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We report the design and theoretical analysis of Wannier-Stark ladders of diamond Lamb wave resonators (LWRs) that can feature compression modes with ultralow damping rates and host spin qubits with excellent optical and spin properties. Three nearest-neighbor coupling schemes with distinct geometric configurations and a large range of coupling rates have been explored for the realization of Wannier-Stark ladders of LWRs, potentially enabling long-range connectivity between spin qubits through their interactions with mechanical vibrations. Additional analysis on the effects of disorder indicates that the proposed Wannier-Stark ladder can be robust against realistic experimental imperfections. The development of mechanical quantum networks of spin qubits with long-range connectivity can open the door to the implementation of newly developed quantum low-density parity-check codes in solid-state systems.

We report the development of an all-optical approach that excites the fundamental compression mode in a diamond Lamb wave resonator with an optical gradient force and detects the induced vibrations via strain coupling to a silicon vacancy center, specifically, via phonon sidebands in the optical excitation spectrum of the silicon vacancy. Sideband optical interferometry has also been used for the detection of the in-plane mechanical vibrations, for which conventional optical interferometry is not effective. These experiments demonstrate a GHz fundamental compression mode with a Q-factor >10^7 at temperatures near 7 K, providing a promising platform for reaching the quantum regime of spin mechanics, especially phononic cavity QED of electron spins.

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing randomized techniques are first devised under the order-d (d >= 3) T-SVD framework. On this basis, we then further investigate the robust high-order tensor completion (RHTC) problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem. Empirical studies on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

We report experimental studies of a driven spin-mechanical system, in which a nitrogen vacancy (NV) center couples to out-of-plane vibrations of a diamond cantilever through the excited-state deformation potential. Photoluminescence excitation studies show that in the unresolved sideband regime and under strong resonant mechanical driving, the excitation spectra of a NV optical transition feature two spectrally sharp peaks, corresponding to the two turning points of the oscillating cantilever. In the limit that the strain-induced frequency separation between the two peaks far exceeds the NV zero-phonon linewidth, the spectral position of the individual peak becomes sensitive to minute detuning between the mechanical resonance and the external driving force. For a fixed optical excitation frequency near the NV transition, NV fluorescence as a function of mechanical detuning features resonances with a linewidth that can be orders of magnitude smaller than the intrinsic linewidth of the mechanical mode. This enhanced sensitivity to mechanical detuning can potentially provide an effective mechanism for mechanical sensing, for example, mass sensing via measurements of induced changes in the mechanical oscillator frequency.

The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent research have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint L+S models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularization term, which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping this single regularizer into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion (TC) and tensor robust principal component analysis (TRPCA). To the best of our knowledge, this should be the first exact-recovery results among all related L+S methods for tensor recovery. Significant recovery accuracy improvements over many other SOTA methods in several TC and TRPCA tasks with various kinds of visual tensor data are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely large, e.g., 99.5%, for the color image inpainting task, while all its peers totally fail in such challenging case.

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

We report the design, fabrication, and characterization of diamond cantilevers attached to a phononic square lattice. We show that the robust protection of mechanical modes by phononic band gaps leads to a three-orders-of-magnitude increase in mechanical Q-factors, with the Q-factors exceeding 10^6 at frequencies as high as 100 MHz. Temperature dependent studies indicate that the Q-factors obtained at a few K are still limited by the materials loss. The high-Q diamond nanomechanical resonators provide a promising hybrid quantum system for spin-mechanics studies.

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$. For the data-based methods, they perform very fast on new test data once they have been trained. However, their generalization ability is always insufficient. In this paper, we propose a fast model-based HSI denoising approach. Specifically, we propose a novel regularizer named Representative Coefficient Total Variation (RCTV) to simultaneously characterize the low rank and local smooth properties. The RCTV regularizer is proposed based on the observation that the representative coefficient matrix $\mathbf{U}\in\mathbb{R}^{MN\times R} (R\ll B)$ obtained by orthogonally transforming the original HSI $\mathbf{X}$ can inherit the strong local-smooth prior of $\mathbf{X}$. Since $R/B$ is very small, the HSI denoising model based on the RCTV regularizer has lower time complexity. Additionally, we find that the representative coefficient matrix $\mathbf{U}$ is robust to noise, and thus the RCTV regularizer can somewhat promote the robustness of the HSI denoising model. Extensive experiments on mixed noise removal demonstrate the superiority of the proposed method both in denoising performance and denoising speed compared with other state-of-the-art methods. Remarkably, the denoising speed of our proposed method outperforms all the model-based techniques and is comparable with the deep learning-based approaches., Comment: 16 pages, 18 figures, 5 tables, 1 theorem

We present H-band (1.65 $\mu$m) and SOFIA HAWC+ 154 $\mu$m polarization observations of the low-mass core L483. Our H-band observations reveal a magnetic field that is overwhelmingly in the E-W direction, which is approximately parallel to the bipolar outflow that is observed in scattered IR light and in single-dish $^{12}$CO observations. From our 154 $\mu$m data, we infer a $\sim$ 45$^{\circ}$ twist in the magnetic field within the inner 5" (1000 au) of L483. We compare these new observations with published single-dish 350 $\mu$m polarimetry and find that the 10,000 au scale H-band data match the smaller scale 350 $\mu$m data, indicating that the collapse of L483 is magnetically regulated on these larger scales. We also present high-resolution 1.3 mm ALMA data of L483 which reveals it is a close binary star with a separation of 34 au. The plane of the binary of L483 is observed to be approximately parallel to the twisted field in the inner 1000 au. Comparing this result to the $\sim$ 1000 au protostellar envelope, we find that the envelope is roughly perpendicular to the 1000 au HAWC+ field. Using the data presented, we speculate that L483 initially formed as a wide binary and the companion star migrated to its current position, causing an extreme shift in angular momentum thereby producing the twisted magnetic field morphology observed. More observations are needed to further test this scenario., Comment: Accepted for publication in ApJ

We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic field from the time series of the observed photon counts, which are correlated with the underlying field. The proof-of-principle experiment uses an external time-varying magnetic field that follows an Ornstein-Uhlenbeck (OU) process. By taking into consideration the statistical properties of the OU process, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.