2,724 research outputs found
Energy spectra of vortex distributions in two-dimensional quantum turbulence
We theoretically explore key concepts of two-dimensional turbulence in a
homogeneous compressible superfluid described by a dissipative two-dimensional
Gross-Pitaeveskii equation. Such a fluid supports quantized vortices that have
a size characterized by the healing length . We show that for the
divergence-free portion of the superfluid velocity field, the kinetic energy
spectrum over wavenumber may be decomposed into an ultraviolet regime
() having a universal scaling arising from the vortex
core structure, and an infrared regime () with a spectrum that
arises purely from the configuration of the vortices. The Novikov power-law
distribution of intervortex distances with exponent -1/3 for vortices of the
same sign of circulation leads to an infrared kinetic energy spectrum with a
Kolmogorov power law, consistent with the existence of an inertial
range. The presence of these and power laws, together with
the constraint of continuity at the smallest configurational scale
, allows us to derive a new analytical expression for the
Kolmogorov constant that we test against a numerical simulation of a forced
homogeneous compressible two-dimensional superfluid. The numerical simulation
corroborates our analysis of the spectral features of the kinetic energy
distribution, once we introduce the concept of a {\em clustered fraction}
consisting of the fraction of vortices that have the same sign of circulation
as their nearest neighboring vortices. Our analysis presents a new approach to
understanding two-dimensional quantum turbulence and interpreting similarities
and differences with classical two-dimensional turbulence, and suggests new
methods to characterize vortex turbulence in two-dimensional quantum fluids via
vortex position and circulation measurements.Comment: 19 pages, 8 figure
How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters
It is often said that constraints affect creative production, both in terms
of form and quality. Online social media platforms frequently impose
constraints on the content that users can produce, limiting the range of
possible contributions. Do these restrictions tend to push creators towards
producing more or less successful content? How do creators adapt their
contributions to fit the limits imposed by social media platforms? To answer
these questions, we conduct an observational study of a recent event: on
November 7, 2017, Twitter changed the maximum allowable length of a tweet from
140 to 280 characters, thereby significantly altering its signature constraint.
In the first study of this switch, we compare tweets with nearly or exactly 140
characters before the change to tweets of the same length posted after the
change. This setup enables us to characterize how users alter their tweets to
fit the constraint and how this affects their tweets' success. We find that in
response to a length constraint, users write more tersely, use more
abbreviations and contracted forms, and use fewer definite articles. Also,
although in general tweet success increases with length, we find initial
evidence that tweets made to fit the 140-character constraint tend to be more
successful than similar-length tweets written when the constraint was removed,
suggesting that the length constraint improved tweet quality.Comment: To appear in the Proceedings of AAAI ICWSM 201
Reddit in the Time of COVID
When the COVID-19 pandemic hit, much of life moved online. Platforms of all
types reported surges of activity, and people remarked on the various important
functions that online platforms suddenly fulfilled. However, researchers lack a
rigorous understanding of the pandemic's impacts on social platforms, and
whether they were temporary or long-lasting. We present a conceptual framework
for studying the large-scale evolution of social platforms and apply it to the
study of Reddit's history, with a particular focus on the COVID-19 pandemic. We
study platform evolution through two key dimensions: structure vs. content and
macro- vs. micro-level analysis. Structural signals help us quantify how much
behavior changed, while content analysis clarifies exactly how it changed.
Applying these at the macro-level illuminates platform-wide changes, while at
the micro-level we study impacts on individual users. We illustrate the value
of this approach by showing the extraordinary and ordinary changes Reddit went
through during the pandemic. First, we show that typically when rapid growth
occurs, it is driven by a few concentrated communities and within a narrow
slice of language use. However, Reddit's growth throughout COVID-19 was spread
across disparate communities and languages. Second, all groups were equally
affected in their change of interest, but veteran users tended to invoke
COVID-related language more than newer users. Third, the new wave of users that
arrived following COVID-19 was fundamentally different from previous cohorts of
new users in terms of interests, activity, and likelihood of staying active on
the platform. These findings provide a more rigorous understanding of how an
online platform changed during the global pandemic.Comment: 12 pages, published in ICWSM 202
Snell's Law for a vortex dipole in a Bose-Einstein condensate
A quantum vortex dipole, comprised of a closely bound pair of vortices of
equal strength with opposite circulation, is a spatially localized travelling
excitation of a planar superfluid that carries linear momentum, suggesting a
possible analogy with ray optics. We investigate numerically and analytically
the motion of a quantum vortex dipole incident upon a step-change in the
background superfluid density of an otherwise uniform two-dimensional
Bose-Einstein condensate. Due to the conservation of fluid momentum and energy,
the incident and refracted angles of the dipole satisfy a relation analogous to
Snell's law, when crossing the interface between regions of different density.
