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Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically., Comment: Accepted in SCIENCE CHINA Information Sciences

The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed to capture semantics at both the token and statement levels simultaneously, which is crucial for achieving more accurate multi-granular vulnerability detection. Additionally, CodeBERT-HLS efficiently processes longer code token sequences, making it more suited to real-world vulnerability detection. Comprehensive experiments demonstrate that our method enhances the performance of vulnerability detection at both coarse- and fine-grained levels. Specifically, in coarse-grained vulnerability detection, StagedVulBERT achieves an F1 score of 92.26%, marking a 6.58% improvement over the best-performing methods. At the fine-grained level, our method achieves a Top-5% accuracy of 65.69%, which outperforms the state-of-the-art methods by up to 75.17%., Comment: 18 pages,13 figures

Wi-Fi based human activity recognition is a technology with immense potential in home automation, advanced caregiving, and enhanced security systems. It can distinguish human activity in environments with poor lighting and obstructions. However, most current Wi-Fi based human activity recognition methods are data-driven, leading to a continuous increase in the size of datasets. This results in a significant increase in the resources and time required to store and utilize these datasets. To address this issue, we propose WiDistill, a large-scale Wi-Fi datasets distillation method. WiDistill improves the distilled dataset by aligning the parameter trajectories of the distilled data with the recorded expert trajectories. WiDistill significantly reduces the need for the original large-scale Wi-Fi datasets and allows for faster training of models that approximate the performance of the original network, while also demonstrating robust performance in cross-network environments. Extensive experiments on the Widar3.0, XRF55, and MM-Fi datasets demonstrate that WiDistill outperforms other methods. The code can be found in https://github.com/the-sky001/WiDistill., Comment: 5 pages, 2 figures, 3tables

Clean one-dimensional electron systems can exhibit quantized conductance. The plateau conductance doubles if the transport is dominated by Andreev reflection. Here, we report quantized conductance observed in both Andreev and normal-state transports in PbTe-Pb and PbTe-In hybrid nanowires. The Andreev plateau is observed at $4e^2/h$, twice of the normal plateau value of $2e^2/h$. In comparison, Andreev conductance in the best-optimized III-V nanowires is non-quantized due to mode-mixing induced dips (a disorder effect), despite the quantization of normal-state transport. The negligible mode mixing in PbTe hybrids indicates an unprecedented low-disorder transport regime for nanowire devices, beneficial for Majorana researches.

Network structures by semiconductor nanowires hold great promise for advanced quantum devices, especially for applications in topological quantum computing. In this study, we created networks of PbTe nanowires arranged in loop configurations. Using shadow-wall epitaxy, we defined superconducting quantum interference devices (SQUIDs) using the superconductor Pb. These SQUIDs exhibit oscillations in supercurrent upon the scanning of a magnetic field. Most of the oscillations can be fitted assuming a sinusoidal current-phase relation for each Josephson junction. Under certain conditions, the oscillations are found to be skewed, suggesting possible deviation from a sinusoidal behavior. Our results highlight the potential of PbTe nanowires for building complex quantum devices in the form of networks.

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, reflected as emergence of the plateau. This degeneracy and its tunable lifting were challenging to observe in previous nanowire experiments, possibly due to disorder. Numerical simulations can qualitatively capture our observation, shedding light on device parameters for future applications.

Superconductivity in semiconductor nanostructures contains fascinating physics due to the interplay between Andreev reflection, spin, and orbital interactions. New material hybrids can access new quantum regimes and phenomena. Here, we report the realization of epitaxial indium thin films on PbTe nanowires.The film is continuous and forms an atomically sharp interface with PbTe.Tunneling devices reveal a hard superconducting gap.The gap size, 1.08 to 1.18 meV, is twice as large as bulk indium (around 0.5 meV), due to the presence of PbTe. A similar enhancement is also observed in the critical temperature of In on a PbTe substrate. Zero bias conductance peaks appear at finite magnetic fields. The effective g-factor (15 to 45) is notably enhanced compared to bare PbTe wires (less than 10) due to the presence of In, differing from Al-hybrids. Josephson devices exhibit gate-tunable supercurrents. The PbTe-In hybrid enhances the properties of both, the superconductivity of In and g-factors of PbTe, and thus may enable exotic phases of matter such as topological superconductivity.

