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#AreHashtagsWords? Structure, position, and syntactic integration of hashtags in (English) tweets
No full textAs social media use continues to increase its presence in our lives, so does the language of such platforms. One of the most salient features of social media discourse is the hashtag. Starting its life on Twitter (X) about 15 years ago, the hashtag has seeped from online to offline communication. Yet, it is not clear whether hashtags are words, tags, or something else altogether, nor is it clear what morphological process gives rise to them. This study presents an extensive analysis of 3,423 hashtags from 1,216 English-language tweets, each manually coded for various linguistics features, including position in the tweet, grammatical function, and syntactic integration. Our findings suggest that hashtags are extremely varied and we propose that they are indeed words, arising through a process of hashtagging (which is distinct from compounding). We also argue that some hashtags are syntactically integrated while others constitute parenthetical material
Decision usefulness of SME financial statements in Sri Lanka
No full textThis paper examines the users of Sri Lankan small and medium-sized entities' (SMEs) financial statements, and their information needs. Semi-structured interviews found the main recipients of SME financial information are banks, the Inland Revenue Department and other government institutions. However, the users' concerns, such as manipulation of financial results, tax orientation, insufficient detail, and out-of-date information, hinder the decision usefulness of SME financial statements. Given this context, the widespread problems of unreliability and non-compliance with accounting principles and standards present a substantial hurdle in evaluating the decision usefulness of SMEs' financial statements in Sri Lanka. The Chartered Accountants of Sri Lanka, regulatory bodies and government authorities need to pay greater attention to SMEs' financial reporting and take steps to make it credible and relevant to users' needs
Computational Nanobiosensing – Drawing Analogies Between Optimisation and Nanobiosensing for Smart Tumour Targeting
No full textNanotechnology has been rapidly developing for early diagnosis and treatment of cancer, with nanoparticles being a large focus. However, traditional drug delivery mechanisms are passive and inefficient, with only 0.7% of nanoparticles reaching the tumour through blood vasculature. In vivo computation, also known as computational nanobiosensing (CONA), replaces nanoparticles with swarms of externally manipulable nanorobots whose movement is controlled by an external actuating system. The biological problem of smart tumour targeting is viewed from the computational perspective as an optimisation problem: nanorobot swarms (computational agents) explore the blood vasculature of high-risk tissue (search space) to locate the tumour (global optimum). Tumour biological gradient fields (BGF) create a fitness landscape, which can be analysed with fitness landscape analysis (FLA) to select and tune appropriate search algorithms for in vivo computation.
Key limitations of previous work for in vivo computation are a lack of realistic BGFs that reflect the tumour microenvironment to test search algorithms on; and for FLA, no available measures that consider physical constraints of the in vivo environment.
Two realistic tumour BGF models were created using COMSOL Multiphysics software (CFD Module), one highly vascularised, the other less vascularised. The vascular architecture was based on in vivo blood vessel networks in healthy and tumour regions, and blood velocity was used as a BGF. Blood velocity was found to be lowest in the tumour region, not exceeding 100 µm/s, confirming its applicability as a BGF for search algorithm testing.
Three new FLA measures were created and validated with numerical simulations on two possible tumour vascular landscapes. These measures addressed the physical constraints of discrete search space, unidirectional blood flow, and nanorobot steering imperfections when using a uniform magnetic field. The less vascularized landscape was found to be more discrete, more heterogeneous, and contain a smaller countercurrent frequency of search direction. This indicated it would be more challenging to solve for than the highly vascularised landscape.
