55 research outputs found
Verbal Memory and IQ Predict Adaptive Behavior in Children and Adolescents with High-Functioning Autism Spectrum Disorders
Adaptive deficits are commonly found in high functioning autism spectrum disorders (HF-ASD) despite of cognitive potential. Most studies have focused on the relationships between adaptive behavior and intellectual quotient (IQ) and have used correlations to study relationships between them. Few studies have analyzed cognitive variables other than IQ as potential predictors of adaptive behavior in HF-ASD using regression methods. This study aimed to analyze the impact of several cognitive variables on adaptive behavior in a sample of children and adolescents with HF-ASD. METHODS: Sample included 16 child and adolescent boys with HF-ASD (age between 7-17 years). Cognitive assessment included measures of general intelligence, visual memory, verbal memory, working memory and problem solving/flexibility tests. Vineland Adaptive Behavior Scales (VABS) was used to evaluate adaptive behavior. To establish the predictive capacity of the cognitive variables for adaptive functioning, linear regression models were fitted for each adaptive domain using a stepwise method. RESULTS: Verbal memory and IQ emerged as the main independent predictors for VABS adaptive scores. The 41% of the variance in Communication was predicted by IQ. The 35% of the variance in Daily Living Skills was predicted by verbal memory. Almost half of the variance in Socialization was predicted by both, verbal memory and IQ (49%). No other cognitive functions were associated with adaptive scores. CONCLUSIONS: The results highlight the strong impact of IQ and verbal memory on adaptive behavior in HF-ASD patients. These findings could contribute to identify potential targets of intervention
Hydrogen Epoch of Reionization Array (HERA)
The Hydrogen Epoch of Reionization Array (HERA) is a staged experiment to
measure 21 cm emission from the primordial intergalactic medium (IGM)
throughout cosmic reionization (), and to explore earlier epochs of our
Cosmic Dawn (). During these epochs, early stars and black holes
heated and ionized the IGM, introducing fluctuations in 21 cm emission. HERA is
designed to characterize the evolution of the 21 cm power spectrum to constrain
the timing and morphology of reionization, the properties of the first
galaxies, the evolution of large-scale structure, and the early sources of
heating. The full HERA instrument will be a 350-element interferometer in South
Africa consisting of 14-m parabolic dishes observing from 50 to 250 MHz.
Currently, 19 dishes have been deployed on site and the next 18 are under
construction. HERA has been designated as an SKA Precursor instrument.
In this paper, we summarize HERA's scientific context and provide forecasts
for its key science results. After reviewing the current state of the art in
foreground mitigation, we use the delay-spectrum technique to motivate
high-level performance requirements for the HERA instrument. Next, we present
the HERA instrument design, along with the subsystem specifications that ensure
that HERA meets its performance requirements. Finally, we summarize the
schedule and status of the project. We conclude by suggesting that, given the
realities of foreground contamination, current-generation 21 cm instruments are
approaching their sensitivity limits. HERA is designed to bring both the
sensitivity and the precision to deliver its primary science on the basis of
proven foreground filtering techniques, while developing new subtraction
techniques to unlock new capabilities. The result will be a major step toward
realizing the widely recognized scientific potential of 21 cm cosmology.Comment: 26 pages, 24 figures, 2 table
Mitigating Internal Instrument Coupling for 21 cm Cosmology. II. A Method Demonstration with the Hydrogen Epoch of Reionization Array
We present a study of internal reflection and cross-coupling systematics in Phase I of the Hydrogen Epoch of Reionization Array (HERA). In a companion paper, we outlined the mathematical formalism for such systematics and presented algorithms for modeling and removing them from the data. In this work, we apply these techniques to data from HERA's first observing season as a method demonstration. The data show evidence for systematics that, without removal, would hinder a detection of the 21 cm power spectrum for the targeted Epoch of Reionization (EoR) line-of-sight modes in the range 0.2 h −1 Mpc−1 < < 0.5 h −1 Mpc−1. In particular, we find evidence for nonnegligible amounts of spectral structure in the raw autocorrelations that overlaps with the EoR window and is suggestive of complex instrumental effects. Through systematic modeling on a single night of data, we find we can recover these modes in the power spectrum down to the integrated noise floor, achieving a dynamic range in the EoR window of 106 in power (mK2 units) with respect to the bright galactic foreground signal. Future work with deeper integrations will help determine whether these systematics can continue to be mitigated down to EoR levels. For future observing seasons, HERA will have upgraded analog and digital hardware to better control these systematics in the field
Detection of Cosmic Structures using the Bispectrum Phase. II. First Results from Application to Cosmic Reionization Using the Hydrogen Epoch of Reionization Array
Characterizing the epoch of reionization (EoR) at via the
redshifted 21 cm line of neutral Hydrogen (HI) is critical to modern
astrophysics and cosmology, and thus a key science goal of many current and
