553 research outputs found
Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations
The network scale-up method enables researchers to estimate the size of
hidden populations, such as drug injectors and sex workers, using sampled
social network data. The basic scale-up estimator offers advantages over other
size estimation techniques, but it depends on problematic modeling assumptions.
We propose a new generalized scale-up estimator that can be used in settings
with non-random social mixing and imperfect awareness about membership in the
hidden population. Further, the new estimator can be used when data are
collected via complex sample designs and from incomplete sampling frames.
However, the generalized scale-up estimator also requires data from two
samples: one from the frame population and one from the hidden population. In
some situations these data from the hidden population can be collected by
adding a small number of questions to already planned studies. For other
situations, we develop interpretable adjustment factors that can be applied to
the basic scale-up estimator. We conclude with practical recommendations for
the design and analysis of future studies
The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda.
Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
Breaking a one-dimensional chain: fracture in 1 + 1 dimensions
The breaking rate of an atomic chain stretched at zero temperature by a
constant force can be calculated in a quasiclassical approximation by finding
the localized solutions ("bounces") of the equations of classical dynamics in
imaginary time. We show that this theory is related to the critical cracks of
stressed solids, because the world lines of the atoms in the chain form a
two-dimensional crystal, and the bounce is a crack configuration in (unstable)
mechanical equilibrium. Thus the tunneling time, Action, and breaking rate in
the limit of small forces are determined by the classical results of Griffith.
For the limit of large forces we give an exact bounce solution that describes
the quantum fracture and classical crack close to the limit of mechanical
stability. This limit can be viewed as a critical phenomenon for which we
establish a Levanyuk-Ginzburg criterion of weakness of fluctuations, and
propose a scaling argument for the critical regime. The post-tunneling dynamics
is understood by the analytic continuation of the bounce solutions to real
time.Comment: 15 pages, 5 figure
How simple rules determine pedestrian behavior and crowd disasters
With the increasing size and frequency of mass events, the study of crowd
disasters and the simulation of pedestrian flows have become important research
areas. Yet, even successful modeling approaches such as those inspired by
Newtonian force models are still not fully consistent with empirical
observations and are sometimes hard to calibrate. Here, a novel cognitive
science approach is proposed, which is based on behavioral heuristics. We
suggest that, guided by visual information, namely the distance of obstructions
in candidate lines of sight, pedestrians apply two simple cognitive procedures
to adapt their walking speeds and directions. While simpler than previous
approaches, this model predicts individual trajectories and collective patterns
of motion in good quantitative agreement with a large variety of empirical and
experimental data. This includes the emergence of self-organization phenomena,
such as the spontaneous formation of unidirectional lanes or stop-and-go waves.
Moreover, the combination of pedestrian heuristics with body collisions
generates crowd turbulence at extreme densities-a phenomenon that has been
observed during recent crowd disasters. By proposing an integrated treatment of
simultaneous interactions between multiple individuals, our approach overcomes
limitations of current physics-inspired pair interaction models. Understanding
crowd dynamics through cognitive heuristics is therefore not only crucial for a
better preparation of safe mass events. It also clears the way for a more
realistic modeling of collective social behaviors, in particular of human
crowds and biological swarms. Furthermore, our behavioral heuristics may serve
to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA
Quantifying Social Influence in an Online Cultural Market
We revisit experimental data from an online cultural market in which 14,000 users interact to download songs, and develop a simple model that can explain seemingly complex outcomes. Our results suggest that individual behavior is characterized by a two-step process–the decision to sample and the decision to download a song. Contrary to conventional wisdom, social influence is material to the first step only. The model also identifies the role of placement in mediating social signals, and suggests that in this market with anonymous feedback cues, social influence serves an informational rather than normative role
Why We Read Wikipedia
Wikipedia is one of the most popular sites on the Web, with millions of users
relying on it to satisfy a broad range of information needs every day. Although
it is crucial to understand what exactly these needs are in order to be able to
meet them, little is currently known about why users visit Wikipedia. The goal
of this paper is to fill this gap by combining a survey of Wikipedia readers
with a log-based analysis of user activity. Based on an initial series of user
surveys, we build a taxonomy of Wikipedia use cases along several dimensions,
capturing users' motivations to visit Wikipedia, the depth of knowledge they
are seeking, and their knowledge of the topic of interest prior to visiting
Wikipedia. Then, we quantify the prevalence of these use cases via a
large-scale user survey conducted on live Wikipedia with almost 30,000
responses. Our analyses highlight the variety of factors driving users to
Wikipedia, such as current events, media coverage of a topic, personal
curiosity, work or school assignments, or boredom. Finally, we match survey
responses to the respondents' digital traces in Wikipedia's server logs,
enabling the discovery of behavioral patterns associated with specific use
cases. For instance, we observe long and fast-paced page sequences across
topics for users who are bored or exploring randomly, whereas those using
Wikipedia for work or school spend more time on individual articles focused on
topics such as science. Our findings advance our understanding of reader
motivations and behavior on Wikipedia and can have implications for developers
aiming to improve Wikipedia's user experience, editors striving to cater to
their readers' needs, third-party services (such as search engines) providing
access to Wikipedia content, and researchers aiming to build tools such as
recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table
Inhibition of reverse transcription in rat liver intracisternal A-particles by thymidine derivatives
AbstractThe thymidine derivatives araAzT, dTTP(3′N3), TTP(3′NH2), and araTTP(3′N3), were studied as inhibitors of the reverse transcription taking place within endogenous retroviral A-type particles, where retroviral RNAs served as templates and primers. dTTP(3′N3) was shown to be the most efficient inhibitor of retroviral particle reverse transcription. Termination of DNA chain elongation is the basic mechanism of the inhibitory action of dTTP(3′N3). The compound has a very low inhibitory effect on mammalian DNA-dependent DNA polymerases α, β and γ
Bias reduction in traceroute sampling: towards a more accurate map of the Internet
Traceroute sampling is an important technique in exploring the internet
router graph and the autonomous system graph. Although it is one of the primary
techniques used in calculating statistics about the internet, it can introduce
bias that corrupts these estimates. This paper reports on a theoretical and
experimental investigation of a new technique to reduce the bias of traceroute
sampling when estimating the degree distribution. We develop a new estimator
for the degree of a node in a traceroute-sampled graph; validate the estimator
theoretically in Erdos-Renyi graphs and, through computer experiments, for a
wider range of graphs; and apply it to produce a new picture of the degree
distribution of the autonomous system graph.Comment: 12 pages, 3 figure
Quantum Breaking of Elastic String
Breaking of an atomic chain under stress is a collective many-particle
tunneling phenomenon. We study classical dynamics in imaginary time by using
conformal mapping technique, and derive an analytic formula for the probability
of breaking. The result covers a broad temperature interval and interpolates
between two regimes: tunneling and thermal activation. Also, we consider the
breaking induced by an ultrasonic wave propagating in the chain, and propose to
observe it in an STM experiment.Comment: 8 pages, RevTeX 3.0, Landau Institute preprint 261/643
- …