SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model

The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively exploit the pre-trained knowledge for each incremental task while maintaining its generalizability remains an open question. In this work, we present an extensive analysis for continual learning on a pre-trained model (CLPM), and attribute the key challenge to a progressive overfitting problem. Observing that selectively reducing the learning rate can almost resolve this issue in the representation layer, we propose a simple but extremely effective approach named Slow Learner with Classifier Alignment (SLCA), which further improves the classification layer by modeling the class-wise distributions and aligning the classification layers in a post-hoc fashion. Across a variety of scenarios, our proposal provides substantial improvements for CLPM (e.g., up to 49.76%, 50.05%, 44.69% and 40.16% on Split CIFAR-100, Split ImageNet-R, Split CUB-200 and Split Cars-196, respectively), and thus outperforms state-of-the-art approaches by a large margin. Based on such a strong baseline, critical factors and promising directions are analyzed in-depth to facilitate subsequent research.Comment: 11 pages, 8 figures, accepted by ICCV 202

StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data

The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions. Current methodologies often rely on annotations derived from benchmark datasets to construct image-dialogue datasets for training purposes, akin to instruction tuning in LLMs. However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models. In an effort to mitigate these limitations, we propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models to yield a diverse and controllable dataset with varied image content. This not only provides greater flexibility compared to existing methodologies but also significantly enhances several model capabilities. Our research includes comprehensive experiments conducted on various datasets using the open-source LLAVA model as a testbed for our proposed pipeline. Our results underscore marked enhancements across more than ten commonly assessed capabilities,Comment: Project page: https://github.com/icoz69/StableLLAV

Disentangled Pre-training for Image Matting

Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by proposing a self-supervised pretraining approach that can leverage infinite numbers of data to boost the matting performance. The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective. The pre-trained model is then used as an initialisation of the downstream matting task for fine-tuning. Extensive experimental evaluations show that the proposed approach outperforms both the state-of-the-art matting methods and other alternative self-supervised initialisation approaches by a large margin. We also show the robustness of the proposed approach over different backbone architectures. Our project page is available at https://crystraldo.github.io/dpt-mat/.</p

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research

OGM: Online Gaussian Graphical Models on the Fly

Gaussian Graphical Model is widely used to understand the dependencies between variables from high-dimensional data and can enable a wide range of applications such as principal component analysis, discriminant analysis, and canonical analysis. With respect to the streaming nature of big data, we study a novel Online Gaussian Graphical Model (OGM) that can estimate the inverse covariance matrix over the high-dimensional streaming data, in this paper. Specifically, given a small number of samples to initialize the learning process, OGM first estimates a low-rank estimation of inverse covariance matrix; then, when each individual new sample arrives, it updates the estimation of inverse covariance matrix using a low-complexity updating rule, without using the past data and matrix inverse. The significant edges of Gaussian graphical models can be discovered through thresholding the inverse covariance matrices. Theoretical analysis shows the convergence rate of OGM to the true parameters is guaranteed under Bernstein-style with mild conditions. We evaluate OGM using extensive experiments. The evaluation results backup our theory

Influence of pre-transplant minimal residual disease on prognosis after Allo-SCT for patients with acute lymphoblastic leukemia: systematic review and meta-analysis

Abstract Background This meta-analysis was performed to explore the impact of minimal residual disease (MRD) prior to transplantation on the prognosis for patients with acute lymphoblastic leukemia (ALL). Methods A systematic search of PubMed, Embase, and the Cochrane Library was conducted for relevant studies from database inception to March 2016. A total of 21 studies were included. Results Patients with positive MRD prior to allogeneic stem cell transplantation (allo-SCT) had a significantly higher rate of relapse compared with those with negative MRD (HR = 3.26; P <  0.05). Pre-transplantation positive MRD was a significant negative predictor of relapse-free survival (RFS) (HR = 2.53; P <  0.05), event-free survival (EFS) (HR = 4.77; P < 0.05), and overall survival (OS) (HR = 1.98; P < 0.05). However, positive MRD prior to transplantation was not associated with a higher rate of nonrelapse mortality. Conclusions Positive MRD before allo-SCT was a predictor of poor prognosis after transplantation in ALL. Trial registration Not applicable

A new NASH model in aged mice with rapid progression of steatohepatitis and fibrosis.

Non-alcoholic fatty liver disease (NAFLD) has a high prevalence worldwide, with a significant proportion of patients progressing into non-alcoholic steatohepatitis (NASH) and further into cirrhosis and hepatocellular carcinoma (HCC). Most of the current animal models of NASH have limitations, such as incompatibility with human pathogenesis characteristics or long induction periods, which severely limit the development of new drugs and preclinical studies for NASH. We investigated the progression of NASH and fibrosis, as well as metabolic indicators, at different time points in aged mice induced by the Gubra Amylin NASH (GAN) diet, a high-fat, high-sugar, high-cholesterol diet, and attempted to establish a rapid and useful mouse model of NASH. Young and aged C57BL/6 mice were induced on a normal chow or GAN diet for 12 and 21 weeks, respectively. After 12 weeks of induction, aged mice developed NASH, including hepatic steatosis, lobular inflammation and hepatic ballooning, and the phenotype was more severe compared with young mice. After 21 weeks of induction, aged mice developed hepatic fibrosis, which greatly shortened the induction time compared with young mice. Furthermore, analysis of immune cell infiltration in the liver by flow cytometry elucidated the changes of multiple immune cells during the pathogenesis of NASH. These findings suggest that aged mice may develop NASH and fibrosis more rapidly under GAN diet induction, which may significantly shorten the period for preclinical studies of NASH

dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms

MicroRNAs (miRNAs) are important regulators in gene expression. The dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells. A large number of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published studies with high-throughput experiment information, the database of DEMs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, which contains two-fold more data entries than the second version and now includes also data from mice and rats. The dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained by using experimentally validated targets. The annotations can be of great benefit to the intensive analysis of the roles of DEMs in cancer. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC