Publications

\textbf{Background:} No microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive. \textbf{Results:} Here, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network. \textbf{Conclusions:} For the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Quantum entities manifest themselves as either particles or waves. In a physical system containing a very large number of identical particles, such as electrons in a material, individualistic (particle-like) behavior prevails at high temperatures. At low temperatures, collective behavior emerges, and excitations of the system in this regime are best described as waves—long-lived phenomena that are periodic in both space and time and often dubbed “coherent excitations” by physicists. On page 186 of this issue, Goremychkin et al. (1) used experiment and theory to describe the emergence of coherent excitations in a complex quantum system with strong interactions. They studied a ceriumpalladium compound, CePd3, in which the very localized electrons of 4f orbitals of Ce interact with the much more itinerant conduction electrons of the extended d orbitals of Pd at low temperatures to create a wavelike state.

We introduce a simple scheme to efficiently compute photon exchange-correlation contributions due to the coupling to transversal photons as formulated in the newly developed quantum-electrodynamical density functional theory (QEDFT). Our construction employs the optimized-effective potential (OEP) approach by means of the Sternheimer equation to avoid the explicit calculation of unoccupied states. We demonstrate the efficiency of the scheme by applying it to an exactly solvable GaAs quantum ring model system, a single azulene molecule, and chains of sodium dimers, all located in optical cavities and described in full real space. While the first example is a two-dimensional system and allows to benchmark the employed approximations, the latter two examples demonstrate that the correlated electron-photon interaction appreciably distorts the ground-state electronic structure of a real molecule. By using this scheme, we not only construct typical electronic observables, such as the electronic ground-state density, but also illustrate how photon observables, such as the photon number, and mixed electron-photon observables, e.g. electron-photon correlation functions, become accessible in a DFT framework. This work constitutes the first three-dimensional ab-initio calculation within the new QEDFT formalism and thus opens up a new computational route for the ab-initio study of correlated electron-photon systems in quantum cavities.

We present the measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect in Fourier space, rather than in real space. We measure the density-weighted pairwise kSZ power spectrum, the first use of this promising approach, by cross-correlating a cleaned Cosmic Microwave Background (CMB) temperature map, which jointly uses both Planck Release 2 and Wilkinson Microwave Anisotropy Probe nine-year data, with the two galaxy samples, CMASS and LOWZ, derived fr om the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12. With the current data, we constrain the average optical depth τ multiplied by the ratio of the Hubble parameter at redshift z and the present day, E=H/H0; we find τE=(3.95±1.62)×10−5 for LOWZ and τE=(1.25±1.06)×10−5 for CMASS, with the optimal angular radius of an aperture photometry filter to estimate the CMB temperature distortion associ ated with each galaxy. By repeating the pairwise kSZ power analysis for various aperture radii, we measure the optical depth as a function of aperture ra dii. While this analysis results in the kSZ signals with only evidence for a detection, S/N=2.54 for LOWZ and 1.24 for CMASS, the combination of future CMB and spectroscopic galaxy surveys should enable precision measurements. We estimate that the combination of CMB-S4 and data from DESI shoul d yield detections of the kSZ signal with S/N=70−100, depending on the resolution of CMB-S4.

We search for an isotropic stochastic gravitational-wave background (GWB) in the newly released 11-year dataset from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). While we find no significant evidence for a GWB, we place constraints on a GWB from a population of supermassive black-hole binaries, from cosmic strings, and from a primordial GWB. For the first time, we find that the GWB upper limits and detection statistics are sensitive to the Solar System ephemeris (SSE) model used, and that SSE errors can mimic a GWB signal. To mitigate this effect, we developed and implemented a novel approach that bridges systematic SSE differences, producing the first PTA constraints that are robust against SSE uncertainties. We place a 95% upper limit on the GW strain amplitude of AGWB<1.45×10−15 at a frequency of f=1 yr−1 for a fiducial f−2/3 power-law spectrum, and with inter-pulsar correlations modeled. This is a factor of ∼2 improvement over the NANOGrav 9-year limit, calculated using the same procedure. Previous PTA upper limits on the GWB will need revision in light of SSE systematic uncertainties. We also characterize the combined influence of the mass-density of stars in galactic cores, the eccentricity of binaries at formation, and the relation between the mass of the central supermassive black hole and the galactic bulge (the MBH−Mbulge relation). We constrain cosmic-string tension on the basis of recent simulations, yielding an SSE-marginalized 95\% upper limit on the cosmic string tension of Gμ<5.3×10−11---a factor of ∼2 better than the NANOGrav 9-year constraints. We then use our new Bayesian SSE model to limit the energy density of primordial GWBs, corresponding to ΩGWB(f)h2<3.4×10−10 for a radiation-dominated inflationary era.

