Publications

We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from $z=0.001$ to 2.26. This work features an increased sample size, increased redshift span, and improved treatment of systematic uncertainties in comparison to the original Pantheon analysis and results in a factor of two improvement in cosmological constraining power. For a Flat$Λ$CDM model, we find $Ω_M=0.334\pm0.018$ from SNe Ia alone. For a Flat$w_0$CDM model, we measure $w_0=-0.90\pm0.14$ from SNe Ia alone, H$_0=73.5\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including the Cepheid host distances and covariance (SH0ES), and $w_0=-0.978^{+0.024}_{-0.031}$ when combining the SN likelihood with constraints from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO); both $w_0$ values are consistent with a cosmological constant. We also present the most precise measurements to date on the evolution of dark energy in a Flat$w_0w_a$CDM universe, and measure $w_a=-0.1^{+0.9}_{-2.0}$ from Pantheon+ alone, H$_0=73.3\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including SH0ES, and $w_a=-0.65^{+0.28}_{-0.32}$ when combining Pantheon+ with CMB and BAO data. Finally, we find that systematic uncertainties in the use of SNe Ia along the distance ladder comprise less than one third of the total uncertainty in the measurement of H$_0$ and cannot explain the present "Hubble tension" between local measurements and early-Universe predictions from the cosmological model.

<div>Abstract<p>Altered lipid metabolism is a common hallmark of various cancers, including intrahepatic cholangiocarcinoma (ICC), a highly lethal carcinoma that lacks effective treatment options. To elucidate the lipid metabolism changes in ICC, we coupled the expression of the firefly luciferase gene (FFL) to <i>AKT1</i> (<i>AKT-FFL</i>) via an IRES linker, and then hydrodynamically injected mice with AKT-FFL and <i>Notch1</i> intracellular cytoplasmic domain (<i>NICD</i>) to establish a luciferase-positive ICC model. This model not only enabled us to monitor and quantify tumor growth by injecting the mice with luciferin, but also allowed us to assess the fatty acid uptake rate by injecting the mice with free fatty acid luciferin (FFA-Luc). The ICC model exhibited robust uptake of exogenous fatty acids compared with the HCC model induced by AKT-FFL/ neuroblastoma <i>Ras</i> (<i>Ras</i>). Lipidomics analysis showed a dramatically higher level of fatty acid in ICC, further supporting the increased fatty acids uptake. Mechanistic studies identified FATP5 as the predominant mediator of fatty acid uptake required for ICC growth using <i>Fatp5</i> knockout mice and AAV-based shRNA silencing of <i>Fatp5</i>. Our study discovered a novel therapeutic target for the treatment of ICC and shed light on the contributions of lipid metabolism to ICC development.</p>Implications:<p>This study provides the first <i>in vivo</i> evidence that FATP5 is a potential therapeutic target for treating ICC.</p></div>

The evolution of a Type IIn supernova (SN IIn) is governed by the interaction between the SN ejecta and a hydrogen-rich circumstellar medium. The SNe IIn thus allow us to probe the late-time mass-loss history of their progenitor stars. We present a sample of SNe IIn from the untargeted, magnitude-limited surveys of the Palomar Transient Factory (PTF) and its successor, the intermediate PTF (iPTF). To date, statistics on SN IIn optical light-curve properties have generally been based on small (≲10 SNe) samples from targeted SN surveys. The SNe IIn found and followed by the PTF/iPTF were used to select a sample of 42 events with useful constraints on the rise times as well as with available post-peak photometry. The sample SNe were discovered in 2009−2016 and have at least one low-resolution classification spectrum, as well as photometry from the P48 and P60 telescopes at Palomar Observatory. We study the light-curve properties of these SNe IIn using spline fits (for the peak and the declining portion) and template matching (for the rising portion). We study the peak-magnitude distribution, rise times, decline rates, colour evolution, host galaxies, and K-corrections of the SNe in our sample. We find that the typical rise times are divided into fast and slow risers at 20 ± 6 d and 50 ± 11 d, respectively. The decline rates are possibly divided into two clusters (with slopes 0.013 ± 0.006 mag d−1 and 0.040 ± 0.010 mag d−1), but this division has weak statistical significance. We find no significant correlation between the peak luminosity of SNe IIn and their rise times, but the more luminous SNe IIn are generally found to be more long-lasting. Slowly rising SNe IIn are generally found to decline slowly. The SNe in our sample were hosted by galaxies of absolute magnitude −22 ≲ Mg ≲ −13 mag. The K-corrections at light-curve peak of the SNe IIn in our sample are found to be within 0.2 mag for the observer’s frame r-band, for SNe at redshifts z < 0.25. By applying K-corrections and also including ostensibly “superluminous” SNe IIn, we find that the peak magnitudes are $ M_{\mathrm{peak}}^{r} = -19.18\pm1.32 $ mag. We conclude that the occurrence of conspicuous light-curve bumps in SNe IIn, such as in iPTF13z, are limited to $ 1.4^{+14.6}_{-1.0}\% $ of the SNe IIn. We also investigate a possible sub-type of SNe IIn with a fast rise to a ≳50 d plateau followed by a slow, linear decline.

