Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled images, as a fundamental and classic machine learning problem, still lacks effective solution, particularly for large-scale datasets. In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale. We show that the pre-trained features are significantly more structured by further optimizing the rate reduction objective. The resulting features may significantly improve the clustering accuracy, e.g., from 57\% to 66\% on ImageNet-1k. Furthermore, by leveraging CLIP's image-text binding, we show how the new clustering method leads to a simple yet effective self-labeling algorithm that successfully works on unlabeled large datasets such as MS-COCO and LAION-Aesthetics. We will release the code in https://github.com/LeslieTrue/CPP.Comment: 21 pages, 13 figure

Unsupervised Manifold Linearizing and Clustering

We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data

Else-Net:Elastic Semantic Network for Continual Action Recognition from Skeleton Data

Most of the state-of-the-art action recognition methods focus on offline learning, where the samples of all types of actions need to be provided at once. Here, we address continual learning of action recognition, where various types of new actions are continuously learned over time. This task is quite challenging, owing to the catastrophic forgetting problem stemming from the discrepancies between the previously learned actions and current new actions to be learned. Therefore, we propose Else-Net, a novel Elastic Semantic Network with multiple learning blocks to learn diversified human actions over time. Specifically, our ElseNet is able to automatically search and update the most relevant learning blocks w.r.t. the current new action, or explore new blocks to store new knowledge, preserving the unmatched ones to retain the knowledge of previously learned actions and alleviates forgetting when learning new actions. Moreover, even though different human actions may vary to a large extent as a whole, their local body parts can still share many homogeneous features. Inspired by this, our proposed Else-Net mines the shared knowledge of the decomposed human body parts from different actions, which benefits continual learning of actions. Experiments show that the proposed approach enables effective continual action recognition and achieves promising performance on two large-scale action recognition dataset

Physiologically based pharmacokinetic modelling and simulation to predict the plasma concentration profile of schaftoside after oral administration of total flavonoids of Desmodium styracifolium

Introduction: The total flavonoids of Desmodium styracifolium (TFDS) are the flavonoid extracts purified from Desmodii Styracifolii Herba. The capsule of TFDS was approved for the treatment of urolithiasis by NMPA in 2022. Schaftoside is the representative compound of TFDS that possesses antilithic and antioxidant effects. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of schaftoside to simulate its plasma concentration profile in rat and human after oral administration of the total flavonoids of Desmodium styracifolium.Methods: The physiologically based pharmacokinetic model of schaftoside was firstly developed and verified by the pharmacokinetic data in rats following intravenous injection and oral administration of the total flavonoids of Desmodium styracifolium. Then the PBPK model was extrapolated to human with PK-Sim® software. In order to assess the accuracy of the extrapolation, a preliminary multiple-dose clinical study was performed in four healthy volunteers aged 18–45 years old. The predictive performance of PBPK model was mainly evaluated by visual predictive checks and fold error of Cmax and AUC0-t of schaftoside (the ratio of predicted to observed). Finally, the adult PBPK model was scaled to several subpopulations including elderly and renally impaired patients.Results: Schaftoside underwent poor metabolism in rat and human liver microsomes in vitro, and in vivo it was extensively excreted into urine and bile as an unchanged form. By utilizing literature and experimental data, the PBPK model of schaftoside was well established in rat and human. The predicted plasma concentration profiles of schaftoside were consistent with the corresponding observed data, and the fold error values were within the 2-fold acceptance criterion. No significant pharmacokinetic differences were observed after extrapolation from adult (18–40 years old) to elderly populations (71–80 years) in PK-Sim®. However, the plasma concentration of schaftoside was predicted to be much higher in renally impaired patients. The maximum steady-state plasma concentrations in patients with chronic kidney disease stage 3, 4 and 5 were 3.41, 12.32 and 23.77 times higher, respectively, than those in healthy people.Conclusion: The established PBPK model of schaftoside provided useful insight for dose selection of the total flavonoids of Desmodium styracifolium in different populations. This study provided a feasible way for the assessment of efficacy and safety of herbal medicines

Fault2SeisGAN: A method for the expansion of fault datasets based on generative adversarial networks

The development of supervised deep learning technology in seismology and related fields has been restricted due to the lack of training sets. A large amount of unlabeled data is recorded in seismic exploration, and their application to network training is difficult, e.g., fault identification. To solve this problem, herein, we propose an end-to-end training data set generative adversarial network Fault2SeisGAN. This network can expand limited labeled datasets to improve the performance of other neural networks. In the proposed method, the Seis-Loss is used to constrain horizon and amplitude information, Fault-Loss is used to constrain fault location information, and the Wasserstein distance is added to stabilize the network training to generate seismic amplitude data with fault location labels. A new fault identification network model was trained with a combination of expansion and original data, and the model was tested using actual seismic data. The results show that the use of the expanded dataset generated in this study improves the performance of the deep neural network with respect to seismic data prediction. Our method solves the shortage of training data set problem caused by the application of deep learning technology in seismology to a certain extent, improves the performance of neural networks, and promotes the development of deep learning technology in seismology

Engineering hibiscus-like riboflavin/ZIF-8 microsphere composites to enhance transepithelial corneal cross-linking

Riboflavin-5-phosphate (RF) is the most commonly used photosensitizer in corneal cross-linking (CXL), but its hydrophilicity and negative charge limit its penetration through the corneal epithelium into the stroma. To enhance the corneal permeability of RF and promote its efficacy in the treatment of keratoconus, novel hibiscus-like RF@ZIF-8 microsphere composites [6RF@ZIF-8 NF (nanoflake)] are prepared using ZIF-8 nanomaterials as carriers, which are characterized by their hydrophobicity, positive potential, biocompatibility, high loading capacities, and large surface areas. Both hematoxylin and eosin endothelial staining and TUNEL assays demonstrate excellent biocompatibility of 6RF@ZIF-8 NF. In in vivo studies, the 6RF@ZIF-8 NF displayed excellent corneal permeation, and outstanding transepithelial CXL (TE-CXL) efficacy, slightly better than the conventional CXL protocol. Furthermore, the special hibiscus-like structures of 6RF@ZIF-8 NF meant that it has better TE-CXL efficacy than that of 6RF@ZIF-8 NP (nanoparticles) due to the larger contact area with the epithelium and the shorter RF release passage. These results suggest that the 6RF@ZIF-8 NF are promising for transepithelial corneal cross-linking, avoiding the need for epithelial debridement

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network

This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data. To do this, the cycle generative adversarial network with special symmetric generation countermeasure network cycle mechanism and "cycle consistency loss" was selected. We improved the performance of the network learning and training by optimizing the network structure. Next, based on the optimized shallow profile sample set training network, random noise was removed from the shallow profile data and the signal-to-noise ratio of the data was improved. The effectiveness and adaptability of this method for denoising shallow profile data were verified by trial calculations of experimental and actual data and by comparison with the traditional band-pass filtering method