Publications

The rupture potential of an atherosclerotic plaque is dependent on both the plaque's composition and the shear stresses it encounters from blood flow. Because plaques move and deform throughout the cardiac cycle, resulting in changes to plaque position and shape as well as to the encountered shear stresses, concurrent imaging of both risk factors over time is required to accurately predict plaque vulnerability. To evaluate the potential to achieve as much, multi-angle plane wave (PW) ARFI and least-squares vector Doppler data were acquired in a calibrated flow phantom with channels of 4–8 mm diameters and flow rates of 100–600 ml/min. The wall shear stress (WSS) was measured to within 15% of the ground-truth analytical solutions. The same methods were then implemented in an excised human cadaveric carotid with a x% stenotic plaque. ARFI VoA detected plaque regions of calcium and intraplaque hemorrhage that were validated by spatially-matched histology. Concurrent vector Doppler yielded a peak WSS of 5.2 Pa on the plaque shoulder, which was consistent with the 6.4 Pa WSS predicted by an immersed interface fluid-solid interation (FSI) model developed using the specific geometry of the examined cadaveric carotid. Overall our results demonstrate the feasibility of concurrent imaging of carotid plaque composition by ARFI VoA, vector flow, and WSS to better assess stroke risk.

We examine the ground-state phase diagram and thermal phase transitions in a plaquettized fully frustrated bilayer spin-1/2 Heisenberg model. Based on a combined analysis from sign-problem free quantum Monte Carlo simulations, perturbation theory and free-energy arguments, we identify a first-order quantum phase transition line that separates two competing quantum-disordered ground states with dominant singlet formations on inter-layer dimers and plaquettes, respectively. At finite temperatures, this line extends to form a wall of first-order thermal transitions, which terminates in a line of thermal critical points. From a perturbative approach in terms of an effective Ising model description, we identify a quadratic suppression of the critical temperature scale in the strongly plaquettized region. Based on free-energy arguments we furthermore obtain the full phase boundary of the low-temperature dimer-singlet regime, which agrees well with the quantum Monte Carlo data.

Recent experiments of chemical reactions in optical cavities have shown great promise to alter and steer chemical reactions but still remain poorly understood theoretically. In particular the origin of resonant effects between the cavity and certain vibrational modes in the collective limit is still subject to active research. In this paper, we study unimolecular dissociation reactions of many molecules collectively interacting with an infrared cavity mode through their vibrational dipole moment. We find that the reaction rate can slow down by increasing the number of aligned molecules if the cavity mode is resonant with a vibrational frequency of the molecules. We also discover a simple scaling relation that scales with the collective Rabi splitting to estimate the onset of reaction rate modification by collective vibrational strong coupling and numerically demonstrate these effects for up to 10,000 molecules.

Theoretical research into many-body quantum systems has mostly focused on regular structures which have a small, simple unit cell and where a vanishingly small number of pairs of the constituents directly interact. Motivated by advances in control over the pairwise interactions in many-body simulators, we determine the fate of spin systems on more general, arbitrary graphs. Placing the minimum possible constraints on the underlying graph, we prove how, with certainty in the thermodynamic limit, such systems behave like a single collective spin. We thus understand the emergence of complex many-body physics as dependent on `exceptional', geometrically constrained structures such as the low-dimensional, regular ones found in nature. Within the space of dense graphs we identify hitherto unknown exceptions via their inhomogeneity and observe how complexity is heralded in these systems by entanglement and highly non-uniform correlation functions. Our work paves the way for the discovery and exploitation of a whole class of geometries which can host uniquely complex phases of matter.