Material challenges are the key issue in Majorana nanowires where surface disorder constrains device performance. Here, we tackle this challenge by embedding PbTe nanowires within a latticematched crystal, an oxide-free environment. The wire edges are shaped by self-organized growth instead of lithography, resulting in nearly-atomic-flat facets along both cross-sectional and longitudinal directions. Quantized conductance plateaus are observed at zero magnetic field with channel lengths reaching 1.54 $\mu$m, significantly surpassing the state-of-the-art of III-V nanowires (nearly an order-of-magnitude improvement compared to InSb). Coupling PbTe to a Pb film unveils a flat interface spanning microns and a large superconducting gap of 1 meV. Our results meet the stringent low-disorder requirement for the definitive observation of Majoranas.

We present a first step towards 4D (3D and time) human video stylization, which addresses style transfer, novel view synthesis and human animation within a unified framework. While numerous video stylization methods have been developed, they are often restricted to rendering images in specific viewpoints of the input video, lacking the capability to generalize to novel views and novel poses in dynamic scenes. To overcome these limitations, we leverage Neural Radiance Fields (NeRFs) to represent videos, conducting stylization in the rendered feature space. Our innovative approach involves the simultaneous representation of both the human subject and the surrounding scene using two NeRFs. This dual representation facilitates the animation of human subjects across various poses and novel viewpoints. Specifically, we introduce a novel geometry-guided tri-plane representation, significantly enhancing feature representation robustness compared to direct tri-plane optimization. Following the video reconstruction, stylization is performed within the NeRFs' rendered feature space. Extensive experiments demonstrate that the proposed method strikes a superior balance between stylized textures and temporal coherence, surpassing existing approaches. Furthermore, our framework uniquely extends its capabilities to accommodate novel poses and viewpoints, making it a versatile tool for creative human video stylization., Comment: Under Review

Planar Josephson junctions are predicted to host Majorana zero modes. The material platforms in previous studies are two dimensional electron gases (InAs, InSb, InAsSb and HgTe) coupled to a superconductor such as Al or Nb. Here, we introduce a new material platform for planar JJs, the PbTe-Pb hybrid. The semiconductor, PbTe, was grown as a thin film via selective area epitaxy. The Josephson junction was defined by a shadow wall during the deposition of the superconductor Pb. Scanning transmission electron microscopy reveals a sharp semiconductor-superconductor interface. Gate-tunable supercurrent and multiple Andreev reflections are observed. A perpendicular magnetic field causes interference patterns of the switching current, exhibiting Fraunhofer-like and SQUID-like behaviors. We further demonstrate a prototype device for Majorana detection, wherein phase bias and tunneling spectroscopy are applicable.

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods., Comment: Accepted to ICASSP 2023 (5 pages, 2 figures, 3 table)

In this report, we focus on reconstructing clothed humans in the canonical space given multiple views and poses of a human as the input. To achieve this, we utilize the geometric prior of the SMPLX model in the canonical space to learn the implicit representation for geometry reconstruction. Based on the observation that the topology between the posed mesh and the mesh in the canonical space are consistent, we propose to learn latent codes on the posed mesh by leveraging multiple input images and then assign the latent codes to the mesh in the canonical space. Specifically, we first leverage normal and geometry networks to extract the feature vector for each vertex on the SMPLX mesh. Normal maps are adopted for better generalization to unseen images compared to 2D images. Then, features for each vertex on the posed mesh from multiple images are integrated by MLPs. The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space. Finally, latent code for each 3D point is extracted and utilized to calculate the SDF. Our work for reconstructing the human shape on canonical pose achieves 3rd performance on WCPA MVP-Human Body Challenge., Comment: 8 pages, 3 figures, published to ECCV2022 WCPA Workshop