Autonomous Driving and Simultaneous Localization and Mapping(SLAM) are becoming increasingly important in real world, where point cloud-based large scale place recognition is the spike of them. Previous place recognition methods have achieved accepta
ble performances by regarding the task as a point cloud retrieval problem. However, all of them are suffered from a common defect: they cant handle the situation when the point clouds are rotated, which is common, e.g, when viewpoints or motorcycle types are changed. To tackle this issue, we propose an Attentive Rotation Invariant Convolution (ARIConv) in this paper. The ARIConv adopts three kind of Rotation Invariant Features (RIFs): Spherical Signals (SS), Individual-Local Rotation Invariant Features (ILRIF) and Group-Local Rotation Invariant features (GLRIF) in its structure to learn rotation invariant convolutional kernels, which are robust for learning rotation invariant point cloud features. Whats more, to highlight pivotal RIFs, we inject an attentive module in ARIConv to give different RIFs different importance when learning kernels. Finally, utilizing ARIConv, we build a DenseNet-like network architecture to learn rotation-insensitive global descriptors used for retrieving. We experimentally demonstrate that our model can achieve state-of-the-art performance on large scale place recognition task when the point cloud scans are rotated and can achieve comparable results with most of existing methods on the original non-rotated datasets.
Theories and experiments on dirty superconductors are sophisticated but important for both fundamentals and applications. It becomes more challenging when magnetic fields are present, because the field distribution, the electron density of states, an
d the superconducting pairing potentials are nonuniform. Here we present tunneling microspectroscopic experiments on NbC single crystals and show that NbC is a homogeneous dirty superconductor. When applying magnetic fields to the sample, we observe that the zero-energy local density of states and the pairing energy gap follow an explicit scale relation proposed by de Gennes for homogeneous dirty superconductors in high magnetic fields. Surprisingly, our experimental findings suggest that the validity of the scale relation extends to magnetic field strengths far below the upper critical field and call for new nonperturbative understanding of this fundamental property in dirty superconductors. On the practical side, we use the observed scale relation to drive a simple and straightforward experimental scheme for extracting the superconducting coherence length of a dirty superconductor in magnetic fields.
Discontinuous quantum phase transitions and the associated metastability play central roles in diverse areas of physics ranging from ferromagnetism to false vacuum decay in the early universe. Using strongly-interacting ultracold atoms in an optical
lattice, we realize a driven many-body system whose quantum phase transition can be tuned from continuous to discontinuous. Resonant shaking of a one-dimensional optical lattice hybridizes the lowest two Bloch bands, driving a novel transition from a Mott insulator to a $pi$-superfluid, i.e., a superfluid state with staggered phase order. For weak shaking amplitudes, this transition is discontinuous (first-order) and the system can remain frozen in a metastable state, whereas for strong shaking, it undergoes a continuous transition toward a $pi$-superfluid. Our observations of this metastability and hysteresis are in good quantitative agreement with numerical simulations and pave the way for exploring the crucial role of quantum fluctuations in discontinuous transitions.
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM). Although many models have been proposed and have achieved good performance by learning short-range local features,
long-range contextual properties have often been neglected. Moreover, the model size has also become a bottleneck for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net for large scale place recognition. Specifically, on top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed to learn both short-range local features and long-range contextual features in this model. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art on benchmark datasets in terms of both accuracy and speed with a super-light model size (0.9M). Meanwhile, two simplifi
Strong spin-orbital-coupling magnetic systems with the honeycomb structure can provide bond-directional interactions which may result in Kitaev quantum spin liquids and exotic anyonic excitations. However, one of the key ingredients in real materials
$-$disorders$-$has been much less studied in Kitaev systems. Here we synthesized a trigonal SrIr$_2$O$_{6-delta}$ with $delta approx 0.25$, which consists of two-dimensional honeycomb Ir planes with edge-sharing IrO$_6$ octahedra. First-principles computation and experimental measurements suggest that the electronic system is gapped, and there should be no magnetic moment as the Ir$^{5+}$ ion has no unpaired electrons. However, significant magnetism has been observed in the material, and it can be attributed to disorders that are most likely from oxygen vacancies. No magnetic order is found down to 0.05 K, and the low-temperature magnetic properties exhibit power-law behaviors in magnetic susceptibility and zero-field specific heat, and a single-parameter scaling of the specific heat under magnetic fields. These results provide strong evidence for the existence of the random singlet phase in SrIr$_2$O$_{6-delta}$, which offers a different member to the family of spin-orbital entangled iridates and Kitaev materials.