The pseudo-fermion representation for S=1/2 quantum spins introduces unphysical states in the Hilbert space which can be projected out using the Popov-Fedotov trick. However, state-of-the-art implementation of the functional renormalization group method for pseudo-fermions have so far omitted the Popov-Fedotov projection. Instead, restrictions to zero temperature were made and absence of unphysical contributions to the ground-state was assumed. We question this belief by exact diagonalization of several small-system counterexamples where unphysical states do contribute to the ground state. We then introduce Popov-Fedotov projection to pseudo-fermion functional renormalization, enabling finite temperature computations with only minor technical modifications to the method. At large and intermediate temperatures, our results are perturbatively controlled and we confirm their accuracy in benchmark calculations. At lower temperatures, the accuracy degrades due to truncation errors in the hierarchy of flow equations. Interestingly, these problems cannot be alleviated by switching to the parquet approximation. We introduce the spin projection as a method-intrinsic quality check. We also show that finite temperature magnetic ordering transitions can be studied via finite-size scaling.

We present a promising route to realize spontaneous magnetic order on the surface of a 3D topological insulator by applying a superlattice potential. The superlattice potential creates tunable van Hove singularities, which, when combined with strong spin-orbit coupling and Coulomb repulsion give rise to a topological meron lattice spin texture. The periodicity of this designer meron lattice can be tuned by varying the periodicity of the superlattice potential. We characterize the magnetic order using Ginzburg-Landau theory and show that the magnetic transition temperature reaches experimentally accessible values. Our work introduces a new direction to realize exotic quantum order by engineering interacting Dirac electrons in a superlattice potential, with promising applications to spintronics.

Precise calculations of dynamics in the homogeneous electron gas (jellium model) are of fundamental importance for design and characterization of new materials. We introduce a diagrammatic Monte Carlo technique based on algorithmic Matsubara integration that allows us to compute frequency and momentum resolved finite temperature response directly in the real frequency domain using a series of connected Feynman diagrams. The data for charge response at moderate electron density are used to extract the frequency dependence of the exchange-correlation kernel at finite momenta and temperature. These results are as important for development of the time-dependent density functional theory for materials dynamics as ground state energies are for the density functional theory.

We study the Cooper instability in jellium model in the controlled regime of small to intermediate values of the Coulomb parameter r

Visual information arriving at the retina is transmitted to the brain by signals in the optic nerve, and the brain must rely solely on these signals to make inferences about the visual world. Previous work has probed the content of these signals by directly reconstructing images from retinal activity using linear regression or nonlinear regression with neural networks. Maximum a posteriori (MAP) reconstruction using retinal encoding models and separately-trained natural image priors offers a more general and principled approach. We develop a novel method for approximate MAP reconstruction that combines a generalized linear model for retinal responses to light, including their dependence on spike history and spikes of neighboring cells, with the image prior implicitly embedded in a deep convolutional neural network trained for image denoising. We use this method to reconstruct natural images from ex vivo simultaneously-recorded spikes of hundreds of retinal ganglion cells uniformly sampling a region of the retina. The method produces reconstructions that match or exceed the state-of-the-art in perceptual similarity and exhibit additional fine detail, while using substantially fewer model parameters than previous approaches. The use of more rudimentary encoding models (a linear-nonlinear-Poisson cascade) or image priors (a 1/f spectral model) significantly reduces reconstruction performance, indicating the essential role of both components in achieving high-quality reconstructed images from the retinal signal.

Attentional gating is a core mechanism supporting behavioral flexibility, but its biological implementation remains uncertain. Gain modulation of neural responses is likely to play a key role, but simply boosting relevant neural responses can be insufficient for improving behavioral outputs, especially in hierarchical circuits. Here we propose a variation of attentional gating that relies on stochastic gain modulation as a dedicated indicator of task relevance. We show that targeted stochastic modulation can be effectively learned and used to fine-tune hierarchical architectures, without reorganization of the underlying circuits. Simulations of such networks demonstrate improvements in learning efficiency and performance in novel tasks, relative to traditional attentional mechanisms based on deterministic gain increases. The effectiveness of this approach relies on the availability of representational bottlenecks in which the task relevant information is localized in small subpopulations of neurons. Overall, this work provides a new mechanism for constructing intelligent systems that can flexibly and robustly adapt to changes in task structure.