ASU Scholarship Showcase

Vesta is a unique, intermediate class of rocky body in the Solar System, between terrestrial planets and small asteroids, because of its size (average radius of ∼263 km) and differentiation, with a crust, mantle and core. Vesta’s low surface gravity (0.25 m/s²) has led to the continual absence of a protective atmosphere and consequently impact cratering and impact-related processes are prevalent. Previous work has shown that the formation of the Rheasilvia impact basin induced the equatorial Divalia Fossae, whereas the formation of the Veneneia impact basin induced the northern Saturnalia Fossae. Expanding upon this earlier work, we conducted photogeologic mapping of the Saturnalia Fossae, adjacent structures and geomorphic units in two of Vesta’s northern quadrangles: Caparronia and Domitia. Our work indicates that impact processes created and/or modified all mapped structures and geomorphic units. The mapped units, ordered from oldest to youngest age based mainly on cross-cutting relationships, are: (1) Vestalia Terra unit, (2) cratered highlands unit, (3) Saturnalia Fossae trough unit, (4) Saturnalia Fossae cratered unit, (5) undifferentiated ejecta unit, (6) dark lobate unit, (7) dark crater ray unit and (8) lobate crater unit. The Saturnalia Fossae consist of five separate structures: Saturnalia Fossa A is the largest (maximum width of ∼43 km) and is interpreted as a graben, whereas Saturnalia Fossa B-E are smaller (maximum width of ∼15 km) and are interpreted as half grabens formed by synthetic faults. Smaller, second-order structures (maximum width of <1 km) are distinguished from the Saturnalia Fossae, a first-order structure, by the use of the general descriptive term ‘adjacent structures’, which encompasses minor ridges, grooves and crater chains. For classification purposes, the general descriptive term ‘minor ridges’ characterizes ridges that are not part of the Saturnalia Fossae and are an order of magnitude smaller (maximum width of <1 km vs. maximum width of ∼43 km). Shear deformation resulting from the large-scale (diameter of <100 km) Rheasilvia impact is proposed to form minor ridges (∼2 km to ∼25 km in length), which are interpreted as the surface expression of thrust faults, as well as grooves (∼3 km to ∼25 km in length) and pit crater chains (∼1 km to ∼25 km in length), which are interpreted as the surface expression of extension fractures and/or dilational normal faults. Secondary crater material, ejected from small-scale and medium-scale impacts (diameters of <100 km), are interpreted to form ejecta ray systems of grooves and crater chains by bouncing and scouring across the surface. Furthermore, seismic shaking, also resulting from small-scale and medium-scale impacts, is interpreted to form minor ridges because seismic shaking induces flow of regolith, which subsequently accumulates as minor ridges that are roughly parallel to the regional slope. In this work we expand upon the link between impact processes and structural features on Vesta by presenting findings of a photogeologic, structural mapping study which highlights how impact cratering and impact-related processes are expressed on this unique, intermediate Solar System body.

We produced a geologic map of the Av-9 Numisia quadrangle of asteroid Vesta using Dawn spacecraft data to serve as a tool to understand the geologic relations of surface features in this region. These features include the plateau Vestalia Terra, a hill named Brumalia Tholus, and an unusual “dark ribbon” material crossing the majority of the map area. Stratigraphic relations suggest that Vestalia Terra is one of the oldest features on Vesta, despite a model crater age date similar to that of much of the surface of the asteroid. Cornelia, Numisia and Drusilla craters reveal bright and dark material in their walls, and both Cornelia and Numisia have smooth and pitted terrains on their floors suggestive of the release of volatiles during or shortly after the impacts that formed these craters. Cornelia, Fabia and Teia craters have extensive bright ejecta lobes. While diogenitic material has been identified in association with the bright Teia and Fabia ejecta, hydroxyl has been detected in the dark material within Cornelia, Numisia and Drusilla. Three large pit crater chains appear in the map area, with an orientation similar to the equatorial troughs that cut the majority of Vesta. Analysis of these features has led to several interpretations of the geological history of the region. Vestalia Terra appears to be mechanically stronger than the rest of Vesta. Brumalia Tholus may be the surface representation of a dike-fed laccolith. The dark ribbon feature is proposed to represent a long-runout ejecta flow from Drusilla crater.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on–off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Recently, the phenomenon of quantum-classical correspondence breakdown was uncovered in optomechanics, where in the classical regime the system exhibits chaos but in the corresponding quantum regime the motion is regular - there appears to be no signature of classical chaos whatsoever in the corresponding quantum system, generating a paradox. We find that transient chaos, besides being a physically meaningful phenomenon by itself, provides a resolution. Using the method of quantum state diffusion to simulate the system dynamics subject to continuous homodyne detection, we uncover transient chaos associated with quantum trajectories. The transient behavior is consistent with chaos in the classical limit, while the long term evolution of the quantum system is regular. Transient chaos thus serves as a bridge for the quantum-classical transition (QCT). Strikingly, as the system transitions from the quantum to the classical regime, the average chaotic transient lifetime increases dramatically (faster than the Ehrenfest time characterizing the QCT for isolated quantum systems). We develop a physical theory to explain the scaling law.

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.