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4th Southern African Conference for Artificial Intelligence Research, SACAIR 2023

作者： du Toit, Jaco Dunaiski, Marcel Department of Mathematical Science Computer Science Division Stellenbosch University Stellenbosch South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

ISBN: (纸本)9783031490019

Hierarchical text classification (HTC) is a natural language processing task with the objective of categorising text documents into a set of classes from a structured class hierarchy. Recent HTC approaches combine encodings of the class hierarchy with the language understanding capabilities of pre-trained language models (PLMs) to improve classification performance. Furthermore, label-wise attention mechanisms have been shown to improve performance in HTC tasks by placing larger weight on more important parts of the document for each class individually to obtain label-specific representations of the document. However, using label-wise attention mechanisms to fine-tune PLMs for downstream HTC tasks has not been comprehensively investigated in previous work. In this paper, we evaluate the performance of a HTC approach which adds label-wise attention mechanisms, along with a label-wise classification layer, to a PLM which is subsequently fine-tuned on the downstream HTC dataset. We evaluate several existing label-wise attention mechanisms and propose an adaptation to one of the approaches which separates the attention mechanisms for the different levels of the hierarchy. Our proposed approach allows the prediction task at a certain level to leverage the information gained from the predictions performed at all ancestor levels. We compare the different label-wise attention mechanisms on three HTC benchmark datasets and show that our proposed approach generally outperforms the other label-wise attention mechanisms. Furthermore, we show that without using the complex techniques proposed in recent HTC approaches, our relatively simple approach outperforms state-of-the-art approaches on two of the three benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Accelerating VQE Algorithm via Parameters and Measurement Reuse 8

Accelerating VQE Algorithm via Parameters and Measurement Re...

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8th IEEE International Conference on Rebooting Computing, ICRC 2023

作者： Li, Xinpeng Liu, Ji Hansen, Ethan H. Xu, Shuai Hovland, Paul Chaudhary, Vipin Case Western Reserve University Department of Computer and Data Science United States Argonne National Laboratory Mathematics and Computational Research Division United States

ISBN: (纸本)9798350382044

The Variational Quantum Eigensolver (VQE), a quantum-classical hybrid algorithm, holds significant potential for advancements in quantum chemistry and optimization tasks. Our study focuses on accelerating the VQE in combinatorial optimization tasks such as Max-Cut, k-Clique, and k-SAT, while addressing the challenges of scalability and measurement overhead in quantum devices. Notably, while solving one problem, it allows us to concurrently compute essential information like expectation values and gradients for multiple other problems. This capability is made feasible because combinatorial optimization problems often share the same measurement basis. Drawing inspiration from this insight, we introduce a method that efficiently solves multiple problems by appropriately selecting their initial points. Subsequently, we propose two selection strategies for the method. Employing these strategies can, to some extent, help avoid the issue of becoming trapped in local optima, thus leading to improved accuracy. Our tests on 5-node and 10-node graphs for Max-Cut and k-Clique show that, in some cases, our method outperforms random initialization in terms of both accuracy and number of iterations. © 2023 IEEE.

关键词： Combinatorial optimization

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Optimal Design of Color Laparoscopic Super-Resolution Image Quality Based on Generative Adversarial Networks 1

Optimal Design of Color Laparoscopic Super-Resolution Image ...

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1st International Conference on Computer Graphics and Image Processing, CGIP 2023

作者： Kawabata, Norifumi Nakaguchi, Toshiya Hokkaido University Education and Research Center for Mathematical and Data Science Sapporo Japan Computational Imaging Lab Research Division of Computational Imaging Sapporo Japan Chiba University Center for Frontier Medical Engineering Chiba Japan

ISBN: (纸本)9798350333183

The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristics, though this generate unreal data by learning characteristics from data. As past our study, we discussed from the viewpoint of image quality for super-resolution of color laparoscopic image including SRCNN (Super-Resolution Convolutional Neural Network). However, it was not enough to compare to other neural network methods in our discussion. We consider that it is possible to support the medical image diagnosis by measuring whether the difference of both neural network method and image contents is affected or not for image quality. In this paper, first we carried out the objective image quality assessment by designing optimally of color laparoscopic super-resolution image using Generative Adversarial Networks (GAN). And then, we discussed for performance between methods comparing to result of SRCNN. On the other hand, from a view of information science, we consider that we need to verify experimentally for affect between network learning effect and generated image, and then to improve method. Therefore, we also discussed relationship between image quality and learning effect in color laparoscopic image generation using SRGAN. © 2023 IEEE.