The predictions of the analogue Snell's law relation are confirmed for a wide
range of incident angles by systematic numerical simulations of the
Gross-Piteavskii equation. Near the critical angle for total internal
reflection, we identify a regime of anomalous Snell's law behaviour where the
finite size of the dipole causes transient capture by the interface.
Remarkably, despite the extra complexity of the surface interaction, the
incoming and outgoing dipole paths obey Snell's law.Comment: 16 pages, 7 figures, Scipost forma
Auditing Search Engines for Differential Satisfaction Across Demographics
Many online services, such as search engines, social media platforms, and
digital marketplaces, are advertised as being available to any user, regardless
of their age, gender, or other demographic factors. However, there are growing
concerns that these services may systematically underserve some groups of
users. In this paper, we present a framework for internally auditing such
services for differences in user satisfaction across demographic groups, using
search engines as a case study. We first explain the pitfalls of na\"ively
comparing the behavioral metrics that are commonly used to evaluate search
engines. We then propose three methods for measuring latent differences in user
satisfaction from observed differences in evaluation metrics. To develop these
methods, we drew on ideas from the causal inference literature and the
multilevel modeling literature. Our framework is broadly applicable to other
online services, and provides general insight into interpreting their
evaluation metrics.Comment: 8 pages Accepted at WWW 201
Sparsify-then-Classify: From Internal Neurons of Large Language Models To Efficient Text Classifiers
Among the many tasks that Large Language Models (LLMs) have revolutionized is
text classification. However, existing approaches for applying pretrained LLMs
to text classification predominantly rely on using single token outputs from
only the last layer of hidden states. As a result, they suffer from limitations
in efficiency, task-specificity, and interpretability. In our work, we
contribute an approach that uses all internal representations by employing
multiple pooling strategies on all activation and hidden states. Our novel
lightweight strategy, Sparsify-then-Classify (STC) first sparsifies
task-specific features layer-by-layer, then aggregates across layers for text
classification. STC can be applied as a seamless plug-and-play module on top of
existing LLMs. Our experiments on a comprehensive set of models and datasets
demonstrate that STC not only consistently improves the classification
performance of pretrained and fine-tuned models, but is also more efficient for
both training and inference, and is more intrinsically interpretable.Comment: 23 pages, 5 figures, 8 tables Code available at
https://github.com/difanj0713/Sparsify-then-Classif
Capturing Dynamics in Online Public Discourse: A Case Study of Universal Basic Income Discussions on Reddit
Societal change is often driven by shifts in public opinion. As citizens
evolve in their norms, beliefs, and values, public policies change too. While
traditional opinion polling and surveys can outline the broad strokes of
whether public opinion on a particular topic is changing, they usually cannot
capture the full multi-dimensional richness and diversity of opinion present in
a large heterogeneous population. However, an increasing fraction of public
discourse about public policy issues is now occurring on online platforms,
which presents an opportunity to measure public opinion change at a
qualitatively different scale of resolution and context.
In this paper, we present a conceptual model of observed opinion change on
online platforms and apply it to study public discourse on Universal Basic
Income (UBI) on Reddit throughout its history. UBI is a periodic,
no-strings-attached cash payment given to every citizen of a population. We
study UBI as it is a clearly-defined policy proposal that has recently
experienced a surge of interest through trends like automation and events like
the COVID-19 pandemic. We find that overall stance towards UBI on Reddit
significantly declined until mid-2019, when this historical trend suddenly
reversed and Reddit became substantially more supportive. Using our model, we
find the most significant drivers of this overall stance change were shifts
within different user cohorts, within communities that represented similar
affluence levels, and within communities that represented similar partisan
leanings. Our method identifies nuanced social drivers of opinion change in the
large-scale public discourse that now regularly occurs online, and could be
applied to a broad set of other important issues and policies
- …