Inspired by coarse-graining approaches used in physics, we show how similar algorithms can be adapted for data. The resulting algorithms are based on layered tree tensor networks and scale linearly with both the dimension of the input and the training set size. Computing most of the layers with an unsupervised algorithm, then optimizing just the top layer for supervised classification of the MNIST and fashion-MNIST data sets gives very good results. We also discuss mixing a prior guess for supervised weights together with an unsupervised representation of the data, yielding a smaller number of features nevertheless able to give good performance.

Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet derivations of single-layer networks with such local learning rules from principled optimization objectives became possible only recently, with the introduction of similarity matching objectives. What explains the success of similarity matching objectives in deriving neural networks with local learning rules? Here, using dimensionality reduction as an example, we introduce several variable substitutions that illuminate the success of similarity matching. We show that the full network objective may be optimized separately for each synapse using local learning rules in both the offline and online settings. We formalize the long-standing intuition of the rivalry between Hebbian and anti-Hebbian rules by formulating a min-max optimization problem. We introduce a novel dimensionality reduction objective using fractional matrix exponents. To illustrate the generality of our approach, we apply it to a novel formulation of dimensionality reduction combined with whitening. We confirm numerically that the networks with learning rules derived from principled objectives perform better than those with heuristic learning rules.

Insights from spectroscopic experiments led to the development of quantum mechanics as the common theoretical framework for describing the physical and chemical properties of atoms, molecules and materials. Later, a full quantum description of charged particles, electromagnetic radiation and special relativity was developed, leading to quantum electrodynamics (QED). This is, to our current understanding, the most complete theory describing photon–matter interactions in correlated many–body systems. In the low-energy regime, simplified models of QED have been developed to describe and analyse spectra over a wide spatiotemporal range as well as physical systems. In this Review, we highlight the interrelations and limitations of such theoretical models, thereby showing that they arise from low-energy simplifications of the full QED formalism, in which antiparticles and the internal structure of the nuclei are neglected. Taking molecular systems as an example, we discuss how the breakdown of some simplifications of low-energy QED challenges our conventional understanding of light–matter interactions. In addition to high-precision atomic measurements and simulations of particle physics problems in solid-state systems, new theoretical features that account for collective QED effects in complex interacting many-particle systems could become a material-based route to further advance our current understanding of light–matter interactions.

We introduce a simple ansatz for the wavefunction of a many-body system based on coupled forward and backward-propagating semiclassical trajectories. This method is primarily aimed at, but not limited to, treating nonequilibrium dynamics in electron-phonon systems. The time-evolution of the system is obtained from the Euler-Lagrange variational principle, and we show that this ansatz yields Ehrenfest mean field theory in the limit that the forward and backward trajectories are orthogonal, and in the limit that they coalesce. We investigate accuracy and performance of this method by simulating electronic relaxation in the spin-boson model and the Holstein model. Although this method involves only pairs of semiclassical trajectories, it shows a substantial improvement over mean field theory, capturing quantum coherence of nuclear dynamics as well as electron-nuclear correlations. This improvement is particularly evident in nonadiabatic systems, where the accuracy of this coupled trajectory method extends well beyond the perturbative electron-phonon coupling regime. This approach thus provides an attractive route forward to the ab-initio description of relaxation processes, such as thermalization, in condensed phase systems.

The Auger decay is a relevant recombination channel during the first few femtoseconds of molecular targets impinged by attosecond XUV or soft X-ray pulses. Including this mechanism in time--dependent simulations of charge--migration processes is a difficult task, and Auger scatterings are often ignored altogether. In this work we present an advance of the current state-of-the-art by putting forward a real--time approach based on nonequilibrium Green's functions suitable for first-principles calculations of molecules with tens of active electrons. To demonstrate the accuracy of the method we report comparisons against accurate grid simulations of one-dimensional systems. We also predict a highly asymmetric profile of the Auger wavepacket, with a long tail exhibiting ripples temporally spaced by the inverse of the Auger energy.