High-sensitivity imaging of ultracold atoms is often challenging when interference patterns are imprinted on the imaging light. Such image noises result in low signal-to-noise ratio and limit the capability to extract subtle physical quantities. Here
we demonstrate an advanced fringe removal algorithm for absorption imaging of ultracold atoms, which efficiently suppresses unwanted fringe patterns using a small number of sample images without taking additional reference images. The protocol is based on an image decomposition and projection method with an extended image basis. We apply this scheme to raw absorption images of degenerate Fermi gases for the measurement of atomic density fluctuations and temperatures. The quantitative analysis shows that image noises can be efficiently removed with only tens of reference images, which manifests the efficiency of our protocol. Our algorithm would be of particular interest for the quantum emulation experiments in which several physical parameters need to be scanned within a limited time duration.
Lip sync has emerged as a promising technique for generating mouth movements from audio signals. However, synthesizing a high-resolution and photorealistic virtual news anchor is still challenging. Lack of natural appearance, visual consistency, and
processing efficiency are the main problems with existing methods. This paper presents a novel lip-sync framework specially designed for producing high-fidelity virtual news anchors. A pair of Temporal Convolutional Networks are used to learn the cross-modal sequential mapping from audio signals to mouth movements, followed by a neural rendering network that translates the synthetic facial map into a high-resolution and photorealistic appearance. This fully trainable framework provides end-to-end processing that outperforms traditional graphics-based methods in many low-delay applications. Experiments also show the framework has advantages over modern neural-based methods in both visual appearance and efficiency.
Blurring the boundary between bosons and fermions lies at the heart of a wide range of intriguing quantum phenomena in multiple disciplines, ranging from condensed matter physics and atomic, molecular and optical physics to high energy physics. One s
uch example is a multi-component Fermi gas with SU($N$) symmetry that is expected to behave like spinless bosons in the large $N$ limit, where the large number of internal states weakens constraints from the Pauli exclusion principle. However, bosonization in SU($N$) fermions has never been established in high dimensions where exact solutions are absent. Here, we report direct evidence for bosonization in a SU($N$) fermionic ytterbium gas with tunable $N$ in three dimensions (3D). We measure contacts, the central quantity controlling dilute quantum gases, from the momentum distribution, and find that the contact per spin approaches a constant with a 1/$N$ scaling in the low fugacity regime consistent with our theoretical prediction. This scaling signifies the vanishing role of the fermionic statistics in thermodynamics, and allows us to verify bosonization through measuring a single physical quantity. Our work delivers a highly controllable quantum simulator to exchange the bosonic and fermionic statistics through tuning the internal degrees of freedom in any generic dimensions. It also suggests a new route towards exploring multi-component quantum systems and their underlying symmetries with contacts.
We measure collective excitations of a harmonically trapped two-dimensional (2D) SU($N$) Fermi gas of $^{173}$Yb confined to a stack of layers formed by a one-dimensional optical lattice. Quadrupole and breathing modes are excited and monitored in th
e collisionless regime $lvertln(k_F a_{2D})rvertgg 1$ with tunable spin. We observe that the quadrupole mode frequency decreases with increasing number of spin components due to the amplification of the interaction effect by $N$ in agreement with a theoretical prediction based on 2D kinetic equations. The breathing mode frequency, however, is measured to be twice the dipole oscillation frequency regardless of $N$. We also follow the evolution of collective excitations in the dimensional crossover from two to three dimensions and characterize the damping rate of quadrupole and breathing modes for tunable SU($N$) fermions, both of which reveal the enhanced inter-particle collisions for larger spin. Our result paves the way to investigate the collective property of 2D SU($N$) Fermi liquid with enlarged spin.
Observation of topological phases beyond two-dimension (2D) has been an open challenge for ultracold atoms. Here, we realize for the first time a 3D spin-orbit coupled nodal-line semimetal in an optical lattice and observe the bulk line nodes with ul
tracold fermions. The realized topological semimetal exhibits an emergent magnetic group symmetry. This allows to detect the nodal lines by effectively reconstructing the 3D topological band from a series of measurements of integrated spin textures, which precisely render spin textures on the parameter-tuned magnetic-group-symmetric planes. The detection technique can be generally applied to explore 3D topological states of similar symmetries. Furthermore, we observe the band inversion lines from topological quench dynamics, which are bulk counterparts of Fermi arc states and connect the Dirac points, reconfirming the realized topological band. Our results demonstrate the first approach to effectively observe 3D band topology, and open the way to probe exotic topological physics for ultracold atoms in high dimensions.
