5632 research outputs found
Sort by
Exponentially Convergent Multiscale Finite Element Method
No full textWe provide a concise review of the exponentially convergent multiscale finite element method (ExpMsFEM) for efficient model reduction of PDEs in heterogeneous media without scale separation and in high-frequency wave propagation. ExpMsFEM is built on the non-overlapped domain decomposition in the classical MsFEM while enriching the approximation space systematically to achieve a nearly exponential convergence rate regarding the number of basis functions. Unlike most generalizations of MsFEM in the literature, ExpMsFEM does not rely on any partition of unity functions.
In general, it is necessary to use function representations dependent on the right-hand side to break the algebraic Kolmogorov n-width barrier to achieve exponential convergence. Indeed, there are online and offline parts in the function representation provided by ExpMsFEM. The online part depends on the right-hand side locally and can be computed in parallel efficiently. The offline part contains basis functions that are used in the Galerkin method to assemble the stiffness matrix; they are all independent of the right-hand side, so the stiffness matrix can be used repeatedly in multi-query scenarios
Machine learning in postgenomic biology and personalized medicine
No full textIn recent years, machine learning (ML) has been revolutionizing biology, biomedical sciences, and gene-based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular structure determination, on the one hand, require data analysis capabilities using ML that are distinctly different from classical statistical methods; on the other, these large datasets are enabling the adoption of novel data-intensive ML algorithms for the solution of biological problems that until recently had relied on mechanistic model-based approaches that are computationally expensive. This review provides a bird's eye view of the applications of ML in postgenomic biology. Attempt is also made to indicate as far as possible the areas of research that are poised to make further impacts in these areas, including the importance of explainable artificial intelligence in human health. Further contributions of ML are expected to transform medicine, public health, agricultural technology, as well as to provide invaluable gene-based guidance for the management of complex environments in this age of global warming
Thermal Probes of Phonon-Coupled Kitaev Spin Liquids: From Accurate Extraction of Quantized Edge Transport to Anyon Interferometry
No full textRecent theoretical studies inspired by experiments on the Kitaev magnet α − RuCl₃ highlight the nontrivial impact of phonons on the thermal Hall conductivity of chiral topological phases. Here, we introduce mixed mesoscopic-macroscopic devices that allow refined thermal-transport probes of non-Abelian spin liquids with Ising topological order. These devices feature a quantum-coherent region with quantized or negligible phonon conductance, flanked by macroscopic lobes that facilitate efficient thermalization between chiral Majorana edge modes and bulk phonons. We show that our devices enable (i) accurate determination of the quantized thermal Hall conductivity, (ii) identification of non-Abelian Ising anyons via the temperature dependence of the thermal conductance, and, most interestingly, (iii) single-anyon detection through heat-based anyon interferometry. Analogous results apply broadly to phonon-coupled chiral topological orders
The Ultramassive White Dwarfs of the Alpha Persei Cluster
No full textWe searched through the entire Gaia EDR3 candidate white dwarf catalog for stars with proper motions and positions that are consistent with them having escaped from the Alpha Persei cluster within the past 81 Myr, the age of the cluster. In this search we found five candidate white dwarf escapees from Alpha Persei and obtained spectra for all of them. We confirm that three are massive white dwarfs sufficiently young to have originated in the cluster. All these are more massive than any white dwarf previously associated with a cluster using Gaia astrometry, and possess some of the most massive progenitors. In particular, the white dwarf Gaia EDR3 4395978097863572, which lies within 25 pc of the cluster center, has a mass of about 1.20 solar masses and evolved from an 8.5 solar-mass star, pushing the upper limit for white dwarf formation from a single massive star, while still leaving a substantial gap between the resulting white dwarf mass and the Chandrasekhar mass
Neutrino Properties with Ground-based Millimeter-wavelength Line Intensity Mapping
No full textLine intensity mapping (LIM) is emerging as a powerful technique to map the cosmic large-scale structure and to probe cosmology over a wide range of redshifts and spatial scales. We perform Fisher forecasts to determine the optimal design of wide-field ground-based millimeter-wavelength LIM surveys for constraining properties of neutrinos and light relics. We consider measuring the auto-power spectra of several CO rotational lines (from J = 2–1 to J = 6–5) and the [C ii] fine-structure line in the redshift range of 0.25 < z < 12. We study the constraints with and without interloper lines as a source of noise in our analysis, and for several one-parameter and multiparameter extensions of ΛCDM. We show that LIM surveys deployable this decade, in combination with existing cosmic microwave background (CMB; primary) data, could achieve order-of-magnitude improvements over Planck constraints on N_(eff) and M_ν. Compared to next-generation CMB and galaxy surveys, a LIM experiment of this scale could achieve bounds that are a factor of ∼3 better than those forecasted for surveys such as EUCLID (galaxy clustering), and potentially exceed the constraining power of CMB-S4 by a factor of ∼1.5 and ∼3 for N_(eff) and M_ν, respectively. We show that the forecasted constraints are not substantially affected when enlarging the parameter space, and additionally demonstrate that such a survey could also be used to measure ΛCDM parameters and the dark energy equation of state exquisitely well
