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IEEE International Conference on Big data

作者： Gunjan Mahindre Rasika Karkare Randy Paffenroth Anura Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins CO USA Data Science Worcester Polytechnic Institute Worcester MA USA Mathematical Sciences Computer Science & Data Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (纸本)9781665445993

In this paper, we propose a novel method to make distance predictions in real-world social networks. As predicting missing distances is a difficult problem, we take a two-stage approach. Structural parameters for families of synthetic networks are first estimated from a small set of measurements of a real-world network and these synthetic networks are then used to pre-train the predictive neural networks. Since our model first searches for the most suitable synthetic graph parameters which can be used as an “oracle” to create arbitrarily large training data sets, we call our approach “Oracle Search Pre-training” (OSP). Such an approach enables us to evaluate the robustness of the autoencoder to artificial training data while making predictions on real-world social *** example, many real-world networks exhibit a Power law structure in their node degree distribution, so a Power law model can provide a foundation for the desired oracle to generate synthetic pre-training networks, if the appropriate Power law graph parameters can be estimated. Accordingly, we conduct experiments on real-world Facebook, Email, and Train Bombing networks and show that OSP outperforms models without pre-training, models pre-trained with inaccurate parameters, and other distance prediction schemes such as Low-rank Matrix Completion. In particular, we achieve a prediction error of less than one hop with only 1% of sampled distances from the social network. OSP can be easily extended to other domains such as random networks by choosing an appropriate model to generate synthetic training data, and therefore promises to impact many different network learning problems.

关键词： Analytical models Adaptation models Social networking (online) Neural networks Training data Predictive models Big data

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Towards Detecting Cascades of Biased Medical Claims on Twitter

Towards Detecting Cascades of Biased Medical Claims on Twitt...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Libby Tiderman Juan Sanchez Mercedes Fiona Romanoschi Fabricio Murai Dept. of Mathematics University of Arizona Tucson AZ USA Dept. of Information Systems & Analytics Bryant University Smithfield RI USA School of Information Univerisity of Texas at Austin Austin TX USA Data Science & Computer Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread. We propose a machine learning framework that uses two models in tandem: RoBERTa to detect medical claims and DistilBERT to classify bias. After identifying original biased medical claims, we conducted a retweet cascade analysis, computing their individual reach and rate of spread. Tweets containing biased claims were found to circulate faster and further than unbiased claims.

关键词： Correlation Social networking (online) Computational modeling Blogs Machine learning Diseases

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Electronic Structures and Thermoelectric Properties of Heavily Doped N-Type Zrcobi from First Principles Calculations

SSRN

引用

SSRN 2022年

作者： Ibrahim, Nura Ahmed, Ridwan A. Adri, Huda Reisya, Ichwani Department of Physics Ahmadu Bello University Zaria Nigeria Materials Science and Engineering Program Department of Mechanical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

We present a theoretical investigation on the electronic structures and the thermoelectric properties of doped ZrCoBi half-Heusler compound using a combination of density functional theory calculations, semi classical Boltzmann transport theory and deformation potential theory. The effect of doping is described by a combination of rigid band approximation (RBA) and a model that explicitly includes the dopant atom within the lattice of ZrCoBi. The results demonstrate a high power factor with considerable n- and p-doping concentrations at high temperatures. Our findings further reveal that the electronic transport coefficients of ZrCo0.75M0.25Bi (M = Ni, Pd, Pt) are comparable because of their identical electronic structures. At 900 K, a high figure of merit (ZT) of ~ 0.35, ~ 0.38, and ~ 0.40 are obtained for ZrCo0.75Ni0.25Bi, ZrCo0.75Pd0.25Bi and ZrCo0.75Pt0.25Bi respectively. © 2022, The Authors. All rights reserved.

关键词： Electronic structure

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Tensor neural networks for high-dimensional Fokker-Planck equations

arXiv

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arXiv 2024年

作者： Wang, Taorui Hu, Zheyuan Kawaguchi, Kenji Zhang, Zhongqiang Karniadakis, George Em Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science National University of Singapore 119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of several selected radial basis functions. The use of tensor feedforward networks allows us to efficiently exploit auto-differentiation (in physical variables) in major Python packages while using radial basis functions can fully avoid auto-differentiation, which is rather expensive in high dimensions. We then use the physics-informed neural networks and stochastic gradient descent methods to learn the tensor networks. One essential step is to determine a proper bounded domain or numerical support for the Fokker-Planck equation. To better train the tensor radial basis function networks, we impose some constraints on parameters, which lead to relatively high accuracy. We demonstrate numerically that the tensor neural networks in physics-informed machine learning are efficient for steady-state Fokker-Planck equations from two to ten dimensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

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Neural network ensembles: Theory, training, and the importance of explicit diversity

arXiv

引用

arXiv 2021年

作者： Li, Wenjing Paffenroth, Randy C. Berthiaume, David Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Department of Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute WorcesterMA01609 United States

Ensemble learning is a process by which multiple base learners are strategically generated and combined into one composite learner. There are two features that are essential to an ensemble’s performance, the individual accuracies of the component learners and the overall diversity in the ensemble. The right balance of learner accuracy and ensemble diversity can improve the performance of machine learning tasks on benchmark and real-world data sets, and recent theoretical and practical work has demonstrated the subtle trade-off between accuracy and diversity in an ensemble. In this paper, we extend the extant literature by providing a deeper theoretical understanding for assessing and improving the optimality of any given ensemble, including random forests and deep neural network ensembles. We also propose a training algorithm for neural network ensembles and demonstrate that our approach provides improved performance when compared to both state-of-the-art individual learners and ensembles of state-of-the-art learners trained using standard loss functions. Our key insight is that it is better to explicitly encourage diversity in an ensemble, rather than merely allowing diversity to occur by happenstance, and that rigorous theoretical bounds on the trade-off between diversity and learner accuracy allow one to know when an optimal arrangement has been achieved. © 2021, CC BY.

关键词： Decision trees

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IEEE International Conference on Big data

作者： Jidapa Thadajarassiri Cansu Sen Thomas Hartvigsen Xiangnan Kong Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Publicly available pre-trained word embeddings are rich sources for turning critical high-dimensional representations of huge text data repositories into meaningful compact vectors essential for text mining applications. With many of such pre-trained embedding sources available, each faces limitations in the appropriateness of their language use for the downstream text-mining tasks. Meta-embeddings aim to tackle this ambiguity challenge by fusing multiple embedding sources into one feature space. However, current meta-embedding methods assume vocabularies across sources are similar or even identical; which unfortunately stands in sharp contrast to the fact that many sources barely overlap. Further, these methods encode a meta-embedding for each word by reconstructing its actual embedding values (word-encoder), while valuable information of relationships (distances) among words within each source are not directly considered. In this work, we instead propose a novel relation-encoder learning approach called Similarity-Preserving Meta-Embedding (SimME) that directly integrates word-pair relationships from partially overlapping embedding sources. SimME embeds words such that their similarities are learned from those observed in multiple pre-trained sources. To handle relations between words that are not present in all sources, we introduce maskout, a new loss term, that steers the learning selectively to the sources containing said relations. SimME consistently outperforms state-of-the-art methods by 10% on average and with up to 20% across several core metrics in 4 popular mining tasks on 23 datasets.

关键词： Text mining Training Vocabulary Transforms Big data Turning Task analysis

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INTOSIS: Interactive Observation of Smartphone Inferred Symptoms for In-The-Wild data

INTOSIS: Interactive Observation of Smartphone Inferred Symp...

