New topic detection in microblogs and topic model evaluation using topical alignment

textThis thesis deals with topic model evaluation and new topic detection in microblogs. Microblogs are short and thus may not carry any contextual clues. Hence it becomes challenging to apply traditional natural language processing algorithms on such data. Graphical models have been traditionally used for topic discovery and text clustering on sets of text-based documents. Their unsupervised nature allows topic models to be trained easily on datasets meant for specific domains. However the advantage of not requiring annotated data comes with a drawback with respect to evaluation difficulties. The problem aggravates when the data comprises microblogs which are unstructured and noisy. We demonstrate the application of three types of such models to microblogs - the Latent Dirichlet Allocation, the Author-Topic and the Author-Recipient-Topic model. We extensively evaluate these models under different settings, and our results show that the Author-Recipient-Topic model extracts the most coherent topics. We also addressed the problem of topic modeling on short text by using clustering techniques. This technique helps in boosting the performance of our models. Topical alignment is used for large scale assessment of topical relevance by comparing topics to manually generated domain specific concepts. In this thesis we use this idea to evaluate topic models by measuring misalignments between topics. Our study on comparing topic models reveals interesting traits about Twitter messages, users and their interactions and establishes that joint modeling on author-recipient pairs and on the content of tweet leads to qualitatively better topic discovery. This thesis gives a new direction to the well known problem of topic discovery in microblogs. Trend prediction or topic discovery for microblogs is an extensive research area. We propose the idea of using topical alignment to detect new topics by comparing topics from the current week to those of the previous week. We measure correspondence between a set of topics from the current week and a set of topics from the previous week to quantify five types of misalignments: \textit{junk, fused, missing} and \textit{repeated}. Our analysis compares three types of topic models under different settings and demonstrates how our framework can detect new topics from topical misalignments. In particular so-called \textit{junk} topics are more likely to be new topics and the \textit{missing} topics are likely to have died or die out. To get more insights into the nature of microblogs we apply topical alignment to hashtags. Comparing topics to hashtags enables us to make interesting inferences about Twitter messages and their content. Our study revealed that although a very small proportion of Twitter messages explicitly contain hashtags, the proportion of tweets that discuss topics related to hashtags is much higher.Computer Science

Fracture Classification Associated with the Orthopaedic Trauma

This study aimed at exploring the fracture classification associated with the orthopaedic trauma as the provision of care associated with orthopedic trauma shares an important goal, which is to restore and preserve function. A focused assessment that embodies subjective and objective data will assist the healthcare professional to determine a patient’s needs and deliver the most appropriate level of care. Learning to collect data about factors associated with an orthopedic injury is an integral part of providing care for individuals who have sustained an orthopedic trauma. Fracture classification is the categorization of a fracture. It is used for documentation and research and gives surgeons and patients information about treatment options and prognosis. The process of obtaining this documentation is the process of diagnosis

Sympathetic Activation in Deadlines of Deskbound Research - A Study in the Wild

Paper and proposal deadlines are important milestones, conjuring up emotional memories to researchers. The question is if in the daily challenging world of scholarly research, deadlines truly incur higher sympathetic loading than the alternative. Here we report results from a longitudinal, in the wild study of n = 10 researchers working in the presence and absence of impeding deadlines. Unlike the retrospective, questionnaire-based studies of research deadlines in the past, our study is real-time and multimodal, including physiological, observational, and psychometric measurements. The results suggest that deadlines do not significantly add to the sympathetic loading of researchers. Irrespective of deadlines, the researchers' sympathetic activation is strongly associated with the amount of reading and writing they do, the extent of smartphone use, and the frequency of physical breaks they take. The latter likely indicates a natural mechanism for regulating sympathetic overactivity in deskbound research, which can inform the design of future break interfaces

79th AIOC 2021: All India Ophthalmological Society

CONFERENCE PROCEEDING

Gendered Spaces in the Public Sphere: A Micro Study of Bangalore’s Malls, Airport, Railways, and Educational Institutes

This study focuses on public spaces and analyses them to reveal their gendered nature. It is organized around the following public spaces: educational institutions, malls, railway stations, and the airport. Architectural designs, facilities provided, and gender-specific organization are some of the aspects of these spaces that are under study. Our study identified The discriminatory patterns in some of these places suggesting that there are long-term effects of discrimination on the human psyche, particularly when these spaces do not accommodate gender diversity. This paper highlights some of the discriminations and their effects on the LGBTQ+, gender-fluid and gender non-conforming communities. We observed architectural spaces to trace the subtlety with which gendered structures were incorporated into their architectural design. Specifically, we surveyed architectural designs of railway stations, airports, malls, and selected educational institutions. Each public space accommodates the needs of a specific gender while often overlooking the necessities of the under-represented genders. Despite a nearly balanced ratio that exists between males and females, there is minimal representation of the latter in several public spaces. Shared public spaces generally disregard non-dominant genders, and instead, align themselves with the dominant identities of the gender binary. The indifference towards the presence of genders other than men and women emphasizes the importance of acknowledging evolving identities and their specific needs. For such reasons, the development of gender-neutral spaces and openness towards the multiplicity of genders is of paramount importance in order to incorporate the needs and necessities of all individuals

Assessment of microbial contamination in pediatric oral liquid formulations marketed in Katsina State, Nigeria

No Abstract

BLP 2023 Task 2: Sentiment Analysis

We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, a total of 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging Large Language Model (LLMs) in zero- and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a brief overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domainComment: Accepted in BLP Workshop at EMNLP-2

Quantumlike description of the nonlinear and collective effects on relativistic electron beams in strongly magnetized plasmas

A numerical analysis of the self-interaction induced by a relativistic electron/positron beam in the presence of an intense external longitudinal magnetic field in plasmas is carried out. Within the context of the Plasma Wake Field theory in the overdense regime, the transverse beam-plasma dynamics is described by a quantumlike Zakharov system of equations in the long beam limit provided by the Thermal Wave Model. In the limiting case of beam spot size much larger than the plasma wavelength, the Zakharov system is reduced to a 2D Gross-Pitaevskii-type equation, where the trap potential well is due to the external magnetic field. Vortices, "beam halos" and nonlinear coherent states (2D solitons) are predicted.Comment: Poster presentation P5.021 at the 38th EPS Conference on Plasma Physics, Strasbourg, France, 26 June - 1 July, 201