Probing the order parameter symmetry of two-dimensional superconductors by twisted Josephson interferometry

Probing the superconducting order parameter symmetry is a crucial step towards understanding the pairing mechanism in unconventional superconductors. Inspired by the recent discoveries of superconductivity in various van der Waals materials, and the availability of the relative twist angle as a continuous tuning knob in these systems, we propose a general setup for probing the order parameter symmetry of two-dimensional superconductors in twisted Josephson junctions. The junction is composed of an anisotropic s-wave superconductor as a probe and another superconductor with an unknown order parameter symmetry. Assuming momentum-resolved tunneling, we investigate signatures of different order parameter symmetries in the twist angle dependence of the critical current, the current-phase relations, and magnetic field dependence. As a concrete example, we study a twisted Josephson junction between NbSe2 and magic angle twisted bilayer graphene.Comment: 15 pages, 5 figure

Chirality driven topological electronic structure of DNA-like materials

Topological aspects of the geometry of DNA and similar chiral molecules have received a lot of attention, while the topology of their electronic structure is less explored. Previous experiments have revealed that DNA can efficiently filter spin-polarized electrons between metal contacts, a process called chiral-induced spin-selectivity (CISS). However, the underlying correlation between chiral structure and electronic spin remains elusive. In this work, we reveal an orbital texture in the band structure, a topological characteristic induced by the chirality. We find that this orbital texture enables the chiral molecule to polarize the quantum orbital. This orbital polarization effect (OPE) induces spin polarization assisted by the spin-orbit interaction from a metal contact and leads to magnetorestistance and chiral separation. The orbital angular momentum of photoelectrons also plays an essential role in related photoemission experiments. Beyond CISS, we predict that OPE can induce spin-selective phenomena even in achiral but inversion-breaking materials.Comment: 24 pages, 4 figures, and Supplementary Material

Low-complexity Resource Allocation for User Paired RSMA in Future 6G Wireless Networks

Rate-splitting multiple access (RSMA) uplink requires optimization of decoding order and power allocation, while decoding order is a discrete variable, and it is very complex to find the optimal decoding order if the number of users is large enough. This letter proposes a low-complexity user pairing-based resource allocation algorithm with the objective of minimizing the maximum latency, which significantly reduces the computational complexity and also achieves similar performance to unpaired uplink RSMA. A closed-form expression for power and bandwidth allocation is first derived, and then a bisection method is used to determine the optimal resource allocation. Finally, the proposed algorithm is compared with unpaired RSMA, paired NOMA and unpaired NOMA. The results demonstrate the effectiveness of the proposed algorithm

Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios

Underwater degraded images greatly challenge existing algorithms to detect objects of interest. Recently, researchers attempt to adopt attention mechanisms or composite connections for improving the feature representation of detectors. However, this solution does \textit{not} eliminate the impact of degradation on image content such as color and texture, achieving minimal improvements. Another feasible solution for underwater object detection is to develop sophisticated deep architectures in order to enhance image quality or features. Nevertheless, the visually appealing output of these enhancement modules do \textit{not} necessarily generate high accuracy for deep detectors. More recently, some multi-task learning methods jointly learn underwater detection and image enhancement, accessing promising improvements. Typically, these methods invoke huge architecture and expensive computations, rendering inefficient inference. Definitely, underwater object detection and image enhancement are two interrelated tasks. Leveraging information coming from the two tasks can benefit each task. Based on these factual opinions, we propose a bilevel optimization formulation for jointly learning underwater object detection and image enhancement, and then unroll to a dual perception network (DPNet) for the two tasks. DPNet with one shared module and two task subnets learns from the two different tasks, seeking a shared representation. The shared representation provides more structural details for image enhancement and rich content information for object detection. Finally, we derive a cooperative training strategy to optimize parameters for DPNet. Extensive experiments on real-world and synthetic underwater datasets demonstrate that our method outputs visually favoring images and higher detection accuracy

Learning Heavily-Degraded Prior for Underwater Object Detection

Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as haze-like effects, low visibility, and color distortions. Therefore, we commit to resolving the issue of underwater object detection with compounded environmental degradations. Typical approaches attempt to develop sophisticated deep architecture to generate high-quality images or features. However, these methods are only work for limited ranges because imaging factors are either unstable, too sensitive, or compounded. Unlike these approaches catering for high-quality images or features, this paper seeks transferable prior knowledge from detector-friendly images. The prior guides detectors removing degradations that interfere with detection. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps while the lightly degraded regions of them overlap each other. Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater- images, and make the mapping as a heavily degraded prior (HDP) for underwater detection. Since the statistical properties are independent to image content, HDP can be learned without the supervision of semantic labels and plugged into popular CNNbased feature extraction networks to improve their performance on underwater object detection. Without bells and whistles, evaluations on URPC2020 and UODD show that our methods outperform CNN-based detectors by a large margin. Our method with higher speeds and less parameters still performs better than transformer-based detectors. Our code and DFUI dataset can be found in https://github.com/xiaoDetection/Learning-Heavily-Degraed-Prior

Monopole-like orbital-momentum locking and the induced orbital transport in topological chiral semimetals

The interplay between chirality and topology nurtures many exotic electronic properties. For instance, topological chiral semimetals display multifold chiral fermions that manifest nontrivial topological charge and spin texture. They are an ideal playground for exploring chirality-driven exotic physical phenomena. In this work, we reveal a monopole-like orbital-momentum locking texture on the three-dimensional Fermi surfaces of topological chiral semimetals with B20 structures (e.g., RhSi and PdGa). This orbital texture enables a large orbital Hall effect (OHE) and a giant orbital magnetoelectric (OME) effect in the presence of current flow. Different enantiomers exhibit the same OHE which can be converted to the spin Hall effect by spin-orbit coupling in materials. In contrast, the OME effect is chirality-dependent and much larger than its spin counterpart. Our work reveals the crucial role of orbital texture for understanding OHE and OME effects in topological chiral semimetals and paves the path for applications in orbitronics, spintronics, and enantiomer recognition.Comment: 23 pages, 5 figure