Detecting Important Life Events on Twitter Using Frequent Semantic and Syntactic Subgraphs

Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married)

Simulation, Design, and Test of Square, Apodized Photon Sieves for High-Contrast, Exoplanet Imaging

A photon sieve is a lightweight, diffractive optic which is well-suited to be a deployable primary for a space telescope. Point spread functions (PSFs) can be altered by shaping and apodizing an aperture, and a PSF that drops rapidly from the peak is desirable for high-contrast imaging. For this reason, square apodized photon sieves were simulated, designed, and tested for high-contrast performance and use in an exoplanet imaging telescope. These sieves were shown to outperform conventional optics and unapodized sieves for high-contrast imaging in a number of tests. New methods were developed for apodizing sieves, measuring PSFs, and characterizing high-contrast performance. Tests indicated that square apodized sieves could detect exoplanets with irradiance below 10-3.69 of the star\u27s PSF peak within ten diffraction limits of separation. This was not sufficient for directly imaging earth-like exoplanets, but will be useful for other high-contrast applications. The Fresnel diffraction simulation conducted for the sieves was shown to agree closely with the experimental results. The ability to accurately apply apodizations and conduct simulations for photon sieves, measure PSFs across an extreme dynamic range, and conduct high-contrast imaging performance analyses will drive new PSF design and be useful for future high-contrast imaging work

Detecting Personal Life Events from Social Media

Social media has become a dominating force over the past 15 years, with the rise of sites such as Facebook, Instagram, and Twitter. Some of us have been with these sites since the start, posting all about our personal lives and building up a digital identify of ourselves. But within this myriad of posts, what actually matters to us, and what do our digital identities tell people about ourselves? One way that we can start to filter through this data, is to build classifiers that can identify posts about our personal life events, allowing us to start to self reflect on what we share online. The advantages of this type of technology also have direct merits within marketing, allowing companies to target customers with better products. We also suggest that the techniques and methodologies built throughout this thesis also have opportunities to support research within other areas such as cyber bullying, and radicalisation detection. The aim of this thesis is to build upon the under researched area of life event detection, specifically targeting Twitter, and Instagram. Our goal is to develop classifiers that identify a list of life events inspired by cognitive psychology, where we target a total of seven within this thesis. To achieve this we look to answer three research questions covered in each of our empirical chapters. In our first empirical chapter, we ask; What features would improve the classification of important life events. To answer this, we look at first extracting a new dataset from Twitter targeting the following events: Getting Married, Having Children, Starting School, Falling in Love, and Death of a Parent. We look at three new feature sets: interactions, content, and semantic features, and compare against a current state of the art technique. In our second empirical chapter, we draw inspiration from cheminformatics, and frequent sub-graph mining to ask; Could the inclusion of semantic and syntactic patterns improve performance in our life event classifier. Here we look at expanding our tweets into semantic networks, as well as consider two forms of syntactic relationships between tokens. We then mine for frequent sub-graphs amongst our tweet graphs, and use these as features in our classifier. Our results produce F1 scores of between 0.65 and 0.77, providing an improvement between 0.01 and 0.04 against the current state of the art. In our final empirical chapter, we look to answer our third research question; How can we detect important life events from other social media sites, such as Instagram?. We ask this question, as we believe Instagram to be a preferred environment to share personal life events. In this chapter, we extract a new dataset, targeting the following events: Getting Married, Having Children, Starting School, Graduation, and Buying a House. Our results find that our methodology provides F1 scores between 0.78, and 0.82, an improvement in F1 score between 0.01 and 0.04 against the current state of the art

Animal Consortium

Article published in the Tenn. Law Review

I Never Saw a Moor

https://digitalcommons.library.umaine.edu/mmb-me/1792/thumbnail.jp

Mt Grand Station – Wildlife Conservation

This document is a collection of reports from students of ECOL609 Conservation Biology (Semester 1, 2023). The aim of this course is to investigate the challenges and future options for nature conservation management within the agricultural and policy framework and the landscape mosaic of the New Zealand High Country. The focus of the course this year was a case study of the Lincoln University’s High Country Station in Hawea, Central Otago. A 4-day residential field course was attended by more than 30 students with the support of five academic staff from the Faculty of Agriculture and Life Sciences and the Farm Manager. This paper typically attracts students from several different disciplines and postgraduate study programmes, mostly Masters programmes. Overseas students accounted for a large proportion of the group, particularly from our Master of International Nature Conservation (MINC) joint programme with University of Gottingen in Germany, together with a good number of New Zealand students from various postgraduate study programmes including MINC. Overseas visitors were from a diverse range of countries including USA, Sweden and Kazakhstan. Each student identified and developed their own research project that formed the practical component of the course. Although these were individual research projects, much value was placed on broader learning, sharing of knowledge, discussion, debate and teamwork. The breadth of research topics reflects the varied interests of the students, but all projects have a primary focus on some aspect of Conservation Biology at Mt Grand. These reports provide an original and unique contribution to knowledge of the agroecology of this beautiful landscape and, in our view, fully justify their collation

Development of a Compact Neutron Source based on Field Ionization Processes

The authors report on the use of carbon nanofiber nanoemitters to ionize deuterium atoms for the generation of neutrons in a deuterium-deuterium reaction in a preloaded target. Acceleration voltages in the range of 50-80 kV are used. Field emission of electrons is investigated to characterize the emitters. The experimental setup and sample preparation are described and first data of neutron production are presented. Ongoing experiments to increase neutron production yields by optimizing the field emitter geometry and surface conditions are discussed.Comment: 4 pages, 5 figures; IVNC 201

A refined model for spinning dust radiation

We present a comprehensive treatment of the spectrum of electric dipole emission from spinning dust grains, updating the commonly used model of Draine and Lazarian. Grain angular velocity distributions are computed using the Fokker-Planck equation; we revisit the drift and diffusion coefficients for the major torques on the grain, including collisions, grain-plasma interactions, and infrared emission. We use updated grain optical properties and size distributions. The theoretical formalism is implemented in the companion code, SPDUST, which is publicly available. The effect of some environmental and grain parameters on the emissivity is shown and analysed.Comment: Minor corrections. Matches the MNRAS published version (except for a typo in Eq.(74) corrected here). The companion code, SPDUST, can be downloaded from http://www.tapir.caltech.edu/~yacine/spdust/spdust.htm

Generating Narratives from Personal Digital Data: Using Sentiment, Themes, and Named Entities to Construct Stories

As the quantity and variety of personal digital data shared on social media continues to grow, how can users make sense of it? There is growing interest among HCI researchers in using narrative techniques to support interpretation and understanding. This work describes our prototype application, ReelOut, which uses narrative techniques to allow users to understand their data as more than just a database. The online service extracts data from multiple social media sources and augments it with semantic information such as sentiment, themes, and named entities. The interactive editor automatically constructs a story by using unit selection to fit data units to a simple narrative structure. It allows the user to change the story interactively by rejecting certain units or selecting a new narrative target. Finally, images from the story can be exported as a video clip or a collage