Learning Sample Difficulty from Pre-trained Models for Reliable Prediction

Large-scale pre-trained models have achieved remarkable success in many applications, but how to leverage them to improve the prediction reliability of downstream models is undesirably under-explored. Moreover, modern neural networks have been found to be poorly calibrated and make overconfident predictions regardless of inherent sample difficulty and data uncertainty. To address this issue, we propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. Pre-trained models that have been exposed to large-scale datasets and do not overfit the downstream training classes enable us to measure each training sample's difficulty via feature-space Gaussian modeling and relative Mahalanobis distance computation. Importantly, by adaptively penalizing overconfident prediction based on the sample difficulty, we simultaneously improve accuracy and uncertainty calibration across challenging benchmarks (e.g., +0.55% ACC and -3.7% ECE on ImageNet1k using ResNet34), consistently surpassing competitive baselines for reliable prediction. The improved uncertainty estimate further improves selective classification (abstaining from erroneous predictions) and out-of-distribution detection.Comment: NeurIPS 202

TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective

Vision Transformers (ViTs) have demonstrated powerful representation ability in various visual tasks thanks to their intrinsic data-hungry nature. However, we unexpectedly find that ViTs perform vulnerably when applied to face recognition (FR) scenarios with extremely large datasets. We investigate the reasons for this phenomenon and discover that the existing data augmentation approach and hard sample mining strategy are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information. To remedy these problems, this paper proposes a superior FR model called TransFace, which employs a patch-level data augmentation strategy named DPAP and a hard sample mining strategy named EHSM. Specially, DPAP randomly perturbs the amplitude information of dominant patches to expand sample diversity, which effectively alleviates the overfitting problem in ViTs. EHSM utilizes the information entropy in the local tokens to dynamically adjust the importance weight of easy and hard samples during training, leading to a more stable prediction. Experiments on several benchmarks demonstrate the superiority of our TransFace. Code and models are available at https://github.com/DanJun6737/TransFace.Comment: Accepted by ICCV 202

5,5′-[(1,4-Phenyl­enedimethyl­ene)bis­(sulfanedi­yl)]bis­(1-methyl-1H-1,2,3,4-tetra­zole)

The title mol­ecule, C12H14N8S2, has point symmetry since it is situated on a crystallographic centre of symmetry. The 1-meth­yl/5-thio groups are in an anti­periplanar conformation. The dihedral angle between the benzene and tetra­zole rings is 84.33 (2)°. In the crystal, C—H⋯N hydrogen bonds link mol­ecules into ladder-like chains running along the b axis. There are also C—H⋯π inter­actions present in the crystal structure

L2hgdh Deficiency Accumulates l-2-Hydroxyglutarate with Progressive Leukoencephalopathy and Neurodegeneration

l-2-Hydroxyglutarate aciduria (L-2-HGA) is an autosomal recessive neurometabolic disorder caused by a mutation in the l-2-hydroxyglutarate dehydrogenase (L2HGDH) gene. In this study, we generated L2hgdh knockout (KO) mice and observed a robust increase of l-2-hydroxyglutarate (L-2-HG) levels in multiple tissues. The highest levels of L-2-HG were observed in the brain and testis, with a corresponding increase in histone methylation in these tissues. L2hgdh KO mice exhibit white matter abnormalities, extensive gliosis, microglia-mediated neuroinflammation, and an expansion of oligodendrocyte progenitor cells (OPCs). Moreover, L2hgdh deficiency leads to impaired adult hippocampal neurogenesis and late-onset neurodegeneration in mouse brains. Our data provide in vivo evidence that L2hgdh mutation leads to L-2-HG accumulation, leukoencephalopathy, and neurodegeneration in mice, thereby offering new insights into the pathophysiology of L-2-HGA in humans

Comparative transcriptomic analysis reveals the molecular mechanism underlying seedling heterosis and its relationship with hybrid contemporary seeds DNA methylation in soybean

Heterosis is widely used in crop production, but phenotypic dominance and its underlying causes in soybeans, a significant grain and oil crop, remain a crucial yet unexplored issue. Here, the phenotypes and transcriptome profiles of three inbred lines and their resulting F1 seedlings were analyzed. The results suggest that F1 seedlings with superior heterosis in leaf size and biomass exhibited a more extensive recompilation in their transcriptional network and activated a greater number of genes compared to the parental lines. Furthermore, the transcriptional reprogramming observed in the four hybrid combinations was primarily non-additive, with dominant effects being more prevalent. Enrichment analysis of sets of differentially expressed genes, coupled with a weighted gene co-expression network analysis, has shown that the emergence of heterosis in seedlings can be attributed to genes related to circadian rhythms, photosynthesis, and starch synthesis. In addition, we combined DNA methylation data from previous immature seeds and observed similar recompilation patterns between DNA methylation and gene expression. We also found significant correlations between methylation levels of gene region and gene expression levels, as well as the discovery of 12 hub genes that shared or conflicted with their remodeling patterns. This suggests that DNA methylation in contemporary hybrid seeds have an impact on both the F1 seedling phenotype and gene expression to some extent. In conclusion, our study provides valuable insights into the molecular mechanisms of heterosis in soybean seedlings and its practical implications for selecting superior soybean varieties

Exploring the pathogenesis of colorectal carcinoma complicated with hepatocellular carcinoma via microarray data analysis

Background: Despite the increasing number of research endeavors dedicated to investigating the relationship between colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), the underlying pathogenic mechanism remains largely elusive. The aim of this study is to shed light on the molecular mechanism involved in the development of this comorbidity.Methods: The gene expression profiles of CRC (GSE90627) and HCC (GSE45267) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely, functional annotation, protein‐protein interaction (PPI) network and module construction, and hub gene identification, survival analysis and co-expression analysis.Results: A total of 150 common downregulated differentially expressed genes and 148 upregulated differentially expressed genes were selected for subsequent analyses. The significance of chemokines and cytokines in the pathogenesis of these two ailments is underscored by functional analysis. Seven gene modules that were closely connected were identified. Moreover, the lipopolysaccharide-mediated signaling pathway is intricately linked to the development of both diseases. Finally, 10 important hub genes were identified using cytoHubba, including CDK1, KIF11, CDC20, CCNA2, TOP2A, CCNB1, NUSAP1, BUB1B, ASPM, and MAD2L1.Conclusion: Our study reveals the common pathogenesis of colorectal carcinoma and hepatocellular carcinoma. These common pathways and hub genes may provide new ideas for further mechanism research

The complete mitochondrial genome of Xystrocera globosa (Coleoptera: Cerambycidae) and its phylogeny

The complete mitochondrial genome of Xystrocera globosa is 15,706 bp in length, containing 13 protein-coding genes, 22 transfer RNAs, two ribosomal RNAs and the A + T-rich region. The overall base composition is 72.7% AT and 27.3% GC, and the AT content of the control region is 79.3%. In ML and BI phylogenetic trees, X. globosa was a sister clade to X. grayii. The monophyly of Lamiinae and Prioninae were supported in ML analyses, but nevertheless, the monophyly of Cerambycinae was not recovered