Artificial Gauge Field and Quantum Spin Hall States in a Conventional Two-dimensional Electron Gas

Based on the Born-Oppemheimer approximation, we divide total electron Hamiltonian in a spinorbit coupled system into slow orbital motion and fast interband transition process. We find that the fast motion induces a gauge field on slow orbital motion, perpendicular to electron momentum, inducing a topological phase. From this general designing principle, we present a theory for generating artificial gauge field and topological phase in a conventional two-dimensional electron gas embedded in parabolically graded GaAs/In$_{x}$Ga$_{1-x}$As/GaAs quantum wells with antidot lattices. By tuning the etching depth and period of antidot lattices, the band folding caused by superimposed potential leads to formation of minibands and band inversions between the neighboring subbands. The intersubband spin-orbit interaction opens considerably large nontrivial minigaps and leads to many pairs of helical edge states in these gaps.Comment: 9 pages and 4 figure

Two Stream Scene Understanding on Graph Embedding

The paper presents a novel two-stream network architecture for enhancing scene understanding in computer vision. This architecture utilizes a graph feature stream and an image feature stream, aiming to merge the strengths of both modalities for improved performance in image classification and scene graph generation tasks. The graph feature stream network comprises a segmentation structure, scene graph generation, and a graph representation module. The segmentation structure employs the UPSNet architecture with a backbone that can be a residual network, Vit, or Swin Transformer. The scene graph generation component focuses on extracting object labels and neighborhood relationships from the semantic map to create a scene graph. Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GAT) are employed for graph representation, with an emphasis on capturing node features and their interconnections. The image feature stream network, on the other hand, focuses on image classification through the use of Vision Transformer and Swin Transformer models. The two streams are fused using various data fusion methods. This fusion is designed to leverage the complementary strengths of graph-based and image-based features.Experiments conducted on the ADE20K dataset demonstrate the effectiveness of the proposed two-stream network in improving image classification accuracy compared to conventional methods. This research provides a significant contribution to the field of computer vision, particularly in the areas of scene understanding and image classification, by effectively combining graph-based and image-based approaches

Enabling Large Language Models to Learn from Rules

Large language models (LLMs) have shown incredible performance in completing various real-world tasks. The current knowledge learning paradigm of LLMs is mainly based on learning from examples, in which LLMs learn the internal rule implicitly from a certain number of supervised examples. However, the learning paradigm may not well learn those complicated rules, especially when the training examples are limited. We are inspired that humans can learn the new tasks or knowledge in another way by learning from rules. That is, humans can grasp the new tasks or knowledge quickly and generalize well given only a detailed rule and a few optional examples. Therefore, in this paper, we aim to explore the feasibility of this new learning paradigm, which encodes the rule-based knowledge into LLMs. We propose rule distillation, which first uses the strong in-context abilities of LLMs to extract the knowledge from the textual rules and then explicitly encode the knowledge into LLMs' parameters by learning from the above in-context signals produced inside the model. Our experiments show that making LLMs learn from rules by our method is much more efficient than example-based learning in both the sample size and generalization ability.Comment: In progres

C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction

There is an emerging effort to combine the two popular 3D frameworks using Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific focus on the few-shot / sparse view setting. In this paper, we introduce a novel integration scheme that combines the multi-view stereo with neural signed distance function representations, which potentially overcomes the limitations of both methods. MVS uses per-view depth estimation and cross-view fusion to generate accurate surfaces, while NIS relies on a common coordinate volume. Based on this strategy, we propose to construct per-view cost frustum for finer geometry estimation, and then fuse cross-view frustums and estimate the implicit signed distance functions to tackle artifacts that are due to noise and holes in the produced surface reconstruction. We further apply a cascade frustum fusion strategy to effectively captures global-local information and structural consistency. Finally, we apply cascade sampling and a pseudo-geometric loss to foster stronger integration between the two architectures. Extensive experiments demonstrate that our method reconstructs robust surfaces and outperforms existing state-of-the-art methods.Comment: Accepted by ICCV202

Comparative Effects and Safety of Full-Endoscopic Versus Microscopic Spinal Decompression for Lumbar Spinal Stenosis: A Meta-Analysis and Statistical Power Analysis of 6 Randomized Controlled Trials

