Large deviations for local times and intersection local times of fractional Brownian motions and Riemann-Liouville processes

In this paper we prove exact forms of large deviations for local times and intersection local times of fractional Brownian motions and Riemann-Liouville processes. We also show that a fractional Brownian motion and the related Riemann-Liouville process behave like constant multiples of each other with regard to large deviations for their local and intersection local times. As a consequence of our large deviation estimates, we derive laws of iterated logarithm for the corresponding local times. The key points of our methods: (1) logarithmic superadditivity of a normalized sequence of moments of exponentially randomized local time of a fractional Brownian motion; (2) logarithmic subadditivity of a normalized sequence of moments of exponentially randomized intersection local time of Riemann-Liouville processes; (3) comparison of local and intersection local times based on embedding of a part of a fractional Brownian motion into the reproducing kernel Hilbert space of the Riemann-Liouville process.Comment: To appear in the Annals of Probabilit

Distributed Multi-authority Attribute-based Encryption Scheme for Friend Discovery in Mobile Social Networks

AbstractIn recent years, the rapid expansion of the capability of portable devices, cloud servers and cellular network technologies is the wind beneath the wing of mobile social networks. Compared to traditional web-based online social networks, the mobile social networks can assist users to easily discover and make new social interaction with others. A challenging task is to protect the privacy of the users’ profiles and communications. Existing works are mainly based on traditional cryptographic methods, such as homomorphic and group signatures, which are very computationally costly. In this paper, we propose a novel distributed multi-authority attribute-based encryption scheme to efficiently achieve privacy-preserving without additional special signatures. In addition, the proposed scheme can achieve fine-grained and flexible access control. Detailed analysis demonstrates the effectiveness and practicability of our scheme

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM and provide an outlook on future development of segmentation tasks. Note that our work does not intend to propose new algorithms or theories, but rather provide a comprehensive view of SAM in practice. This work is expected to provide insights that facilitate future research activities toward generic segmentation.Comment: Tech Repor

GEOMETRY CONSTRUCTION METHOD OF HEX-TRI RECIPROCAL FRAME

In this paper the geometrical characteristics and construction method of hex-tri reciprocal frame are studied. The naming rules of structural units, members and joints of hex-tri reciprocal frame are proposed. Based on the study of relationships between the structural geometrical parameters, the formulas for the joint coordinates of the structural unit are derived, and the calculating method for normalized direction vectors of the unit members is also developed. The geometry construction method of hex-tri reciprocal frame is established in this paper. By this method the whole structure is formed as an assembly of the units arranged in rings about the structural center. The influences of the diameter, the length and the binding length of the member on the rise of hex-tri reciprocal frame are analyzed by an example. If the rise of hex-tri reciprocal frame needs to be specified in the design, the values of the diameter, the length and the binding length of the structural member need to be coordinated to meet the design requirements

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images. We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images. Specifically, we customize an inpainting-oriented transformer block, where the attention module aggregates non-local information only from partial valid tokens, indicated by a dynamic mask. Extensive experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets. Code is released at https://github.com/fenglinglwb/MAT.Comment: Accepted to CVPR2022 Ora