planned low-frequency radio telescopes. The primary challenge to detecting this
signal is the overwhelmingly bright foreground emission at these frequencies,
placing stringent requirements on the knowledge of the instruments and
inaccuracies in analyses. Results from these experiments have largely been
limited not by thermal sensitivity but by systematics, particularly caused by
the inability to calibrate the instrument to high accuracy. The interferometric
bispectrum phase is immune to antenna-based calibration and errors therein, and
presents an independent alternative to detect the EoR HI fluctuations while
largely avoiding calibration systematics. Here, we provide a demonstration of
this technique on a subset of data from the Hydrogen Epoch of Reionization
Array (HERA) to place approximate constraints on the brightness temperature of
the intergalactic medium (IGM). From this limited data, at we infer
"" upper limits on the IGM brightness temperature to be
"pseudo" mK at "pseudo" Mpc (data-limited)
and "pseudo" mK at "pseudo" Mpc
(noise-limited). The "pseudo" units denote only an approximate and not an exact
correspondence to the actual distance scales and brightness temperatures. By
propagating models in parallel to the data analysis, we confirm that the
dynamic range required to separate the cosmic HI signal from the foregrounds is
similar to that in standard approaches, and the power spectrum of the
bispectrum phase is still data-limited (at dynamic range)
indicating scope for further improvement in sensitivity as the array build-out
continues.Comment: 22 pages, 12 figures (including sub-figures). Published in PhRvD.
Abstract may be slightly abridged compared to the actual manuscript due to
length limitations on arXi
Optimizing Sparse RFI Prediction using Deep Learning
Radio Frequency Interference (RFI) is an ever-present limiting factor among
radio telescopes even in the most remote observing locations. When looking to
retain the maximum amount of sensitivity and reduce contamination for Epoch of
Reionization studies, the identification and removal of RFI is especially
important. In addition to improved RFI identification, we must also take into
account computational efficiency of the RFI-Identification algorithm as radio
interferometer arrays such as the Hydrogen Epoch of Reionization Array grow
larger in number of receivers. To address this, we present a Deep Fully
Convolutional Neural Network (DFCN) that is comprehensive in its use of
interferometric data, where both amplitude and phase information are used
jointly for identifying RFI. We train the network using simulated HERA
visibilities containing mock RFI, yielding a known "ground truth" dataset for
evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model
is performed on observations from the 67 dish build-out, HERA-67, and achieves
a data throughput of 1.6 HERA time-ordered 1024 channeled
visibilities per hour per GPU. We determine that relative to an amplitude only
network including visibility phase adds important adjacent time-frequency
context which increases discrimination between RFI and Non-RFI. The inclusion
of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and
score of 0.75 as applied to our HERA-67 observations.Comment: 11 pages, 7 figure
What does an interferometer really measure? Including instrument and data characteristics in the reconstruction of the 21cm power spectrum
Combining the visibilities measured by an interferometer to form a
cosmological power spectrum is a complicated process in which the window
functions play a crucial role. In a delay-based analysis, the mapping between
instrumental space, made of per-baseline delay spectra, and cosmological space
is not a one-to-one relation. Instead, neighbouring modes contribute to the
power measured at one point, with their respective contributions encoded in the
window functions. To better understand the power spectrum measured by an
interferometer, we assess the impact of instrument characteristics and analysis
choices on the estimator by deriving its exact window functions, outside of the
delay approximation. Focusing on HERA as a case study, we find that
observations made with long baselines tend to correspond to enhanced low-k
tails of the window functions, which facilitate foreground leakage outside the
wedge, whilst the choice of bandwidth and frequency taper can help narrow them
down. With the help of simple test cases and more realistic visibility
simulations, we show that, apart from tracing mode mixing, the window functions
can accurately reconstruct the power spectrum estimator of simulated
visibilities. We note that the window functions depend strongly on the
chromaticity of the beam, and less on its spatial structure - a Gaussian
approximation, ignoring side lobes, is sufficient. Finally, we investigate the
potential of asymmetric window functions, down-weighting the contribution of
low-k power to avoid foreground leakage. The window functions presented in this
work correspond to the latest HERA upper limits for the full Phase I data. They
allow an accurate reconstruction of the power spectrum measured by the
instrument and can be used in future analyses to confront theoretical models
and data directly in cylindrical space.Comment: 18 pages, 18 figures, submitted to MNRAS. Comments welcome
Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization
Radio Frequency Interference (RFI) is one of the systematic challenges
preventing 21cm interferometric instruments from detecting the Epoch of
Reionization. To mitigate the effects of RFI on data analysis pipelines,
numerous inpaint techniques have been developed to restore RFI corrupted data.
