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This paper focuses on radar waveform optimization for minimizing the Cram\'er-Rao bound (CRB) in a multiple-input multiple-output (MIMO) radar system. In contrast to conventional approaches relying on semi-definite programming (SDP) and optimization toolboxes like CVX, we introduce a pioneering and efficient waveform optimization approach in this paper. Our proposed algorithm first applies sequential linear approximation to transform the original CRB-based problem with the transmit power constraint into a sequence of convex subproblems. By introducing a proximal term and further leveraging the Karush-Kuhn-Tucker (KKT) conditions, we derive the optimal closed-form solution for each subproblem. The convergence of the proposed algorithm is then proved rigorously. Numerical results demonstrate that the proposed approach significantly reduces computational complexity -- at least two orders of magnitude lower than the baseline algorithms while maintaining the same radar sensing accuracy.

The unique optical properties of graphene, with broadband absorption and ultrafast response, make it a critical component of optoelectronic and spintronic devices. Using time-resolved momentum microscopy with high data rate and high dynamic range, we report momentum-space measurements of electrons promoted to the graphene conduction band with visible light, and their subsequent relaxation. We observe a pronounced non-thermal distribution of nascent photoexcited electrons with lattice pseudospin polarization in remarkable agreement with results of simple tight-binding theory. By varying the excitation fluence, we vary the relative importance of electron-electron vs. electron-phonon scattering in the relaxation of the initial distribution. Increasing the excitation fluence results in increased noncollinear electron-electron scattering and reduced pseudospin polarization, although up-scattered electrons retain a degree of polarization. These detailed momentum-resolved electron dynamics in graphene demonstrate the capabilities of high-performance time-resolved momentum microscopy in the study of 2D materials and can inform the design of graphene devices., Comment: 19 pages, 5 figures

Polaritons are light-matter quasiparticles that govern the optical response of quantum materials and enable their nanophotonic applications. We have studied a new type of polaritons arising in magnetized graphene encapsulated in hexagonal boron nitride (hBN). These polaritons stem from hybridization of Dirac magnetoexciton modes of graphene with waveguide phonon modes of hBN crystals. We refer to these quasiparticles as the Landau-phonon polaritons (LPPs). Using infrared magneto nanoscopy, we imaged LPPs and controlled their real-space propagation by varying the magnetic field. These LLPs have large in-plane momenta and are not bound by the conventional optical selection rules, granting us access to the "forbidden" inter-Landau level transitions (ILTs). We observed avoided crossings in the LPP dispersion - a hallmark of the strong coupling regime - occurring when the magnetoexciton and hBN phonon frequencies matched. Our LPP-based nanoscopy also enabled us to resolve two fundamental many-body effects: the graphene Fermi velocity renormalization and ILT-dependent magnetoexciton binding energies. These results indicate that magnetic-field-tuned Dirac heterostructures are promising platforms for precise nanoscale control and sensing of light-matter interaction.

In this paper, we formally analyze global convergence in the realm of distributed consensus optimization. Current solutions have explored such analysis, particularly focusing on consensus alternating direction method of multipliers (CADMM), including convex and non-convex cases. While such efforts on non-convexity offer elegant theory guaranteeing global convergence, they entail strong assumptions and complicated proof techniques that are increasingly pose challenges when adopted to real-world applications. To resolve such tension, we propose a novel bi-level globalization strategy that not only guarantees global convergence but also provides succinct proofs, all while requiring mild assumptions. We begin by adopting such a strategy to perform global convergence analysis for the non-convex cases in C-ADMM. Then, we employ our proposed strategy in consensus augmented Lagrangian based alternating direction inexact Newton method (C-ALADIN), a more recent and generalization of C-ADMM. Surprisingly, our analysis shows that C-ALADIN globally converges to local optimizer, complementary to the prior work on C-ALADIN, which had primarily focused on analyzing local convergence for non-convex cases.

