RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task

Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks. However, the inference process is often not interpretable. Most existing explanation techniques are limited to understanding GNN behaviors in classification tasks. In this work, we seek an explanation to interpret the graph regression models (XAIG-R). We show that existing methods overlook the distribution shifting and continuously ordered decision boundary, which hinders them away from being applied in the regression tasks. To address these challenges, we propose a novel objective based on the information bottleneck theory and introduce a new mix-up framework, which could support various GNNs in a model-agnostic manner. We further present a contrastive learning strategy to tackle the continuously ordered labels in regression task. To empirically verify the effectiveness of the proposed method, we introduce three benchmark datasets and a real-life dataset for evaluation. Extensive experiments show the effectiveness of the proposed method in interpreting GNN models in regression tasks

Self-Consistent Learning: Cooperation between Generators and Discriminators

Using generated data to improve the performance of downstream discriminative models has recently gained popularity due to the great development of pre-trained language models. In most previous studies, generative models and discriminative models are trained separately and thus could not adapt to any changes in each other. As a result, the generated samples can easily deviate from the real data distribution, while the improvement of the discriminative model quickly reaches saturation. Generative adversarial networks (GANs) train generative models via an adversarial process with discriminative models to achieve joint training. However, the training of standard GANs is notoriously unstable and often falls short of convergence. In this paper, to address these issues, we propose a $\textit{self-consistent learning}$ framework, in which a discriminator and a generator are cooperatively trained in a closed-loop form. The discriminator and the generator enhance each other during multiple rounds of alternating training until a scoring consensus is reached. This framework proves to be easy to train and free from instabilities such as mode collapse and non-convergence. Extensive experiments on sentence semantic matching demonstrate the effectiveness of the proposed framework: the discriminator achieves 10+ AP of improvement on the zero-shot setting and new state-of-the-art performance on the full-data setting

Contrastive Graph Pooling for Explainable Classification of Brain Networks

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions

Tumor promoter TPA activates Wnt/β-catenin signaling in a casein kinase 1-dependent manner.

The tumor promoter 12-O-tetra-decanoylphorbol-13-acetate (TPA) has been defined by its ability to promote tumorigenesis on carcinogen-initiated mouse skin. Activation of Wnt/β-catenin signaling has a decisive role in mouse skin carcinogenesis, but it remains unclear how TPA activates Wnt/β-catenin signaling in mouse skin carcinogenesis. Here, we found that TPA could enhance Wnt/β-catenin signaling in a casein kinase 1 (CK1) ε/δ-dependent manner. TPA stabilized CK1ε and enhanced its kinase activity. TPA further induced the phosphorylation of LRP6 at Thr1479 and Ser1490 and the formation of a CK1ε-LRP6-axin1 complex, leading to an increase in cytosolic β-catenin. Moreover, TPA increased the association of β-catenin with TCF4E in a CK1ε/δ-dependent way, resulting in the activation of Wnt target genes. Consistently, treatment with a selective CK1ε/δ inhibitor SR3029 suppressed TPA-induced skin tumor formation in vivo, probably through blocking Wnt/β-catenin signaling. Taken together, our study has identified a pathway by which TPA activates Wnt/β-catenin signaling

Association of obesity with the development of end stage renal disease in IgA nephropathy patients

Background and aimImmunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. We aimed to evaluate whether obesity is a risk factor for IgAN patients.MethodsA total of 1054 biopsy-proven IgAN patients were analyzed in this retrospective study. Patients were divided into four groups according to their body weight index (BMI) at the period of renal biopsy: underweight group (BMI< 18.5, N=75), normal weight group (18.5≤BMI<24, N=587), overweight group (24≤BMI<28, N=291) and obesity group (28≤BMI, N=101). The endpoint of our study was end stage renal disease (ESRD: eGFR <15 mL/min/1.73 m2 or having renal replacement treatment). Kaplan-Meier analyses and Cox proportional hazard models were performed to evaluate renal survival. Propensity-score matching (PSM) was performed to get the matched cohort to evaluate the role of obesity in IgAN patients. Besides, the effect modification of obesity and hypertension in IgAN patients was clarified by the synergy index.ResultsIgAN patients complicated with obesity had more severe renal dysfunction at the time of renal biopsy than those with optimal body weight. In addition, patients with obesity tended to have higher risk of metabolic disorders, such as hyperuricemia (64.4% vs 37%, p<0.001), hypertriglyceridemia (71.3% vs 32.5%, p<0.001) and hypercholesterolemia (46.5% vs 35.6%, p=0.036). It was observed that obesity patients had higher rate of unhealthy behaviors, such as smoking (27.7% vs 16.4%, p=0.006) and alcohol drinking (29.7% vs 19.9%, p=0.027). Although obesity was not confirmed as an independent risk factor for IgAN patients, we found that IgAN patients with obesity presented with higher incidence of hypertension, as well as lower event-free renal survival rate (log-rank p < 0.001), especially in patients with 24-h urine protein ≥ 1g (log-rank p =0.002). In addition, the synergy index showed that there was positive interaction between obesity and hypertension in IgAN.ConclusionObesity is an important risk factor for IgAN patients when combined with hypertension. Hypertension appears to be common in obese IgAN patients

Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian

IntroductionLand use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized.MethodsA quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted.ResultsThe quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm.ConclusionThe findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area

Hallucination Augmented Contrastive Learning for Multimodal Large Language Model

Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically, we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples, naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively, showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark, our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl