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Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions.

Efficient polyp segmentation in healthcare plays a critical role in enabling early diagnosis of colorectal cancer. However, the segmentation of polyps presents numerous challenges, including the intricate distribution of backgrounds, variations in polyp sizes and shapes, and indistinct boundaries. Defining the boundary between the foreground (i.e. polyp itself) and the background (surrounding tissue) is difficult. To mitigate these challenges, we propose Multi-Scale Edge-Guided Attention Network (MEGANet) tailored specifically for polyp segmentation within colonoscopy images. This network draws inspiration from the fusion of a classical edge detection technique with an attention mechanism. By combining these techniques, MEGANet effectively preserves high-frequency information, notably edges and boundaries, which tend to erode as neural networks deepen. MEGANet is designed as an end-to-end framework, encompassing three key modules: an encoder, which is responsible for capturing and abstracting the features from the input image, a decoder, which focuses on salient features, and the Edge-Guided Attention module (EGA) that employs the Laplacian Operator to accentuate polyp boundaries. Extensive experiments, both qualitative and quantitative, on five benchmark datasets, demonstrate that our EGANet outperforms other existing SOTA methods under six evaluation metrics. Our code is available at \url{//github.com/UARK-AICV/MEGANet}.

Tinnitus is a prevalent hearing disorder that can be caused by various factors such as age, hearing loss, exposure to loud noises, ear infections or tumors, certain medications, head or neck injuries, and psychological conditions like anxiety and depression. While not every patient requires medical attention, about 20% of sufferers seek clinical intervention. Early diagnosis is crucial for effective treatment. New developments have been made in tinnitus detection to aid in early detection of this illness. Over the past few years, there has been a notable growth in the usage of electroencephalography (EEG) to study variations in oscillatory brain activity related to tinnitus. However, the results obtained from numerous studies vary greatly, leading to conflicting conclusions. Currently, clinicians rely solely on their expertise to identify individuals with tinnitus. Researchers in this field have incorporated various data modalities and machine-learning techniques to aid clinicians in identifying tinnitus characteristics and classifying people with tinnitus. The purpose of writing this article is to review articles that focus on using machine learning (ML) to identify or predict tinnitus patients using EEG signals as input data. We have evaluated 11 articles published between 2016 and 2023 using a systematic literature review (SLR) method. This article arranges perfect summaries of all the research reviewed and compares the significant aspects of each. Additionally, we performed statistical analyses to gain a deeper comprehension of the most recent research in this area. Almost all of the reviewed articles followed a five-step procedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the open affairs and challenges in this method of tinnitus recognition or prediction and suggest future directions for research.

Virtual Reality (VR) can support effective and scalable training of psychomotor skills in manufacturing. However, many industry training modules offer experiences that are close-ended and do not allow for human error. We aim to address this gap in VR training tools for psychomotor skills training by exploring an open-ended approach to the system design. We designed a VR training simulation prototype to perform open-ended practice of drilling using a 3-axis milling machine. The simulation employs near "end-to-end" instruction through a safety module, a setup and drilling tutorial, open-ended practice complete with warnings of mistakes and failures, and a function to assess the geometries and locations of drilled holes against an engineering drawing. We developed and conducted a user study within an undergraduate-level introductory fabrication course to investigate the impact of open-ended VR practice on learning outcomes. Study results reveal positive trends, with the VR group successfully completing the machining task of drilling at a higher rate (75% vs 64%), with fewer mistakes (1.75 vs 2.14 score), and in less time (17.67 mins vs 21.57 mins) compared to the control group. We discuss our findings and limitations and implications for the design of open-ended VR training systems for learning psychomotor skills.

Background: Given that VR is applied in multiple domains, understanding the effects of cyber-sickness on human cognition and motor skills and the factors contributing to cybersickness gains urgency. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20-45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to CSQ-VR and performed VR-based cognitive and psychomotor tasks. Post-VR session, participants completed the CSQ-VR again. Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience in videogaming was a significant predictor of both cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall cybersickness', nausea and vestibular symptoms' intensities significantly decreased after removing the VR headset. Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears as a cybersickness' biomarker. Cybersickness negatively affects visuospatial working memory and psychomotor skills. Cybersickness and its effects on performance should be examined during and not after immersion.

Aerosols play a critical role in atmospheric chemistry, and affect clouds, climate, and human health. However, the spatial coverage of satellite-derived aerosol optical depth (AOD) products is limited by cloud cover, orbit patterns, polar night, snow, and bright surfaces, which negatively impacts the coverage and accuracy of particulate matter modeling and health studies relying on air pollution characterization. We present a random forest model trained to capture spatial dependence of AOD and produce higher coverage through imputation. By combining the models with and without the mean filters, we are able to create full-coverage high-resolution daily AOD in the conterminous U.S., which can be used for aerosol estimation and other studies leveraging air pollutant concentration levels.

Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of the leading infectious causes of mortality worldwide. To prevent TB from spreading within the body, which causes life-threatening complications, timely and effective anti-TB treatment is crucial. Cough, an objective biomarker for TB, is a triage tool that monitors treatment response and regresses with successful therapy. Current gold standards for TB diagnosis are slow or inaccessible, especially in rural areas where TB is most prevalent. In addition, current machine learning (ML) diagnosis research, like utilizing chest radiographs, is ineffective and does not monitor treatment progression. To enable effective diagnosis, an ensemble model was developed that analyzes, using a novel ML architecture, coughs' acoustic epidemiologies from smartphones' microphones to detect TB. The architecture includes a 2D-CNN and XGBoost that was trained on 724,964 cough audio samples and demographics from 7 countries. After feature extraction (Mel-spectrograms) and data augmentation (IR-convolution), the model achieved AUROC (area under the receiving operator characteristic) of 88%, surpassing WHO's requirements for screening tests. The results are available within 15 seconds and can easily be accessible via a mobile app. This research helps to improve TB diagnosis through a promising accurate, quick, and accessible triaging tool.

Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.

Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other Natural Language Processing (NLP) tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we address the problem by incorporating dictionaries into deep neural networks for the Chinese CNER task. Two different architectures that extend the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network and five different feature representation schemes are proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.

Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation and classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we propose a method to segment hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral cubes to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of three-dimensional data cubes. Effective piecewise training is applied in order to avoid the computationally expensive iterative CRF inference. Furthermore, we introduce a deep deconvolution network that improves the segmentation masks. We also introduce a new dataset and experimented our proposed method on it along with several widely adopted benchmark datasets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.