Transition-metal distribution in kagome antiferromagnet CoCu3(OH)6Cl2 revealed by resonant x-ray diffraction

The distribution of chemically similar transition-metal ions is a fundamental issue in the study of herbertsmithite-type kagome antiferromagnets. Using synchrotron radiation, we have performed resonant powder x-ray diffractions on newly synthesized CoCu3(OH)6Cl2, which provide an exact distribution of transition-metal ions in the frustrated antiferromagnet. Both magnetic susceptibility and specific heat measurements are quantitatively consistent with the occupation fractions determined by resonant x-ray diffraction. The distribution of transition-metal ions and residual magnetic entropy suggest a novel low temperature (T < 4 K) magnetism, where the interlayer triangular spins undergo a spin-glass freezing while the kagome spins still keep highly frustrated.Comment: 18 pages, 4 figures and 2 table

Matrix Recovery with Implicitly Low-Rank Data

In this paper, we study the problem of matrix recovery, which aims to restore a target matrix of authentic samples from grossly corrupted observations. Most of the existing methods, such as the well-known Robust Principal Component Analysis (RPCA), assume that the target matrix we wish to recover is low-rank. However, the underlying data structure is often non-linear in practice, therefore the low-rankness assumption could be violated. To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an implicit feature space but high-rank or even full-rank in its original form. Namely, our method pursues the low-rank structure of the target matrix in an implicit feature space. By making use of the specifics of an accelerated proximal gradient based optimization algorithm, the proposed method could recover the target matrix with non-linear structures from its corrupted version. Comprehensive experiments on both synthetic and real datasets demonstrate the superiority of our method

Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: https://xialipku.github.io/RESCAN .Comment: Accepted by ECC

Differentiable Linearized ADMM

Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the convergence behaviors of learning-based optimization. In particular, most existing analyses are specific to unconstrained problems but cannot apply to the more general cases where some variables of interest are subject to certain constraints. In this paper, we propose Differentiable Linearized ADMM (D-LADMM) for solving the problems with linear constraints. Specifically, D-LADMM is a K-layer LADMM inspired deep neural network, which is obtained by firstly introducing some learnable weights in the classical Linearized ADMM algorithm and then generalizing the proximal operator to some learnable activation function. Notably, we rigorously prove that there exist a set of learnable parameters for D-LADMM to generate globally converged solutions, and we show that those desired parameters can be attained by training D-LADMM in a proper way. To the best of our knowledge, we are the first to provide the convergence analysis for the learning-based optimization method on constrained problems.Comment: Accepted by ICML201

Near-term performance of quantum repeaters with imperfect ensemble-based quantum memories

We study the feasibility of meaningful proof-of-principle demonstrations of several quantum repeater protocols with photon (single-photon and photon-pair) sources and atomic-ensemble based quantum memories. We take into account non-unit memory efficiencies that decay exponentially with time, which complicates the calculation of repeater rates. We discuss implementations based on quantum dots, parametric down-conversion, rare-earth-ion doped crystals, and Rydberg atoms. Our results provide guidance for the near-term implementation of long-distance quantum repeater demonstrations, suggesting that such demonstrations are within reach of current technology.Comment: 14 pages, 7 figure

Nano watermill driven by the revolving charge

Using molecular dynamics simulations, we propose a novel nanoscale watermill for unidirectional transport of water molecules through a curved single-walled carbon nanotube (SWNT). In this nanoscale system, a revolving charge is introduced to drive water chain confined inside the SWNT, which is served as nano waterwheel and nano engine. A resonance-like phenomenon is found that the revolving frequency of the charge plays a key role in pumping water chain. The water flux across the SWNT increases with respect to the revolving frequency of the external charge and reaches the maximum when the frequency is 4 THz. Correspondingly, the number of the hydrogen bonds of water chain inside the SWNT decreases dramatically with the frequency ranging from 4 THz to 25 THz. The mechanism behind the resonant phenomenon has been investigated systematically. Our findings are helpful for designing nanoscale fluidic devices and energy converters.Comment: 10 pages, 4 figure

Image Inspired Poetry Generation in XiaoIce

Vision is a common source of inspiration for poetry. The objects and the sentimental imprints that one perceives from an image may lead to various feelings depending on the reader. In this paper, we present a system of poetry generation from images to mimic the process. Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems. Our approach is evaluated by human assessors and compared to other generation baselines. The results show that our method can generate poems that are more artistic than the baseline methods. This is one of the few attempts to generate poetry from images. By deploying our proposed approach, XiaoIce has already generated more than 12 million poems for users since its release in July 2017. A book of its poems has been published by Cheers Publishing, which claimed that the book is the first-ever poetry collection written by an AI in human history

Mechanical Creep Instability of Nanocrystalline Methane Hydrates

Mechanical creep behaviors of natural gas hydrates (NGHs) are of importance for understanding mechanical instability of gas hydrate-bearing sediments on Earth. Limited by the experimental challenges, intrinsic creep mechanisms of nanocrystalline methane hydrates remain largely unknown yet at molecular scale. Herein, using large-scale molecular dynamics (MD) simulations, mechanical creep behaviors of nanocrystalline methane hydrates are investigated. It is revealed that mechanical creep responses are greatly dictated by internal microstructures of crystalline grain size and external conditions of temperature and static stress. Interestingly, a long steady-state creep is observed in nanocrystalline methane hydrates, which can be described by a modified constitutive Bird-Dorn-Mukherjee model. Microstructural analysis show that deformations of crystalline grains, grain boundary (GB) diffusion and GB sliding collectively govern the mechanical creep behaviors of nanocrystalline methane hydrates. Furthermore, structural transformation also appears important in their mechanical creep mechanisms. This study sheds new insights into understanding the mechanical creep scenarios of gas hydrates

UniNeXt: Exploring A Unified Architecture for Vision Recognition

Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we observe that a well-designed general architecture can significantly improve the performance of the entire backbone, regardless of which spatial token mixer is equipped. In this paper, we propose UniNeXt, an improved general architecture for the vision backbone. To verify its effectiveness, we instantiate the spatial token mixer with various typical and modern designs, including both convolution and attention modules. Compared with the architecture in which they are first proposed, our UniNeXt architecture can steadily boost the performance of all the spatial token mixers, and narrows the performance gap among them. Surprisingly, our UniNeXt equipped with naive local window attention even outperforms the previous state-of-the-art. Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone. All models and codes will be publicly available

Maximum-and-Concatenation Networks

While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance. In this work, we propose a novel architecture called Maximum-and-Concatenation Networks (MCN) to try eliminating bad local minima and improving generalization ability as well. Remarkably, we prove that MCN has a very nice property; that is, \emph{every local minimum of an $(l+1)$-layer MCN can be better than, at least as good as, the global minima of the network consisting of its first $l$ layers}. In other words, by increasing the network depth, MCN can autonomously improve its local minima's goodness, what is more, \emph{it is easy to plug MCN into an existing deep model to make it also have this property}. Finally, under mild conditions, we show that MCN can approximate certain continuous functions arbitrarily well with \emph{high efficiency}; that is, the covering number of MCN is much smaller than most existing DNNs such as deep ReLU. Based on this, we further provide a tight generalization bound to guarantee the inference ability of MCN when dealing with testing samples.Comment: Accepted by ICML202