Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer.

Pressure sensor-impregnated walkways transform a person's footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and without neuromotor pathology. Evidence shows that plantar pressure distributions are altered in aging and diabetic peripheral neuropathy, but less is known about pressure dynamics in chemotherapy-induced peripheral neuropathy (CIPN), a condition leading to falls in cancer survivors. Studying pressure dynamics longitudinally as people develop CIPN will require a composite model that can accurately characterize a survivor's gait consistencies before chemotherapy, even in the presence of normal step-to-step variation. In this paper, we present a state-of-the-art data-driven learning technique to identify consistencies in an individual's plantar pressure dynamics. We apply this technique to a database of steps taken by each of 16 women before they begin a new course of neurotoxic chemotherapy for breast or gynecologic cancer. After extracting gait features by decomposing spatiotemporal plantar pressure data into low-rank dynamic modes characterized by three features: frequency, a decay rate, and an initial condition, we employ a machine-learning model to identify consistencies in each survivor's walking pattern using the centroids for each feature. In this sample, our approach is at least 86% accurate for identifying the correct individual using their pressure dynamics, whether using the right or left foot, or data from trials walked at usual or fast speeds. In future work, we suggest that persistent deviation from a survivor's pre-chemotherapy step consistencies could be used to automate the identification of peripheral neuropathy and other chemotherapy side effects that impact mobility.

Ferromagnetic Mott state in Twisted Graphene Bilayers at the Magic Angle.

We address the effective tight-binding Hamiltonian that describes the insulating Mott state of twisted graphene bilayers at a magic angle. In that configuration, twisted bilayers form a honeycomb superlattice of localized states, characterized by the appearance of flat bands with fourfold degeneracy. After calculating the maximally localized superlattice Wannier wave functions, we derive the effective spin model that describes the Mott state. We suggest that the system is an exotic ferromagnetic Mott insulator, with well-defined experimental signatures.

Emergence of Majorana and Dirac particles in ultracold fermions via tunable interactions, spin-orbit effects, and Zeeman fields.

We discuss the emergence of rings of zero-energy excitations in momentum space for superfluid phases of ultracold fermions when spin-orbit effects, Zeeman fields, and interactions are varied. We show that phases containing rings of nodes possess nontrivial topological invariants, and that phase transitions between distinct topological phases belong to the Lifshitz class. Upon crossing phase boundaries, existing massless Dirac fermions in the gapless phase annihilate to produce bulk zero-mode Majorana fermions at phase boundaries, and then become massive Dirac fermions in the gapped phase. We characterize these tunable topological phase transitions via several spectroscopic properties, including excitation spectrum, spectral function, and momentum distribution.

Pairing symmetry in a two-orbital exchange coupling model of oxypnictides.

We study the pairing symmetry of a two-orbital J1-J2 model for FeAs layers in oxypnictides. We show that the mixture of an intraorbital unconventional s_{x;{2}y;{2}} approximately cos(k_{x})cos(k_{y}) pairing symmetry, which changes sign between the electron and hole Fermi surfaces, and a very small d_{x;{2}-y;{2}} approximately cos(k_{x})-cos(k_{y}) component is favored in a large part of the J1-J2 phase diagram. A pure s_{x;{2}y;{2}} pairing state is favored for J2>J1. The signs of the d_{x;{2}-y;{2}} order parameters in the two different orbitals are opposite. While a small d_{xy} approximately sin(k_{x})sin(k_{y}) interorbital pairing coexists in the above phases, the intraorbital d_{xy} pairing is not favored even for large J2.