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Alternating twisted multilayer graphene presents a compelling multiband system for exploring superconductivity. Here we investigate robust superconductivity in alternating twisted quadralayer graphene, elucidating carrier contributions from both flat and dispersive bands. The superconductivity is robust, with a strong electrical field tunability, a maximum BKT transition temperature of 1.6 K, and high critical magnetic fields beyond the Pauli limit. We disentangle the carrier density of Dirac bands and flat bands from the Landau fan diagram. Moreover, we could estimate the flatband Fermi velocity from the obtained high critical current near half filling when superconductivity is killed at finite magnetic fields, and further quantify the superfluid stiffness from the low critical current in the superconducting regime. Our results exhibit the electric field tunable coupling strength within the superconducting phase, revealing unconventional properties with vanishing Fermi velocity and large superfluid stiffness. These phenomena, attributed to substantial quantum metric contributions, offer new insights into the mechanisms underlying unconventional superconductivity in moire systems.

Large vision-language models (LVLMs) have witnessed significant progress on visual understanding tasks. However, they often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. To tackle this issue, we first identify the drawbacks of existing solutions (i.e., insufficient and irrelevant visual descriptions, and limited multi-modal capacities). We then decompose visual reasoning process into two stages: visual perception (i.e., eyesight) and textual reasoning (i.e., wisdom), and introduce a novel visual reasoning framework named ProReason. This framework features multi-run proactive perception and decoupled vision-reasoning capabilities. Briefly, given a multi-modal question, ProReason iterates proactive information collection and reasoning until the answer can be concluded with necessary and sufficient visual descriptions. Notably, the disassociation of capabilities allows seamless integration of existing large language models (LLMs) to compensate for the reasoning deficits of LVLMs. Our extensive experiments demonstrate that ProReason outperforms both existing multi-step reasoning frameworks and passive peer methods on a wide range of benchmarks for both open-source and closed-source models. In addition, with the assistance of LLMs, ProReason achieves a performance improvement of up to 15% on MMMU benchmark. Our insights into existing solutions and the decoupled perspective for feasible integration of LLMs illuminate future research on visual reasoning techniques, especially LLM-assisted ones.

The rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served simultaneously. Targeting more parameter-efficient low-rank adaptation (LoRA), parameter sharing presents a promising solution. Empirically, our research into high-level sharing principles highlights the indispensable role of differentiation in reversing the detrimental effects of pure sharing. Guided by this finding, we propose Mixture of Shards (MoS), incorporating both inter-layer and intra-layer sharing schemes, and integrating four nearly cost-free differentiation strategies, namely subset selection, pair dissociation, vector sharding, and shard privatization. Briefly, it selects a designated number of shards from global pools with a Mixture-of-Experts (MoE)-like routing mechanism before sequentially concatenating them to low-rank matrices. Hence, it retains all the advantages of LoRA while offering enhanced parameter efficiency, and effectively circumvents the drawbacks of peer parameter-sharing methods. Our empirical experiments demonstrate approximately 8x parameter savings in a standard LoRA setting. The ablation study confirms the significance of each component. Our insights into parameter sharing and MoS method may illuminate future developments of more parameter-efficient finetuning methods.

High-harmonic generation (HHG), an extreme nonlinear effect, introduces an unprecedented paradigm to detect emergent quantum phases and electron dynamics inconceivable in the framework of linear and low-order nonlinear processes. As an important manifestation, the optical HHG (o-HHG) enables extraordinary opportunities to underpin attosecond physics. In addition to nonlinear optics, emerging nonlinear electric transport has been demonstrated recently and opens new paradigms to probe quantum phase transition, symmetry breaking, band geometrical and topological properties. Thus far, only electrical second-/third-harmonic generation in perturbative regime has been elucidated, while the electrical HHG (e-HHG) that can advance to extreme non-perturbative physics remains elusive. Here we report the observation of e-HHG up to 300th-order. Remarkably, the e-HHG shows a clear non-perturbative character and exhibits periodic oscillations with the reciprocal of driving current. Further, theoretical simulations corroborate the experiments, suggesting the contribution of singular distribution of Berry curvature near band edges. Our results demonstrate e-HHG in extreme nonlinear regime and may shed light on a plethora of exotic physics and applications, such as extreme non-equilibrium quantum phenomena, ultra-fast and coherent electrical signal generations and detections., Comment: 15 pages, 4 figures