We examine the qualitative and quantitative errors introduced into the
visibilities and power spectrum due to inpainting. We perform our analysis on
simulated data as well as real data from the Hydrogen Epoch of Reionization
Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural
network that capable of inpainting RFI corrupted data in interferometric
instruments. We train our network on simulated data and show that our network
is capable at inpainting real data without requiring to be retrained. We find
that techniques that incorporate high wavenumbers in delay space in their
modeling are best suited for inpainting over narrowband RFI. We also show that
with our fiducial parameters Discrete Prolate Spheroidal Sequences (DPSS) and
CLEAN provide the best performance for intermittent ``narrowband'' RFI while
Gaussian Progress Regression (GPR) and Least Squares Spectral Analysis (LSSA)
provide the best performance for larger RFI gaps. However we caution that these
qualitative conclusions are sensitive to the chosen hyperparameters of each
inpainting technique. We find these results to be consistent in both simulated
and real visibilities. We show that all inpainting techniques reliably
reproduce foreground dominated modes in the power spectrum. Since the
inpainting techniques should not be capable of reproducing noise realizations,
we find that the largest errors occur in the noise dominated delay modes. We
show that in the future, as the noise level of the data comes down, CLEAN and
DPSS are most capable of reproducing the fine frequency structure in the
visibilities of HERA data.Comment: 26 pages, 18 figure
Diversity and carbon storage across the tropical forest biome
Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.Additional co-authors: Kofi Affum-Baffoe, Shin-ichiro Aiba, Everton Cristo de Almeida, Edmar Almeida de Oliveira, Patricia Alvarez-Loayza, Esteban Álvarez Dávila, Ana Andrade, Luiz E. O. C. Aragão, Peter Ashton, Gerardo A. Aymard C., Timothy R. Baker, Michael Balinga, Lindsay F. Banin, Christopher Baraloto, Jean-Francois Bastin, Nicholas Berry, Jan Bogaert, Damien Bonal, Frans Bongers, Roel Brienen, José Luís C. Camargo, Carlos Cerón, Victor Chama Moscoso, Eric Chezeaux, Connie J. Clark, Álvaro Cogollo Pacheco, James A. Comiskey, Fernando Cornejo Valverde, Eurídice N. Honorio Coronado, Greta Dargie, Stuart J. Davies, Charles De Canniere, Marie Noel Djuikouo K., Jean-Louis Doucet, Terry L. Erwin, Javier Silva Espejo, Corneille E. N. Ewango, Sophie Fauset, Ted R. Feldpausch, Rafael Herrera, Martin Gilpin, Emanuel Gloor, Jefferson S. Hall, David J. Harris, Terese B. Hart, Kuswata Kartawinata, Lip Khoon Kho, Kanehiro Kitayama, Susan G. W. Laurance, William F. Laurance, Miguel E. Leal, Thomas Lovejoy, Jon C. Lovett, Faustin Mpanya Lukasu, Jean-Remy Makana, Yadvinder Malhi, Leandro Maracahipes, Beatriz S. Marimon, Ben Hur Marimon Junior, Andrew R. Marshall, Paulo S. Morandi, John Tshibamba Mukendi, Jaques Mukinzi, Reuben Nilus, Percy Núñez Vargas, Nadir C. Pallqui Camacho, Guido Pardo, Marielos Peña-Claros, Pascal Pétronelli, Georgia C. Pickavance, Axel Dalberg Poulsen, John R. Poulsen, Richard B. Primack, Hari Priyadi, Carlos A. Quesada, Jan Reitsma, Maxime Réjou-Méchain, Zorayda Restrepo, Ervan Rutishauser, Kamariah Abu Salim, Rafael P. Salomão, Ismayadi Samsoedin, Douglas Sheil, Rodrigo Sierra, Marcos Silveira, J. W. Ferry Slik, Lisa Steel, Hermann Taedoumg, Sylvester Tan, John W. Terborgh, Sean C. Thomas, Marisol Toledo, Peter M. Umunay, Luis Valenzuela Gamarra, Ima Célia Guimarães Vieira, Vincent A. Vos, Ophelia Wang, Simon Willcock & Lise Zemagh
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