Reviewerly/exHarmony huggingface dataset:
Zero-Shot Meta-Learning for Small-Scale Data From Human Subjects

While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as zero-shot learning. To address this challenge, we develop an end-to-end framework using a meta-learning approach, which enables the model to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. We use three real-world small-scale human subjects datasets (two randomized control studies and one observational study), for which we predict treatment outcomes for held-out treatment groups. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multitask predictions. We show that our model performs the best holistically for each held-out group and especially when the test group is distinctly different from the training group. Our model has implications for improved generalization of small-size human studies to the wider population.
Long COVID Challenge: Predictive Modeling of Noisy Clinical Tabular Data

We present an end-to-end machine learning pipeline for aggregating, analyzing, and modeling National COVID Cohort Collaborative (N3C) data on the Enclave system as part of the NIH Long COVID Computational Challenge (L3C). The challenge’s goal is to determine the probability of patients who have tested positive for SARS-CoV-2 in an outpatient or hospital setting (ICU or non-ICU) developing PASC/Long COVID. To achieve this, we have utilized state-of-the-art machine learning algorithms to process millions of clinical observations and identify the most impactful attributes that support accurate prediction modeling. The pipeline is optimized for deployment on N3C Enclave and aims to inform clinical decisions for managing and preventing PASC/Long COVID by identifying the most relevant factors. The study implements four state-of-the-art machine learning methods in PySpark on the Enclave for processing noisy tabular data and a novel robust cascaded fusion model. Results show improved modeling performance for high noise levels in clinical data sources and the highest number of true positives and the lowest number of true negatives for the cascaded model. Multiple conditions, observations, and drugs relevant to Long COVID diagnoses and treatment were also identified.
Learning Physician’s Treatment for Alzheimer's Disease based on Electronic Health Records and Reinforcement Learning

Alzheimer's Disease (AD) is a progressive neurological disorder that necessitates physicians with sophisticated skills and knowledge to effectively care for AD patients. In this study, we adopted reinforcement learning (RL) to learn a physician's treatment plan for AD by utilizing Electronic Health Records (EHR). By defining states, actions, and rewards, we modeled the data of 1,736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) into an RL problem. We evaluated the RL-based learning model across four patient cohorts: the entire dataset, AD-only data, AD-hypertension data, and AD-hypertension-depression data. The RL learning models demonstrated promising outcomes in generating an optimal physician policy, which represents the treatment plan, in comparison to the clinician policy obtained from transitional probability. For instance, the q-learning-based policy achieved a score of -2.48, whereas the clinician policy scored -3.57. This research highlights the potential of RL-based treatment learning to enhance the management of Alzheimer's Disease.
Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction

Supervised contrastive learning provides superior performance over self-supervised learning by considering label information in classification tasks. However, this process suffers from collapsing embedding space since the positive samples are randomly selected from the labeled group and are pulled together. In this work, we theoretically guarantee that any pre-training methods that maintain a mixture of sub-class distribution could consistently outperform supervised contrastive pre-training. Furthermore, based on our theoretical analysis, we propose a new pre-training method by adopting an efficient Expectation Maximization learning strategy. Finally, we empirically evaluated our proposed method of sepsis prediction from the PhysioNet/Computing in Cardiology Challenge dataset and showed its superior performance to the state-of-the-art from various perspectives.
Estimation of the Target State with Passive Measurements and Prior Range Information

This work demonstrates a simple but effective method by which prior information on the target range can be included in the likelihood function for bearings-only as well as Doppler-bearings target motion analysis. The prior information is treated as a pseudomeasurement with a Gaussian distribution. An estimator is derived as an extension to the well-known maximum likelihood estimator. The performance bounds naturally follow as an extension to the Cramer-Rao lower bound. Utilization of the range pseudomeasurement adds new design parameters to the estimator, for which practical methodology and illustrative examples are provided. The estimation efficiency is confirmed via the use of Monte-Carlo simulations.
Validating Transformers for Redaction of Text from Electronic Health Records in Real-World Healthcare

Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text. Rule-based methods have been widely used, but their precision is often low causing over-redaction of text and frequently not being adaptable enough for non-standardised or unconventional structures of personal health information. Deep learning techniques have emerged as a promising solution, but implementing them in real-world environments poses challenges due to the differences in patient record structure and language across different departments, hospitals, and countries.In this study, we present AnonCAT, a transformer-based model and a blueprint on how deidentification models can be deployed in real-world healthcare. AnonCAT was trained through a process involving manually annotated redactions of real-world documents from three UK hospitals with different electronic health record systems and 3116 documents. The model achieved high performance in all three hospitals with a Recall of 0.99, 0.99 and 0.96.Our findings demonstrate the potential of deep learning techniques for improving the efficiency and accuracy of redaction in global healthcare data and highlight the importance of building workflows which not just use these models but are also able to continually fine-tune and audit the performance of these algorithms to ensure continuing effectiveness in real-world settings. This approach provides a blueprint for the real-world use of de-identifying algorithms through fine-tuning and localisation, the code together with tutorials is available on GitHub (https://github.com/CogStack/MedCAT).
Training Recurrent Neural Network-Based Model to Predict COVID-19 Patient Risk for PASC using Pytorch_EHR – Hands-on Tutorial on N3C

Pytorch_EHR is a codebase enabling fast prototyping of deep learning-based predictive models using electronic health records structured data. Rather than a collection of vertical pipelines implementing methods from papers claiming state-of the-art results, Pytorch_EHR offers efficient implementations of data flow, padding, embeddings, choices of popular recurrent neural networks (RNN) cells and layers structures, prediction heads, and also predictions explanation and visualization. This tutorial will provide participants with a clear understanding of deep learning theoretical concepts behind Pytorch_EHR different components, as well as hands-on experience in building an explainable RNN-based model to solve a real-world clinical problem on a cloud-based platform hosting the National COVID Cohort Collaborative (N3C) data. URL: https://github.com/ZhiGroup/pytorch_ehr
Integrating and Expanding the Gulf of Mexico Alliance Community with ArcGIS Hub

The Gulf of Mexico Alliance (GOMA) is using ArcGIS Hub to share data and maps from a variety of public and private authoritative sources across the five Gulf states. The Gulf of Mexico Open Data Platform (GOMOD) uses ArcGIS Hub to provide the tools needed to create a single point of entry for users to discover, explore, and access Gulf-wide data from a variety of sources. GOMA is extending collaboration with State and Local Government, Academic Institutions, and Non-Profit Agencies across the Gulf by collaboratively designing common geospatial schema across Communities of Practice, and sharing them as Feature Services in Open Data Groups. GOMOD also leverages ArcGIS Online (AGOL) web mapping and application tools, such as Experience Builder, Survey123, and StoryMaps. This paper will discuss the technical methods used to overcome a wide variety of configuration, editing and maintenance challenges, including the use of Jupyter Notebook and Python scripting to access and update remote large, derived Water Quality Feature Services from the Environmental Protection Agency's (EPS) Water Quality Portal. These Python tools can run on regular intervals to update the resulting Feature Services, which gather and present spatially, with all associated collection methodology and tabular data, water quality submissions from a national community of stakeholders and local agencies, which are then reduced to include only the National Ocean and Atmospheric Administration's (NOAA) Coastal Watershed Counties for the five Gulf of Mexico States. The paper will also discuss the use and strategy for using Esri's Experience Builder Application for sharing web maps, as well as the advantages for only sourcing open public ArcGIS Online content and REST services for populating all GOMOD Theme maps and other data products, to support a strategy of connecting to data sources vs collecting (and hosting) geospatial datasets. Notes on collaboration strategies will be shared, as well as the significant efforts across multiple Communities of Practice to minimize tasking and update anomalies, while maximizing exposure and usefulness of data contribution with ArcGIS Hub. A discussion of the overall workflow for GOMOD will cover three main purpose-oriented topics: 1) Theme Maps, a curated set of maps targeting specific GOMA community needs, and leveraging open data sources. 2) Community of Practice AGOL Groups, a place for members to collaborate on geospatial data in shared data environments in the cloud. 3) An offering of additional GOMA tools and projects, all available in one place.
Refined Datasets and Saliency Map Analysis for Underwater Object Detection

Underwater operations performed by divers are inefficient and dangerous. This encourages the development of underwater drones to replace human operations. Underwater object detection technology, in particular, plays a role in estimating the current position, controlling, and grabbing in the underwater drone. In this paper, we propose underwater object detection algorithms using refined underwater datasets to investigate model selection and hyperparameters. From the simulation results, the optimal parameter set for YOLOX is determined and a saliency map generation method is integrated into the YOLOX framework for the validity of the reasoning basis.
A Zero Shot Detection Based Approach for Fish Species Recognition in Underwater Environments