关键词： Generative adversarial networks

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3D CG Image Region of Interest Estimation and Visual Attention Based on Saliency Map 29

3D CG Image Region of Interest Estimation and Visual Attenti...

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29th International Display Workshops, IDW 2022

作者： Kawabata, Norifumi Education and Research Center for Mathematical and Data Science Hokkaido University Sapporo Japan Research Division of Computational Imaging Computational Imaging Lab Sapporo Japan

In this paper, we estimated that there is the region of interest (ROI) in which object or background region of 3D CG image from the view of stochastic model for saliency map, and then we evaluated and discussed quanti... 详细信息

关键词： Stochastic models

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Integrating machine learning with simultaneous quantile regression for uncertainty estimation

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Leading Edge 2025年第2期44卷 133-141页

作者： Liu, Yanhua Zhang, Xitong Tsvankin, Ilya Lin, Youzuo Los Alamos National Laboratory Theoretical Division Los AlamosNM United States Colorado School of Mines Center for Wave Phenomena GoldenCO United States Michigan State University Computational Mathematics Science and Engineering East LansingMI United States University of North Carolina at Chapel Hill School of Data Science and Society Chapel HillNC United States

Accurate velocity models are crucial for understanding subsurface structures, which is vital for various subsurface activities including hydrocarbon exploration, development, and CO2 storage. The application of data-driven techniques based on machine learning (ML) to velocity model building has garnered significant attention due to their computational efficiency. However, most existing ML implementations provide only a single prediction for a given input, which may not adequately reflect the distribution of the testing data. The simultaneous quantile regression method can estimate the entire conditional distribution of the target variable given the input using pinball loss. In this study, we incorporate this technique into seismic inversion and test the proposed method on synthetic Kimberlina data. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also introduce an innovative data-augmentation method that leverages uncertainty to enhance prediction accuracy. The results validate the reliability of the calculated uncertainty in the proposed InvNet_UQ model. The method remains robust, even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error. © 2025 by The Society of Exploration Geophysicists.

关键词： Adversarial machine learning

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Interpolatory model reduction of dynamical systems with root mean squared error

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IFAC-PapersOnLine 2025年第1期59卷 385-390页

作者： Sean Reiter Steffen W.R. Werner Department of Mathematics Virginia Tech Blacksburg VA 24061 USA Department of Mathematics and Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech Blacksburg VA 24061 USA

The root mean squared error is an important measure used in a variety of applications like structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive. Model order reduction techniques resolve this issue via the construction of surrogate models that emulate the root mean squared error measure using an intermediate linear system. However, classical approaches require a large number of system outputs, which is disadvantageous in the design of reduced-order models. In this work, we consider directly the root mean squared error as the quantity of interest using the concept of quadratic-output models and propose several new model reduction techniques for the construction of appropriate surrogates. Numerical tests are performed on a model of a plate with tuned vibration absorbers.

关键词： model order reduction root mean squared error rational interpolation quadratic terms nonlinear systems

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A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

A Decision Support System to Compile Environmental Mitigatio...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yoon, Hong-Jun Ruggles, Tom Zhao, Huanhuan Singh, Debjani Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37830 United States Oak Ridge National Laboratory Environmental Sciences Division Oak RidgeTN37830 United States University of Tennessee Knoxville Data Science and Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9798350362480

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and ***'re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the *** system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States. © 2024 IEEE.

关键词： Natural language processing systems

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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GPT Models Can Perform Thematic Analysis in Public Health Studies,Akin to Qualitative Researchers

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Journal of Social Computing 2024年第4期5卷 293-312页

作者： Yuyi Yang Charles Alba Chenyu Wang Xi Wang Jami Anderson Ruopeng An Division of Computational and Data Science’s Washington University in St.LouisSt.Louis 63130MOUSA School Washington University in St.LouisSt.Louis 63130MOUSA School of Medicine Washington University in St.LouisSt.Louis 63130MOUSA Implementation Science Center for Cancer Control Washington University in St.LouisSt.Louis 63130MOUSA Constance and Martin Silver Center on Data Science and Social Equity Silver School of Social WorkNew York UniversityNew York 10003NYUSA

Conducting thematic analysis in qualitative research can be laborious and *** propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview *** engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study’s *** evaluation using a 5-point Likert scale(0−4)assessed GPTgenerated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of *** all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant *** these promising results,practices such as complementing outputs with field-specific knowledge are recommended.

关键词： social computing applications in healthcare and public health ethnographic and qualitative methodologies machine learning data mining computational linguistics

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