Double layer quantum systems are promising platforms for realizing novel quantum phases. Here, we report a study of quantum oscillations (QOs) in a weakly coupled double layer system, composed of a large angle twisted double bilayer graphene (TDBG). We observe two different QOs at low temperature, one with a periodicity in carrier density (n), i.e. Shubnikov de Haas oscillation (SdHO) due to Landau quantization, and the other one in displacement field (D), resulting a grid pattern. We quantify the interlayer coupling strength by measuring the interlayer capacitance from the grid pattern with a capacitance model, revealing an electron hole asymmetry. At high temperature when SdHO are thermal smeared, we observe resistance peaks when LLs from two minivalleys in the moir\'e Brillion zone are aligned, regardless of carrier density; eventually, it results in a two fold increase of oscillating frequency in D, serving as a smoking gun evidence of the magneto intersubband oscillations (MISO) in a double layer system. The temperature dependence of MISO suggests electron-electron interaction between two minivalleys play a crucial rule in the scattering, and the scattering times obtained from MISO thermal damping are found to be correlated with the interlayer coupling strength. Our study reveals an intriguing interplay among Landau quantization, moir\'e band structure, and scatterings.

This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, possibly as multiple disjoint continuum ranges, and under both disturbances and stochastic noise. To ensure the worst-case fault sensitivity in the considered frequency ranges and mitigate the effects of disturbances and noise, an optimization framework incorporating a mixed H_/H2 performance index is developed to compute the optimal detection filter. Moreover, a thresholding rule is proposed to guarantee both the false alarm rate (FAR) and the fault detection rate (FDR). Next, shifting attention to fault estimation in specific frequency ranges, an exact reformulation of the optimal estimation filter design using the restricted Hinf performance index is derived, which is inherently non-convex. However, focusing on finite frequency samples and fixed poles, a lower bound is established via a highly tractable quadratic programming (QP) problem. This lower bound together with an alternating optimization (AO) approach to the original estimation problem leads to a suboptimality gap for the overall estimation filter design. The effectiveness of the proposed approaches is validated through applications of a non-minimum phase hydraulic turbine system and a multi-area power system., Comment: 31pages, 15 figures

This article reports a comparative study of bulk and surface properties in the transition metal dichalcogenide 1T-TaS$_2$. When heating the sample, the surface displays an intermediate insulating phase that persists for $\sim 10$ K on top of a metallic bulk. The weaker screening of Coulomb repulsion and stiffer Charge Density Wave (CDW) explain such resilience of a correlated insulator in the topmost layers. Both time resolved ARPES and transient reflectivity are employed to investigate the dynamics of electrons and CDW collective motion. It follows that the amplitude mode is always stiffer at the surface and displays variable coupling to the Mott-Peierls band, stronger in the low temperature phase and weaker in the intermediate one.

Stacking order plays a crucial role in determining the crystal symmetry and has significant impacts on electronic, optical, magnetic, and topological properties. Electron-phonon coupling, which is central to a wide range of intriguing quantum phenomena, is expected to be intricately connected with stacking order. Understanding the stacking order-dependent electron-phonon coupling is essential for understanding peculiar physical phenomena associated with electron-phonon coupling, such as superconductivity and charge density waves. In this study, we investigate the effect of stacking order on electron-infrared phonon coupling in graphene trilayers. By using gate-tunable Raman spectroscopy and excitation frequency-dependent near-field infrared nanoscopy, we show that rhombohedral ABC-stacked trilayer graphene has a significantly stronger electron-infrared phonon coupling strength than the Bernal ABA-stacked trilayer graphene. Our findings provide novel insights into the superconductivity and other fundamental physical properties of rhombohedral ABC-stacked trilayer graphene, and can enable nondestructive and high-throughput imaging of trilayer graphene stacking order using Raman scattering.