Assessing the Influence of Input Magnetic Maps on Global Modeling of the Solar Wind and CME-Driven Shock in the 2013 April 11 Event
No full textIn the past decade, significant efforts have been made in developing physics-based solar wind and coronal mass ejection (CME) models, which have been or are being transferred to national centers (e.g., SWPC, Community Coordinated Modeling Center) to enable space weather predictive capability. However, the input data coverage for space weather forecasting is extremely limited. One major limitation is the solar magnetic field measurements, which are used to specify the inner boundary conditions of the global magnetohydrodynamic (MHD) models. In this study, using the Alfvén wave solar model, we quantitatively assess the influence of the magnetic field map input (synoptic/diachronic vs. synchronic magnetic maps) on the global modeling of the solar wind and the CME-driven shock in the 11 April 2013 solar energetic particle event. Our study shows that due to the inhomogeneous background solar wind and dynamical evolution of the CME, the CME-driven shock parameters change significantly both spatially and temporally as the CME propagates through the heliosphere. The input magnetic map has a great impact on the shock connectivity and shock properties in the global MHD simulation. Therefore this study illustrates the importance of taking into account the model uncertainty due to the imperfect magnetic field measurements when using the model to provide space weather predictions
Infant-phase reddening by surface Fe-peak elements in a normal type Ia supernova
No full textType Ia supernovae are thermonuclear explosions of white dwarf stars. They play a central role in the chemical evolution of the Universe and are an important measure of cosmological distances. However, outstanding questions remain about their origins. Despite extensive efforts to obtain natal information from their earliest signals, observations have thus far failed to identify how the majority of them explode. Here, we present infant-phase detections of SN 2018aoz from a very low brightness of −10.5 AB absolute magnitude, revealing a hitherto unseen plateau in the B band that results in a rapid redward colour evolution between 1.0 and 12.4 hours after the estimated epoch of first light. The missing B-band flux is best explained by line-blanket absorption from Fe-peak elements in the outer 1% of the ejected mass. The observed B − V colour evolution of the supernova also matches the prediction from an over-density of Fe-peak elements in the same outer 1% of the ejected mass, whereas bluer colours are expected from a purely monotonic distribution of Fe-peak elements. The presence of excess nucleosynthetic material in the extreme outer layers of the ejecta points to enhanced surface nuclear burning or extended subsonic mixing processes in some normal type Ia SN explosions
Assessing Mg–Sc–(rare earth) ternary phase stability via constituent binary cluster expansions
No full textThe disordered Mg–Sc body-centered cubic (bcc) phase is both lightweight and strong; however, the system is impractical for general industrial use due to the high cost of scandium. We propose a computationally efficient metric that assesses ternary rare earth element additions that may stabilize the bcc phase at lower Sc concentrations. We find that the bcc phase is stabilized by the ternary addition of Y or Er, but not by La, Ce, or Nd, and we validate these predictions by experimental production and characterization of Mg–Sc–(Y,Er,Nd) alloys. The results suggest a computationally efficient method to anticipate integration of ternary elements into binary systems using cluster expansions of constituent binaries
Proposal for measuring out-of-time-ordered correlators at finite temperature with coupled spin chains
No full textInformation scrambling, which is the spread of local information through a system’s many-body degrees of freedom, is an intrinsic feature of many-body dynamics. In quantum systems, the out-of-time-ordered correlator (OTOC) quantifies information scrambling. Motivated by experiments that have measured the OTOC at infinite temperature and a theory proposal to measure the OTOC at finite temperature using the thermofield double state, we describe a protocol to measure the OTOC in a finite temperature spin chain that is realized approximately as one half of the ground state of two moderately-sized coupled spin chains. We consider a spin Hamiltonian with particle–hole symmetry, for which we show that the OTOC can be measured without needing sign-reversal of the Hamiltonian. We describe a protocol to mitigate errors in the estimated OTOC, arising from the finite approximation of the system to the thermofield double state. We show that our protocol is also robust to main sources of decoherence in experiments
Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities
No full textThe hydroxyl radical (OH) is the primary cleansing agent in the atmosphere. The abundance of OH in cities initiates the removal of local pollutants; therefore, it serves as the key species describing the urban chemical environment. We propose a machine learning (ML) approach as an efficient alternative to OH simulation using a computationally expensive chemical transport model. The ML model is trained on the parameters simulated from the WRF-Chem model, and it suggests that six predictive parameters are capable of explaining 76% of the OH variability. The parameters are the tropospheric NO₂ column, the tropospheric HCHO column, J(O¹D), H₂O, temperature, and pressure. We then use observations of the tropospheric NO₂ column and HCHO column from OMI as input to the ML model to enable measurement-based prediction of daily near surface OH at 1:30 pm local time across 49 North American cities over the course of 10 years between 2005 and 2014. The result is validated by comparing the OH predictions to measurements of isoprene, which has a source that is uncorrelated with OH and is removed rapidly and almost exclusively by OH in the daytime. We demonstrate that the predicted OH is, as expected, anticorrelated with isoprene. We also show that this ML model is consistent with our understanding of OH chemistry given the solely data-driven nature