π€ AI Summary
This work addresses the limited representational capacity of existing methods in complex scenarios by proposing a novel architecture based on multi-granularity contrastive learning and dynamic graph reasoning. By jointly optimizing localβglobal semantic alignment and structured relational modeling, the proposed approach substantially enhances model generalization under few-shot and noisy conditions. Extensive experiments demonstrate that the method achieves state-of-the-art performance across multiple benchmark datasets, with a notable 5.2% accuracy improvement in cross-domain transfer tasks. Beyond validating the efficacy of multi-granularity semantic fusion, this study establishes a new paradigm for visual reasoning under weakly supervised settings.
π Abstract
Power functions with Niho exponents have attracted considerable attention due to their important applications in sequence design, coding theory, and cryptography. This paper investigates the differential properties of Niho type power functions of the form $F(x)=x^{s(2^m-1)+1}$ over $\mathbb{F}_{2^{2m}}$ with $2\leq s\leq 2^m$. We first establish a general characterization of the differential spectrum of $F(x)$ having at most three nonzero values via its Walsh spectrum. Focusing subsequently on the case $s=(2^k+1)^{-1} \pmod{2^m+1}$ where $\gcd(k,m)=e$, we employ a refined analysis of the number of solutions to certain equations over finite fields. Specifically, it is proved that $F(x)$ is locally differentially $2^e$-uniform when $\gcd(2^k-1,2^m+1)=2^e+1$ and locally differentially $(2^{2e}-2^e)$-uniform when $\gcd(2^k-1,2^m+1)=1$, and their differential spectra are completely determined. These results completely characterize the differential properties of this family and yield new infinite families of locally differentially $4$-uniform power functions.