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IEEE International Conference on Big data

作者： Hamid Mansoor Walter Gerych Luke Buquicchio Abdulaziz Alajaji Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Worcester Polytechnic Institute Worcester MA Data Science Program Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Current research in passive health monitoring utilizes machine learning methods to infer users' symptoms and health status from smartphone-sensed data, which can be gathered on a large scale. However, reasoning about smartphone-sensed health behaviors should engage health experts who may not want to solely rely on computational approaches as they provide limited insights. We designed and proposed a visualization framework for the INTeractive Observation of Smartphone-Inferred Symptoms (INTOSIS), that supports contextualization of symptomatic days by presenting a holistic picture of complex smartphone data for analysts to find c oncerning b ehavior patterns. For instance, while sedentary behavior caused by the flu is concerning, sedentary behaviors on holidays are non-concerning. INTOSIS visualizes multiple smartphone sensor data channels such as geo-location, app usage and screen usage. It uses visual metaphors to effectively represent the data to help analysts derive important human-understandable spatio-temporal contexts and assign health consequences with corresponding semantic labels. INTOSIS provides timeline visualizations of contextual clues such as screen and app usage at night that support an analyst in being able to reason about and then extract plausible explanations for the occurrence of certain symptoms, such as, sleep problems. We validate INTOSIS with intuitive use cases, using a real-world smartphone-sensed dataset, along with expert evaluation.

关键词： Visual analytics Semantics data visualization Psychology Big data Tools Task analysis

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Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks

arXiv

引用

arXiv 2024年

作者： Hu, Zheyuan Kawaguchi, Kenji Zhang, Zhongqiang Em Karniadakis, George Department of Computer Science National University of Singapore 119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence;in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions. © 2024, CC BY.

关键词： Monte Carlo methods

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Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Lévy Equations

arXiv

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arXiv 2024年

作者： Hu, Zheyuan Zhang, Zhongqiang Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We introduce an innovative approach for solving high-dimensional Fokker-Planck-Lévy (FPL) equations in modeling non-Brownian processes across disciplines such as physics, finance, and ecology. We utilize a fractional score function and Physical-informed neural networks (PINN) to lift the curse of dimensionality (CoD) and alleviate numerical overflow from exponentially decaying solutions with dimensions. The introduction of a fractional score function allows us to transform the FPL equation into a second-order partial differential equation without fractional Laplacian and thus can be readily solved with standard physics-informed neural networks (PINNs). We propose two methods to obtain a fractional score function: fractional score matching (FSM) and score-fPINN for fitting the fractional score function. While FSM is more cost-effective, it relies on known conditional distributions. On the other hand, score-fPINN is independent of specific stochastic differential equations (SDEs) but requires evaluating the PINN model's derivatives, which may be more costly. We conduct our experiments on various SDEs and demonstrate numerical stability and effectiveness of our method in dealing with high-dimensional problems, marking a significant advancement in addressing the CoD in FPL equations. © 2024, CC BY.

关键词： Cost effectiveness

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Using System Dynamics to Understand Long-Term Impact of Sustainable Mobility Policies: An Analysis Pre- and Post-COVID-19 Pandemic

SSRN

引用

SSRN 2022年

作者： Fontoura, Wlisses Bonela Radzicki, Michael J. Ribeiro, Glaydston Mattos Transportation Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Social Science & Policy Studies Department Worcester Polytechnic Institute WorcesterMA United States

The current urban mobility pattern in many cities around the world is the result of disorderly urban growth, favoring car trips and intensifying the urban sprawl. In these cases, transportation planning is fundamental for detecting, diagnosing, and anticipating problems in urban transport systems. In addition to planning, governments must develop sustainable mobility policies to adapt transport capacity to existing and future travel demand. In this context, this study aims to develop a strategy support model, using System Dynamics, to verify the effects of sustainable transport policies in urban transport systems, focusing on congestion and air pollution. In addition, it is carried out a case study in Rio de Janeiro in two realities: pre- and post-pandemic. The results highlight the importance of sustainable mobility policies to reduce urban transport externalities. More specifically, the results highlight the efficiency of encouraging high-capacity public transport, followed by car ownership control. © 2022, The Authors. All rights reserved.

关键词： Urban growth

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