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  1. Free, publicly-accessible full text available June 1, 2023
  2. Abstract

    We present the analysis of ∼100 pc scale compact radio continuum sources detected in 63 local (ultra)luminous infrared galaxies (U/LIRGs;LIR≥ 1011L), using FWHM ≲ 0.″1–0.″2 resolution 15 and 33 GHz observations with the Karl G. Jansky Very Large Array. We identify a total of 133 compact radio sources with effective radii of 8–170 pc, which are classified into four main categories—“AGN” (active galactic nuclei), “AGN/SBnuc” (AGN-starburst composite nucleus), “SBnuc” (starburst nucleus), and “SF” (star-forming clumps)—based on ancillary data sets and the literature. We find that “AGN” and “AGN/SBnuc” more frequently occur in late-stage mergers and have up to 3 dex higher 33 GHz luminosities and surface densities compared with “SBnuc” and “SF,” which may be attributed to extreme nuclear starburst and/or AGN activity in the former. Star formation rates (SFRs) and surface densities (ΣSFR) are measured for “SF” and “SBnuc” using both the total 33 GHz continuum emission (SFR ∼ 0.14–13Myr−1, ΣSFR∼ 13–1600Myr−1kpc−2) and the thermal free–free emission from Hiiregions (median SFRth∼ 0.4Myr−1,ΣSFRth44Myr−1kpc−2). These values are 1–2 dex higher than those measured for similar-sized clumps in nearby normal (non-U/LIRGs). The latter also have a much flatter median 15–33 GHz spectral index (∼−0.08) compared withmore »“SBnuc” and “SF” (∼−0.46), which may reflect higher nonthermal contribution from supernovae and/or interstellar medium densities in local U/LIRGs that directly result from and/or lead to their extreme star-forming activities on 100 pc scales.

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  3. Free, publicly-accessible full text available April 21, 2023
  4. While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion re- covery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained with recovery likelihood, which maximizes the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. Optimizing re- covery likelihood is more tractable than marginal likelihood, as sampling from the conditional distributions is much easier than sampling from the marginal distribu- tions. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method gener- ates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of datamore »even for high-dimensional datasets. Our implementation is avail- able at https://github.com/ruiqigao/recovery_likelihood.« less
  5. The properties of concretes are controlled by the rate of reaction of their precursors, the chemical composition of the binding phase(s), and their structure at different scales. However, the complex and multiscale structure of the cementitious hydrates and the dissimilar rates of numerous chemical reactions make it challenging to eluci- date such linkages. In particular, reliable predictions of strength development in concretes remain unavailable. As an alternative route to physics- or chemistry-based models, machine learning (ML) offers a means to develop powerful predictive models for materials using existing data. Here, it is shown that ML models can be used to accurately predict concrete’s compressive strength at 28 days. This approach relies on the analysis of a large data set (>10,000 observations) of measured compressive strengths for industrially produced concretes, based on knowledge of their mixture proportions. It is demonstrated that these models can readily predict the 28-day compressive strength of any concrete based merely on the knowledge of the mixture proportions with an accuracy of approximately ±4.4 MPa (as captured by the root- mean-square error). By comparing the performance of select ML algorithms, the balance between accuracy, simplicity, and inter- pretability in ML approaches is discussed.
  6. Abstract Nuclear rings are excellent laboratories for studying intense star formation. We present results from a study of nuclear star-forming rings in five nearby normal galaxies from the Star Formation in Radio Survey (SFRS) and four local LIRGs from the Great Observatories All-sky LIRG Survey at sub-kiloparsec resolutions using Very Large Array high-frequency radio continuum observations. We find that nuclear ring star formation (NRSF) contributes 49%–60% of the total star formation of the LIRGs, compared to 7%–40% for the normal galaxies. We characterize a total of 57 individual star-forming regions in these rings, and find that with measured sizes of 10–200 pc, NRSF regions in the LIRGs have star formation rate (SFR) and Σ SFR up to 1.7 M ⊙ yr −1 and 402 M ⊙ yr −1 kpc −2 , respectively, which are about 10 times higher than in NRSF regions in the normal galaxies with similar sizes, and comparable to lensed high- z star-forming regions. At ∼100–300 pc scales, we estimate low contributions (<50%) of thermal free–free emission to total radio continuum emission at 33 GHz in the NRSF regions in the LIRGs, but large variations possibly exist at smaller physical scales. Finally, using archival sub-kiloparsec resolution COmore »( J = 1–0) data of nuclear rings in the normal galaxies and NGC 7469 (LIRG), we find a large scatter in gas depletion times at similar molecular gas surface densities, which tentatively points to a multimodal star formation relation on sub-kiloparsec scales.« less
  7. A systematic analysis was used to understand electrical drift occurring in field-effect transistor (FET) dissolved-analyte sensors by investigating its dependence on electrode surface-solution combinations in a remote-gate (RG) FET configuration. Water at pH 7 and neat acetonitrile, having different dipoles and polarizabilities, were applied to the RG surface of indium tin oxide (ITO), SiO2, hexamethyldisilazane-modified SiO2, polystyrene, poly(styrene-co-acrylic acid), poly(3-hexylthiophene-2,5-diyl) (P3HT), and poly [3-(3-carboxypropyl)thiophene-2,5-diyl] (PT-COOH). We discovered that in some cases a slow reorientation of dipoles at the interface induced by gate electric fields caused severe drift and hysteresis because of induced interface potential changes. Conductive and charged P3HT and PT-COOH increased electrochemical stability by promoting fast surface equilibrations. We also demonstrated pH sensitivity of P3HT (17 mV/pH) as an indication of proton doping. PT-COOH showed further enhanced pH sensitivity (30 mV/pH). This combination of electrochemical stability and pH response in PT-COOH are proposed as advantageous for polymer-based biosensors.