The Bloch electron energy spectrum of a crystalline solid is determined by the underlying lattice structure at the atomic level. In a 2-dimensional (2d) crystal it is possible to impose a superlattice with nanometer-scale periodicity, allowing to tune the fundamental Bloch electron spectrum, and enabling novel physical properties which are not accessible in the original crystal. In recent years, a top-down approach for creating 2d superlattices on monolayer graphene by means of nanopatterned electric gates has been studied, which allows the formation of isolated energy bands and Hofstadter Butterfly physics in quantizing magnetic fields. Within this approach, however, evidence of electron correlations which drive many problems at the forefront of physics research remains to be uncovered. In this work we demonstrate signatures of a correlated insulator phase in Bernal-stacked bilayer graphene (BLG) modulated by a gate-defined superlattice potential, manifested as a set of resistance peaks centered at carrier densities of integer multiples of a single electron per unit cell of the superlattice potential. We associate the correlated insulator phase to the formation of flat energy bands due to the superlattice potential combined with inversion symmetry breaking. Inducing correlated electron phases with nanopatterning defined electric gates paves the way to custom-designed superlattices with arbitrary geometries and symmetries for studying band structure engineering and strongly correlated electrons in 2d materials.

This paper investigates algorithms for solving distributed consensus optimization problems that are non-convex. Since Typical ALADIN (Typical Augmented Lagrangian based Alternating Direction Inexact Newton Method, T-ALADIN for short) [1] is a well-performed algorithm treating distributed optimization problems that are non-convex, directly adopting T-ALADIN to those of consensus is a natural approach. However, T-ALADIN typically results in high communication and computation overhead, which makes such an approach far from efficient. In this paper, we propose a new variant of the ALADIN family, coined consensus ALADIN (C-ALADIN for short). C-ALADIN inherits all the good properties of T-ALADIN, such as the local linear or super-linear convergence rate and the local convergence guarantees for non-convex optimization problems; besides, C-ALADIN offers unique improvements in terms of communication efficiency and computational efficiency. Moreover, C-ALADIN involves a reduced version, in comparison with Consensus ADMM (Alternating Direction Method of Multipliers) [3], showing significant convergence performance, even without the help of second-order information. We also propose a practical version of C-ALADIN, named FedALADIN, that seamlessly serves the emerging federated learning applications, which expands the reach of our proposed C-ALADIN. We provide numerical experiments to demonstrate the effectiveness of C-ALADIN. The results show that C-ALADIN has significant improvements in convergence performance.

Microgrids are increasingly being utilized to improve the resilience and operational flexibility of power grids, and act as a backup power source during grid outages. However, it necessitates that the microgrid itself could provide power to the critical loads. This paper presents an algorithm named alternating optimization based sequential boolean quadratic programming tailored for solving optimal demand shut-offs problems arising in microgrids. Moreover, we establish local superlinear convergence of the proposed approximate Boolean quadratic programming method over nonconvex problems. In the end, the performance of the proposed method is illustrated on the modified IEEE 30-bus case study.

Fault localization is challenging in an online service system due to its monitoring data's large volume and variety and complex dependencies across or within its components (e.g., services or databases). Furthermore, engineers require fault localization solutions to be actionable and interpretable, which existing research approaches cannot satisfy. Therefore, the common industry practice is that, for a specific online service system, its experienced engineers focus on localization for recurring failures based on the knowledge accumulated about the system and historical failures. Although the above common practice is actionable and interpretable, it is largely manual, thus slow and sometimes inaccurate. In this paper, we aim to automate this practice through machine learning. That is, we propose an actionable and interpretable fault localization approach, DejaVu, for recurring failures in online service systems. For a specific online service system, DejaVu takes historical failures and dependencies in the system as input and trains a localization model offline; for an incoming failure, the trained model online recommends where the failure occurs (i.e., the faulty components) and which kind of failure occurs (i.e., the indicative group of metrics) (thus actionable), which are further interpreted by both global and local interpretation methods (thus interpretable). Based on the evaluation on 601 failures from three production systems and one open-source benchmark, in less than one second, DejaVu can on average rank the ground truths at 1.66-th to 5.03-th among a long candidate list, outperforming baselines by at least 51.51%., Comment: Accepted by ESEC/FSE 2022

