1,250 research outputs found
Uncertainty quantification for radio interferometric imaging: II. MAP estimation
Uncertainty quantification is a critical missing component in radio
interferometric imaging that will only become increasingly important as the
big-data era of radio interferometry emerges. Statistical sampling approaches
to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
can in principle recover the full posterior distribution of the image, from
which uncertainties can then be quantified. However, for massive data sizes,
like those anticipated from the Square Kilometre Array (SKA), it will be
difficult if not impossible to apply any MCMC technique due to its inherent
computational cost. We formulate Bayesian inference problems with
sparsity-promoting priors (motivated by compressive sensing), for which we
recover maximum a posteriori (MAP) point estimators of radio interferometric
images by convex optimisation. Exploiting recent developments in the theory of
probability concentration, we quantify uncertainties by post-processing the
recovered MAP estimate. Three strategies to quantify uncertainties are
developed: (i) highest posterior density credible regions; (ii) local credible
intervals (cf. error bars) for individual pixels and superpixels; and (iii)
hypothesis testing of image structure. These forms of uncertainty
quantification provide rich information for analysing radio interferometric
observations in a statistically robust manner. Our MAP-based methods are
approximately times faster computationally than state-of-the-art MCMC
methods and, in addition, support highly distributed and parallelised
algorithmic structures. For the first time, our MAP-based techniques provide a
means of quantifying uncertainties for radio interferometric imaging for
realistic data volumes and practical use, and scale to the emerging big-data
era of radio astronomy.Comment: 13 pages, 10 figures, see companion article in this arXiv listin
Exploring auditory-motor interactions in normal and disordered speech
Auditory feedback plays an important role in speech motor learning and in the online correction of speech movements. Speakers can detect and correct auditory feedback errors at the segmental and suprasegmental levels during ongoing speech. The frontal brain regions that contribute to these corrective movements have also been shown to be more active during speech in persons who stutter (PWS) compared to fluent speakers. Further, various types of altered auditory feedback can temporarily improve the fluency of PWS, suggesting that atypical auditory-motor interactions during speech may contribute to stuttering disfluencies. To investigate this possibility, we have developed and improved Audapter, a software that enables configurable dynamic perturbation of the spatial and temporal content of the speech auditory signal in real time. Using Audapter, we have measured the compensatory responses of PWS to static and dynamic perturbations of the formant content of auditory feedback and compared these responses with those from matched fluent controls. Our findings indicate deficient utilization of auditory feedback by PWS for short-latency online control of the spatial and temporal parameters of articulation during vowel production and during running speech. These findings provide further evidence that stuttering is associated with aberrant auditory-motor integration during speech.Published versio
Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
A crucial aspect of mass-mapping, via weak lensing, is quantification of the
uncertainty introduced during the reconstruction process. Properly accounting
for these errors has been largely ignored to date. We present results from a
new method that reconstructs maximum a posteriori (MAP) convergence maps by
formulating an unconstrained Bayesian inference problem with Laplace-type
-norm sparsity-promoting priors, which we solve via convex
optimization. Approaching mass-mapping in this manner allows us to exploit
recent developments in probability concentration theory to infer theoretically
conservative uncertainties for our MAP reconstructions, without relying on
assumptions of Gaussianity. For the first time these methods allow us to
perform hypothesis testing of structure, from which it is possible to
distinguish between physical objects and artifacts of the reconstruction. Here
we present this new formalism, demonstrate the method on illustrative examples,
before applying the developed formalism to two observational datasets of the
Abel-520 cluster. In our Bayesian framework it is found that neither Abel-520
dataset can conclusively determine the physicality of individual local massive
substructure at significant confidence. However, in both cases the recovered
MAP estimators are consistent with both sets of data
Distributed and parallel sparse convex optimization for radio interferometry with PURIFY
Next generation radio interferometric telescopes are entering an era of big
data with extremely large data sets. While these telescopes can observe the sky
in higher sensitivity and resolution than before, computational challenges in
image reconstruction need to be overcome to realize the potential of
forthcoming telescopes. New methods in sparse image reconstruction and convex
optimization techniques (cf. compressive sensing) have shown to produce higher
fidelity reconstructions of simulations and real observations than traditional
methods. This article presents distributed and parallel algorithms and
implementations to perform sparse image reconstruction, with significant
practical considerations that are important for implementing these algorithms
for Big Data. We benchmark the algorithms presented, showing that they are
considerably faster than their serial equivalents. We then pre-sample gridding
kernels to scale the distributed algorithms to larger data sizes, showing
application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to
50 billion visibilities, and find that the run-times for the distributed
algorithms range from 100 milliseconds to 3 minutes per iteration. This work
presents an important step in working towards computationally scalable and
efficient algorithms and implementations that are needed to image observations
of both extended and compact sources from next generation radio interferometers
such as the SKA. The algorithms are implemented in the latest versions of the
SOPT (https://github.com/astro-informatics/sopt) and PURIFY
(https://github.com/astro-informatics/purify) software packages {(Versions
3.1.0)}, which have been released alongside of this article.Comment: 25 pages, 5 figure
DND1 Expression and Function in the Porcine Ovary, Oocyte and Embryo
DND1 (dead end homolog 1), belonging to the RNA binding protein family, can impact miRNA:mRNA functional pathway and in turn may contribute to maintaining normal oocyte growth and quality as well as embryo development following fertilization. To characterize DND1 in pig maturing oocytes and cumulus cells, and early embryos, we examined DND1 mRNA and protein expression using quantitative RT-PCR, Western blot and immunostaining. We found: (1) DND1 protein is expressed during pig follicle development; (2) DND1 is dynamically expressed at both mRNA and protein level in the maturing oocyte and early in vitro fertilized embryos; (3) DND1 mRNA is expressed in cumulus cells surrounding the maturing oocyte; and (4) DND1 protein is localized in the cytoplasm of pig maturing oocytes and early embryos. Our work provides useful data for functional study of DND1 proteins in female gametogenesis and developing embryos, which will benefit animal reproduction health and provides foundational knowledge for improving swine reproductive efficiency
Reward Collapse in Aligning Large Language Models
The extraordinary capabilities of large language models (LLMs) such as
ChatGPT and GPT-4 are in part unleashed by aligning them with reward models
that are trained on human preferences, which are often represented as rankings
of responses to prompts. In this paper, we document the phenomenon of
\textit{reward collapse}, an empirical observation where the prevailing
ranking-based approach results in an \textit{identical} reward distribution
\textit{regardless} of the prompts during the terminal phase of training. This
outcome is undesirable as open-ended prompts like ``write a short story about
your best friend'' should yield a continuous range of rewards for their
completions, while specific prompts like ``what is the capital of New Zealand''
should generate either high or low rewards. Our theoretical investigation
reveals that reward collapse is primarily due to the insufficiency of the
ranking-based objective function to incorporate prompt-related information
during optimization. This insight allows us to derive closed-form expressions
for the reward distribution associated with a set of utility functions in an
asymptotic regime. To overcome reward collapse, we introduce a prompt-aware
optimization scheme that provably admits a prompt-dependent reward distribution
within the interpolating regime. Our experimental results suggest that our
proposed prompt-aware utility functions significantly alleviate reward collapse
during the training of reward models
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