Ground fault detection in inverter-based microgrid (IBM) systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when there are partially decoupled disturbances and modeling uncertainties. The conventional solution of installing more relays to obtain additional measurements is costly and also increases the complexity of the system. In this paper, we propose a data-assisted diagnosis scheme based on an optimization-based fault detection filter with the output current as the only measurement. Modeling the microgrid dynamics and the diagnosis filter, we formulate the filter design as a quadratic programming (QP) problem that accounts for decoupling partial disturbances, robustness to non-decoupled disturbances and modeling uncertainties by training with data, and ensuring fault sensitivity simultaneously. To ease the computational effort, we also provide an approximate but analytical solution to this QP. Additionally, we use classical statistical results to provide a thresholding mechanism that enjoys probabilistic false-alarm guarantees. Finally, we implement the IBM system with Simulink and Real Time Digital Simulator (RTDS) to verify the effectiveness of the proposed method through simulations., Comment: 18 pages, 9 figures

Here we perform angle and time-resolved photoelectron spectroscopy on the commensurate Charge Density Wave (CDW) phase of 1T-TaS$_2$. Data with different probe pulse polarization are employed to map the dispersion of electronic states below and above the chemical potential. Upon photoexcitation, the fluctuations of CDW order erase the band dispersion near to the chemical potential and halve the charge gap size. This transient phase sets within half a period of the coherent lattice motion and is favored by strong electronic correlations. The experimental results are compared to Density-Functional Theory (DFT) calculations with a self-consistent evaluation of the Coulomb repulsion. Our simulations indicate that the screening of Coulomb repulsion depends on the stacking order of the TaS$_2$ layers. The entanglement of such degrees of freedom suggest that both the structural order and electronic repulsion are locally modified by the photoinduced CDW fluctuations.

We study a diagnosis scheme to reliably detect the active mode of discrete-time, switched affine systems in the presence of measurement noise and asynchronous switching. The proposed scheme consists of two parts: (i) the construction of a bank of filters, and (ii) the introduction of a residual/threshold-based diagnosis rule. We develop an exact finite optimization-based framework to numerically solve an optimal bank of filters in which the contribution of measurement noise to the residual is minimized. The design problem is safely approximated through linear matrix inequalities and thus becomes tractable. We further propose a thresholding policy along with probabilistic false-alarm guarantees to estimate the active system mode in real-time. In comparison with the existing results, the guarantees improve from a polynomial dependency in the probability of false alarm to a logarithmic form. This improvement is achieved under the additional assumption of sub-Gaussianity, which is expected in many applications. The performance of the proposed approach is validated through a numerical example and an application of the building radiant system., Comment: 25 pages, 15 figures

Terahertz (THz) spin-to-charge conversion has become an increasingly important process for THz pulse generation and as a tool to probe ultrafast spin interactions at magnetic interfaces. However, its relation to traditional, steady state, ferromagnetic resonance techniques is poorly understood. Here we investigate nanometric trilayers of Co/X/Pt (X=Ti, Au or Au0:85W0:15) as a function of the 'X' layer thickness, where THz emission generated by the inverse spin Hall effect is compared to the Gilbert damping of the ferromagnetic resonance. Through the insertion of the 'X' layer we show that the ultrafast spin current injected in the non-magnetic layer defines a direct spin conductance, whereas the Gilbert damping leads to an effective spin mixing-conductance of the trilayer. Importantly, we show that these two parameters are connected to each other and that spin-memory losses can be modeled via an effective Hamiltonian with Rashba fields. This work highlights that magneto-circuits concepts can be successfully extended to ultrafast spintronic devices, as well as enhancing the understanding of spin-to-charge conversion processes through the complementarity between ultrafast THz spectroscopy and steady state techniques.