4,309 research outputs found
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.Comment: 8 pages (11 including references), 2 figure
A new SOLT calibration method for leaky on-wafer measurements using a 10-term error model
We present a new Short-Open-Load-Thru (SOLT) calibration method for on-wafer S-parameter measurements. The new calibration method is based on a 10-term error model which is a simplified version of the 16-term error model. Compared with the latter, the former ignores all signal leakages except the ones between the probes. Experimental results show that this is valid for modern vector network analyzers (VNA). The advantage of using this 10-term error model is that the exact values of all error terms can be obtained by using the same calibration standards as the conventional SOLT method. This avoids not only the singularity problem with approximate methods, such as least squares, but also the usage of additional calibration standards. In this paper, we first demonstrate how the 10-term error model is developed and then the experimental verification of the theory is given. Finally, a practical application of the error model using a 10 dB attenuator from 140 GHz to 220 GHz is presented. Compared with the conventional SOLT calibration method without crosstalk corrections, the new method shows approximately 1 dB improvement in the transmission coefficients of the attenuator at 220 GHz
The Kinematic Sunyaev-Zel'dovich Effect with Projected Fields II: prospects, challenges, and comparison with simulations
The kinematic Sunyaev-Zel'dovich (kSZ) signal is a powerful probe of the
cosmic baryon distribution. The kSZ signal is proportional to the integrated
free electron momentum rather than the electron pressure (which sources the
thermal SZ signal). Since velocities should be unbiased on large scales, the
kSZ signal is an unbiased tracer of the large-scale electron distribution, and
thus can be used to detect the "missing baryon" that evade most observational
techniques. While most current methods for kSZ extraction rely on the
availability of very accurate redshifts, we revisit a method that allows
measurements even in the absence of redshift information for individual
objects. It involves cross-correlating the square of an appropriately filtered
cosmic microwave background (CMB) temperature map with a projected density map
constructed from a sample of large-scale structure tracers. We show that this
method will achieve high signal-to-noise when applied to the next generation of
high-resolution CMB experiments, provided that component separation is
sufficiently effective at removing foreground contamination. Considering
statistical errors only, we forecast that this estimator can yield 3, 120 and over 150 for Planck, Advanced ACTPol, and hypothetical Stage-IV
CMB experiments, respectively, in combination with a galaxy catalog from WISE,
and about 20% larger for a galaxy catalog from the proposed SPHEREx
experiment. This work serves as a companion paper to the first kSZ measurement
with this method, where we used CMB temperature maps constructed from Planck
and WMAP data, together with galaxies from the WISE survey, to obtain a 3.8 -
4.5 detection of the kSZ amplitude.Comment: 14 pages, 10 figures. Comments welcom
The Kinematic Sunyaev-Zel'dovich Effect with Projected Fields: A Novel Probe of the Baryon Distribution with Planck, WMAP, and WISE Data
The kinematic Sunyaev-Zel'dovich (kSZ) effect --- the Doppler boosting of
cosmic microwave background (CMB) photons due to Compton-scattering off free
electrons with non-zero bulk velocity --- probes the abundance and distribution
of baryons in the Universe. All kSZ measurements to date have explicitly
required spectroscopic redshifts. Here, we implement a novel estimator for the
kSZ -- large-scale structure cross-correlation based on projected fields: it
does not require redshift estimates for individual objects, allowing kSZ
measurements from large-scale imaging surveys. We apply this estimator to
cleaned CMB temperature maps constructed from Planck and Wilkinson Microwave
Anisotropy Probe data and a galaxy sample from the Wide-field Infrared Survey
Explorer (WISE). We measure the kSZ effect at 3.8-4.5 significance,
depending on the use of additional WISE galaxy bias constraints. We verify that
our measurements are robust to possible dust emission from the WISE galaxies.
Assuming the standard CDM cosmology, we directly constrain (statistical error
only) at redshift , where is the fraction of matter in
baryonic form and is the free electron fraction. This is the
tightest kSZ-derived constraint reported to date on these parameters. The
consistency between the value found here and the values inferred from
analyses of the primordial CMB and Big Bang nucleosynthesis verifies that
baryons approximately trace the dark matter distribution down to Mpc
scales. While our projected-field estimator is already competitive with other
kSZ approaches when applied to current datasets (because we are able to use the
full-sky WISE photometric survey), it will yield enormous signal-to-noise when
applied to upcoming high-resolution, multi-frequency CMB surveys.Comment: 5 pages + references, 2 figures; v2: matches PRL accepted version,
results unchange
Happy times: measuring happiness using response times
Surveys measuring happiness or preferences generate discrete ordinal data. Ordered response models, which are used to analyze such data, suffer from an identification problem. Their conclusions depend on distributional assumptions about a latent variable. We propose using response times to solve that problem. Response times contain information about the distribution of the latent variable through a chronometric effect. Using an online survey experiment, we verify the chronometric effect. We then provide theoretical conditions for testing conventional distributional assumptions. These assumptions are rejected in some cases, but overall our evidence is consistent with the qualitative validity of the conventional models
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