Examining the Relationships Among Categorization, Stereotype Activation, and Stereotype Application.

Increased category salience is associated with increased stereotyping. Prior research has not examined the processes that may account for this relationship. That is, it is unclear whether category salience leads to increased stereotyping by increasing stereotype activation (i.e., increased accessibility of stereotypic information), application (i.e., increasing the tendency to apply activated stereotypes), or both processes simultaneously. We examined this question across three studies by manipulating category salience in an implicit stereotyping measure and by applying a process model that provides independent estimates of stereotype activation and application. Our results replicated past findings that category salience increases stereotyping. Modeling results showed that category salience consistently increased the extent of stereotype application but increased stereotype activation in more limited contexts. Implications for models of social categorization and stereotyping are discussed

Orientation and experience in the perception of form: A study with the arizona whale-kangaroo

When subjects are presented with the Arizona whale-kangaroo, an ambiguous figure, perception of the whale is more common than perception of the kangaroo. However, this difference is smaller in Australian than American subjects. Perception of the kangaroo is more orientation dependent than perception of the whale, which is perceived at all orientations of the stimulus. Together with the difference between subject populations, this effect reveals an influence of past experience on the perception of this new ambiguous figure. Perception of the whale versus the kangaroo differs in both reconstrual of parts and realignment of the object-centered reference frame. Observers report reference frame reconstruals before reference frame reversals, shedding light on the organization of object memory

Human Papillomavirus (HPV) Vaccination Status Among University Freshmen in Hawai‘i

Purpose/Background: The HPV vaccine provides immunity against nine HPV strains that cause cancer and genital warts. It is recommended for 11 to 12 year olds, and catch-up immunization is recommended for females 13 to 26 years old and males 13 to 21 years old. College students represent an important population for HPV vaccination due to their increased risk for HPV infection. Despite the benefits of the HPV vaccine, its coverage rates are low in Hawaii. Hawai‘i is the home of two large universities on two islands that are representative of Hawai‘i’s populations, including Native Hawaiians, Filipinos, and Pacific Islanders. The purpose of this study was to assess the current HPV and HPV vaccine knowledge, barriers and beliefs among incoming Freshmen university students at University of Hawai‘i at Mānoa and University of Hawai‘i at Hilo. Materials & Methods: In 2016, 200 University of Hawai‘i at Mānoa (UHM) and University of Hawai‘i at Hilo (UHH) Freshmen students responded to a survey that assessed their knowledge and awareness of HPV, the HPV vaccine, their current vaccination status, and barriers and motivators to vaccination. Descriptive statistics were used to summarize each survey variable first for all students and then separately for each campus. Results: Overall 76% of Freshmen from both campuses heard of the HPV vaccine and 54% reported hearing it from their health care provider. Only 28% UHM and 23% UHH Freshmen students have received partial (1-2 shots) or completed doses of the HPV vaccine. For those who received the vaccine, 45% reported that they were told by their parent and 43% were told by their doctor. For the 147 students who did not receive the vaccine, 28% reported that they are still not sure to get it and 20% need more information. Their main reasons for not receiving the HPV vaccine were: their doctor did not mention the vaccine to him/her (44%), he/she never knew about the vaccine (18%), and they don\u27t know enough about the vaccine (17%). Discussion/Conclusion: Although the HPV vaccine has been available for 13 years, young adults remain unvaccinated. Freshmen students reported that they are informed about the vaccine, but were not vaccinated because of the lack of parental and/or healthcare provider recommendation. With no active education campaigns in Hawaii promoting the HPV vaccine at college campuses, a first step to increasing vaccination rates is to develop a health education campaign to inform students of the HPV vaccine and its availability at campus clinics and neighboring pharmacies

Discovering Mixtures of Structural Causal Models from Time Series Data

In fields such as finance, climate science, and neuroscience, inferring causal relationships from time series data poses a formidable challenge. While contemporary techniques can handle nonlinear relationships between variables and flexible noise distributions, they rely on the simplifying assumption that data originates from the same underlying causal model. In this work, we relax this assumption and perform causal discovery from time series data originating from mixtures of different causal models. We infer both the underlying structural causal models and the posterior probability for each sample belonging to a specific mixture component. Our approach employs an end-to-end training process that maximizes an evidence-lower bound for data likelihood. Through extensive experimentation on both synthetic and real-world datasets, we demonstrate that our method surpasses state-of-the-art benchmarks in causal discovery tasks, particularly when the data emanates from diverse underlying causal graphs. Theoretically, we prove the identifiability of such a model under some mild assumptions

Students’ Contextualization on Technology Use in Learning

This research determines the level of agreement of learners on technology use as a tutor and as a learning tool.  It also discussed how the constructivist theory supports the two domains of technology use as a tutor and as a learning tool.  A questionnaire was used in this descriptive research.  Pilot testing was performed before real information collection involving 112 learners registered from Gulf Medical University, Ajman, UAE medical departments.  The answers for Cronbach's tau-equivalent reliability were calculated using SPSS AMOS software version 23. It was discovered that the coefficient of reliability was 0.71.  This value falls into an acceptable category.  The real collection of information used purposeful sampling involving 138 learners of medical imaging.  A six-point Likert scale has been used to categorize the two primary factors; technology as a tutor and as a learning tool. The results were presented as weighted mean values.  Technology as a tutor is a useful and efficient educational instrument for learners with different abilities. They agreed heavily on its use.  Besides technology as a learning tool fosters cooperation among students.  In the same instance, it motivates learners to participate more in learning operations.  They are very much in agreement with this domain.  The constructivist theory supports that learning takes place when learners are actively involved in classroom activities and other locations conducive to them. Then learning is backed up for a lifetime by real-life experiences

Paid parental leave evaluation: Phase 2 report

Since 1 January 2011, most Australian families in which a mother was in paid employment before the birth or adoption of a child have been eligible for the new Australian Government funded Paid Parental Leave (PPL) scheme.2 The scheme provides eligible parents with up to 18 weeks of Parental Leave Pay (PLP), paid at the rate of the National Minimum Wage, following the birth or recent adoption of a child. The PPL scheme brings Australia into line with all other Organisation for Economic Cooperation and Development (OECD) countries, except the United States, in having a national scheme for paid leave available to mothers following childbirth. This report describes the results of an evaluation of the initial operation of the scheme

A New Implementation of Federated Learning for Privacy and Security Enhancement

Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parameter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm

Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset

In this work, we compare two simple methods of tagging scientific publications with labels reflecting their content. As a first source of labels Wikipedia is employed, second label set is constructed from the noun phrases occurring in the analyzed corpus. We examine the statistical properties and the effectiveness of both approaches on the dataset consisting of abstracts from 0.7 million of scientific documents deposited in the ArXiv preprint collection. We believe that obtained tags can be later on applied as useful document features in various machine learning tasks (document similarity, clustering, topic modelling, etc.)