Identification of fish species is vital for fisheries management, stock assessments, protection of endangered species, and ecosystem management. Image based surveys often deploy video cameras that are used to collect large image datasets that are reviewed by a human observer to identify species and generate a numerical count at each station. One main challenge in labeling or annotating such a large dataset is that it requires a huge amount of time, cost, and human effort. Recently, general adversarial network (GAN) based generative techniques have drawn much attention in zero-shot object detection (ZSD) because of superior performance in localizing and simultaneously recognizing objects without training a model on unseen (few target) classes. In this work, a zero-shot detection for Fish Species Recognition (ZSD-FR) in underwater environments is utilized for object detection. This approach can localize and recognize objects when the model is not trained on “unseen” classes. Generative models like GAN can be trained on data with “seen” classes for generating unseen class samples depending upon the semantics (attributes) learned from data with seen classes. The results obtained on SEAMAPD21 illustrate that the zero shot detection (ZSD) based approach of object detection can successfully transfer knowledge from seen classes to unseen classes with better detection accuracy.
Predicting underwater vehicle manipulator system dynamics using time-series methods

In this study, we apply two time-series models, Long Short-Term Memory (LSTM) and Vector AutoRgressive with eXogenous Variables (VARX) models to predict underwater vehicle manipulator system (UVMS) dynamics simulated in a ROS/Gazebo environment under two environmental conditions commonly encountered in the wave zone, namely splash zone conditions, and wave loads on the UVMS when it is fully submerged, but close to the surface of the water. We also investigate the effect of dataset size and heterogeneity on the performance of these models.
Privacy-Preserving Multi-Agent Marine Data Collection via Differential Privacy

Data collection is a resource-intensive process that significantly impacts the detection and mitigation of natural disasters. To overcome limitations in available datasets, it becomes necessary to involve mutually distrusting parties in collaborative data collection missions. This paper presents two contributions. Firstly, we propose a pipeline based on differential privacy to enable privacy-preserving multi-agent data collection in marine environments. This pipeline ensures that data collected by external participants, acting as semi-honest third parties, is securely pooled and anonymized by a trusted data steward. Secondly, we demonstrate the implementation of two cost-effective unmanned surface vessel designs using readily available components for conducting hardware-based experiments. The resulting anonymized data can be utilized in various marine applications, thereby facilitating improved decision-making processes. Through this research, we aim to address the challenges associated with data collection in marine environments and foster collaboration among stakeholders for effective disaster management.
ChatSim: Underwater Simulation with Natural Language Prompting

Robots are becoming an essential part of many operations including marine exploration or environmental monitoring. However, the underwater environment presents many challenges, including high pressure, limited visibility, and harsh conditions that can damage equipment. Real-world experimentation can be expensive and difficult to execute. Therefore, it is essential to simulate the performance of underwater robots in comparable environments to ensure their optimal functionality within practical real-world contexts.OysterSim generates photo-realistic images and segmentation masks of objects in marine environments, providing valuable training data for underwater computer vision applications. By integrating ChatGPT into underwater simulations, users can convey their thoughts effortlessly and intuitively create desired underwater environments without intricate coding. The objective of ChatSim is to integrate Large Language Models (LLM) with a simulation environment (OysterSim), enabling direct control of the simulated environment via natural language input. This advancement can greatly enhance the capabilities of underwater simulation, with far-reaching benefits for marine exploration and broader scientific research endeavors.
Optimized Path Planning and Control for Autonomous Surface Vehicles using B-Splines and Nonlinear Model Predictive Control

In this paper, B-splines are used to solve the problem of online path planning for Autonomous Surface Vehicles (ASVs). The proposed approach is based on bi-directional B-spline curves that takes into account path curvature and involves defining a cost function that minimizes the overall curvature of the path, while ensuring that the final point of the path at each time step is as close as possible to the final goal. Path curvature is especially critical for vehicles that lack high maneuverability, such as some ASVs. The proposed method determines a temporary path using B-spline curves and constrained optimization. The path is updated at each time step based on range sensor data and with constraints defined to avoid discontinuity in the first and second derivatives of the path with respect to time and thus, making the path feasible to track. To integrate the generated path with the ASV, Nonlinear Model Predictive Control (NMPC) is used to ensure path tracking. To evaluate the performance of the proposed approach, simulation experiments are provided which demonstrate the effectiveness and robustness of the integrated path planning and control algorithm in generating feasible paths and accurately controlling the ASV for successful trajectory tracking.
Hybrid Search Algorithm of ABC and DE Based on Improved Rate

The Artificial bee colony algorithm (ABC) has low search efficiency in solving complex optimization problems, while the Differential evolution algorithm (DE) has high search efficiency in solving complex optimization problems, so in order to improve the performance of ABC on complex functions, this paper combines DE and ABC. A hybrid search algorithm of ABC and DE based on improved rate is proposed. In the algorithm, the mean of ABC and DE improvement rates is compared every 100 cycles, and the next generation is adaptively selected to use the ABC or DE algorithm. At the same time, the parametric recession factor is introduced to determine the change range between ABC and DE improvement rate, so as to avoid the influence of improvement rate decreasing with the number of iterations. Through the experiments under the CEC2014 test set, it is verified that the hybrid search algorithm proposed in this paper is effective in improving the performance of ABC on complex functions.
FPGA Optimized Architecture of XNOR-POPCOUNT

Binarized neural network (BNN) is a neural network whose input activations and weights are quantified to 1-bit. It has very low parameters compared with other neural networks. What’s more, the multiplication and accumulation calculation of the convolutional layer and the fully connected layer of BNN can be replaced by XNOR-POPCOUNT calculation. FPGA has become a major hardware deployment platform for BNN due to its programmability and parallelism. However, the basic logic cell of the FPGA cannot directly complete the XNOR-POPCOUNT calculation. This paper proposes an architecture that saves numerous look-up tables (LUT), and it improves the efficiency of the logic cell by allowing the LUTs to complete more logical operations. Compared with other architectures, the proposed architecture can save 8%-32% of logic resources.
Learnable Position-based Joint Entity-Relation Extraction Filtering Model

The goal of entity relation joint extraction is to simultaneously extract entities and their relationships from text, representing this information in a structured manner, typically presented in the form of triples (entity1, relationship, entity2). This task holds significant importance in natural language processing applications such as knowledge graph construction and expansion, automatic question answering, information retrieval, and text mining. However, existing relation extraction methods still face several challenges. The core idea behind Seq2Seq models is to transform input sequences into output sequences, typically consisting of an encoder and a decoder. While Seq2Seq models excel in multiple tasks, they still encounter certain issues. For instance, they may be limited in dealing with challenges like redundancy, long-distance dependencies, and polysemy. To address these challenges, this study introduces an innovative approach. Firstly, we incorporate a "filter layer" inspired by signal processing to alleviate the decline in extraction performance caused by data redundancy. Secondly, we adopt variable-length positional encoding techniques to effectively capture word positions in sequences, enhancing the model's understanding of text. Additionally, we introduce dense connections, successfully mitigating the vanishing gradient problem in deep learning networks, thereby improving information flow and performance. Through these innovative methods, our model demonstrates significant performance improvements on two benchmark datasets, affirming its effectiveness and practicality in entity relation joint extraction tasks. These contributions are expected to drive advancements in the field of relation extraction, enhance the effectiveness of natural language processing tasks and hold significant implications for knowledge graph construction and expansion, among other domains.
A Logistic-Regression-Based Reduced Model for Predicting Online News Popularity

Consuming news through social media has become an integral part of our lives, thus online news popularity prediction is now a crucial issue for marketing strategies and social media platforms. This paper proposes a real-time prediction model, based on a dataset from Mashable, it uses regression analysis to find out whether an article is of high-popularity or low-popularity. In this paper, we first solve the problem of imbalanced data via Synthetic Minority Oversampling Technique (SMOTE); then various feature selection and engineering methods are employed to extract key features. A comparison experiment is conducted to verify the validity of the data enhancement. And the results show that the reduced logistic model with SMOTE and dimension reduction outperforms other models with an Accuracy of 80.6% and a Recall of 78.6%. Our work can help the software application on evaluating the popularity of online news.
Low-Cost AUV Visual System for Marine Ecosystem Monitoring and Analysis

Marine protected areas (MPAs) play a vital role in safeguarding marine ecosystems and preserving their ecological and societal benefits. Japan, with its expansive marine territory, has designated a substantial area as MPAs. However, monitoring and analyzing these vast regions present challenges, necessitating significant resources and workforce. In recent years, the use of low-cost Autonomous Underwater Vehicles (AUVs) has emerged as a promising approach for data collection in MPAs, especially when coupled with visual data collection. This study aims to develop a cost-efficient visual system to attach to a low-cost AUV, enabling effective data gathering and analysis for ecological and economic purposes. Algorithms that use monocular and stereo image inputs were used to generate data such as 3D mosaic of the seafloor and dimension estimation of marine animals. By utilizing easy-to-obtain hardware visual systems, this research contributes to enhancing the efficiency and cost-effectiveness of data collection and analysis of MPAs.