultures. With the historical changes and social development, the ethnic languages of the Oroqen nationality and the Evenki nationality are seriously on the danger of disappearing. In this regard, China and Russia have adopted institutionalized and systematic protection measures and formed their own distinctive language protection modes from three aspects: policies and laws, theoretical research and educational practice, which has alleviated the former endangered situation of ethnic languages. Based on the necessity of cross-border ethnic language protection, this paper explores the protection modes of these two ethnic languages and puts forward suggestions for further strengthening the protection in the future, in order to provide help for improving the soft power of national culture and enhancing the friendship between China and Russia

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.Comment: 35 pages, 14 figure

New Gedanken experiment on Reissner-Nordstr\"om AdS Black Holes surrounded by quintessence

In this paper, we apply the new Gedanken experiment to investigate the weak cosmic censorship conjecture for Reissner-Nordstr\"om AdS black holes surrounded by quintessence. Since the perturbation of matter fields doesn't affect the spacetime geometry, we propose the stability condition and assume the process of matter fields falling into the black hole satisfies the null energy condition. Based on Iyer-Wald formalism we can derive the first order and second-order variational identities. From the two identities and the above two conditions lead to the first-order and second-order perturbation inequalities, and under the second-order approximation of matter fields perturbation, we find that the weak cosmic censorship conjecture is still satisfied

MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative Models on Medical Conversation Tasks

Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of research have been come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable Medical Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism aims to assist general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are public: https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanis

A Boolean based Question Answering System

The search engine searches the information according to the key words and provides users with related links, which need users to review and find the direct information among a large number of webpages. To avoid this drawback and improve the search results from search engine, we implemented a Boolean based Question Answering System. This system used Boolean Retrieval Model to analyze and match the text information from corresponding webpages in the document indexing step when users ask a Boolean expression based question. To evaluate system and analyze Boolean Retrieval Model, we used the data set from TREC (Text Retrieval Conference) to finish our experiment. Different Boolean operators in the questions such as AND, OR has been evaluated separately which is clear to analyze the effectiveness for each of them. We also evaluate the overall performance for this system

Analyzing eventual leader election protocols for dynamic systems by probabilistic model checking

Leader election protocols have been intensively studied in distributed computing, mostly in the static setting. However, it remains a challenge to design and analyze these protocols in the dynamic setting, due to its high uncertainty, where typical properties include the average steps of electing a leader eventually, the scalability etc. In this paper, we propose a novel model-based approach for analyzing leader election protocols of dynamic systems based on probabilistic model checking. In particular, we employ a leading probabilistic model checker, PRISM, to simulate representative protocol executions. We also relax the assumptions of the original model to cover unreliable channels which requires the introduction of probability to our model. The experiments confirm the feasibility of our approach

Electrical transport across metal/two-dimensional carbon junctions: Edge versus side contacts

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV \propto V), whereas the latter is characterized by a nonlinear dependence, dI/dV \propto V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices