38 research outputs found
The Approximate Capacity of the Many-to-One and One-to-Many Gaussian Interference Channels
Recently, Etkin, Tse, and Wang found the capacity region of the two-user
Gaussian interference channel to within one bit/s/Hz. A natural goal is to
apply this approach to the Gaussian interference channel with an arbitrary
number of users. We make progress towards this goal by finding the capacity
region of the many-to-one and one-to-many Gaussian interference channels to
within a constant number of bits. The result makes use of a deterministic model
to provide insight into the Gaussian channel. The deterministic model makes
explicit the dimension of signal scale. A central theme emerges: the use of
lattice codes for alignment of interfering signals on the signal scale.Comment: 45 pages, 16 figures. Submitted to IEEE Transactions on Information
Theor
When is it Better to Compare than to Score?
When eliciting judgements from humans for an unknown quantity, one often has
the choice of making direct-scoring (cardinal) or comparative (ordinal)
measurements. In this paper we study the relative merits of either choice,
providing empirical and theoretical guidelines for the selection of a
measurement scheme. We provide empirical evidence based on experiments on
Amazon Mechanical Turk that in a variety of tasks, (pairwise-comparative)
ordinal measurements have lower per sample noise and are typically faster to
elicit than cardinal ones. Ordinal measurements however typically provide less
information. We then consider the popular Thurstone and Bradley-Terry-Luce
(BTL) models for ordinal measurements and characterize the minimax error rates
for estimating the unknown quantity. We compare these minimax error rates to
those under cardinal measurement models and quantify for what noise levels
ordinal measurements are better. Finally, we revisit the data collected from
our experiments and show that fitting these models confirms this prediction:
for tasks where the noise in ordinal measurements is sufficiently low, the
ordinal approach results in smaller errors in the estimation
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Data in the form of pairwise comparisons arises in many domains, including
preference elicitation, sporting competitions, and peer grading among others.
We consider parametric ordinal models for such pairwise comparison data
involving a latent vector that represents the
"qualities" of the items being compared; this class of models includes the
two most widely used parametric models--the Bradley-Terry-Luce (BTL) and the
Thurstone models. Working within a standard minimax framework, we provide tight
upper and lower bounds on the optimal error in estimating the quality score
vector under this class of models. The bounds depend on the topology of
the comparison graph induced by the subset of pairs being compared via its
Laplacian spectrum. Thus, in settings where the subset of pairs may be chosen,
our results provide principled guidelines for making this choice. Finally, we
compare these error rates to those under cardinal measurement models and show
that the error rates in the ordinal and cardinal settings have identical
scalings apart from constant pre-factors.Comment: 39 pages, 5 figures. Significant extension of arXiv:1406.661
How is Fatherhood Framed Online in Singapore?
The proliferation of discussion about fatherhood in Singapore attests to its
significance, indicating the need for an exploration of how fatherhood is
framed, aiding policy-making around fatherhood in Singapore. Sound and holistic
policy around fatherhood in Singapore may reduce stigma and apprehension around
being a parent, critical to improving the nations flagging birth rate. We
analyzed 15,705 articles and 56,221 posts to study how fatherhood is framed in
Singapore across a range of online platforms (news outlets, parenting forums,
Twitter). We used NLP techniques to understand these differences. While
fatherhood was framed in a range of ways on the Singaporean online environment,
it did not seem that fathers were framed as central to the Singaporean family
unit. A strength of our work is how the different techniques we have applied
validate each other
Predicting Opioid Use Outcomes in Minoritized Communities
Machine learning algorithms can sometimes exacerbate health disparities based
on ethnicity, gender, and other factors. There has been limited work at
exploring potential biases within algorithms deployed on a small scale, and/or
within minoritized communities. Understanding the nature of potential biases
may improve the prediction of various health outcomes. As a case study, we used
data from a sample of 539 young adults from minoritized communities who engaged
in nonmedical use of prescription opioids and/or heroin. We addressed the
indicated issues through the following contributions: 1) Using machine learning
techniques, we predicted a range of opioid use outcomes for participants in our
dataset; 2) We assessed if algorithms trained only on a majority sub-sample
(e.g., Non-Hispanic/Latino, male), could accurately predict opioid use outcomes
for a minoritized sub-sample (e.g., Latino, female). Results indicated that
models trained on a random sample of our data could predict a range of opioid
use outcomes with high precision. However, we noted a decrease in precision
when we trained our models on data from a majority sub-sample, and tested these
models on a minoritized sub-sample. We posit that a range of cultural factors
and systemic forms of discrimination are not captured by data from majority
sub-samples. Broadly, for predictions to be valid, models should be trained on
data that includes adequate representation of the groups of people about whom
predictions will be made. Stakeholders may utilize our findings to mitigate
biases in models for predicting opioid use outcomes within minoritized
communities
ChatGPT and Bard Responses to Polarizing Questions
Recent developments in natural language processing have demonstrated the
potential of large language models (LLMs) to improve a range of educational and
learning outcomes. Of recent chatbots based on LLMs, ChatGPT and Bard have made
it clear that artificial intelligence (AI) technology will have significant
implications on the way we obtain and search for information. However, these
tools sometimes produce text that is convincing, but often incorrect, known as
hallucinations. As such, their use can distort scientific facts and spread
misinformation. To counter polarizing responses on these tools, it is critical
to provide an overview of such responses so stakeholders can determine which
topics tend to produce more contentious responses -- key to developing targeted
regulatory policy and interventions. In addition, there currently exists no
annotated dataset of ChatGPT and Bard responses around possibly polarizing
topics, central to the above aims. We address the indicated issues through the
following contribution: Focusing on highly polarizing topics in the US, we
created and described a dataset of ChatGPT and Bard responses. Broadly, our
results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more
likely to provide responses around polarizing topics. Bard seemed to have fewer
guardrails around controversial topics, and appeared more willing to provide
comprehensive, and somewhat human-like responses. Bard may thus be more likely
abused by malicious actors. Stakeholders may utilize our findings to mitigate
misinformative and/or polarizing responses from LLM
Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990-2019, for 204 countries and territories: the Global Burden of Diseases Study 2019
Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic.
Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0).
Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was 1·94 (1·76–2·12). No regions met suggested thresholds for progress. Interpretation: Sub-Saharan Africa had both the highest HIV burden and the greatest progress between 1990 and 2019. The number of incident cases and deaths in males and females approached parity in 2019, although there remained more females with HIV than males with HIV. Globally, the HIV epidemic is far from the UNAIDS benchmarks on progress metrics.
Funding: The Bill & Melinda Gates Foundation, the National Institute of Mental Health of the US National Institutes of Health (NIH), and the National Institute on Aging of the NIH
Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019
Background Ending the global tobacco epidemic is a defining challenge in global health. Timely and comprehensive estimates of the prevalence of smoking tobacco use and attributable disease burden are needed to guide tobacco control efforts nationally and globally. Methods We estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We modelled multiple smoking-related indicators from 3625 nationally representative surveys. We completed systematic reviews and did Bayesian meta-regressions for 36 causally linked health outcomes to estimate non-linear dose-response risk curves for current and former smokers. We used a direct estimation approach to estimate attributable burden, providing more comprehensive estimates of the health effects of smoking than previously available. Findings Globally in 2019, 1.14 billion (95% uncertainty interval 1.13-1.16) individuals were current smokers, who consumed 7.41 trillion (7.11-7.74) cigarette-equivalents of tobacco in 2019. Although prevalence of smoking had decreased significantly since 1990 among both males (27.5% [26. 5-28.5] reduction) and females (37.7% [35.4-39.9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0.99 billion (0.98-1.00) in 1990. Globally in 2019, smoking tobacco use accounted for 7.69 million (7.16-8.20) deaths and 200 million (185-214) disability-adjusted life-years, and was the leading risk factor for death among males (20.2% [19.3-21.1] of male deaths). 6.68 million [86.9%] of 7.69 million deaths attributable to smoking tobacco use were among current smokers. Interpretation In the absence of intervention, the annual toll of 7.69 million deaths and 200 million disability-adjusted life-years attributable to smoking will increase over the coming decades. Substantial progress in reducing the prevalence of smoking tobacco use has been observed in countries from all regions and at all stages of development, but a large implementation gap remains for tobacco control. Countries have a dear and urgent opportunity to pass strong, evidence-based policies to accelerate reductions in the prevalence of smoking and reap massive health benefits for their citizens. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe