Multipolar condensates and multipolar Josephson effects

When single-particle dynamics are suppressed in certain strongly correlated systems, dipoles arise as elementary carriers of quantum kinetics. These dipoles can further condense, providing physicists with a rich realm to study fracton phases of matter. Whereas recent theoretical discoveries have shown that an unconventional lattice model may host a dipole condensate as the ground state, fundamental questions arise as to whether dipole condensation is a generic phenomenon rather than a specific one unique to a particular model and what new quantum macroscopic phenomena a dipole condensate may bring us with. Here, we show that dipole condensates prevail in bosonic systems. Because of a self-proximity effect, where single-particle kinetics inevitably induces a finite order parameter of dipoles, dipole condensation readily occurs in conventional normal phases of bosons. Our findings allow experimentalists to manipulate the phase of a dipole condensate and deliver dipolar Josephson effects, where supercurrents of dipoles arise in the absence of particle flows. The self-proximity effects can also be utilized to produce a generic multipolar condensate. The kinetics of the $n$-th order multipoles unavoidably creates a condensate of the $(n+1)$-th order multipoles, forming a hierarchy of multipolar condensates that will offer physicists a whole new class of macroscopic quantum phenomena

Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Journal version of arXiv:2010.05300 (NeurIPS 2020). The first two authors contributed equall

Coal seam thickness prediction based on transition probability of structural elements

Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Then, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m which significantly improves the prediction accuracy in comparison to the existing BP neural networks, support vector machine and Gaussian process regression methods

Electronic analog of chiral metamaterial: Helicity-resolved filtering and focusing of Dirac fermions in thin films of topological materials

Control over the helicity degree of freedom of Dirac fermions is identified in thin films of topological materials which act as a tunable “chiral-metamaterial-like” platform to tame left- and right-handed Dirac fermions in two dimensions. Using topological crystalline insulator SnTe(111) thin films as an example, we perform the first-principles calculations and show that giant helicity splitting in the band structures can be induced under moderate electric field. Based on this result, helicity-resolved functionalities, including pronounced electron dichroism, helicity switching, helical negative refraction, and birefraction, are demonstrated, where the intrahelical scattering always dominates over the interhelical one. Such intriguing control strategy for helical Dirac fermions may hold great promise for the applications of helicity-based electron optics and nanoelectronics.National Natural Science Foundation (China) (Grant 11204154)National Natural Science Foundation (China) (Grant 11334006)Ministry of Science and Technology of the People's Republic of China (Grant 2011CB921901)Ministry of Science and Technology of the People's Republic of China (Grant 2011CB606405

Type-II Ising Pairing in Few-Layer Stanene

Spin-orbit coupling has proven indispensable in realizing topological materials and more recently Ising pairing in two-dimensional superconductors. This pairing mechanism relies on inversion symmetry breaking and sustains anomalously large in-plane polarizing magnetic fields whose upper limit is expected to diverge at low temperatures, although experimental demonstration of this has remained elusive due to the required fields. In this work, the recently discovered superconductor few-layer stanene, i.e. epitaxially strained $\alpha$-Sn, is shown to exhibit a new type of Ising pairing between carriers residing in bands with different orbital indices near the $\Gamma$-point. The bands are split as a result of spin-orbit locking without the participation of inversion symmetry breaking. The in-plane upper critical field is strongly enhanced at ultra-low temperature and reveals the sought for upturn

Experimental observation of Dirac-like surface states and topological phase transition in Pb1−x_{1-x}Snx_xTe(111) films

The surface of a topological crystalline insulator (TCI) carries an even number of Dirac cones protected by crystalline symmetry. We epitaxially grew high quality Pb$_{1-x}$Sn$_x$Te(111) films and investigated the TCI phase by in-situ angle-resolved photoemission spectroscopy. Pb$_{1-x}$Sn$_x$Te(111) films undergo a topological phase transition from trivial insulator to TCI via increasing the Sn/Pb ratio, accompanied by a crossover from n-type to p-type doping. In addition, a hybridization gap is opened in the surface states when the thickness of film is reduced to the two-dimensional limit. The work demonstrates an approach to manipulating the topological properties of TCI, which is of importance for future fundamental research and applications based on TCI

The evolution of the day-of-the-week effect and the implications in the crude oil market

The movement of prices in the crude oil market are important to the international economy and financial asset returns. Day-of- the-week effect is a great challenge to the market's effectiveness. We examine the evolution of day-of-the-week and investigate its implications based on the West Texas Intermediate crude oil return from 14 May 2007 to 14 May 2021. We have obtained convincing findings that there is abnormal positive return on Wednesdays because the inventory shock schedule. Abnormal negative return on Mondays disappears sometimes, because bad sentiment is not the only decisive factor, as it is also determined by reactions to good sentiment. The results provide implication for the day-of-the-week effect and new evidence that crude oil market's efficiency changes over time. Policy makers, investors and producers can benefit from this

Neo-sex chromosomes in the black muntjac recapitulate incipient evolution of mammalian sex chromosomes

The nascent neo-sex chromosomes of black muntjacs show that regulatory mutations could accelerate the degeneration of the Y chromosome and contribute to the further evolution of dosage compensation