Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases

Towards Natural And Robust Human-Robot Interaction Using Sketch And Speech

For centuries, we have dreamt of intelligent machines that could someday co-exist with humans as autonomous agents, working for, with, and sometimes ˇ even against us. Since Karel Capek's play, R.U.R. (Rossum's Universal Robots) was written in 1920 [1], robots have permeated science fiction books, movies and television, giving rise to famous characters such as Robbie in I, Robot [2], Johnny 5 in Short Circuit [3], and C-3PO in Star Wars [4]. However, the fields of robotics and artificial intelligence are still a long way off from producing fullyautonomous machines like Rosie from The Jetsons [5] that can behave and interact as humans do. Today, getting computer agents to perform even the simplest of tasks requires designing an interface that is able to translate what the human wants into what the computer can do. Traditionally, this has been accomplished by constraining human users to communicate in a specific and unambiguous way, such as pressing buttons or selecting options from a menu. This type of interaction is rigid and unnatural, and is far from how humans communicate with one another. In recent years, there has been growing interest in the development of more natural and flexible human-robot interfaces, allowing humans to communicate with machines using means such as speech, drawing, gesturing, etc. These methods are still in their infancy, and while they offer more human-like interaction with computers, ensuring that the user's intentions are correctly inter- preted places limits on the flexibility of expression allowed by such systems. For example, despite recent advances in speech recognition technology, natural language interfaces are still largely confined to simple applications in which the speaker's intentions are disambiguated through the use of pre-defined phrases (e.g., "Call home"), or do not need to be interpreted at all, such as for data entry or speech-to-text processing. In this dissertation, a number of algorithms are proposed with the aim of allowing users to naturally communicate with a semi-autonomous robot while placing as few restrictions on the user's input as possible. The methods presented here reside in the domains of sketch and speech, which are flexible in their expressiveness and take advantage of how humans communicate with each other. The application considered in this work is mobile robot navigation, i.e., instructing a semi-autonomous robot to move to a specific location within its environment, where it will presumably undertake some useful task. By allowing the user to use speak and sketch naturally, the burden of recognition is shifted from human to machine, allowing the user to focus attention on the task at hand. This dissertation develops a probabilistic framework for sketch and speech recognition, the model for which is learned from training data such that recognition is accurate and robust. It also introduces a method for qualitative navigation, allowing the human user to give navigation instructions using an approximate sketched map. These approaches encourage the robot to understand how humans communicate, rather than to force the human to conform to a communication structure designed for the robot, taking a small step towards truly natural human-robot interaction

Patient navigation to improve breast cancer screening in Bosnian refugees and immigrants

Refugee women have low breast cancer screening rates. This study highlights the culturally competent implementation and reports the outcomes of a breast cancer screening patient navigation program for refuge/immigrant women from Bosnia. Refugees/immigrant women from Bosnia age 40–79 were contacted by a Serbo-Croatian speaking patient navigator who addressed patient-reported barriers to breast cancer screening and, using individually tailored interventions, helped women obtain screening. The proportion of women up-to-date for mammography was compared at baseline and after 1-year using McNemar’s Chi-Square test. 91 Serbo-Croatian speaking women were eligible for mammography screening. At baseline, 44.0% of women had a mammogram within the previous year, with the proportion increasing to 67.0% after 1-year (P = 0.001). A culturally-tailored, language-concordant navigator program designed to overcome specific barriers to breast cancer screening can significantly improve mammography rates in refugees/immigrants

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally