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International Conference on Machine Learning and Applications (ICMLA)

作者： Evan Witz Maria Barger Randy Paffenroth Mathematical Sciences Worcester Polytechnic Institute Worcester MA Data Science Worcester Polytechnic Institute Worcester MA Mathematical Sciences Computer Science Data Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665443388

Neural networks are widely applied in domains such as image processing, natural language processing, and time series forecasting. However, neural networks have seen less use in problems arising in the physical sciences. This is unfortunate, since the physical domain has a wealth of problems that can benefit from application of neural networks. These problems hold substantial significance to many areas such as manufacturing, material science, and many others. In the current text we demonstrate that knowledge of the physical systems of interest can be combined with effective data preprocessing and neural network training to achieve prediction effectiveness which is greater than the sum of its parts. In particular, we study the challenging problem of range estimation from the measurement of electromagnetic scattering of radio waves reflected off the surface of the ocean and the atmosphere. Our key finding is a that good performance can only be achieved by combining physical principles with careful data preprocessing and network training.

关键词： Training Knowledge engineering Atmospheric measurements Surface waves Ducts Neural networks data preprocessing

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Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

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Journal of Integrative Agriculture 2023年第9期22卷 2865-2881页

作者： ZHANG Sha YANG Shan-shan WANG Jing-wen WU Xi-fang Malak HENCHIRI Tehseen JAVED ZHANG Jia-hua BAI Yun Research Center for Space Information and Big Earth Data College of Computer Science and TechnologyQingdao UniversityQingdao 266071P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000P.R.China Department of Environmental Sciences Kohat University of Science and TechnologyKohat 26000Pakistan

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited ***,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing *** approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and ***,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the *** IPPs were determined using statistical yield data of reference years(2010–2015)over the *** findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),*** data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–*** addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city ***,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

关键词： approximating irrigations process-based model remote sensing winter wheat yield North China Plain

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Realistic data Generation for 6D Pose Estimation of Surgical Instruments

arXiv

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arXiv 2024年

作者： Barragan, Juan Antonio Zhang, Jintan Zhou, Haoying Munawar, Adnan Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks;yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59 mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Bounded diameter variations of Ryser’s conjecture

arXiv

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arXiv 2025年

作者： Gyárfás, András Sárközy, Gábor N. Alfréd Rényi Institute of Mathematics Budapest P.O. Box 127 BudapestH-1364 Hungary Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

In this paper we study bounded diameter variations of the following form of Ryser’s conjecture. For every graph G = (V, E) with independence number α(G) = α and integer r ≥ 2, in every r-edge coloring of G there is a cover of V (G) by the vertices of (r − 1)α monochromatic connected components. Milićević initiated the question whether the diameters of the covering components can be bounded. For any graph G with α(G) = 2 we show that in every 2-coloring of the edges, V (G) can be covered by the vertices of two monochromatic subgraphs of diameter at most 4. This improves a result of DeBiasio et al., which in turn improved a result of Milićević. It remains open whether diameter 4 can be strengthened to diameter 3, we could do this only for certain graphs, including odd antiholes. We propose also a somewhat orthogonal aspect of the problem. Suppose that we fix the diameter d of the monochromatic components, how many do we need to cover the vertex set? For d = 2, 2 ≤ r ≤ 3, the exact answer is rα and for d = 4, r = 2, we prove the upper bound ⌊3α/2⌋. Copyright © 2025, The Authors. All rights reserved.

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Biomarker Trajectory Prediction and Causal Analysis of the Impact of the Covid-19 Pandemic on CVD Patients using Machine Learning

Biomarker Trajectory Prediction and Causal Analysis of the I...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Trusting Inekwe Winnie Mkandawire Brian Wee Emmanuel Agu Andrés Colubri Computer Science Dept Worcester Polytechnic Institute Worcester MA Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA Chemical and Physical Biology Harvard College Cambridge Massachusetts

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

Background: The COVID-19 pandemic disrupted healthcare services, increasing the susceptibility of high-risk patients including those with cardiovascular Diseases (CVDs), to adverse outcomes. Biomarkers provide insights into patients' underlying health status. However, few studies have investigated the effects of the COVID-19 pandemic on CVD biomarker trajectories using predictive modeling and causal analyses frameworks. Prior research explored the impacts of the COVID-19 pandemic on CVD severity and prognosis but did not investigate biomarker trajectories using Machine Learning (ML), which can discover complex multivariate relationships in multi-modal data. Objective: This study aimed to compare six ML regression models to select the best performing models for predicting biomarker trajectories in CVD patients using retrospective data. Subsequently, these models were used to assess the COVID-19 pandemic's impact on CVD patients and for causal analyses Approach: Using ML regression and causal inference, this study investigated the pandemic's impact on biomarker values of 80,917 CVD patients and 77,332 non-CVD controls, treated at two hospitals in Central Massachusetts between May 2018 and December 2021. ML regression algorithms, including Neural Networks (NN), Decision Trees (DT), Random Forests (RF), XGBoost, CATBoost and ADABoost, were trained and compared. Important CVD biomarkers (HbA1c, LDL cholesterol, BMI, and BP) were predicted as outcome variables with patients' risk factors (age, race, gender, socioeconomic status) as input variables. Shapley feature importance analyses identified the most predictive features, which were then utilized in Causal Analysis. A Difference-in-Differences (DID) approach within a Double/Debiased Machine Learning (DML) method isolated the pandemic's impact on biomarkers, while minimizing the effects of confounding factors. Results: CATBoost and XGBoost were the most predictive ML models for LDL cholesterol and HbA1c, yielding R 2

关键词： COVID-19 Radio frequency Analytical models Pandemics Biological system modeling Biomarkers Predictive models

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CCLNet: Causal and Contrastive Learning Framework for Enhanced Pulmonary Embolism Detection

CCLNet: Causal and Contrastive Learning Framework for Enhanc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zhipeng Ge, Ruiquan Yu, Jianxun Qin, Feiwei Tian, Yuan Li, Nannan Min, Wenwen Elazab, Ahmed Wang, Changmiao Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China Dalian Polytechnic University School of Management Dalian China Chinese University of Hong Kong Second Affiliated Hospital Shenzhen China Macau University School of Science and Technology China Yunnan University School of Information Science and Engineering Yunnan China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350386226

The fusion of multimodal medical data is crucial for helping doctors make accurate treatment decisions. For example, combining Computed Tomography Pulmonary Angiography (CTPA) with Electronic Health Records (EHR) can significantly improve the accuracy of Pulmonary Embolism (PE) detection, thereby increasing patient survival rates. Although multimodal learning has advantages in PE diagnosis, the heterogeneity of multimodal data poses a significant challenge to accurate diagnosis. The natural semantic and structural differences between data modalities make it difficult to effectively integrate their information. In addition, within a single modality, the existence of redundant and irrelevant information introduces unnecessary variability, making the data more complex, and making stable diagnosis challenging. To address these issues, we propose a new framework called CCLNet, which includes a contrastive learning component for addressing inter-modality heterogeneity and a causal learning component for handling intra-modality heterogeneity. Specifically, we achieve precise alignment between visual and tabular modalities by using global-level information to soften labels during contrastive learning. In addition, by using causal intervention methods to eliminate the influence of heterogeneous factors within the modality, we can accurately reveal the causal relationship between features and targets, thereby improving the accuracy and stability of the model. Experimental results demonstrate that our method performs excellently, achieving the best results. Our code is available at https://***/LeavingStarW/CLPE. © 2024 IEEE.

关键词： Contrastive Learning

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Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks

Evaluating Energy Efficiency of GPUs using Machine Learning ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Brett Foster Shubbhi Taneja Joseph Manzano Kevin Barker Department of Computer Science Worcester Polytechnic Institute High Performance Computing Group Pacific Northwest National Laboratory

As we enter the exascale era, the energy efficiency and performance of High-Performance Computing (HPC) systems, especially running Machine Learning (ML) applications, are becoming increasingly important. Nvidia recently released its 9th-generation HPC-grade Graphics Processing Unit (GPU) microarchitecture, Ampere, claiming significant improvements over the previous generation’s Volta architecture. In this paper, we perform fine-grained power collection and assess the performance of these two HPC architectures’ performance by profiling ML benchmarks. In addition, we analyze various hyperparameters, primarily the batch size and the number of GPUs, to determine their impact on these systems’ performance and power efficiency. While Ampere is 3.16x more energy-efficient than Volta in isolation, this is counteracted by the PCIe interconnects of the A100s as the ML tasks are parallelized to run on more GPUs.

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Detection of Influential Features of Hard Landing Based on QAR data 3

Detection of Influential Features of Hard Landing Based on Q...

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3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Chen, Ruofan Jin, Jun Department of Data Science Worcester Polytechnic Institute WorcesterMA United States Department of Statistics University of Connecticut StorrsCT United States

ISBN: (纸本)9781665425186

Based on the static (scalar measurement) and aggregated QAR data, the group carried out the quantile regression to explore the influential features that affects the maximum vertical acceleration during landing and used the sufficient dimension reduction methods to verify the findings. Nine features were suggested to be influential and the patterns how they affect the hard landing were analyzed. © 2021 IEEE

关键词： Landing

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HDSS: a healthcare decision support system on combining domain knowledge and data analytics for predicting potential risk of mental health

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International Journal of Applied Decision sciences 2022年第4期15卷 465-491页

作者： Ngan, Chun-Kit Paat, Yok-Fong Green, Rachel Data Science Program Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Social Work The University of Texas at El Paso 500 West University Avenue El PasoTX79968 United States Diocesan Migrant & Refugee Services Inc. 2400A East Yandell Drive El PasoTX79903 United States

We develop an HDSS to determine if a patient is at risk of mental health (MH) problems. This HDSS combines the strengths of both domain experts’ knowledge and data analytics techniques to conduct the risk identification for patients. Our contributions are three-fold: 1) develop a two-tier hybrid-based prediction process to construct a classification model to predict if a person suffers from a particular symptom;and then determine if that person is at risk of suffering from MH problems based upon the domain experts’ diagnostic criteria;2) execute experiments to show that our approach is more effective and produces the results that are slightly less accurate than those of domain experts’ diagnostic criteria but superior to those of the data analytics approach;3) develop a dashboard that delivers descriptive visuals from diverse perspectives to assist physicians in obtaining a more comprehensive view of patients’ MH status to provide them with better treatment and support. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Health risks

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Adversarial Human Context Recognition: Evasion Attacks and Defenses

Adversarial Human Context Recognition: Evasion Attacks and D...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Abdulaziz Alajaji Walter Gerych Kavin Chandrasekaran Luke Buquicchio Emmanuel Agu Elke Rundensteiner Data Science Program Worcester Polytechnic Institute United States Department of Information Systems King Saud University Saudi Arabia

Human Context Recognition (HCR) from smartphone sensor data is a crucial task for Context-Aware (CA) systems, such as those targeting the healthcare and security domains. HCR models deployed in the wild are susceptible to adversarial attacks, wherein an adversary perturbs input sensor values to cause malicious mis-classifications. In this study, we demonstrate evasion attacks that can be perpetuated during model inference, particularly input perturbations that are adversarially calibrated to fool classifiers. In contrast to white-box methods that require impractical levels of system access, black-box evasion attacks merely require the ability to query the model with arbitrary inputs. Specifically, we generate adversarial perturbations using only class confidence scores, as in the Zoo attack, or only class decisions, as in the HopSkipJump (HSJ) attack that correspond with plausible scenarios of possible adversarial attacks. We empirically demonstrate that sophisticated adversarial evasion attacks can significantly impair the accuracy of HCR models, resulting in a performance drop of up to 60% in f1-score. We also propose RobustHCR, an innovative framework for demonstrating and defending against black box evasion threats using a provable defense based on a duality-based network. RobustHCR is able to make reliable predictions regardless of whether its input is under attack or not, effectively mitigating the potential negative impacts caused by adversarial attacks. Rigorous evaluation on both scripted and in-the-wild smartphone HCR datasets demonstrates that RobustHCR can significantly improve the HCR model’s robustness and protect it from possible evasion attacks while maintaining acceptable performance on "clean" inputs. In particular, an HCR model with integrated RobustHCR defenses experienced an f1-score reduction of about 3% as opposed to a reduction of over 50% for an HCR model without a defense.

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