Radical Belonging: School as Communion of Peoples, Place, and Power

In wondering “How are decolonizing, place/land-based, and community-grown learning places created and sustained as alternatives to dominant settler-colonial systems, and what stories would they share about their creation and existence?”, I formed relationships with two alternative, autonomous, decolonizing schools through a teacher-guide at each school who served as guides for me to enter their spaces with invitation. In developing these relationships over 2-3 years and spending 2-3 weeks alongside each of them at their school sites, I was able to sustain natural and deep conversation with my teacher-guides, who then served as co-storyers of this research to collectively consider research questions through the lens of their stories and lived realities in their schools. This study was carried out through narrative storywork, Indigenous and culturally responsive methodologies, and critical autoethnography, as my experience of entering these school communities and forming these relationships over time became a supporting contribution to the data. Data is regarded as all the stories, conversations, reflections, observations, intuited moments, and elements of portraiture that were gathered through this process of sustained relationship with my co-storyers and my dedicated time in being within and experiencing each school space. I identified four major themes as emergent from the data: (1) a necessary process, (2) school as communion, (3) a radical existence, and (4) belonging. Dialogue with my co-storyers about the emergent themes suggests that this work of creating decolonizing, community-grown, place-specific alternatives to settler-state educational systems is necessary across many communities; yet, entering this work requires a necessary process of individual and collective work to align to place-appropriate, decolonized, and Indigenous principals of place, community, culture, and work. Data also suggests that creating such schools is radical yet sustainable and that these schools embody a paradigmatic shift from colonizing, individualistic systems toward collective, communal systems aligned with Indigenous and anti-colonial communities. Furthermore, the data and dialogue suggest that within this work of growing such place-specific communal schools, members of the community are often afforded a greater sense of belonging and collective ownership over their educational experience. Both schools in the study also demonstrated a positive impact on the place and land on which their school was situated. Therefore, this study implicates that there is value in seeking and growing schools outside of the dominant system and that communities who seek to grow such place and person-specific schools can experience great benefit for both human and more-than-human members of the community. Keywords: alternative-autonomous school, communal school, school as communion, decolonizing, anti-colonial, Indigenous-aligned, Indigenous methodology, decolonizing communities, portraiture, critical autoethnography, co-storying research, narrative storywork, belonging, culturally responsive methodologies, place-based, land-based, resisting settler-state, sustainable systems thinking, Hālau Kū Māna, Angeles Workshop School, revolutionary schools, diverse communities, students of colo

Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation

Obtaining sufficient labeled data for training deep models is often challenging in real-life applications. To address this issue, we propose a novel solution for single-source domain generalized semantic segmentation. Recent approaches have explored data diversity enhancement using hallucination techniques. However, excessive hallucination can degrade performance, particularly for imbalanced datasets. As shown in our experiments, minority classes are more susceptible to performance reduction due to hallucination compared to majority classes. To tackle this challenge, we introduce a dual-stage Feature Transform (dFT) layer within the Adversarial Semantic Hallucination+ (ASH+) framework. The ASH+ framework performs a dual-stage manipulation of hallucination strength. By leveraging semantic information for each pixel, our approach adaptively adjusts the pixel-wise hallucination strength, thus providing fine-grained control over hallucination. We validate the effectiveness of our proposed method through comprehensive experiments on publicly available semantic segmentation benchmark datasets (Cityscapes and SYNTHIA). Quantitative and qualitative comparisons demonstrate that our approach is competitive with state-of-the-art methods for the Cityscapes dataset and surpasses existing solutions for the SYNTHIA dataset. Code for our framework will be made readily available to the research community

A Dominant-Negative PPARγ Mutant Promotes Cell Cycle Progression and Cell Growth in Vascular Smooth Muscle Cells

PPARγ ligands have been shown to have antiproliferative effects on many cell types. We herein report that a synthetic dominant-negative (DN) PPARγ mutant functions like a growth factor to promote cell cycle progression and cell proliferation in human coronary artery smooth muscle cells (CASMCs). In quiescent CASMCs, adenovirus-expressed DN-PPARγ promoted G1→S cell cycle progression, enhanced BrdU incorporation, and increased cell proliferation. DN-PPARγ expression also markedly enhanced positive regulators of the cell cycle, increasing Rb and CDC2 phosphorylation and the expression of cyclin A, B1, D1, and MCM7. Conversely, overexpression of wild-type (WT) or constitutively-active (CA) PPARγ inhibited cell cycle progression and the activity and expression of positive regulators of the cell cycle. DN-PPARγ expression, however, did not up-regulate positive cell cycle regulators in PPARγ-deficient cells, strongly suggesting that DN-PPARγ effects on cell cycle result from blocking the function of endogenous wild-type PPARγ. DN-PPARγ expression enhanced phosphorylation of ERK MAPKs. Furthermore, the ERK specific-inhibitor PD98059 blocked DN-PPARγ-induced phosphorylation of Rb and expression of cyclin A and MCM7. Our data thus suggest that DN-PPARγ promotes cell cycle progression and cell growth in CASMCs by modulating fundamental cell cycle regulatory proteins and MAPK mitogenic signaling pathways in vascular smooth muscle cells (VSMCs)

Contrastive Clustering

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline

Fast Model Debias with Machine Unlearning

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify social biases, which is especially concerning for automated decision-making processes in healthcare, recruitment, etc., as they could exacerbate unfair economic and social inequalities among different groups. Existing debiasing methods suffer from high costs in bias labeling or model re-training, while also exhibiting a deficiency in terms of elucidating the origins of biases within the model. To this respect, we propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models. The FMD identifies biased attributes through an explicit counterfactual concept and quantifies the influence of data samples with influence functions. Moreover, we design a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets along with experiments with large language models demonstrate that our method achieves superior or competing accuracies compared with state-of-the-art methods while attaining significantly fewer biases and requiring much less debiasing cost. Notably, our method requires only a small external dataset and updating a minimal amount of model parameters, without the requirement of access to training data that may be too large or unavailable in practice