We show that bilayer graphene in the presence of a 2D superlattice potential provides a highly tunable setup that can realize a variety of flat band phenomena. We focus on two regimes: (i) topological flat bands with non-zero Chern numbers, C, including bands with higher Chern numbers |C| > 1; and (ii) an unprecedented phase consisting of a stack of nearly perfect flat bands with C = 0. For realistic values of the potential and superlattice periodicity, this stack can span nearly 100 meV, encompassing nearly all of the low-energy spectrum. We further show that in the topological regime, the topological flat band has a favorable band geometry for realizing a fractional Chern insulator (FCI) and use exact diagonalization to show that the FCI is in fact the ground state at 1/3 filling. Our results provide a realistic guide for future experiments to realize a new platform for flat band phenomena., Comment: 14 pages including supplementary materials. Published version

The wireless mobile communication system is updated and iterated on the whole almost every decade. It is now in the development period of the application scenarios of the fifth generation mobile communication system (5G). Unfortunately, 5G relies on plenty of small base stations with a large number of antennas that consume a lot of energy. In this paper, a novel Boolean variable quadratic programming algorithm is designed for the antenna selection optimization problem to reduce power consumption. Experiments show that the proposed algorithm achieves high complementarity satisfaction accuracy with only a few steps.

This paper is about computationally tractable methods for power system parameter estimation and Optimal Experiment Design (OED). Here, the main motivation is that OED has the potential to significantly increase the accuracy of power system parameter estimates, for example, if only a few batches of data are available. The problem is, however, that solving the exact OED problem for larger power grids turns out to be computationally expensive and, in many cases, even computationally intractable. Therefore, the present paper proposes three numerical approximation techniques, which increase the computational tractability of OED for power systems. These approximation techniques are bench-marked on a 5-bus and a 14-bus case studies.

Coupling between exciton states across the Brillouin zone in monolayer transition metal dichalcogenides can lead to ultrafast valley depolarization. Using time- and angle-resolved photoemission, we present momentum- and energy-resolved measurements of exciton coupling in monolayer WS$_2$. By comparing full 4D ($k_x, k_y, E, t$) data sets after both linearly and circularly polarized excitation, we are able to disentangle intervalley and intravalley exciton coupling dynamics. Recording in the exciton binding energy basis instead of excitation energy, we observe strong mixing between the B$_{1s}$ exciton and A$_{n>1}$ states. The photoelectron energy and momentum distributions observed from excitons populated via intervalley coupling (e.g. K$^-$ $\rightarrow$ K$^+$) indicate that the dominant valley depolarization mechanism conserves the exciton binding energy and center-of-mass momentum, consistent with intervalley Coulomb exchange. On longer timescales, exciton relaxation is accompanied by contraction of the momentum space distribution., Comment: 16 pages: 8 pages main text with 5 figures, 8 pages SI with 6 figures

The geometric phase (Berry phase) of an electronic wave function is the fundamental basis of the topological properties in solids. Modulating band structure provides a tuning knob for the Berry phase, and in the extreme case drives a topological phase transition. Despite the significant developments in topological materials study, it remains a challenge to tune between different topological phases while tracing the impact of the Berry phase on quantum charge transport, in the same material. Here we report both in a magnetotransport study of ZrTe5. By tuning the band structure with uniaxial strain, we directly map a weak- to strong- topological phase transition through a gapless Dirac semimetal phase via quantum oscillations. Moreover, we demonstrate the impact of the strain-tunable spin-dependent Berry phase on the Zeeman effect through the amplitude of the quantum oscillations. We show that such a spin-dependent Berry phase, largely neglected in solid-state systems, is critical in modeling quantum oscillations in Dirac bands in topological materials.

