OTMatch: Improving Semi-Supervised Learning with Optimal Transport

Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning image predictions with specific classes generated through self-training techniques, thereby neglecting the inherent relationships that exist within these classes. In this paper, we present a new approach called OTMatch, which leverages semantic relationships among classes by employing an optimal transport loss function. By utilizing optimal transport, our proposed method consistently outperforms established state-of-the-art methods. Notably, we observed a substantial improvement of a certain percentage in accuracy compared to the current state-of-the-art method, FreeMatch. OTMatch achieves 3.18%, 3.46%, and 1.28% error rate reduction over FreeMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels per class, respectively. This demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting

DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation

Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories are not seen by the model before. This often leads to a relatively uniform distribution of feature values across channels on novel classes, posing challenges in determining channel importance for novel tasks. Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features. However, features with high geometric similarities may carry distinct semantics, especially in the context of few-shot learning. In this paper, we demonstrate that the importance ranking of feature channels is a more reliable indicator for few-shot learning than geometric similarity metrics. We observe that replacing the geometric similarity metric with Kendall's rank correlation only during inference is able to improve the performance of few-shot learning across a wide range of datasets with different domains. Furthermore, we propose a carefully designed differentiable loss for meta-training to address the non-differentiability issue of Kendall's rank correlation. Extensive experiments demonstrate that the proposed rank-correlation-based approach substantially enhances few-shot learning performance

Effects of wave-current interaction on storm surge in the Taiwan Strait: Insights from Typhoon Morakot

The effects of wave-current interaction on storm surge are investigated by a two-dimensional wave-current coupling model through simulations of Typhoon Morakot in the Taiwan Strait. The results show that wind wave and slope of sea floor govern wave setup modulations within the nearshore surf zone. Wave setup during Morakot can contribute up to 24% of the total storm surge with a maximum value of 0.28 m. The large wave setup commonly coincides with enhanced radiation stress gradient, which is itself associated with transfer of wave momentum flux. Water levels are to leading order in modulating significant wave height inside the estuary. High water levels due to tidal change and storm surge stabilize the wind wave and decay wave breaking. Outside of the estuary, waves are mainly affected by the current-induced modification of wind energy input to the wave generation. By comparing the observed significant wave height and water level with the results from uncoupled and coupled simulations, the latter shows a better agreement with the observations. It suggests that wave-current interaction plays an important role in determining the extreme storm surge and wave height in the study area and should not be neglected in a typhoon forecast

Decoding trust: A reinforcement learning perspective

Behavioral experiments on the trust game have shown that trust and trustworthiness are universal among human beings, contradicting the prediction by assuming \emph{Homo economicus} in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations however need to resort to some factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals update their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one's decision making as trustor and trustee respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human's psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness without external factors involved. More importantly, the proposed paradigm shows the potential in deciphering many puzzles in human behaviors.Comment: 12 pages, 11 figures. Comments are appreciate

Time series clustering of mRNA and lncRNA expression during osteogenic differentiation of periodontal ligament stem cells

Background Long noncoding RNAs (lncRNAs) are regulatory molecules that participate in biological processes such as stem cell differentiation. Periodontal ligament stem cells (PDLSCs) exhibit great potential for the regeneration of periodontal tissue and the formation of new bone. However, although several lncRNAs have been found to be involved in the osteogenic differentiation of PDLSCs, the temporal transcriptomic landscapes of mRNAs and lncRNAs need to be mapped to obtain a complete picture of osteoblast differentiation. In this study, we aimed to characterize the time-course expression patterns of lncRNAs during the osteogenic differentiation of PDLSCs and to identify the lncRNAs that are related to osteoblastic differentiation. Methods We cultured PDLSCs in an osteogenic medium for 3, 7, or 14 days. We then used RNA sequencing (RNA-seq) to analyze the expression of the coding and non-coding transcripts in the PDLSCs during osteogenic differentiation. We also utilized short time-series expression miner (STEM) to describe the temporal patterns of the mRNAs and lncRNAs. We then performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to assess the biological relevance of genes in each profile, and used quantitative real-time PCR (qRT-PCR) to validate the differentially expressed mRNAs and lncRNAs that were associated with osteoblast differentiation. Lastly, we performed a knock down of two lncRNAs, MEG8, and MIR22HG, and evaluated the expression of osteogenic markers. Results When PDLSCs were differentiated to osteoblasts, mRNAs associated with bone remodeling, cell differentiation, and cell apoptosis were upregulated while genes associated with cell proliferation were downregulated. lncRNAs showed stage-specific expression, and more than 200 lncRNAs were differentially expressed between the undifferentiated and osteogenically differentiated PDLSCs. Using STEM, we identified 25 temporal gene expression profiles, among which 14 mRNA and eight lncRNA profiles were statistically significant. We found that genes in pattern 12 were associated with osteoblast differentiation. The expression patterns of osteogenic mRNAs (COL6A1, VCAN, RRBP1, and CREB3L1) and lncRNAs (MEG8 and MIR22HG) were consistent between the qRT-PCR and RNA-seq results. Moreover, the knockdown of MEG8 and MIR22HG significantly decreased the expression of osteogenic markers (runt-related transcription factor 2 and osteocalcin). Discussion During the osteogenic differentiation of PDLSCs, both mRNAs and lncRNAs showed stage-specific expression. lncRNAs MEG8 and MIR22HG showed a high correlation with osteoblastogenesis. Our results can be used to gain a more comprehensive understanding of the molecular events regulating osteoblast differentiation and the identification of functional lncRNAs in PDLSCs

Impact of V-ets Erythroblastosis Virus E26 Oncogene Homolog 1 Gene Polymorphisms Upon Susceptibility to Autoimmune Diseases

V-ets erythroblastosis virus E26 oncogene homolog 1 (ETS1) is recognized as a gene of risk to autoimmune diseases (ADs). Two single nucleotide polymorphisms (SNPs) in ETS1 (rs1128334 G\u3eA and rs10893872 T\u3eC) were considered associated with ADs risk. However, the results remain conflicting. We performed a meta-analysis to evaluate more precise estimations of any relationship. We searched PubMed, OvidSP, and Chinese National Knowledge Infrastructure databases (papers published prior to September 12, 2014) and extracted data from eligible studies. Meta-analysis was performed using the STATA 12.0 software. Random effect model or fixed effect model were chosen according to the study heterogeneities. A total of 11 studies including 7359 cases (9660 controls) for rs1128334 and 8 studies including 5419 cases (7122 controls) for rs10893872 were involved in this meta-analysis. Overall, our results showed that there were significant associations for rs1128334 with AD risk in 5 genetic models, both in pooled analysis and in systemic lupus erythematous (SLE) subgroup, and in 3 genetic models of the uveitis subgroup. Although for rs10893872, the results showed that there were significant associations in allele model both in pooled analysis and in SLE subgroup. As a conclusion, this meta-analysis demonstrated that these 2 SNPs (rs1128334 and rs10893872) in ETS1were associated with ADs risk