MCRAGE: Synthetic Healthcare Data for Fairness

In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on class-imbalanced EHR datasets perform significantly worse in deployment for individuals of the minority classes compared to samples from majority classes, which may lead to inequitable healthcare outcomes for minority groups. To address this challenge, we propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE), a novel approach to augment imbalanced datasets using samples generated by a deep generative model. The MCRAGE process involves training a Conditional Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, thereby achieving a more balanced distribution across all classes, which can be used to train an unbiased machine learning model. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC. We provide theoretical justification for our method in terms of recent convergence results for DDPMs with minimal assumptions.Comment: Keywords: synthetic electronic health records, conditional denoising diffusion probabilistic model, healthcare AI, tabular data, fairness, synthetic data. This paper is the result of work completed at the 2023 Emory University Department of Mathematics REU/RET program under the direction of Project Advisor Dr. Xi Yuanzhe. This work is sponsored by NSF DMS 205101

Hemicellulose-g-PAAc/TiO2 Nanocomposite Hydrogel for Dye Removal

Dyes pollution on urban environment is of great concern because of the human health hazards associated with this kind of contaminants, and the use of low-cost photocatalytic composite material is an efficient treatment method to minimize the environmental impact. A novel hemicellulose-g-PAAc/TiO2 composite hydrogel was prepared as a promising alternative material for dye removal. Wheat straw hemicellulose and TiO2 nanoparticles were first modified and then incorporated into hydrogel via covalent bonds. Effects of gel dosage, pH, initial concentration and contact time on the adsorption amount of methylene blue were systematically studied using the prepared hydrogel. The equilibrium adsorption data was fitted well to the Freundlich isotherm model, and Langmuir isotherm analysis indicated that the adsorption capacity of the hemicellulose-g-PAAc/TiO2 composite hydrogel was 389.1 mg/g, and adsorption kinetic study showed that the adsorption process can be described by the pseudo second-order kinetic model. The prepared composite hydrogel exhibited high photodegradation ability for methylene blue under alkaline conditions, and all results indicated that the hemicellulose-g-PAAc/TiO2 composite hydrogel had excellent photocatalytic degradability for dyes, which can be used in practical process

A preliminary study on the mechanism of the Liangzhu culture’s migration across the Yangtze river

The Liangzhu culture (5300–4200 cal BP) was the most famous Neolithic culture of who settled near the lower reaches of the Yangtze River in China. The core and initial distribution area of Liangzhu originated around Taihu Lake, located on the south bank of the Yangtze River delta. Recently, archaeological studies believe that the Jianghuai area and Huanghuai area north of the Yangtze River are also important distribution areas of Liangzhu culture. The route for Liangzhu culture migrating across the Yangtze River is inferred as follows: One would have crossed the Yangtze River from Nanjing-Zhenjiang belt and continued to migrate northward; while the other would have crossed the River near the estuary before moving north along the ancient coastline to the Jianghuai during the late period of Liangzhu, or crossed the Yangtze River from the east of the present Beijing-Hangzhou Grand Canal to Jianghuai and Huanghuai. According to the formation of the Yangtze River delta during the Holocene and the evolution of the estuarine sand bar, it is believed that there were large shoals in the Yangtze River channel in the middle-Holocene. The Liangzhu ancestors around 5000 cal BP had the ability of making canoes over 8 m. Based on the archaeological research of the Neolithic period, the evolution of the Yangtze River channel in the Holocene, the history of ancient Chinese shipbuilding, and the modern examples of crossing the Yangtze River with boat, it can be concluded that the present Changzhou–Jiangyin–Zhangjiagang line should be the main and reasonable route for the Liangzhu culture migrating across the Yangtze River

Uplink Performance of Cell-Free Extremely Large-Scale MIMO Systems

In this paper, we investigate the uplink performance of cell-free (CF) extremely large-scale multiple-input-multipleoutput (XL-MIMO) systems, which is a promising technique for future wireless communications. More specifically, we consider the practical scenario with multiple base stations (BSs) and multiple user equipments (UEs). To this end, we derive exact achievable spectral efficiency (SE) expressions for any combining scheme. It is worth noting that we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio (MR) combining. Numerical results show that the SE performance of the CF XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is interesting that a smaller antenna spacing leads to a higher correlation level among patch antennas. Finally, we prove that increasing the number of UE antennas may decrease the SE performance with MR combining

Prior Bilinear Based Models for Knowledge Graph Completion

Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior properties (based on evidence, e.g. symmetry pattern) while neglecting the prior properties. In this paper, we find a prior property named "the law of identity" that cannot be captured by bilinear based models, which hinders them from comprehensively modeling the characteristics of KGs. To address this issue, we introduce a solution called Unit Ball Bilinear Model (UniBi). This model not only achieves theoretical superiority but also offers enhanced interpretability and performance by minimizing ineffective learning through minimal constraints. Experiments demonstrate that UniBi models the prior property and verify its interpretability and performance

Learning Variational Neighbor Labels for Test-Time Domain Generalization

This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed at unseen target domains. We follow the strict separation of source training and target testing but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on six widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.Comment: Under revie

Association Graph Learning for Multi-Task Classification with Category Shifts

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes. We construct the association graph with nodes representing tasks, classes and instances, and encode the relationships among the nodes in the edges to guide their mutual knowledge transfer. By message passing on the association graph, our model enhances the categorical information of each instance, making it more discriminative. To avoid spurious correlations between task and class nodes in the graph, we introduce an assignment entropy maximization that encourages each class node to balance its edge weights. This enables all tasks to fully utilize the categorical information from related tasks. An extensive evaluation on three general benchmarks and a medical dataset for skin lesion classification reveals that our method consistently performs better than representative baselines

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks.Comment: accepted to ICML 202

Any-Shift Prompting for Generalization over Distributions

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless, conventional prompt learning methods overfit their training distribution and lose the generalization ability on test distributions. To improve generalization across various distribution shifts, we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework, the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass, avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts

A New Method for Estimation of the Sensible Heat Flux Under Unstable Conditions Using Satellite Vector Winds

It has been difficult to estimate the sensible heat flux at the air - sea interface using satellite data because of the difficulty in remotely observing the sea level air temperature. In this study, a new method is developed for estimating the sensible heat flux using satellite observations under unstable conditions. The basic idea of the method is that the air - sea temperature difference is related to the atmospheric convergence. Employed data include the wind convergence, sea level humidity, and sea surface temperature. These parameters can be derived from the satellite wind vectors, Special Sensor Microwave Imager (SSM/I) precipitable water, and Advanced Very High Resolution Radiometer (AVHRR) observations, respectively. The authors selected a region east of Japan as the test area where the atmospheric convergence appears all year. Comparison between the heat fluxes derived from the satellite data and from the National Centers for Environmental Prediction (NCEP) data suggests that the rms difference between the two kinds of sensible heat fluxes has low values in the sea area east of Japan with a minimum of 10.0 W m(-2). The time series of the two kinds of sensible heat fluxes at 10 locations in the area are in agreement, with rms difference ranging between 10.0 and 14.1 W m(-2) and correlation coefficient being higher than 0.7. In addition, the National Aeronautics and Space Administration ( NASA) Goddard Satellite-Based Surface Turbulent Flux (GSSTF) was used for a further comparison. The low-rms region with high correlation coefficient (\u3e0.7) was also found in the region east of Japan with a minimum of 12.2 W m(-2). Considering the nonlinearity in calculation of the sensible monthly means, the authors believe that the comparison with GSSTF is consistent with that with NCEP data