We have investigated the magneto-transport properties of beta-Bi4I4 bulk crystal, which was recently theoretically proposed and experimentally demonstrated to be a topological insulator. At low temperature T and magnetic field B, a series of Shubnikov-De Haas(SdH) oscillations are observed on the magnetoresistivity (MR). The detailed analysis reveals a light cyclotron mass of 0.1 me, and the field angle dependence of MR reveals that the SdH oscillations originate from a convex Fermi surface. In the extreme quantum limit (EQL) region, there is a metal-insulator transition occurring soon after the EQL. We perform the scaling analysis, and all the isotherms fall onto a universal scaling with a fitted critical exponent of 6.5. The enormous value of critical exponent implies this insulating quantum phase originated from strong electron-electron interactions in high fields. However, in the far end of EQL, both the longitudinal and Hall resistivity increase exponentially with B, and the temperature dependence of the MR reveals an energy gap induced by the high magnetic field, signifying a magnetic freeze-out effect. Our findings indicate that bulk beta-Bi4I4 is an excellent candidate for a 3D topological system for exploring EQL physics and relevant exotic quantum phases., Comment: 14 pages, 5 figures

The integration of renewables into electrical grids calls for optimization-based control schemes requiring reliable grid models. Classically, parameter estimation and optimization-based control is often decoupled, which leads to high system operation cost in the estimation procedure. The present work proposes a method for simultaneously minimizing grid operation cost and optimally estimating line parameters based on methods for the optimal design of experiments. This method leads to a substantial reduction in cost for optimal estimation and in higher accuracy in the parameters compared with standard Optimal Power Flow and maximum-likelihood estimation. We illustrate the performance of the proposed method on a benchmark system.

We report localization of fractional quantum Hall (QH) quasiparticles on graphene antidots. By studying coherent tunneling through the localized QH edge modes on the antidot, we measured the QH quasiparticle charges to be approximately $\pm e/3$ at fractional fillings of $\nu = \pm 1/3$. The Dirac spectrum in graphene allows large energy scales and robust quasiparticle localization against thermal excitation. The capability of localizing fractional quasiparticles on QH antidots brings promising opportunities for realizing anyon braiding and novel quantum electronics.

In practical propagation environments, different massive MIMO users can have correlated angles in spatial paths. In this paper, we study the effect of angle correlation on inter-user channel correlation via a combination of measurement and analysis. We show three key results. First, we collect a massive MIMO channel dataset for examining the inter-user channel correlation in a real-world propagation environment; the dataset is now open-access. We observed channel correlation higher than $0.48$ for all close-by users. Additionally, over $30$ % of far-away users, even when they are tens of wavelengths apart, have inter-user channel correlation that is at least twice higher than the correlation in the i.i.d. Rayleigh fading channel. Second, we compute the inter-user channel correlation in closed-form as a function of inter-user angle correlation, the number of base-station antennas, and base-station inter-antenna spacing. Our analysis shows that inter-user angle correlation increases the inter-user channel correlation. Inter-user channel correlation reduces with a larger base-station array aperture, i.e., more antennas and larger inter-antenna spacing. Third, we explain the measurements with numerical experiments to show that inter-user angle correlation can result in significant inter-user channel correlation in practical massive MIMO channels., Comment: accepted to be published by IEEE Transactions on Vehicular Technology

The implementation of aberration-corrected electron beam lithography (AC-EBL) in a 200 keV scanning transmission electron microscope (STEM) is a novel technique that could be used for the fabrication of quantum devices based on 2D atomic crystals with single nanometer critical dimensions, allowing to observe more robust quantum effects. In this work we study electron beam sculpturing of nanostructures on suspended graphene field effect transistors using AC-EBL, focusing on the in situ characterization of the impact of electron beam exposure on device electronic transport quality. When AC-EBL is performed on a graphene channel (local exposure) or on the outside vicinity of a graphene channel (non-local exposure), the charge transport characteristics of graphene can be significantly affected due to charge doping and scattering. While the detrimental effect of non-local exposure can be largely removed by vigorous annealing, local-exposure induced damage is irreversible and cannot be fixed by annealing. We discuss the possible causes of the observed exposure effects. Our results provide guidance to the future development of high-energy electron beam lithography for nanomaterial device fabrication.

The integration of renewables into electrical grids calls for the development of tailored control schemes which in turn require reliable grid models. In many cases, the grid topology is known but the actual parameters are not exactly known. This paper proposes a new approach for online parameter estimation in power systems based on optimal experimental design using multiple measurement snapshots. In contrast to conventional methods, our method computes optimal excitations extracting the maximum information in each estimation step to accelerate convergence. The performance of the proposed method is illustrated on a case study.