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

IP-based virtual private networks and proportional quality of service differentiation

IP-based virtual private networks (VPNs) have the potential of delivering cost-effective, secure, and private network-like services. Having surveyed current enabling techniques, an overall picture of IP VPN implementations is presented. In order to provision the equivalent quality of service (QoS) of legacy connection-oriented layer 2 VPNs (e.g., Frame Relay and ATM), IP VPNs have to overcome the intrinsically best effort characteristics of the Internet. Subsequently, a hierarchical QoS guarantee framework for IP VPNs is proposed, stitching together development progresses from recent research and engineering work. To differentiate IP VPN QoS, the proportional QoS differentiation model, whose QoS specification granularity compromises that of IntServ and Diffserv, emerges as a potential solution. The investigation of its claimed capability of providing the predictable and controllable QoS differentiation is then conducted. With respect to the loss rate differentiation, the packet shortage phenomenon shown in two classical proportional loss rate (PLR) dropping schemes is studied. On the pursuit of a feasible solution, the potential of compromising the system resource, that is, the buffer, is ruled out; instead, an enhanced debt-aware mechanism is suggested to relieve the negative effects of packet shortage. Simulation results show that debt-aware partially curbs the biased loss rate ratios, and improves the queueing delay performance as well. With respect to the delay differentiation, the dynamic behavior of the average delay difference between successive classes is first analyzed, aiming to gain insights of system dynamics. Then, two classical delay differentiation mechanisms, that is,proportional average delay (PAD) and waiting time priority (WTP), are simulated and discussed. Based on observations on their differentiation performances over both short and long time periods, a combined delay differentiation (CDD) scheme is introduced. Simulations are utilized to validate this method. Both loss and delay differentiations are based on a series of differentiation parameters. Though previous work on the selection of delay differentiation parameters has been presented, that of loss differentiation parameters mostly relied on network operators\u27 experience. A quantitative guideline, based on the principles of queueing and optimization, is then proposed to compute loss differentiation parameters. Aside from analysis, the new approach is substantiated by numerical results

Discrete Message via Online Clustering Labels in Decentralized POMDP

Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in Partially-Observable Markov Decision Processes. Existing works often rely on black-box methods to encode local information/features into messages shared with other agents. However, such black-box approaches are unable to provide any quantitative guarantees on the expected return and often lead to the generation of continuous messages with high communication overhead and poor interpretability. In this paper, we establish an upper bound on the return gap between an ideal policy with full observability and an optimal partially-observable policy with discrete communication. This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss. By minimizing the upper bound, we propose a surprisingly simple design of message generation functions in multi-agent communication and integrate it with reinforcement learning using a Regularized Information Maximization loss function. Evaluations show that the proposed discrete communication significantly outperforms state-of-the-art multi-agent communication baselines and can achieve nearly-optimal returns with few-bit messages that are naturally interpretable

Droop Based Control Strategy for an Isolated DC Nanogrid with Boost Type Interfaces

A Nanogrid can integrate Renewable Energy Sources (RESs) easily and power loads locally. DC distribution is more efficient than AC. An isolated DC Nanogrid increases the resilience of a single building, as it can operate in the off-grid mode. Energy storage (ES) units can balance the fluctuating power from RESs and variable load demand. The considered isolated DC Nanogrid consists of one Photovoltaic (PV) unit, two Energy Storage (ES) units, and multiple loads. Hierarchical control is often used. Decentralized current-mode droop control on the primary level achieves power-sharing. State of Charge (SoC) control avoids overcharging and deep discharging for ES units, working on the slope of droop control on the secondary level. Also, voltage regulation on the secondary level maintains the bus voltage at its nominal value by modifying the “no-load” voltages of droop control for the PV unit and ES units respectively. Boost-type Class C converters are used as interfaces for ES units. They allow the ES units to have a lower voltage than the bus voltage. The stability issue related to the Right Half Plane (RHP) zero of the Class C converter is analyzed. Based on a worst-case scenario, the controllers are designed to guarantee the system can be stable under considered circumstances. A new design approach for the output capacitor of the converter is introduced to stabilize the system with a random droop factor. The control strategy is verified in simulations of analog and digital control in different scenarios

Politically Influenced Music in Post-Reform China

This thesis explores the connection between music and politics in post reform China from the point of view of mass communication, and within the fields of politically influenced folk music in Xinjiang, the rapid development of the ‘Chinese Piano Dream’, and the popular music on the central stage of the China Central Television Spring Festival Gala. This is preceded by an introduction to the political, economic and cultural changes since the implementation of the Reform and Opening Up Policy in 1978, which have not only challenged China’s traditional culture but have also altered her self-image and relationship with the outside world. Chinese politicians have realized that communal value and national identity are no longer based only on closed borders and a strong political ideology. Meanwhile, music remains a propaganda tool since the establishment of the Communist Party of China, and continues to serve the mainstream media, interacting with politics to further the Communist agenda and maintain a favourable image of the Party. As a native fieldworker from Xinjiang, I enjoy dual identities and perspectives (‘emic’ and ‘etic’) as insider and outsider to apply to the research of new folk music in Xinjiang: how it is decoded, disseminated and interpreted by its audience, and how it conveys political messages. With China’s rapid economic growth, Western classical music became a new channel of communication between East and West. The renowned pianist Yundi Li is not only a household name but is moreover a symbol of the response to President Xi Jin-ping’s call to the nation to chase the ‘Chinese Dream’ and he also represents China’s ‘soft power’ internationally. My fieldwork with Yundi stemmed from the largest music tour in Chinese history, entitled ‘China Piano Dream’ (2013), and the ‘Emperor - Fantasy’ tour (2014), which gave me a unique insight into how Chinese musicians negotiate the political and social expectations placed on them. Finally, a new crossover resulting from collaborations between well-known musicians from Mainland China and Taiwan has had a significant impact on the most viewed television programme in the world: the China Central Television Spring Festival Gala. Mass communication models, audience reception and a semiotic approach to the analysis of musical extracts are applied to interpret the political meaning of the performance

Scalable Multi-agent Skill Discovery based on Kronecker Graphs

Covering skill (a.k.a., option) discovery has been developed to improve the exploration of RL in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph. Given that joint state space grows exponentially with the number of agents in multi-agent systems, existing researches still relying on single-agent option discovery either become prohibitive or fail to directly discover joint options that improve the connectivity of the joint state space. In this paper, we show how to directly compute multi-agent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector using the Laplacian spectrum of individual agents' transition graphs. Further, considering that directly computing the Laplacian spectrum is intractable for tasks with infinite-scale state spaces, we further propose a deep learning extension of our method by estimating eigenfunctions through NN-based representation learning techniques. The evaluation on multi-agent tasks built with simulators like Mujoco, shows that the proposed algorithm can successfully identify multi-agent options, and significantly outperforms the state-of-the-art. Codes are available at: https://github.itap.purdue.edu/Clan-labs/Scalable_MAOD_via_KP.Comment: Accepted to NeurIPS 2022. arXiv admin note: substantial text overlap with arXiv:2201.0822

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