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Vol. 08, No. 11 [November 2022]
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| Paper Title | :: | A Relation Between the Z – J Functions and The Elzaki and Sumudu Transform for Differential Equations with a Proposed Transformation |
| Author Name | :: | M. Sc. Zenteno Jiménez José Roberto |
| Country | :: | México |
| Page Number | :: | 01-09 |
The following article is only a study and relation on the reduction functions or reduction function that helps to solve more quickly some Parabolic and Hyperbolic PDEs and which function is related to the Laplace Transform and Elzaki Sumudu, also the relation which saves the exponential function with the same solutions of each Equation.
Key Words: Laplace Transform – Elzaki and Sumudu Transform, Exponential Functions, Zenteno – Julia Functions, Transformation Functions
Key Words: Laplace Transform – Elzaki and Sumudu Transform, Exponential Functions, Zenteno – Julia Functions, Transformation Functions
[1]. Una solución analítica para la ecuación de difusión advecciónreacción por medio de la serie de FourierJosé R. Jiménez Instituto Politécnico Nacional IPN ESIA Unidad Ticóman,Méxicohttps://revistas.tec.ac.cr/index.php/matematica/article/view/3525/3168
[2]. Peter V. O’Neil Matemáticas Avanzadas para Ingeniería, Volumen 2 (1999) Cap. 18, 321-429 in Spanish
[3]. M.Sc.José Roberto Zenteno Jiménez Geophysical Engineering, National Polytechnic Institute, México City, ESIA-Unit Ticoman Gustavo A. Madero, General Analytical Solution Unidimensional Advection-Diffusion Reaction Equation Inhomogeneous on a Bounded Domain with an Application in Dispersion of Pollutants http://www.ijlret.com/Papers/Vol-05-issue-02/2.B2019005.pdf
[4]. The New Integral Transform ''Elzaki Transform'' Tarig. M. ElzakiGlobal Journal of Pure and Applied Mathematics ISSN 0973-1768 Volume 7, Number 1 (2011), pp. 57–64© Research India Publicationshttps://www.researchgate.net/publication/289123241_The_new_integral_transform_Elzaki_transform
[5]. Sumudu transform fundamental propertiesinvestigations and applications, Fethi bin Muhammed Belgacem and Ahmed Abdullatif KaraballiHindawi Publishing CorporationJournal of Applied Mathematics and Stochastic AnalysisVolume 2006, Article ID 91083, Pages 1–23 DOI 10.1155/JAMSA/2006/91083https://www.researchgate.net/publication/41448911_Sumudu_transform_fundamental_properties_investigations_and_applications
[2]. Peter V. O’Neil Matemáticas Avanzadas para Ingeniería, Volumen 2 (1999) Cap. 18, 321-429 in Spanish
[3]. M.Sc.José Roberto Zenteno Jiménez Geophysical Engineering, National Polytechnic Institute, México City, ESIA-Unit Ticoman Gustavo A. Madero, General Analytical Solution Unidimensional Advection-Diffusion Reaction Equation Inhomogeneous on a Bounded Domain with an Application in Dispersion of Pollutants http://www.ijlret.com/Papers/Vol-05-issue-02/2.B2019005.pdf
[4]. The New Integral Transform ''Elzaki Transform'' Tarig. M. ElzakiGlobal Journal of Pure and Applied Mathematics ISSN 0973-1768 Volume 7, Number 1 (2011), pp. 57–64© Research India Publicationshttps://www.researchgate.net/publication/289123241_The_new_integral_transform_Elzaki_transform
[5]. Sumudu transform fundamental propertiesinvestigations and applications, Fethi bin Muhammed Belgacem and Ahmed Abdullatif KaraballiHindawi Publishing CorporationJournal of Applied Mathematics and Stochastic AnalysisVolume 2006, Article ID 91083, Pages 1–23 DOI 10.1155/JAMSA/2006/91083https://www.researchgate.net/publication/41448911_Sumudu_transform_fundamental_properties_investigations_and_applications
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| Paper Title | :: | A Benchmark Model for Language Models towards Increased Transparency |
| Author Name | :: | Ayse Kok Arslan |
| Country | :: | US |
| Page Number | :: | 10-16 |
One of the mostly advanced AI technologies in recent year has been language models (LM) which necessitate a comparison or benchmark among many LM to enhance transparency of these models. The purpose of this study is to provide a fuller characterization of LMs rather than to focus on a specific aspect in order to increase societal impact. After a brief overview of the constituents of a benchmark and features of transparency, this study explores main aspects of a model - scenario, adaptation, metric- required to provide a roadmap for how to evaluate language models. Given the lack of studies in the field it is a step towards the design of more sophisticated models and aims to raise awareness of the importance of developing benchmarks for AI models.
