We theoretically investigate twisted structures where each layer is composed of a strongly correlated material. In particular, we study a twisted t-J model of cuprate multilayers within the slave-boson mean field theory. This treatment encompasses th
e Mott physics at small doping and self consistently generates d-wave pairing. Furthermore, including the correct inter-layer tunneling form factor consistent with the symmetry of the Cu $d_{x^2-y^2}$ orbital proves to be crucial for the phase diagram. We find spontaneous time reversal (T) breaking around twist angle of $45^circ$, although only in a narrow window of twist angles. Moreover, the gap obtained is small and the Chern number vanishes, implying a non-topological superconductor. At smaller twist angles, driving an interlayer current however can lead to a gapped topological phase. The energy-phase relation of the interlayer Josephson junction displays notable double-Cooper-pair tunneling which dominates around $45^o$. The twist angle dependence of the Josephson critical current and the Shapiro steps are consistent with recent experiments. Utilizing the moire structure as a probe of correlation physics, in particular of the pair density wave state, is discussed.
In this work we propose an effective preconditioning technique to accelerate the steady-state simulation of large-scale memristor crossbar arrays (MCAs). We exploit the structural regularity of MCAs to develop a specially-crafted preconditioner that
can be efficiently evaluated utilizing tensor products and block matrix inversion. Numerical experiments demonstrate the efficacy of the proposed technique compared to mainstream preconditioners.
We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classification with patch-level labels. However, patch-level labels require precis
e annotations, which is expensive and usually unavailable on clinical data. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label. To address this issue, we posit that WSI analysis can be effectively conducted by integrating information at both high magnification (local) and low magnification (regional) levels. We auto-encode the visual signals in each patch into a latent embedding vector representing local information, and down-sample the raw WSI to hardware-acceptable thumbnails representing regional information. The WSI label is then predicted with a Dual-Stream Network (DSNet), which takes the transformed local patch embeddings and multi-scale thumbnail images as inputs and can be trained by the image-level label only. Experiments conducted on two large-scale public datasets demonstrate that our method outperforms all recent state-of-the-art weakly-supervised WSI classification methods.
With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLAM process. In this paper, based on a novel Lie group structure, a righ
t invariant extended Kalman filter (RI-EKF) for object based SLAM is proposed. The observability analysis shows that the proposed algorithm automatically maintains the correct unobservable subspace, while standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better consistency for the proposed algorithm comparing to Std-EKF. Finally, simulations and real world experiments validate not only the consistency and accuracy of the proposed algorithm, but also the practicability of the proposed RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made publicly available.
Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase si
gnificantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semi-analytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling shown the capability of coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including $mathcal{O}(10^2)$ pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also high beneficial to include DNest in current toolboxes of PTA analysis.
We introduce a new image editing and synthesis framework, Stochastic Differential Editing (SDEdit), based on a recent generative model using stochastic differential equations (SDEs). Given an input image with user edits (e.g., hand-drawn color stroke
s), we first add noise to the input according to an SDE, and subsequently denoise it by simulating the reverse SDE to gradually increase its likelihood under the prior. Our method does not require task-specific loss function designs, which are critical components for recent image editing methods based on GAN inversion. Compared to conditional GANs, we do not need to collect new datasets of original and edited images for new applications. Therefore, our method can quickly adapt to various editing tasks at test time without re-training models. Our approach achieves strong performance on a wide range of applications, including image synthesis and editing guided by stroke paintings and image compositing.
The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterpa
rts including autoregressive models in many tasks such as image generation and audio synthesis, and would be promising for time series imputation. In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. Unlike existing score-based approaches, the conditional diffusion model is explicitly trained for imputation and can exploit correlations between observed values. On healthcare and environmental data, CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, deterministic imputation by CSDI reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods. Furthermore, CSDI can also be applied to time series interpolation and probabilistic forecasting, and is competitive with existing baselines.
Since the advent of graphene ushered the era of two-dimensional materials, many forms of hydrogenated graphene have been reported, exhibiting diverse properties ranging from a tunable band gap to ferromagnetic ordering. Patterned hydrogenated graphen
e with micron-scale patterns has been fabricated by lithographic means. Here we report successful millimeter-scale synthesis of an intrinsically honeycomb patterned form of hydrogenated graphene on Ru(0001) by epitaxial growth followed by hydrogenation. Combining scanning tunneling microscopy observations with density-functional-theory (DFT) calculations, we reveal that an atomic-hydrogen layer intercalates between graphene and Ru(0001). The result is a hydrogen honeycomb structure that serves as a template for the final hydrogenation, which converts the graphene into graphane only over the template, yielding honeycomb-patterned hydrogenated graphene (HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find that the unhydrogenated graphene regions embedded in the patterned graphane exhibit spin-polarized edge states. This type of growth mechanism provides new pathways for the fabrication of intrinsically patterned graphene-based materials.
This work numerically investigates the role of viscosity and resistivity on Rayleigh-Taylor instabilities in magnetized high-energy-density (HED) plasmas for a high Atwood number regime. The numerical simulations are performed using the visco-resisti
ve magnetohydrodynamic (MHD) equations. Results presented here show that the inclusion of self-consistent viscosity and resistivity in the system drastically changes the growth of the Rayleigh-Taylor instability (RTI) as well as modifies its internal structure at smaller scales. It is seen here that the viscosity has a stabilizing effect on the RTI. Moreover, the viscosity inhibits the development of small scale structures and also modifies the morphology of the tip of the RTI spikes. On the other hand, the resistivity reduces the magnetic field stabilization supporting the development of the small scale structures. The morphology of the RTI spikes is seen to be unaffected by the presence of resistivity in the system. An additional novelty of this work is in the disparate viscosity and resistivity profiles that may exist in HED plasmas and their impact on RTI growth, morphology, and the resulting turbulence spectra. Furthermore, it is also found here that the dynamics of magnetic field is independent of viscosity and likewise the resistivity does not affect the dissipation of enstrophy and kinetic energy. In addition, power-law scaling of enstrophy, kinetic energy, and magnetic field energy is provided in both injection range and inertial sub-range which could be useful for understanding RTI induced turbulent mixing in HED laboratory and astrophysical plasmas and could aid in the interpretation of observations of RTI-induced turbulence spectra.
Regularizers helped deep neural networks prevent feature co-adaptations. Dropout,as a commonly used regularization technique, stochastically disables neuron ac-tivations during network optimization. However, such complete feature disposal can affect
the feature representation and network understanding. Toward betterdescriptions of latent representations, we present DropGraph that learns regularization function by constructing a stand-alone graph from the backbone features. DropGraph first samples stochastic spatial feature vectors and then incorporates graph reasoning methods to generate feature map distortions. This add-on graph regularizes the network during training and can be completely skipped during inference. We provide intuitions on the linkage between graph reasoning andDropout with further discussions on how partial graph reasoning method reduces feature correlations. To this end, we extensively study the modeling of graphvertex dependencies and the utilization of the graph for distorting backbone featuremaps. DropGraph was validated on four tasks with a total of 7 different datasets.The experimental results show that our method outperforms other state-of-the-art regularizers while leaving the base model structure unmodified during inference.
