8,116 research outputs found
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
Inter-CubeSat Communication with V-band "Bull's eye" antenna
We present the study of a simple communication scenario between two CubeSats using a V-band “Bull's eye” antenna that we designed for this purpose. The return loss of the antenna has a -10dB bandwidth of 0.7 GHz and a gain of 15.4dBi at 60 GHz. Moreover, the low-profile shape makes it easily integrable in a CubeSat chassis. The communication scenario study shows that, using 0.01W VubiQ modules and V-band “Bull’s eye” antennas, CubeSats can efficiently transmit data within a 500 MHz bandwidth and with a 10-6 BER while being separated by up to 98m, under ideal conditions, or 50m under worst case operating conditions (5° pointing misalignment in E- and H-plane of the antenna, and 5° polarisation misalignment)
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci
201
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
CMB component separation by parameter estimation
We propose a solution to the CMB component separation problem based on
standard parameter estimation techniques. We assume a parametric spectral model
for each signal component, and fit the corresponding parameters pixel by pixel
in a two-stage process. First we fit for the full parameter set (e.g.,
component amplitudes and spectral indices) in low-resolution and high
signal-to-noise ratio maps using MCMC, obtaining both best-fit values for each
parameter, and the associated uncertainty. The goodness-of-fit is evaluated by
a chi^2 statistic. Then we fix all non-linear parameters at their
low-resolution best-fit values, and solve analytically for high-resolution
component amplitude maps. This likelihood approach has many advantages: The
fitted model may be chosen freely, and the method is therefore completely
general; all assumptions are transparent; no restrictions on spatial variations
of foreground properties are imposed; the results may be rigorously monitored
by goodness-of-fit tests; and, most importantly, we obtain reliable error
estimates on all estimated quantities. We apply the method to simulated Planck
and six-year WMAP data based on realistic models, and show that separation at
the muK level is indeed possible in these cases. We also outline how the
foreground uncertainties may be rigorously propagated through to the CMB power
spectrum and cosmological parameters using a Gibbs sampling technique.Comment: 20 pages, 10 figures, submitted to ApJ. For a high-resolution
version, see http://www.astro.uio.no/~hke/docs/eriksen_et_al_fgfit.p
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
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