Title: Meta Distributions–Part 1: Definition and Examples
Meta distributions (MDs) have emerged as a powerful tool in the analysis of wireless networks. Compared to standard distributions, they enable a clean separation of the different sources of randomness, resulting in sharper, more refined results. In particular, they capture the disparity of the performances of individual links or users. In this first part of a two-letter series, we start from first principles and give the formal definition of MDs and present several simple yet illustrative examples. Part 2 [1] explores the properties of the MD in more depth and offers multiple interpretations and applications. more »« less
In the companion letter [1], we have defined and exemplified meta distributions (MDs) as a natural extension of the concepts of the mean and distribution of a random variable. Here we provide an in-depth discussion of the properties and interpretations of MDs. It includes original results on the calculation of MDs in the monotone case and two applications to simple Poisson wireless networks models.
Hsu, Brian; Hosseinzadeh, Griffin; Villar, V. Ashley; Berger, Edo
(, The Astrophysical Journal)
Abstract With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼0.1% of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium Deep Survey (PS1-MDS), classified photometrically with ourSuperRAENNandSuperphotalgorithms. We first construct a subsample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multiband light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are consistent overall, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower-luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way for an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.
Peper, Joseph J; Qiu, Wenzhao; Wang, Lu
(, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL))
We investigate pre-training techniques for abstractive multi-document summarization (MDS), which is much less studied than summarizing single documents. Though recent work has demonstrated the effectiveness of highlighting information salience for pretraining strategy design, they struggle to generate abstractive and reflective summaries, which are critical properties for MDS. To this end, we present PELMS, a pre-trained model that uses pre-training objectives based on semantic coherence heuristics and faithfulness constraints together with unlabeled multi-document inputs, to promote the generation of concise, fluent, and faithful summaries. To support the training of PELMS, we compile MultiPT, a multidocument pre-training corpus containing over 93 million documents to form more than 3 million unlabeled topic-centric document clusters, covering diverse genres such as product reviews, news, and general knowledge. We perform extensive evaluation of PELMS in lowshot settings on a wide range of MDS datasets. Our approach consistently outperforms competitive comparisons with respect to overall informativeness, abstractiveness, coherence, and faithfulness, and with minimal fine-tuning can match performance of language models at a much larger scale (e.g., GPT-4).
Wang, Xinyun; Haenggi, Martin
(, IEEE Transactions on Wireless Communications)
In the analysis of wireless networks, the standard signal-to-interference (SIR) distribution does not capture the performance at the individual link level. The meta distribution (MD) of the SIR resolves this problem by separating different sources of randomness, such as fading and point process(es). While it allows for a much sharper performance characterization, it can in most cases only be calculated based on the moments of the underlying conditional distribution, i.e., by solving a Hausdorff moment problem. Several methods to reconstruct MDs from the moments have been proposed but a rigorous analysis, comparison of their performance, and practical implementations are missing. In addition, a standard is needed for a consistent and objective comparison. This paper addresses the above-mentioned important shortcomings, introduces a tweaking mapping for adjusting approximations, presents terminology to categorize the quality of approximations, proposes the use of the Fourier- Legendre method, which has not previously been applied to MDs, and provides the achievable lower and upper bounds on the MD given the first 𝑛 moments. Further, to facilitate the use of MDs, we give comprehensive guidance on the selection of the best method to determine MDs, and we offer ready-to-use implementations of the proposed algorithms. This study fills an important gap in the literature by rigorously analyzing the MDs, comparing the performance of different methods, and offering user-friendly implementations for recovering MDs from moments.
Palma‐Chavez, Jorge A.; Fuentes, Kevin; Applegate, Brian E.; Jo, Javier A.; Charoenphol, Phapanin
(, Macromolecular Bioscience)
Abstract Vascular‐targeted drug delivery remains an attractive platform for therapeutic and diagnostic interventions in human diseases. This work focuses on the development of a poly‐lactic‐co‐glycolic‐acid (PLGA)‐based multistage delivery system (MDS). MDS consists of two stages: a micron‐sized PLGA outer shell and encapsulated drug‐loaded PLGA nanoparticles. Nanoparticles with average diameters of 76, 119, and 193 nm are successfully encapsulated into 3–6 µm MDS. Sustained in vitro release of nanoparticles from MDS is observed for up to 7 days. Both MDS and nanoparticles arebiocompatible with human endothelial cells. Sialyl‐Lewis‐A (sLeA) is successfully immobilized on the MDS and nanoparticle surfaces to enable specific targeting of inflamed endothelium. Functionalized MDS demonstrates a 2.7‐fold improvement in endothelial binding compared to PLGA nanoparticles from human blood laminar flow. Overall, the presented results demonstrate successful development and characterization of MDS and suggest that MDS can serve as an effective drug carrier, which can enhance the margination of nanoparticles to the targeted vascular wall.
@article{osti_10231801,
place = {Country unknown/Code not available},
title = {Meta Distributions–Part 1: Definition and Examples},
url = {https://par.nsf.gov/biblio/10231801},
DOI = {10.1109/LCOMM.2021.3069662},
abstractNote = {Meta distributions (MDs) have emerged as a powerful tool in the analysis of wireless networks. Compared to standard distributions, they enable a clean separation of the different sources of randomness, resulting in sharper, more refined results. In particular, they capture the disparity of the performances of individual links or users. In this first part of a two-letter series, we start from first principles and give the formal definition of MDs and present several simple yet illustrative examples. Part 2 [1] explores the properties of the MD in more depth and offers multiple interpretations and applications.},
journal = {IEEE Communications Letters},
author = {Haenggi, Martin},
editor = {null}
}
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