INVESTIGATION OF ELECTRIC AND THERMOELECTRIC PROPERTIES OF GRAPHENE NANORIBBON

Master'sMASTER OF SCIENC

Static Deadlock Detection for Rust Programs

Rust relies on its unique ownership mechanism to ensure thread and memory safety. However, numerous potential security vulnerabilities persist in practical applications. New language features in Rust pose new challenges for vulnerability detection. This paper proposes a static deadlock detection method tailored for Rust programs, aiming to identify various deadlock types, including double lock, conflict lock, and deadlock associated with conditional variables. With due consideration for Rust's ownership and lifetimes, we first complete the pointer analysis. Then, based on the obtained points-to information, we analyze dependencies among variables to identify potential deadlocks. We develop a tool and conduct experiments based on the proposed method. The experimental results demonstrate that our method outperforms existing deadlock detection methods in precision

A Compact Dual-Band Circularly Polarized Antenna with Wide HPBWs for CNSS Applications

A compact dual-band circularly polarized antenna with wide half-power beamwidths (HPBWs) for compass navigation satellite system applications is proposed in this paper. The CP radiation is realized by arranging four compact dual-band inverted-F monopoles symmetrically to the center point, where the four monopoles are excited with a 90° phase offset through a compact sequential-phase feeding network. The compactness of the dual-band inverted-F monopole is realized by inserting two chip inductors in the horizontal portion of the monopole. The overall dimension of the antenna is only 0.211λ0 × 0.211λ0 × 0.057λ0, where λ0 is the corresponding free-space wavelength at 1.268 GHz. Experimental results show that the proposed antenna exhibits two overlapped impedance and axial ratio bandwidths of 50 MHz (1.236–1.286 GHz) and 40 MHz (1.532–1.572 GHz). Wide HPBWs of about 120°/125° and 121°/116° (XOZ/YOZ planes) at center frequencies (1.268, 1.561 GHz) of the CNSS-2 B3 and B1 bands are obtained, respectively. With these good performances, the antenna can be a good candidate for CNSS applications

City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web

Existing neural radiance field-based methods can achieve real-time rendering of small scenes on the web platform. However, extending these methods to large-scale scenes still poses significant challenges due to limited resources in computation, memory, and bandwidth. In this paper, we propose City-on-Web, the first method for real-time rendering of large-scale scenes on the web. We propose a block-based volume rendering method to guarantee 3D consistency and correct occlusion between blocks, and introduce a Level-of-Detail strategy combined with dynamic loading/unloading of resources to significantly reduce memory demands. Our system achieves real-time rendering of large-scale scenes at approximately 32FPS with RTX 3060 GPU on the web and maintains rendering quality comparable to the current state-of-the-art novel view synthesis methods.Comment: Project page: https://ustc3dv.github.io/City-on-Web

DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work, we present DiffMorpher, the first approach enabling smooth and natural image interpolation using diffusion models. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories, bridging a critical functional gap that distinguished diffusion models from GANs