An MDL approach to the climate segmentation problem

This paper proposes an information theory approach to estimate the number of changepoints and their locations in a climatic time series. A model is introduced that has an unknown number of changepoints and allows for series autocorrelations, periodic dynamics, and a mean shift at each changepoint time. An objective function gauging the number of changepoints and their locations, based on a minimum description length (MDL) information criterion, is derived. A genetic algorithm is then developed to optimize the objective function. The methods are applied in the analysis of a century of monthly temperatures from Tuscaloosa, Alabama.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS289 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Many adversarial-based UDA methods involve high-instability training and have to carefully tune the optimization procedure. Some non-adversarial UDA methods employ a consistency regularization on the target predictions of a student model and a teacher model under different perturbations, where the teacher shares the same architecture with the student and is updated by the exponential moving average of the student. However, these methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. In this paper, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. By exploiting the latent uncertainty information of the target samples, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. In addition, we further reveal the reason why the current consistency regularization is often unstable in minimizing the distribution discrepancy. We also show that our method can effectively ease this issue by mining the most reliable and meaningful samples with a dynamic weighting scheme of consistency loss. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods on two domain adaptation benchmarks, $i.e.,$ GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes

Context-Aware Mixup for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

DMT: Dynamic Mutual Training for Semi-Supervised Learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https://github.com/voldemortX/DST-CBC .Comment: Reformatte

RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications. The dataset, code, and models are publicly available at https://github.com/dengzhuo-AI/Real-FundusComment: IEEE J-BHI 2022; The First Benchmark and First Transformer-based Method for Real Clinical Fundus Image Restoratio

Good Practices and Common Pitfalls in Climate Time Series Changepoint Techniques:A Review

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to small perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change conclusions. This paper is a review of the changepoint topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features of most climate time series. Thereafter, single and multiple changepoint problems are considered. Several pitfalls are discussed en route and good practices are recommended. While the majority of our applications involve temperature series, other settings are mentioned

Efficient derivation of dopaminergic neurons from SOX1(-) floor plate cells under defined culture conditions.

BACKGROUND: Parkinson's disease (PD) is a severe neurodegenerative disease associated with loss of dopaminergic neurons. Derivation of dopaminergic neurons from human embryonic stem cells (hESCs) could provide new therapeutic options for PD therapy. Dopaminergic neurons are derived from SOX(-) floor plate (FP) cells during embryonic development in many species and in human cell culture in vitro. Early treatment with sonic hedgehog (Shh) has been reported to efficiently convert hESCs into FP lineages. METHODS: In this study, we attempted to utilize a Shh-free approach in deriving SOX1(-) FP cells from hESCs in vitro. Neuroectoderm conversion from hESCs was achieved with dual inhibition of the BMP4 (LDN193189) and TGF-β signaling pathways (SB431542) for 24 h under defined culture conditions. RESULTS: Following a further 5 days of treatment with LDN193189 or LDN193189 + SB431542, SOX1(-) FP cells constituted 70-80 % of the entire cell population. Upon treatment with Shh and FGF8, the SOX1(-) FP cells were efficiently converted to functional Nurr1(+) and TH(+) dopaminergic cells (patterning), which constituted more than 98 % of the entire cell population. However, when the same growth factors were applied to SOX1(+) cells, only less than 4 % of the cells became Nurr1(+), indicating that patterning was effective only if SOX1 expression was down-regulated. After transplanting the Nurr1(+) and TH(+) cells into a hemiparkinsonian rat model, significant improvements were observed in amphetamine induced ipslateral rotations, apomorphine induced contra-lateral rotations and Rota rod motor tests over a duration of 8 weeks. CONCLUSIONS: Our findings thus provide a convenient approach to FP development and functional dopaminergic neuron derivation.published_or_final_versio

Enhanced B7-H4 expression in gliomas with low PD-L1 expression identifies super-cold tumors.

BACKGROUND: Characterizing expression profiles of different immune checkpoint molecules are promising for personalized checkpoint inhibitory immunotherapy. Gliomas have been shown as potential targets for immune checkpoint inhibitors recently. Our study was performed to determine coexpression levels of two major B7 immune regulatory molecules programmed death ligand 1 (PD-L1) and B7-H4, both of which have been demonstrated to inhibit antitumor host immunity in gliomas. METHODS: We assessed tumor tissues from stage II-IV primary gliomas (n=505) by immunohistochemistry (IHC) for protein levels of both PD-L1 and B7-H4. Gene coexpression analysis assessing clusters based on extent of PD-L1/B7-H4 classifier genes expression were investigated in two transcriptome datasets (The Cancer Genome Atlas and Chinese Glioma Genome Atlas). In addition, levels of immune cell infiltrates were estimated with IHC and RNA-seq data for assessing the tumor immune microenvironment of PD-L1/B7-H4 subgroups. RESULTS: High expression of PD-L1 and B7-H4 in gliomas was 23% and 20%, respectively, whereas coexpression of two proteins at high levels was limited to 2% of the cases. Comparable results were seen in RNA-seq datasets where PD-L1 mRNA expression levels negatively correlated with that of B7-H4. Gene coexpression modules clustered within each grade of gliomas demonstrated lack of double-high modules (cluster with high expression of both PD-L1 and B7-H4 classifier genes). B7-H4 mRNA expression levels showed negative correlation with extent of immune cell infiltration and High-B7-H4 module gliomas (high B7-H4 but low PD-L1 classifier genes expression) had less tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). IHC assessment also showed few TILs and TAMs in High-B7-H4 subgroup gliomas. CONCLUSIONS: The majority of gliomas express PD-L1 or B7-H4, however, coexpression of both at high levels is minimal. The high-B7-H4 patients could be considered as \u27super-cold\u27 gliomas with significantly deficient in TILs, suggesting that B7-H4 might inhibit T-cell trafficking into the central nervous system. This study demonstrated that PD-L1 and B7-H4 may serve as mutually compensatory immune checkpoint molecules in gliomas for immune targeted or active-specific immunotherapy. The distinct B7-H4 pathways modulating T-cell function and immune evasion in glioma patients deserved to be further explored in the future during immunotherapy