Platinum nanoparticles size range from 1 to 8 nm deposited on mesoporous silica MCF-17 catalyzed alcohol oxidations were studied in the gas and liquid phases. Among methanol, ethanol, 2- propanol and 2-butanol reactions, the turnover frequency increased with Pt nanoparticle size for all the alcohols utilized. The activation energies for the oxidations were almost same among all alcohol species, but higher in the gas phase than those in the liquid phase. Water coadsorption poisoned the reaction in the gas phase, while it increased the reaction turnover rates in the liquid phase. Sum frequency generation (SFG) vibrational spectroscopy studies and DFT calculations revealed that the alcohol molecules pack horizontally on the metal surface in low concentrations and stand up in high concentrations, which affect the dissociation of β-hydrogen of the alcohols as the critical step in alcohol oxidations.

We present early-time photometric and spectroscopic observations of supernova (SN) 2009kr in NGC 1832. We find that its properties to date support its classification as Type II-linear (SN II-L), a relatively rare subclass of core-collapse supernovae (SNe). We have also identified a candidate for the SN progenitor star through comparison of pre-explosion, archival images taken with WFPC2 onboard the Hubble Space Telescope (HST) with SN images obtained using adaptive optics (AO) plus NIRC2 on the 10-m Keck-II telescope. Although the host galaxy's substantial distance (~26 Mpc) results in large uncertainties in the relative astrometry, we find that if this candidate is indeed the progenitor, it is a highly luminous (M_V = -7.8 mag) yellow supergiant with initial mass ~18-24 M_sun. This would be the first time that a SN II-L progenitor has been directly identified. Its mass may be a bridge between the upper initial mass limit for the more common Type II-plateau SNe (SNe II-P) and the inferred initial mass estimate for one Type II-narrow SN (SN IIn).

One of the key challenges arising when compilers vectorize loops for today's SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the compiler has to determine how many instructions to pack together and how many loop iterations to interleave. Compilers are designed today to use fixed-cost models that are based on heuristics to make vectorization decisions on loops. However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions. Alternatively, software engineers often hand-write the vectorization factors of every loop. This, however, places a huge burden on them, since it requires prior experience and significantly increases the development time. In this work, we explore a novel approach for handling loop vectorization and propose an end-to-end solution using deep reinforcement learning (RL). We conjecture that deep RL can capture different instructions, dependencies, and data structures to enable learning a sophisticated model that can better predict the actual performance cost and determine the optimal vectorization factors. We develop an end-to-end framework, from code to vectorization, that integrates deep RL in the LLVM compiler. Our proposed framework takes benchmark codes as input and extracts the loop codes. These loop codes are then fed to a loop embedding generator that learns an embedding for these loops. Finally, the learned embeddings are used as input to a Deep RL agent, which determines the vectorization factors for all the loops. We further extend our framework to support multiple supervised learning methods. We evaluate our approaches against the currently used LLVM vectorizer and loop polyhedral optimization techniques. Our experiments show 1.29X-4.73X performance speedup compared to baseline and only 3% worse than the brute-force search on a wide range of benchmarks.