Using word embeddings to analyse audience effects and individual differences in parenting Subreddits.

This paper explores how individuals' language use in gender-specific groups ("mothers" and "fathers") compares to their interactions when referred to as "parents." Language adaptation based on the audience is well-documented, yet large-scale studies of naturally-occurring audience effects are rare. To address this, we investigate audience and gender effects in the context of parenting, where gender plays a significant role. We focus on interactions within Reddit, particularly in the parenting Subreddits r/Daddit, r/Mommit, and r/Parenting, which cater to distinct audiences. By analyzing user posts using word embeddings, we measure similarities between user-tokens and word-tokens, also considering differences among high and low self-monitors. Results reveal that in mixed-gender contexts, mothers and fathers exhibit similar behavior in discussing a wide range of topics, while fathers emphasize more on educational and family advice. Single-gender Subreddits see more focused discussions. Mothers in r/Mommit discuss medical care, sleep, potty training, and food, distinguishing themselves. In terms of individual differences, we found that, especially on r/Parenting, high self-monitors tend to conform more to the norms of the Subreddit by discussing more of the topics associated with the Subreddit.

Using word embeddings to investigate cultural biases.

Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologists to word embedding research via a consideration of bias analysis, a topic of central concern in the discipline. We explain how word embeddings are constructed and how they can be used to measure bias along bipolar dimensions that are comparable to semantic differential scales. We review recent studies that show how familiar social biases can be detected in embeddings and how these change over time and in conjunction with real-world discriminatory practices. The evidence suggests that embeddings yield valid and reliable estimates of bias and that they can identify subtle biases that may not be communicated explicitly. We argue that word embedding research can extend scholarship on prejudice and stereotyping, providing measures of the bias environment of human thought and action.

Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report.

OBJECTIVES: The Network for Genomic Surveillance in South Africa (NGS-SA) was formed to investigate the introduction and understand the early transmission dynamics of the SARS-CoV-2 epidemic in South-Africa. DESIGN: This paper presents the first results from this group, which is a molecular epidemiological study of the first 21 SARS-CoV-2 whole genomes sampled in the first port of entry - KwaZulu-Natal (KZN) - during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), it aimed to shed light on the patterns of infections in South Africa. RESULTS: Two of the largest provinces - Gauteng and KZN - had a slow growth rate for the number of detected cases, while the epidemic spread faster in the Western Cape and Eastern Cape. The estimates of transmission potential suggested a decrease towards R = 1 since the first cases and deaths, but a subsequent estimated R average of 1.39 between 6-18 May 2020. It was also demonstrated that early transmission in KZN was associated with multiple international introductions and dominated by lineages B1 and B. Evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic was also provided. CONCLUSION: The COVID-19 pandemic in South Africa was very heterogeneous in its spatial dimension, with many distinct introductions of SARS-CoV2 in KZN and evidence of nosocomial transmission, which inflated early mortality in KZN. The epidemic at the local level was still developing and NGS-SA aimed to clarify the dynamics in South Africa and devise the most effective measures as the outbreak evolved.

Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report.

Background: The emergence of a novel coronavirus, SARS-CoV-2, in December 2019, progressed to become a world pandemic in a few months and reached South Africa at the beginning of March. To investigate introduction and understand the early transmission dynamics of the virus, we formed the South African Network for Genomics Surveillance of COVID (SANGS_COVID), a network of ten government and university laboratories. Here, we present the first results of this effort, which is a molecular epidemiological study of the first twenty-one SARS-CoV-2 whole genomes sampled in the first port of entry, KwaZulu-Natal (KZN), during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), we aim to shed light on the patterns of infections that define the epidemic in South Africa. Methods: R was calculated using positive cases and deaths from reports provided by the four major provinces. Molecular epidemiology investigation involved sequencing viral genomes from patients in KZN using ARCTIC protocols and assembling whole genomes using meticulous alignment methods. Phylogenetic analysis was performed using maximum likelihood (ML) and Bayesian trees, lineage classification and molecular clock calculations. Findings: The epidemic in South Africa has been very heterogeneous. Two of the largest provinces, Gauteng, home of the two large metropolis Johannesburg and Pretoria, and KwaZulu-Natal, home of the third largest city in the country Durban, had a slow growth rate on the number of detected cases. Whereas, Western Cape, home of Cape Town, and the Eastern Cape provinces the epidemic is spreading fast. Our estimates of transmission potential for South Africa suggest a decreasing transmission potential towards R=1 since the first cases and deaths have been reported. However, between 06 May and 18 May 2020, we estimate that R was on average 1.39 (1.04 - 2.15, 95% CI). We also demonstrate that early transmission in KZN, and most probably in all main regions of SA, was associated with multiple international introductions and dominated by lineages B1 and B. The study also provides evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic, which inflated early mortality in KZN. Interpretation: This first report of SANGS_COVID consortium focuses on understanding the epidemic heterogeneity and introduction of SARS-CoV-2 strains in the first month of the epidemic in South Africa. The early introduction of SARS-CoV-2 in KZN included caused a localized outbreak in a hospital, provides potential explanations for the initially high death rates in the province. The current high rate of transmission of COVID-19 in the Western Cape and Eastern Cape highlights the crucial need to strength local genomic surveillance in South Africa. Funding: UKZN Flagship Program entitled: Afrocentric Precision Approach to Control Health Epidemic, by a research Flagship grant from the South African Medical Research Council (MRC-RFA-UFSP-01-2013/UKZN HIVEPI, by the the Technology Innovation Agency and the the Department of Science and Innovation and by National Human Genome Re- search Institute of the National Institutes of Health under Award Number U24HG006941. H3ABioNet is an initiative of the Human Health and Heredity in Africa Consortium (H3Africa).

Quantum Machine Learning in Feature Hilbert Spaces.

A basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely, to efficiently perform computations in an intractably large Hilbert space. In this Letter we explore some theoretical foundations of this link and show how it opens up a new avenue for the design of quantum machine learning algorithms. We interpret the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space. A quantum computer can now analyze the input data in this feature space. Based on this link, we discuss two approaches for building a quantum model for classification. In the first approach, the quantum device estimates inner products of quantum states to compute a classically intractable kernel. The kernel can be fed into any classical kernel method such as a support vector machine. In the second approach, we use a variational quantum circuit as a linear model that classifies data explicitly in Hilbert space. We illustrate these ideas with a feature map based on squeezing in a continuous-variable system, and visualize the working principle with two-dimensional minibenchmark datasets.

The future of quantum biology.

Biological systems are dynamical, constantly exchanging energy and matter with the environment in order to maintain the non-equilibrium state synonymous with living. Developments in observational techniques have allowed us to study biological dynamics on increasingly small scales. Such studies have revealed evidence of quantum mechanical effects, which cannot be accounted for by classical physics, in a range of biological processes. Quantum biology is the study of such processes, and here we provide an outline of the current state of the field, as well as insights into future directions.

Quantum ensembles of quantum classifiers.

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.