Languages adapt to their contextual niche

abstractIt is well established that context plays a fundamental role in how we learn and use language. Here we explore how context links short-term language use with the long-term emergence of different types of language system. Using an iterated learning model of cultural transmission, the current study experimentally investigates the role of the communicative situation in which an utterance is produced (situational context) and how it influences the emergence of three types of linguistic systems: underspecified languages (where only some dimensions of meaning are encoded linguistically), holistic systems (lacking systematic structure), and systematic languages (consisting of compound signals encoding both category-level and individuating dimensions of meaning). To do this, we set up a discrimination task in a communication game and manipulated whether the feature dimension shape was relevant or not in discriminating between two referents. The experimental languages gradually evolved to encode information relevant to the task of achieving communicative success, given the situational context in which they are learned and used, resulting in the emergence of different linguistic systems. These results suggest language systems adapt to their contextual niche over iterated learning.</jats:p

인공지능 관련 뉴스 기사의 프레임, 감정 분석

학위논문(석사) -- 서울대학교대학원 : 사회과학대학 언론정보학과, 2022. 8. 이철주 .This study examines how artificial intelligence (AI) is presented in the news media by examining the frames and emotions expressed in news coverage about AI. For analysis, I used computational text analysis techniques -structural topic model (STM) to extract frames and NRC Emotion Lexicon and Linguistic Inquiry and Word Count (LIWC) to detect emotions. Then I examined their correlations with the political ideology of media outlets (conservative vs. liberal) and media type (newspapers vs TV news). By identifying the frames and the emotions embedded in the news media, it would be possible to predict how they influence the formation of public opinions and attitudes towards AI.본 연구는 컴퓨터 텍스트 분석 기술을 통해 인공지능 (AI)에 대한 뉴스 보도에 드러난 프레임과 감정을 분석하여 인공지능이 뉴스 미디어에서 어떻게 표현되는지를 살펴보는 것을 목적으로 한다. 프레임 추출을 위해 Structural Topic Model (STM) 기법을, 감정 추출을 위해 NRC Emotion Lexicon과 Linguistic Inquiry and Word Count (LIWC) 프로그램을 활용했다. 언론사의 정치 성향(보수 – 진보)과 미디어 유형(신문 – 방송)을 변수로 설정해, 추출된 결과와 각 변수와의 상관관계를 분석했다. 뉴스 미디어에 내재된 프레임과 감정을 파악함으로써, 그것이 AI에 대한 여론 및 태도 형성에 어떤 영향을 미치는지 예측할 수 있을 것이다.Chapter 1. Introduction 1 Chapter 2. Literature Review and Research Aim 2 Chapter 3. Conceptual Framework 8 Chapter 4. Methods 21 Chapter 5. Results 27 Chapter 6. Discussion 45 Appendix. 49 Bibliography. 51 Abstract in Korean. 57석

New likelihoods for shape analysis

We introduce a new kind of likelihood function based on the sequence of moments of the data distribution. Both binned and unbinned data samples are discussed, and the multivariate case is also derived. Building on this approach we lay out the formalism of shape analysis for signal searches. In addition to moment-based likelihoods, standard likelihoods and approximate statistical tests are provided. Enough material is included to make the paper self-contained from the perspective of shape analysis. We argue that the moment-based likelihoods can advantageously replace unbinned standard likelihoods for the search of non-local signals, by avoiding the step of fitting Monte-Carlo generated distributions. This benefit increases with the number of variables simultaneously analyzed. The moment-based signal search is exemplified and tested in various 1D toy models mimicking typical high-energy signal--background configurations. Moment-based techniques should be particularly appropriate for the searches for effective operators at the LHC.Comment: 23 pages, 5 figure

Examining the Trend of Literature on Classification Modelling: A Bibliometric Approach

This paper analyses and reports various types of published works related to classification or discriminant modelling. This paper adopted a bibliometric analysis based on the data obtained from the Scopus online database on 27th July 2019. Based on the ‘keywords’ search results, it yielded 2775 valid documents for further analysis. For data visualisation purposes, we employed VOSviewer. This paper reports the results using standard bibliometric indicators, particularly on the growth rate of publications, research productivity, analysis of the authors and citations. The outcomes revealed that there is an increased growth rate of classification literature over the years since 1968. A total of 2473 (89.12%) documents were from journals (n=1439; 51.86%) and conference proceedings (n=1034; 37.26%) contributed as the top publications in this classification topic. Meanwhile, 2578 (92.9%) documents are multi-authored with an average collaboration index of 3.34 authors per article. However, this classification research field found that the famous numbers of authors’ collaboration in a document are two (with n=758; 27.32%), three (n=752; 27.10%) and four (n=560; 20.18%) respectively. An analysis by country, China with 1146 (41.30%) published documents thus is ranked first in productivity. With respect to the frequency of citations, Bauer and Kohavi (1999)’s article emerged as the most cited article through 1414 total citations with an average of 70.7 citations per year. Overall, the increasing number of works on classification topics indicates a growing awareness of its importance and specific requirements in this research field

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Optimal auditing with scoring: theory and application to insurance fraud

This article makes a bridge between the theory of optimal auditing and the scoring methodology in an asymmetric information setting. Our application is meant for insurance claims fraud, but it can be applied to many other activities that use the scoring approach. Fraud signals are classified based on the degree to which they reveal an increasing probability of fraud. We show that the optimal auditing strategy takes the form of a “Red Flags Strategy” which consists in referring claims to a Special Investigative Unit (SIU) when certain fraud indicators are observed. The auditing policy acts as a deterrence device and we explain why it requires the commitment of the insurer and how it should affect the incentives of SIU staffs. The characterization of the optimal auditing strategy is robust to some degree of signal manipulation by defrauders as well as to the imperfect information of defrauders about the audit frequency. The model is calibrated with data from a large European insurance company. We show that it is possible to improve our results by separating different groups of insureds with different moral costs of fraud. Finally, our results indicate how the deterrence effect of the audit scheme can be taken into account and how it affects the optimal auditing strategy.Audit, scoring, insurance fraud, red flags strategy, fraud indicators, suspicion index, moral cost of fraud, deterrence effect, signal manipulation.

From Microhabitat to Metapopulations: a Model System for Conservation Under Climate Change

Please refer to the 'Front Matter' file for details.Climate Change and Sustainable Futures studentship, University of ExeterNatural Environment Research Council, grant NE⁄G006296⁄