arXiv subjects

Rujie Yin

Publications and source records attributed to Rujie Yin.

6 recordsLinked to original sources

arXiv2025

A Hierarchical Adaptive Diffusion Model for Flexible Protein-Protein Docking

Structural prediction of protein-protein interactions is important to understand the molecular basis of cellular interactions, but it still faces major challenges when significant conformational changes are present. We propose a generative framework of hierarchical adaptive diffusion to improve accuracy and efficiency in such cases. It is hierarchical in separating global inter-protein rigid-body motions and local intra-protein flexibility in diffusion processes, and the distinct local and global noise schedules are designed to mimic the induced-fit effect. It is adaptive in conditioning the local flexibility schedule on predicted levels of conformational change, allowing faster flexing for larger anticipated conformational changes. Furthermore, it couples the local and global diffusion processes through a common score and confidence network with sequence, evolution, structure, and dynamics features as inputs, and maintains rotational or translational invariance or equivariance in outputs. It builds on our newly curated DIPS-AF dataset of nearly 39,000 examples for pre-training. Experiments on the independent docking benchmark dataset DB5.5 show that our model outperforms an AlphaFold2-like iterative transformer (GeoDock) and a diffusion model (DiffDock-PP) in both rigid and flexible cases, with larger improvements in more flexible cases. Ablation studies prove the importance of adaptive schedules, dynamics features, and pre-training. Additional analyses and case studies reveal remaining gaps in sampling, scoring, and conformational resolution.

cs.CE↗

arXiv2017

Stable Phase Retrieval in Infinite Dimensions

The problem of phase retrieval is to determine a signal $f\in \mathcal{H}$, with $\mathcal{H}$ a Hilbert space, from intensity measurements $|F(ω)|$, where $F(ω):=\langle f , φ_ω\rangle$ are measurements of $f$ with respect to a measurement system $(φ_ω)_{ω\in Ω}\subset \mathcal{H}$. Although phase retrieval is always stable in the finite dimensional setting whenever it is possible (i.e. injectivity implies stability for the inverse problem), the situation is drastically different if $\mathcal{H}$ is infinite-dimensional: in that case phase retrieval is never uniformly stable [8, 4]; moreover the stability deteriorates severely in the dimension of the problem [8]. On the other hand, all empirically observed instabilities are of a certain type: they occur whenever the function $|F|$ of intensity measurements is concentrated on disjoint sets $D_j\subset Ω$, i.e., when $F= \sum_{j=1}^k F_j$ where each $F_j$ is concentrated on $D_j$ (and $k \geq 2$). Motivated by these considerations we propose a new paradigm for stable phase retrieval by considering the problem of reconstructing $F$ up to a phase factor that is not global, but that can be different for each of the subsets $D_j$, i.e., recovering $F$ up to the equivalence $$ F \sim \sum_{j=1}^k e^{i α_j} F_j.$$ We present concrete applications (for example in audio processing) where this new notion of stability is natural and meaningful and show that in this setting stable phase retrieval can actually be achieved, for instance if the measurement system is a Gabor frame or a frame of Cauchy wavelets.

math.FA↗

arXiv2016

Directional Wavelet Bases Constructions with Dyadic Quincunx Subsampling

We construct directional wavelet systems that will enable building efficient signal representation schemes with good direction selectivity. In particular, we focus on wavelet bases with dyadic quincunx subsampling. In our previous work, We show that the supports of orthonormal wavelets in our framework are discontinuous in the frequency domain, yet this irregularity constraint can be avoided in frames, even with redundancy factor less than 2. In this paper, we focus on the extension of orthonormal wavelets to biorthogonal wavelets and show that the same obstruction of regularity as in orthonormal schemes exists in biorthogonal schemes. In addition, we provide a numerical algorithm for biorthogonal wavelets construction where the dual wavelets can be optimized, though at the cost of deteriorating the primal wavelets due to the intrinsic irregularity of biorthogonal schemes.

math.FA↗

arXiv2016

A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets

We propose an image representation scheme combining the local and nonlocal characterization of patches in an image. Our representation scheme can be shown to be equivalent to a tight frame constructed from convolving local bases (e.g. wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g. spectral basis induced by nonlinear dimension reduction on patches), and we call the resulting frame elements {\it convolution framelets}. Insight gained from analyzing the proposed representation leads to a novel interpretation of a recent high-performance patch-based image inpainting algorithm using Point Integral Method (PIM) and Low Dimension Manifold Model (LDMM) [Osher, Shi and Zhu, 2016]. In particular, we show that LDMM is a weighted $\ell_2$-regularization on the coefficients obtained by decomposing images into linear combinations of convolution framelets; based on this understanding, we extend the original LDMM to a reweighted version that yields further improved inpainting results. In addition, we establish the energy concentration property of convolution framelet coefficients for the setting where the local basis is constructed from a given nonlocal basis via a linear reconstruction framework; a generalization of this framework to unions of local embeddings can provide a natural setting for interpreting BM3D, one of the state-of-the-art image denoising algorithms.

cs.CV↗

arXiv2016

Construction of Orthonormal Quasi-Shearlets based on quincunx dilation subsampling

We consider the construction of orthonormal directional wavelet bases in the multi-resolution analysis (MRA) framework with quincunx dilation downsampling. We show that the Parseval frame property in MRA is equivalent to the identity summation and shift cancellation conditions on M functions, which essentially characterize the scaling (father) function and all directional (mother) wavelets. Based on these two conditions, we further derive sufficient conditions for orthonormal bases and build a family of quasishearlet orthonormal bases, that has the same frequency support as that of the least redundant shearlet system. In addition, we study the limitation of our proposed bases design due to the shift cancellation conditions.

math.FA↗

arXiv2016

Content Aware Neural Style Transfer

This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural network.

cs.CV↗