Tensor network decomposition for data recovery: Recent advancements and future prospects.

Tensor network (TN) decomposition stands as a pivotal technique for characterizing the essential features of high-dimensional data, attracting significant interest and achieving notable success in high-dimensional data recovery. In recent years, there has been a steady stream of scholarly articles on TN decomposition, reflecting its growing significance. However, a comprehensive review that encapsulates the recent advancements and future prospects of TN decomposition remains conspicuously absent.

Adversarial guided diffusion models for adversarial purification.

Diffusion model (DM) based adversarial purification (AP) has proven to be a powerful defense method that can remove adversarial perturbations and generate a purified example without threats. In principle, the pre-trained DMs can only ensure that purified examples conform to the same distribution of the training data, but it may inadvertently compromise the semantic information of input examples, leading to misclassification of purified examples. Recent advancements introduce guided diffusion techniques to preserve semantic information while removing the perturbations.

Gradient aware adaptive quantization: Locally uniform quantization with learnable clipping thresholds for globally non-uniform weights.

Non-uniform quantization has been shown to achieve promising performance for compressing neural networks, due to its better adaptation to the distribution of weights. However, traditional non-uniform quantization methods rely solely on weight distribution density, resulting in diminished model performance post-quantization. To tackle this challenge, we propose a novel non-uniform quantization method that can not only automatically learn the clipping threshold but also adaptively adjust the quantization levels, which can effectively reduce the quantization error.

Kernel Bayesian tensor ring decomposition for multiway data recovery.

Tensor ring (TR) decomposition has emerged as the prevailing method for tensor completion. Earlier approaches have situated TR decomposition within a probabilistic framework, yielding satisfactory outcomes. However, these methods ignore side information or are inherently incapable of leveraging it.

Are Diffusion Models Effective Good Feature Extractors for MRI Discriminative Tasks?

Purpose: Diffusion models (DMs) excel in pixel-level and spatial tasks and are proven feature extractors for 2D image discriminative tasks when pretrained. However, their capabilities in 3D MRI discriminative tasks remain largely untapped. This study seeks to assess the effectiveness of DMs in this underexplored area.

Low-Rank, High-Order Tensor Completion via t- Product-Induced Tucker (tTucker) Decomposition.

Recently, tensor singular value decomposition (t-SVD)-based methods were proposed to solve the low-rank tensor completion (LRTC) problem, which has achieved unprecedented success on image and video inpainting tasks. The t-SVD is limited to process third-order tensors. When faced with higher-order tensors, it reshapes them into third-order tensors, leading to the destruction of interdimensional correlations.

An information-theoretic approach for heterogeneous differentiable causal discovery.

With the advancement of deep learning, a variety of differential causal discovery methods have emerged, inevitably attracting more attention for their excellent scalability and interpretability. However, these methods often struggle with complex heterogeneous datasets that exhibit environmental diversity and are characterized by shifts in noise distribution. To this end, we introduce a novel information-theoretic approach designed to enhance the robustness of differential causal discovery methods.

Disappearing and reappearing of structure order in colloidal photonic crystals.

Mechanoresponsive colloidal photonic crystals embedded in elastic solid matrices exhibit tunable optical properties under mechanical force, showing great potential for various applications. However, the response of colloidal crystals embedded in a liquid matrix remains largely unexplored. In this study, we investigate the structural and optical transitions of colloidal crystals composed of particles suspended in a liquid oligomer under pressing and shear forces.

Sources analysis and risk assessment of heavy metals in soil in a polymetallic mining area in southeastern Hubei based on Monte Carlo simulation.

This study investigates the pollution characteristics, spatial patterns, causes, and ecological risks of heavy metals in the soils of the southeastern Hubei polymetallic mining areas, specifically the Jilongshan (JLS) and Tonglushan (TLS) regions, located in the middle and lower reaches of the Yangtze River. The main findings are as follows: (1) Among the heavy metals present in the soil, copper (Cu) has the highest average concentration at 278.54 mg/kg, followed by zinc (Zn) at 161.

Tensor ring rank determination using odd-dimensional unfolding.

While tensor ring (TR) decomposition methods have been extensively studied, the determination of TR-ranks remains a challenging problem, with existing methods being typically sensitive to the determination of the starting rank (i.e., the first rank to be optimized).

Unifying complete and incomplete multi-view clustering through an information-theoretic generative model.

Recently, Incomplete Multi-View Clustering (IMVC) has become a rapidly growing research topic, driven by the prevalent issue of incomplete data in real-world applications. Although many approaches have been proposed to address this challenge, most methods did not provide a clear explanation of the learning process for recovery. Moreover, most of them only considered the inter-view relationships, without taking into account the relationships between samples.