These advancements of the CONA framework allow the in vivo search environment to be better visualised and understood for algorithmic development, as well as provide realistic BGFs to test these search algorithms on
Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme
No full textNew Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans
Gradient boosted trees for evolving data streams
No full textGradient Boosting is a widely-used machine learning technique that has proven highly effective in batch learning. However, its effectiveness in stream learning contexts lags behind bagging-based ensemble methods, which currently dominate the field. One reason for this discrepancy is the challenge of adapting the booster to new concept following a concept drift. Resetting the entire booster can lead to significant performance degradation as it struggles to learn the new concept. Resetting only some parts of the booster can be more effective, but identifying which parts to reset is difficult, given that each boosting step builds on the previous prediction. To overcome these difficulties, we propose Streaming Gradient Boosted Trees (Sgbt), which is trained using weighted squared loss elicited in XGBoost. Sgbt exploits trees with a replacement strategy to detect and recover from drifts, thus enabling the ensemble to adapt without sacrificing the predictive performance. Our empirical evaluation of Sgbt on a range of streaming datasets with challenging drift scenarios demonstrates that it outperforms current state-of-the-art methods for evolving data streams
Adaptive prediction interval for data stream regression
No full textPrediction Interval (PI) is a powerful technique for quantifying the uncertainty of regression tasks. However, research on PI for data streams has not received much attention. Moreover, traditional PI-generating approaches are not directly applicable due to the dynamic and evolving nature of data streams. This paper presents AdaPI (ADAptive Prediction Interval), a novel method that can automatically adjust the interval width by an appropriate amount according to historical information to converge the coverage to a user-defined percentage. AdaPI can be applied to any streaming PI technique as a postprocessing step. This paper develops an incremental variant of the pervasive Mean and Variance Estimation (MVE) method for use with AdaPI. An empirical evaluation over a set of standard streaming regression tasks demonstrates AdaPI’s ability to generate compact prediction intervals with a coverage close to the desired level, outperforming alternative methods
Happiness
No full textIn this presentation, Dan introduces the concepts around happiness and resilience and then discusses some of the scientifically supported ways to find a bit more joy, contentment, and peace of mind in this hectic world
Jonathan Sumption's conceptual gaps and misconceptions on historical apologies and judicial diversity
No full textWhich historical wrongs necessitate an apology? What is the value of judicial diversity, and what is the best way to achieve this? Writing for , University of Waikato senior lecturer Alberto Alvarez-Jimenez tackles the treatment of these topics in Jonathan Sumption's recent book , suggesting that the work is characterized by misconceptions and conceptual gaps
“Landlords wouldn’t give my application a second look.” Discrimination exacerbates inequalities in access to private rental housing
No full textThis report and the key insights are based on findings from a housing survey that was conducted with 800 residents in neighbourhoods in Auckland, Hamilton and Christchurch.
• Perceptions of unfairness in Aotearoa’s housing sector are widespread. Renters, Māori, younger people and low-income groups as well as residents in neighbourhoods with high levels of housing deprivation are most likely to think that people are treated unfairly when trying to rent or buy a home in Aotearoa.
• Nearly one in three respondents reported having experienced discrimination when trying to rent or buy a home in Aotearoa. Renters, Māori, younger people and low-income groups as well as residents in neighbourhoods with high levels of housing deprivation are most likely to report experiences of discrimination.
• Advantage and disadvantage in securing a home are determined by a combination of interlocking factors, including income and employment status, age, family status, and race/ethnicity or skin colour. These patterns suggest widespread experiences of potentially unlawful housing discrimination.
• People strategically try to avoid and mitigate discrimination. Expectations of being discriminated against and of being advantaged influence where and how home seekers search for housing. This finding signals that experiences of rejection play a role in constraining people’s housing choices.
• The survey findings suggest that discrimination, as part of tenant selection, contributes to housing precarity and inequalities in access to rental housing. Therefore, this research points to an urgent need to address housing discrimination, especially in the context of high levels of residential mobility among renters and intense competition for rental properties
The Lancet Commission on self-harm
No full textSelf-harm is when someone hurts themselves on purpose, regardless of the reasons for doing this. Often, shame and stigma stop people from seeking help. Self-harming behaviour increases the risk of death by suicide, and it is a common cause of disability in young people. Currently, people attending health services only represent the tip of the iceberg; the proportion of teenagers self-harming has increased over the past 20 years—this is particularly so for young women and girls.
The Lancet Commission on self-harm concludes that our cultures and societies play a major role in driving self-harming behaviours. The public health impact of self-harm has been neglected by governments globally. By delivering transformative shifts in societal attitudes, and initiating radical redesign of mental health care, we can fundamentally improve the lives of people who self-harm. Governments need to act to tackle the societal and commercial determinants of self-harming behaviours. The punishment of people who self-harm must stop. People who self-harm need better access to high-quality, compassionate services for support and treatment. Mainstream and social media outlets need to share information about self-harm responsibly and sympathetically