244 research outputs found
Primordial Gravitational Waves Measurements and Anisotropies of CMB Polarization Rotation
Searching for the signal of primordial gravitational waves in the B-modes
(BB) power spectrum is one of the key scientific aims of the cosmic microwave
background (CMB) polarization experiments. However, this could be easily
contaminated by several foreground issues, such as the thermal dust emission.
In this paper we study another mechanism, the cosmic birefringence, which can
be introduced by a CPT-violating interaction between CMB photons and an
external scalar field. Such kind of interaction could give rise to the rotation
of the linear polarization state of CMB photons, and consequently induce the
CMB BB power spectrum, which could mimic the signal of primordial gravitational
waves at large scales. With the recent polarization data of BICEP2 and the
joint analysis data of BICEP2/Keck Array and Planck, we perform a global
fitting analysis on constraining the tensor-to-scalar ratio by considering
the polarization rotation angle which can be separated into a background
isotropic part and a small anisotropic part. Since the data of BICEP2 and Keck
Array experiments have already been corrected by using the "self-calibration"
method, here we mainly focus on the effects from the anisotropies of CMB
polarization rotation angle. We find that including the anisotropies in the
analysis could slightly weaken the constraints on , when using current CMB
polarization measurements. We also simulate the mock CMB data with the
BICEP3-like sensitivity. Very interestingly, we find that if the effects of the
anisotropic polarization rotation angle can not be taken into account properly
in the analysis, the constraints on will be dramatically biased. This
implies that we need to break the degeneracy between the anisotropies of the
CMB polarization rotation angle and the CMB primordial tensor perturbations, in
order to measure the signal of primordial gravitational waves accurately.Comment: 7 pages, 5 figure
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
We propose Speculative Decoding (SpecDec), for the first time ever, to
formally study exploiting the idea of speculative execution to accelerate
autoregressive (AR) decoding. Speculative Decoding has two innovations:
Spec-Drafter -- an independent model specially optimized for efficient and
accurate drafting -- and Spec-Verification -- a reliable method for verifying
the drafted tokens efficiently in the decoding paradigm. Experimental results
on various seq2seq tasks including machine translation and abstractive
summarization show our approach can achieve around speedup for the
popular Transformer architectures with comparable generation quality to beam
search decoding, refreshing the impression that the draft-then-verify paradigm
introduces only speedup. In addition to the
remarkable speedup, we also demonstrate 3 additional advantages of SpecDec,
revealing its practical value for accelerating generative models in real-world
applications. Our models and codes are available at
https://github.com/hemingkx/SpecDec.Comment: (Early 2022): Initially announced with the name
"Generalized Aggressive Decoding"; (September 2022): Renamed to
"Speculative Decoding" as the ICLR'23 submission
(https://openreview.net/pdf?id=H-VlwsYvVi), marking
"Speculative Decoding" has been publicly proposed. : EMNLP'23
Findings camera read
Lensing reconstruction from the cosmic microwave background polarization with machine learning
The lensing effect of the cosmic microwave background (CMB) is a powerful
tool for our study of the distribution of matter in the universe. Currently,
the quadratic estimator (EQ) method, which is widely used to reconstruct
lensing potential, has been known to be sub-optimal for the low-noise levels
polarization data from next-generation CMB experiments. To improve the
performance of the reconstruction, other methods, such as the maximum
likelihood estimator and machine learning algorithms are developed. In this
work, we present a deep convolutional neural network model named the Residual
Dense Local Feature U-net (RDLFUnet) for reconstructing the CMB lensing
convergence field. By simulating lensed CMB data with different noise levels to
train and test network models, we find that for noise levels less than
K-arcmin, RDLFUnet can recover the input gravitational potential with a
higher signal-to-noise ratio than the previous deep learning and the
traditional QE methods at almost the entire observation scales.Comment: 12 pages, 8 figures, accepted by Ap
Extensible Prompts for Language Models on Zero-shot Language Style Customization
We propose eXtensible Prompt (X-Prompt) for prompting a large language model
(LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL
but also an extensible vocabulary of imaginary words. Registering new imaginary
words allows us to instruct the LLM to comprehend concepts that are difficult
to describe with NL words, thereby making a prompt more descriptive. Also,
these imaginary words are designed to be out-of-distribution (OOD) robust so
that they can be (re)used like NL words in various prompts, distinguishing
X-Prompt from soft prompt that is for fitting in-distribution data. We propose
context-augmented learning (CAL) to learn imaginary words for general
usability, enabling them to work properly in OOD (unseen) prompts. We
experiment X-Prompt for zero-shot language style customization as a case study.
The promising results of X-Prompt demonstrate its potential to facilitate
advanced interaction beyond the natural language interface, bridging the
communication gap between humans and LLMs.Comment: Accepted by NeurIPS 202
Recovering the CMB Signal with Machine Learning
The cosmic microwave background (CMB), carrying the inhomogeneous information
of the very early universe, is of great significance for understanding the
origin and evolution of our universe. However, observational CMB maps contain
serious foreground contaminations from several sources, such as galactic
synchrotron and thermal dust emissions. Here, we build a deep convolutional
neural network (CNN) to recover the tiny CMB signal from various huge
foreground contaminations. Focusing on the CMB temperature fluctuations, we
find that the CNN model can successfully recover the CMB temperature maps with
high accuracy, and that the deviation of the recovered power spectrum
is smaller than the cosmic variance at . We then apply this method to
the current Planck observation, and find that the recovered CMB is quite
consistent with that disclosed by the Planck collaboration, which indicates
that the CNN method can provide a promising approach to the component
separation of CMB observations. Furthermore, we test the CNN method with
simulated CMB polarization maps based on the CMB-S4 experiment. The result
shows that both the EE and BB power spectra can be recovered with high
accuracy. Therefore, this method will be helpful for the detection of
primordial gravitational waves in current and future CMB experiments. The CNN
is designed to analyze two-dimensional images, thus this method is not only
able to process full-sky maps, but also partial-sky maps. Therefore, it can
also be used for other similar experiments, such as radio surveys like the
Square Kilometer Array.Comment: 19 pages, 25 figures, and 3 tables, ApJS, in press. The code
repository is available at https://github.com/Guo-Jian-Wang/cmbNNC
A high mean arterial pressure target is associated with improved microcirculation in septic shock patients with previous hypertension: a prospective open label study
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