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  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 easiermore »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 data 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 tomore »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.« less
  6. ABSTRACT The merger of two or more galaxies can enhance the inflow of material from galactic scales into the close environments of active galactic nuclei (AGNs), obscuring and feeding the supermassive black hole (SMBH). Both recent simulations and observations of AGN in mergers have confirmed that mergers are related to strong nuclear obscuration. However, it is still unclear how AGN obscuration evolves in the last phases of the merger process. We study a sample of 60 luminous and ultra-luminous IR galaxies (U/LIRGs) from the GOALS sample observed by NuSTAR. We find that the fraction of AGNs that are Compton thickmore »(CT; $N_{\rm H}\ge 10^{24}\rm \, cm^{-2}$) peaks at $74_{-19}^{+14}{{\ \rm per\ cent}}$ at a late merger stage, prior to coalescence, when the nuclei have projected separations (dsep) of 0.4–6 kpc. A similar peak is also observed in the median NH [$(1.6\pm 0.5)\times 10^{24}\rm \, cm^{-2}$]. The vast majority ($85^{+7}_{-9}{{\ \rm per\ cent}}$) of the AGNs in the final merger stages (dsep ≲ 10 kpc) are heavily obscured ($N_{\rm H}\ge 10^{23}\rm \, cm^{-2}$), and the median NH of the accreting SMBHs in our sample is systematically higher than that of local hard X-ray-selected AGN, regardless of the merger stage. This implies that these objects have very obscured nuclear environments, with the $N_{\rm H}\ge 10^{23}\rm \, cm^{-2}$ gas almost completely covering the AGN in late mergers. CT AGNs tend to have systematically higher absorption-corrected X-ray luminosities than less obscured sources. This could either be due to an evolutionary effect, with more obscured sources accreting more rapidly because they have more gas available in their surroundings, or to a selection bias. The latter scenario would imply that we are still missing a large fraction of heavily obscured, lower luminosity ($L_{2-10}\lesssim 10^{43}\rm \, erg\, s^{-1}$) AGNs in U/LIRGs.« 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 andmore »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.« less