[1]. Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel. 2020.
[2]. Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres (2019) Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700.
[3]. Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher (2016) Ask me anything: Dynamic memory networks for natural language processing. In International conference on machine learning, pages 1378–1387. PMLR.
[4]. Atoosa Kasirzadeh and Iason Gabriel (2022). In conversation with artificial intelligence: aligning language models with human values.
[5]. Bernard Koch, Emily Denton, Alex Hanna, and Jacob Gates Foster (2021) Reduced, reused and recycled: The life of a dataset in machine learning research. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
[2]. Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres (2019) Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700.
[3]. Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher (2016) Ask me anything: Dynamic memory networks for natural language processing. In International conference on machine learning, pages 1378–1387. PMLR.
[4]. Atoosa Kasirzadeh and Iason Gabriel (2022). In conversation with artificial intelligence: aligning language models with human values.
[5]. Bernard Koch, Emily Denton, Alex Hanna, and Jacob Gates Foster (2021) Reduced, reused and recycled: The life of a dataset in machine learning research. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
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| Paper Title | :: | Designing Frameworks for Reliability in Deep Learning Systems |
| Author Name | :: | Ayse Kok Arslan |
| Country | :: | US |
| Page Number | :: | 24-30 |
There has been a great amount of progress in deep learning models in the last decade. Such models are most accurate when applied to test data drawn from the same distribution as their training set. However, in practice, the data confronting models in real-world settings rarely match the training distribution.
This study explores the use of co-design approaches for developing reliable design frameworks for deep learning systems.It aims to raise awareness on how to develop reliable ML models within the context of recommender systems. While much work needs to be done in this field, the study providessuggestions and practical tips for how to develop reliable ML models such as in the case of recommender systems.
This study explores the use of co-design approaches for developing reliable design frameworks for deep learning systems.It aims to raise awareness on how to develop reliable ML models within the context of recommender systems. While much work needs to be done in this field, the study providessuggestions and practical tips for how to develop reliable ML models such as in the case of recommender systems.
[1]. Assuncao, Marcos D., et al. ―Big Data Computing and Clouds: Challenges, Solutions, and Future Directions.‖ Journal of Parallel and Distributed Computing, vol. 79–80, Dec. 2013, https://doi.org/10.1016/j.jpdc.2014.08.003.
[2]. Athanases, Steven Z., et al. ―Fostering Data Literacy through Preservice Teacher Inquiry in English Language Arts.‖ The Teacher Educator, vol. 48, no. 1, 2013, pp. 8–28.
[3]. Bargagliotti, Anna, et al. Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. American Statistical Association, 2020, https://www.amstat.org/docs/default-source/amstat-documents/gaiseiiprek-12_full.pdf.
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[5]. Bhargava, Rahul, et al. ―Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data.‖ Data-Pop Alliance, Data-Pop Alliance, Nov. 2015, https://datapopalliance.org/item/beyonddata-literacy-reinventing-community-engagement-and-empowerment-in-the-age-of-data.
[2]. Athanases, Steven Z., et al. ―Fostering Data Literacy through Preservice Teacher Inquiry in English Language Arts.‖ The Teacher Educator, vol. 48, no. 1, 2013, pp. 8–28.
[3]. Bargagliotti, Anna, et al. Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. American Statistical Association, 2020, https://www.amstat.org/docs/default-source/amstat-documents/gaiseiiprek-12_full.pdf.
[4]. Bersin Insights Team. Insights from IMPACT 2018. Deloitte Development LLC, 2018, https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/audit/ca-audit-abm-scotia-insights-fromimpact-2018.pdf.
[5]. Bhargava, Rahul, et al. ―Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data.‖ Data-Pop Alliance, Data-Pop Alliance, Nov. 2015, https://datapopalliance.org/item/beyonddata-literacy-reinventing-community-engagement-and-empowerment-in-the-age-of-data.