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20. Construction of M-shaped solitons for a modified regularized long-wave equation via Hirota's bilinear method

Author

Ceesay Baboucarr, Ahmed Nauman, and Macías-Díaz Jorge E.

Subjects
hirota’s bilinear method, solitons, modified regularized long-wave equation, Physics, QC1-999
Abstract

This study examines the effects of various M-shaped water wave shapes on coastal environments for the modified regularized long-wave equation (MRLWE). This work explores the complex dynamics of sediment transport, erosion, and coastal stability influenced by different wave structures using the Hirota bilinear transformation as a basic analytical tool. By providing insightful information about how these wave patterns impact coastal stability, it seeks to broaden our knowledge of dynamic coastlines. As we explore the intricate interactions between water waves and beaches, the knowledge gained from this research could help direct sustainable coastal management and preservation initiatives. For convenience, a range of M-shaped wave structures are depicted, demonstrating the adaptability of the Hirota bilinear transformation approach in recognizing novel wave patterns. Overall, this work contributes to a better understanding of the dynamics of the coastal environment, highlights the wide range of applications for mathematical models in science and engineering, and helps to develop more sensible and practical coastal management and conservation strategies for the protection of coastal areas against changing water wave patterns. Finally, as far as the authors could verify, this is the first work in the literature in which M-shaped soliton solutions are derived for the MRLWE using any method.

Published
2024
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23. SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Author

Park, Seongyeon, Kim, Hajin, Ahmad, Tanveer, Ahmed, Nauman, Al-Ars, Zaid, Hofstee, H. Peter, Kim, Youngsok, and Lee, Jinho

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve workload balancing. The experimental results reveal that SaLoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods., Comment: Published at IPDPS'22

Published
2023

45. KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Author

Mohapatra, Shubhankar, Ahmed, Nauman, and Alencar, Paulo

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Quantitative Finance - Statistical Finance
Abstract

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety., Comment: 7 pages, 8 figures, IEEE Big Data 2019

Published
2020

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