Objective This meta-analysis with statistical power analysis aimed to evaluate the difference between full-endoscopic and microscopic spinal decompression in treating spinal stenosis. Methods We searched PubMed, Embase, CENTRAL (Cochrane Central Register of Controlled Trials), and CNKI (China National Knowledge Infrastructure) for relevant randomized controlled trials (RCTs) regarding the comparison of full-endoscopic versus microscopic spinal decompression in treating lumbar spinal stenosis through February 28, 2022. Two independent investigators selected studies, extracted information, and appraised methodological quality. Meta-analysis was conducted using RevMan 5.4 and STATA 14.0, and statistical power analysis was performed using G*Power 3.1. Results Six RCTs involving 646 patients met selection criteria. Meta-analysis suggested that, compared with microscopic decompression, full-endoscopic spinal decompression achieved more leg pain improvement (mean difference [MD], -0.20; 95% confidence interval [CI], -0.30 to -0.10; p = 0.001), shortened operative time (MD, -12.71; 95% CI, -18.27 to -7.15; p < 0.001), and decreased the incidence of complications (risk ratio, 0.43; 95% CI, 0.22–0.82; p = 0.01), which was supported by a statistical power of 98.57%, 99.97%, and 81.88%, respectively. Conclusion Full-endoscopic spinal decompression is a better treatment for lumbar spinal stenosis, showing more effective leg pain improvement, shorter operative time, and fewer complications than microscopic decompression

Advances in optical molecular imaging for neural visualization

Iatrogenic nerve injury is a significant complication in surgery, which can negatively impact patients’ quality of life. Currently, the main clinical neuroimaging methods, such as computed tomography, magnetic resonance imaging, and high-resolution ultrasonography, do not offer precise real-time positioning images for doctors during surgery. The clinical application of optical molecular imaging technology has led to the emergence of new concepts such as optical molecular imaging surgery, targeted surgery, and molecular-guided surgery. These advancements have made it possible to directly visualize surgical target areas, thereby providing a novel method for real-time identification of nerves during surgery planning. Unlike traditional white light imaging, optical molecular imaging technology enables precise positioning and identifies the cation of intraoperative nerves through the presentation of color images. Although a large number of experiments and data support its development, there are few reports on its actual clinical application. This paper summarizes the research results of optical molecular imaging technology and its ability to realize neural visualization. Additionally, it discusses the challenges neural visualization recognition faces and future development opportunities

Simultaneous improvement of heating efficiency and mechanical strength of a self-healing thermoplastic polymer by hybridizing magnetic particles with conductive fibres

Radio-Frequency (RF) induction heating is a versatile in-situ method for contactless heating of structures by utilizing either magnetic hysteresis loss or eddy-current loss mechanism. Achieving high heating efficiency without degrading mechanical properties is a major challenge. Herein, a RF induction compatible self-healing composite was developed by hybridizing iron oxides (Fe3O4) nanoparticles with carbon fibre veils (CFVs) in poly(ethylene-co-methacrylic acid) (EMAA), which could possess both high magnetic and electrical properties. Owing to the multiscale conductive networks built by Fe3O4 nanoparticles and CFVs, the electrical conductivity of the nanocomposite was found to be higher than the linear combination of the individual contributions, thus creating a synergistic improvement in electrical conductivity and heating efficiency. Furthermore, single lap shear test results demonstrated that the combination of Fe3O4 nanoparticles and CFVs could significantly improve the bonding strength of EMAA polymer. Therefore, the hybridization of magnetic particles with conductive fibres offers a promising technology for a wide range of applications, such as self-healing, reversable bonding, and multiple use bonded composites

Large and tunable magnetoresistance in van der Waals ferromagnet/semiconductor junctions

Magnetic tunnel junctions (MTJs) with conventional bulk ferromagnets separated by a nonmagnetic insulating layer are key building blocks in spintronics for magnetic sensors and memory. A radically different approach of using atomically-thin van der Waals (vdW) materials in MTJs is expected to boost their figure of merit, the tunneling magnetoresistance (TMR), while relaxing the lattice-matching requirements from the epitaxial growth and supporting high-quality integration of dissimilar materials with atomically-sharp interfaces. We report TMR up to 192% at 10 K in all-vdW Fe3GeTe2/GaSe/Fe3GeTe2 MTJs. Remarkably, instead of the usual insulating spacer, this large TMR is realized with a vdW semiconductor GaSe. Integration of semiconductors into the MTJs offers energy-band-tunability, bias dependence, magnetic proximity effects, and spin-dependent optical-selection rules. We demonstrate that not only the magnitude of the TMR is tuned by the semiconductor thickness but also the TMR sign can be reversed by varying the bias voltages, enabling modulation of highly spin-polarized carriers in vdW semiconductors