Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. An example is modeling de facto care pathways that characterize common step-by-step plans for treatment or care. However, clinical event data pose several unique challenges, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, multi-scale event interactions, and the high computational costs associated with long event sequences. Existing neural temporal point processes (TPPs) methods do not effectively capture the multi-scale nature of event interactions, which is common in many real-world clinical applications. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), specifically designed for irregularly timed event data. Our model consists of two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism, where different temporal scales are determined by a bottom-up clustering approach. Extensive experiments on several real-world EHR datasets show that our XTSFormer outperforms multiple baseline methods.

Facial beautification removal presents a formidable inverse challenge due to the inherent diversity and unpredictability of beautification processes. Current methodologies often fall short in effectively restoring facial structural alterations and preserving texture features during makeup removal. This paper introduces DeBeauty, an innovative joint framework for facial beautification removal, comprising two primary workflows: Adversarial De-Makeup Flow (ADF) and Relocation Deformation Flow (RDF). ADF incorporates Multi-Level Perception Collaborative Discrimination (MLPCD) and Discriminator-Guided Spatial Adaptive Multi-Scale Attention (SAMA), which enhance the detection of subtle makeup and facilitate the comprehensive removal of extensive makeup while preserving facial texture. RDF introduces a Hyperplane Relocation Strategy that adjusts the latent code of the input face to align with the original structural distribution. Experimental evaluations on the newly proposed Multivariate Beautified Face dataset demonstrate that this approach effectively restores the original color and structural context of the face while preserving essential facial features, achieving state-of-the-art performance.

Non-duplicate sampling (NDS) is a recently proposed technique that selects items from a data stream with probability p only on their first appearance, effectively handling duplicate items. While NDS has shown promise in the network application on flow cardinality measurement by outperforming sketch-based approaches, it faces limitations in distributed environments where traffic flows span multiple measurement points. To address this challenge, this paper proposes distributed non-duplicate sampling (DNDS), which extends NDS to multiple sampling points by ensuring each item is globally selected with probability p exactly once, regardless of where its appearances occur. We present the first comprehensive study of DNDS, focusing on its application on network-wide flow cardinality estimation. We develop an efficient implementation of DNDS with theoretical guarantees and derive optimal parameter settings. We propose two novel DNDS-based solutions for network-wide flow cardinality estimation. Through extensive experimental evaluation using real network traffic traces, we demonstrate the effectiveness of the DNDS implementation and demonstrate that our solutions achieve up to 10× improvement in estimation accuracy compared to state-of-the-art sketch-based methods.

Over 40% of the global population lives within 100 kilometers of the coast, which contributes more than $8 trillion annually to the global economy. Unfortunately, coastal ecosystems are increasingly vulnerable to more frequent and intense extreme weather events and rising sea levels. Coastal scientists use numerical models to simulate complex physical processes, but these models are often slow and expensive. In recent years, deep learning has become a promising alternative to reduce the cost of numerical models. However, progress has been hindered by the lack of a large-scale, high-resolution coastal simulation dataset to train and validate deep learning models. Existing studies often focus on relatively small datasets and simple processes. To fill this gap, we introduce a decade-long, high-resolution ($<$100m) coastal circulation modeling dataset on a real-world 3D mesh in southwest Florida with around 6 million cells. The dataset contains key oceanography variables (e.g., current velocities, free surface level, temperature, salinity) alongside external atmospheric and river forcings. We evaluated a customized Vision Transformer model that takes initial and boundary conditions and external forcings and predicts ocean variables at varying lead times. The dataset provides an opportunity to benchmark novel deep learning models for high-resolution coastal simulations (e.g., physics-informed machine learning, neural operator learning). The code and dataset can be accessed at https://github.com/spatialdatasciencegroup/CoastalBench.

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration method for improving the coverage of UI scenarios, thereby detecting accessibility issues within the application, and ultimately reducing false negatives. Furthermore, to reduce false positives caused by not considering the runtime context, we propose a context-aware detection method that provides a more fine-grained detection capability. Our experimental results reveal that A11yScan can detect 1.7X more issues surpassing current state-of-the-art approaches like Xbot ($\mathbf{3, 9 9 1}$vs.$\mathbf{2, 3 2 1}$), thereby reducing the false negative rate by$\mathbf{4 1. 8 4 \%}$. Additionally, it outperforms established UI exploration techniques such as SceneDroid (952 vs. 661 UI scenarios), while achieving comparable activity coverage to recent leading GUI testing tools like GPTDroid on the available dataset (73 % vs. 71%). Meanwhile, with the context-aware detection method, A11yScan effectively reduces the false positive rate by 21 %, validated with a 90.56 % accuracy rate through a user study.

Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS), which usually runs on an HPC cluster with multiple CPU cores. However, the process is time-consuming and energy expensive. While coarse-grained ROMS simulations offer faster alternatives, they sacrifice detail and accuracy, particularly in complex coastal environments. Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days). Our approach not only accelerates simulations but also incorporates a physics-based constraint to detect and correct inaccurate results, ensuring reliability while minimizing manual intervention. We develop a fully GPU-accelerated workflow, optimizing the model training and inference pipeline on NVIDIA DGX-2 A100 GPUs. Our experiments demonstrate that our AI surrogate reduces the time cost of$\mathbf{1 2}$-day forecasting of traditional ROMS simulations from 9,908 seconds (on 512 CPU cores) to 22 seconds (on one A100 GPU), achieving over$450 \times$speedup while maintaining high-quality simulation results. This work contributes to oceanographic modeling by offering a fast, accurate, and physically consistent alternative to traditional simulation models, particularly for real-time forecasting in rapid disaster response.