Ultrafast photonic control of colossal magnetoresistive materials

One challenge in condensed matter physics is the active control of quantum phases of functional transition metal oxides (TMOs) using photons. The realization of active control falls into the research field of photo-induced phase transitions using ultrafast spectroscopy, which has attracted research effort for the past two decades. Early research demonstrated photo-control of spin crossover compounds and the neutral-to-ionic transition in organic crystals. The realization of photo-control of the macroscopic properties of metal oxides is not a simple extension of the old subject, but a rebirth and extension of this research area as reflected in the following ways: 1. Ultrafast optical spectroscopy (UOS) provides a dynamical route to analyze the coupled degrees of freedom in the equilibrium state of TMOs, providing a new approach to understand fundamental issues in condensed matter physics. 2. UOS studies of complex materials drive the development of new high precision experimental techniques to facilitate mode-selective excitation. 3. The active control of magnetism, ferro-electricity, superconductivity and metal-insulator transitions in TMOs opens new avenues for the design and application of novel optoelectronic devices. An even greater challenge for photo-induced phase transition is to realize nonthermal switching between the ground state and a meta-stable phase, having a well-defined order parameter. For this purpose we perform ultrafast optical-pump THz-probe spectroscopy on the strain-engineered colossal magnetoresistance (CMR) material La2/3Ca1/3MnO3. We utilize the anisotropic strain applied by NdGaO3 (001) substrates to tune the La2/3Ca1/3MnO3 into a charge ordered insulating (COI) phase, originating from the enhanced orthorhombicity of the lattice, such that Mn-O-Mn bonding angle deviates from 180^o. Both octahedral tilting and Jahn-Teller-like distortion suppress the ferromagnetism and itinerant nature of the d-electrons. Thus, a charge ordered insulating phase dominates at low temperatures. Using ultrafast spectroscopy, we demonstrate a persistent and single-laser-pulse driven insulator-to-metal phase transition in these La2/3Ca1/3MnO3 thin films. The experimental results demonstrate that the light-induced phase transition is of a cooperative nature involving multiple degrees of freedom, with the key ingredient of the switching being magnetoelastic coupling. Such active photo-control provides a dynamical perspective to understand how the delicate balance of competing orders decide the quantum phases in colossal-magnetoresistance materials

Nonlinear terahertz metamaterials via field-enhanced carrier dynamics in GaAs

We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at terahertz frequencies. For SRRs on doped GaAs films, incident terahertz radiation with peak fields of ~20 - 160 kV/cm drives intervalley scattering. This reduces the carrier mobility and enhances the SRR LC response due to a conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field enhancement within the SRR gaps leads to efficient impact ionization, increasing the carrier density and the conductivity which, in turn, suppresses the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude in the carrier density in the SRR gaps on semi-insulating GaAs substrate. Furthermore, we show that the effective permittivity can be swept from negative to positive values with increasing terahertz field strength in the impact ionization regime, enabling new possibilities for nonlinear metamaterials.Comment: 5 pages, 4 figure

Data-driven Tracking of the Bounce-back Path after Disasters: Critical Milestones of Population Activity Recovery and Their Spatial Inequality

The ability to measure and track the speed and trajectory of a community's post-disaster recovery is essential to inform resource allocation and prioritization. The current survey-based approaches to examining community recovery, however, have significant lags and put the burden of data collection on affected people. Also, the existing literature lacks quantitative measures for important milestones to inform the assessment of recovery trajectory. Recognizing these gaps, this study uses location-based data related to visitation patterns and credit card transactions to specify critical recovery milestones related to population activity recovery. Using data from 2017 Hurricane Harvey in Harris County (Texas), the study specifies four critical post-disaster recovery milestones and calculates quantitative measurements of the length of time between the end of a hazard event and when the spatial areas (census tracts) reached these milestones based on fluctuations in visits to essential and non-essential facilities, and essential and non-essential credit card transactions. Accordingly, an integrated recovery metric is created for an overall measurement of each spatial area's recovery progression. Exploratory statistical analyses were conducted to examine whether variations in community recovery progression in achieving the critical milestones is correlated to its flood status, socioeconomic characteristics, and demographic composition. Finally, the extent of spatial inequality is examined. The results show the presence of moderate spatial inequality in population activity recovery in Hurricane Harvey, based upon which the inequality of recovery is measured. Results of this study can benefit post-disaster recovery resource allocation as well as improve community resilience towards future natural hazards

RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture

Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability. To this end, we propose a packet-level network intrusion detection solution that makes novel use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding, which is fed into a DNN-based classifier. To enable explainability and support real-time detections at micro-second speed, we further develop a Software-Hardware Co-Design approach to efficiently realize the proposed solution by converting the learned detection policies into decision trees and implementing them using an emerging architecture based on memristor devices. By jointly optimizing associated software and hardware constraints, we show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level. Evaluation results on real-world datasets (e.g., UNSW and CIC-IDS datasets) demonstrate nearly three-nines detection accuracy with a substantial speedup of nearly four orders of magnitude