Publications

We introduce a mechanism for light-induced Floquet engineering of the Fermi surface to dynamically tip the balance between competing instabilities in correlated condensed matter systems in the vicinity of a Van Hove singularity. We first calculate how the Fermi surface is deformed by an off-resonant, high-frequency light field and then determine the impact of this deformation on the ordering tendencies using an unbiased functional renormalization group approach. As a testbed, we investigate Floquet engineering in cuprates driven by light. We find that the d-wave superconducting ordering tendency in this system can be strongly enhanced over the Mott insulating one. This gives rise to extended regions of induced d-wave superconductivity in the effective phase diagram in the presence of a light field.

Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a "variance is salience" hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The "variance is salience" hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

Malignancy in cancer is a consequence of the progressive accumulation of mutations in a tumor, with profound implications for drug selection and treatment. However, in human studies, inter-patient variability obscures molecular signatures of tumor progression because patients usually present with a single mammary tumor. In contrast, dogs frequently exhibit multiple naturally occurring mammary tumors in the same individual. Moreover, canine mammary tumors (CMTs) and human breast cancer have similar histopathological profiles and clinical presentation. We leverage the CMT model to elucidate genome-wide molecular changes clinically relevant in human breast cancer, focusing on signals underlying tumor development. We develop a robust, generally applicable, computational analysis framework (FREYA) for analysis of CMTs for comparative oncology. Using FREYA, we RNA profile 89 samples from 16 dogs, and demonstrate that CMTs recapitulate human breast cancer subtypes. We then extract molecular profiles of breast cancer progression at three distinct stages (normal, pre-malignant and malignant) and identify signatures of gene expression reflective of tumor progression. Focusing on the transitions to malignancy, we identify transcriptional patterns and biological pathways specific to malignant tumors and distinct from those characterizing pre-malignant tumors or normal tissue. We find that human breast cancer patients whose tumors exhibit strong CMT malignancy signatures have significantly decreased survival, indicative of the importance of the tumor progression processes identified in CMTs to human breast cancer prognosis. Altogether, our comprehensive genomic characterization demonstrates that CMTs are a powerful translational model of breast cancer, providing insights that inform our understanding of tumor development in humans. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we publicly share all of our data and provide FREYA, a robust data processing pipeline and statistical analyses framework, at freya.flatironinstitute.org.

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the $\{{{\rm{\Omega }}}_{{\rm{m}}},{{\rm{\Omega }}}_{{\rm{b}}},h,{n}_{s},{\sigma }_{8},{M}_{\nu },w\}$ hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 ${\left({h}^{-1}\mathrm{Gpc}\right)}^{3};$ each simulation follows the evolution of \(256^{3}, 512^{3}, or 1024^{3}\) particles in a box of 1 h −1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions

We evaluate the performance of approaches that combine coupled-cluster and perturbation theory based on a predefined active space of orbitals. Coupled-cluster theory is used to treat excitations that are internal to the active space and perturbation theory is used for all other excitations, which are at least partially external to the active space. We consider a variety of schemes that differ in how the internal and external excitations are coupled. Such approaches are presented for ground states and excited states within the equation-of-motion formalism. Results are given for the ionisation potentials and electron affinities of a test set of small molecules and for the correlation energy and band gap of a few periodic solids.

Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organism’s PPI network is left out, using other organisms’ network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.

Accretion disks in binary systems can exhibit a tilt instability, arising from the interaction between components of the tidal potential and dissipation. Using a linear analysis, we show that the aspect ratios and outer radii of circumplanetary disks provide favorable conditions for tilt growth. We quantify the growth rate of the instability using particle-based ({\sc phantom}) and grid-based ({\sc athena++}) hydrodynamic simulations. For a disk with outer aspect ratio H/r≃0.1, initially moderate tilts double on a time scale of about 15-30 binary orbits. Our results imply that detached circumplanetary disks, whose evolution is not entirely controlled by accretion from the circumstellar disk, may commonly be misaligned to the planetary orbital plane. We discuss implications for planetary spin evolution, and possible interactions between the tilt instability and Kozai-Lidov dynamics.

Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date are yet to be fully exploited. We employ The Cannon, a data-driven approach to estimating abundances, to obtain detailed abundances from low-resolution (R = 1800) LAMOST spectra, using the GALAH survey as our reference. We deliver five (for dwarfs) or six (for giants) estimated abundances representing five different nucleosynthetic channels, for 3.9 million stars, to a precision of 0.05 - 0.23 dex. Using wide binary pairs, we demonstrate that our abundance estimates provide chemical discriminating power beyond metallicity alone. We show the coverage of our catalogue with radial, azimuthal and dynamical abundance maps, and examine the neutron capture abundances across the disk and halo, which indicate different origins for the in-situ and accreted halo populations. LAMOST has near-complete Gaia coverage and provides an unprecedented perspective on chemistry across the Milky Way.

Animal embryos are patterned by a handful of highly conserved inductive signals. Yet, in most cases, it is unknown which pattern features (i.e., spatial gradients or temporal dynamics) are required to support normal development. An ideal experiment to address this question would be to “paint” arbitrary synthetic signaling patterns on “blank canvas” embryos to dissect their requirements. Here, we demonstrate exactly this capability by combining optogenetic control of Ras/extracellular signal-related kinase (ERK) signaling with the genetic loss of the receptor tyrosine-kinase-driven terminal signaling patterning in early Drosophila embryos. Blue-light illumination at the embryonic termini for 90 min was sufficient to rescue normal development, generating viable larvae and fertile adults from an otherwise lethal terminal signaling mutant. Optogenetic rescue was possible even using a simple, all-or-none light input that reduced the gradient of Erk activity and eliminated spatiotemporal differences in terminal gap gene expression. Systematically varying illumination parameters further revealed that at least three distinct developmental programs are triggered at different signaling thresholds and that the morphogenetic movements of gastrulation are robust to a 3-fold variation in the posterior pattern width. These results open the door to controlling tissue organization with simple optical stimuli, providing new tools to probe natural developmental processes, create synthetic tissues with defined organization, or directly correct the patterning errors that underlie developmental defects.

High order perturbation theory has seen an unexpected recent revival for controlled calculations of quantum many-body systems, even at strong coupling. We adapt integration methods using low-discrepancy sequences to this problem. They greatly outperform state-of-the-art diagrammatic Monte Carlo. In practical applications, we show speed-ups of several orders of magnitude with scaling as fast as $1/N$ in sample number $N$; parametrically faster than $1/\sqrt{N}$ in Monte Carlo. We illustrate our technique with a solution of the Kondo ridge in quantum dots, where it allows large parameter sweeps.