Strain to shine: stretching-induced three-dimensional symmetries in nanoparticle-assembled photonic crystals.

Stretching elastic materials containing nanoparticle lattices is common in research and industrial settings, yet our knowledge of the deformation process remains limited. Understanding how such lattices reconfigure is critically important, as changes in microstructure lead to significant alterations in their performance. This understanding has been extremely difficult to achieve due to a lack of fundamental rules governing the rearrangements.

TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition.

Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions.

Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion.

The recently proposed tensor tubal rank has been witnessed to obtain extraordinary success in real-world tensor data completion. However, existing works usually fix the transform orientation along the third mode and may fail to turn multidimensional low-tubal-rank structure into account. To alleviate these bottlenecks, we introduce two unfolding induced tensor nuclear norms (TNNs) for the tensor completion (TC) problem, which naturally extends tensor tubal rank to high-order data.

Bayesian tensor network structure search and its application to tensor completion.

Tensor network (TN) has demonstrated remarkable efficacy in the compact representation of high-order data. In contrast to the TN methods with pre-determined structures, the recently introduced tensor network structure search (TNSS) methods automatically learn a compact TN structure from the data, gaining increasing attention. Nonetheless, TNSS requires time-consuming manual adjustments of the penalty parameters that control the model complexity to achieve better performance, especially in the presence of missing or noisy data.

Generalized latent multi-view clustering with tensorized bipartite graph.

Tensor-based multi-view spectral clustering algorithms use tensors to model the structure of multi-dimensional data to take advantage of the complementary information and high-order correlations embedded in the graph, thus achieving impressive clustering performance. However, these algorithms use linear models to obtain consensus, which prevents the learned consensus from adequately representing the nonlinear structure of complex data. In order to address this issue, we propose a method called Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG).

A Novel Tensor Ring Sparsity Measurement for Image Completion.

As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix format. Nevertheless, in practical scenarios, much of the data are intrinsically multi-dimensional, and thus, using a matrix format for data representation will inevitably yield sub-optimal outcomes.

Experimental study on the effect of quaternary ammonium salt + monoethylene glycol compound to methane hydrate agglomeration in oil-water system.

Natural gas hydrate has been a critical risk to the safety of offshore oil and gas well test and subsea transportation. Herein, the effect of three quaternary ammonium salt (QAS) surfactants with monoethylene glycol (MEG) to methane hydrate agglomeration in water-oil system was experimentally studied by a rocking cell. Based on the hydrate volume fraction and the slider trajectory, a classification method of the gas hydrate anti-agglomerants was established.

Tensor Robust Kernel PCA for Multidimensional Data.

Recently, the tensor nuclear norm (TNN)-based tensor robust principle component analysis (TRPCA) has achieved impressive performance in multidimensional data processing. The underlying assumption in TNN is the low-rankness of frontal slices of the tensor in the transformed domain (e.g.

Fractional Tensor Recurrent Unit (fTRU): A Stable Forecasting Model With Long Memory.

The tensor recurrent model is a family of nonlinear dynamical systems, of which the recurrence relation consists of a -fold (called degree- ) tensor product. Despite such models frequently appearing in advanced recurrent neural networks (RNNs), to this date, there are limited studies on their long memory properties and stability in sequence tasks. In this article, we propose a fractional tensor recurrent model, where the tensor degree is extended from the discrete domain to the continuous domain, so it is effectively learnable from various datasets.

Nonparametric tensor ring decomposition with scalable amortized inference.

Multi-dimensional data are common in many applications, such as videos and multi-variate time series. While tensor decomposition (TD) provides promising tools for analyzing such data, there still remains several limitations. First, traditional TDs assume multi-linear structures of the latent embeddings, which greatly limits their expressive power.

Construction and application of flow pressure drop model of perforated well considering pressure loss of perforation hole.

Perforating well is one of the main production wells in reservoir development. Perforating effect directly affects well production, so the optimization of perforating parameters has attracted wide attention. Because pressure difference serves as the driving force for fluid flowing from formation to wellbore, it is important to understand the composition of production pressure difference in perforating well, which can guide the optimization of perforating parameters and the evaluation of perforating effect.

Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization.

Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great interpretability. However, most prior NMF-based MRL techniques are shallow models that ignore hierarchical information.

TT-Net: Tensorized Transformer Network for 3D medical image segmentation.

Accurate segmentation of organs, tissues and lesions is essential for computer-assisted diagnosis. Previous works have achieved success in the field of automatic segmentation. However, there exists two limitations.