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Deepepiil13: Deep Learning for Prediction of Il-13 Inducing Epitopes Using Pre-Trained Language Models and Multi-Window Convolutional Neural Networks

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SSRN 2024年

作者： Chuang, Cheng-Che Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

Interleukin-13 (IL-13) is a key cytokine involved in allergic inflammation and the cytokine storm associated with severe COVID-19. Identifying antigenic epitopes capable of inducing IL-13 holds therapeutic potential for developing epitope-based vaccines and immunomodulatory treatments. However, conventional methods for epitope prediction are often inefficient and inaccurate. This study presents DeepEpiIL13, a novel deep learning framework that integrates pre-trained language models and multi-window convolutional neural networks (CNNs) to rapidly and accurately predict IL-13-inducing epitopes from protein sequences. The ProtTrans pre-trained language model was employed to generate high-dimensional embeddings that capture rich contextual information from protein sequences. These embeddings were then inputted into a multi-window CNN architecture, enabling the efficient exploration of local and global sequence patterns associated with IL-13 induction. The proposed approach was rigorously evaluated using benchmark datasets and an independent SARS-CoV-2 dataset. DeepEpiIL13 achieved superior performance compared to traditional methods, with an impressive Matthews correlation coefficient (MCC) of 0.52 and an area under the receiver operating characteristic curve (AUC) of 0.86 on the benchmark dataset. On the independent SARS-CoV-2 dataset, DeepEpiIL13 demonstrated remarkable robustness, attaining an MCC of 0.63 and an AUC of 0.92, highlighting its potential applicability in the context of COVID-19 research. This study introduces a powerful deep learning framework for accurate epitope prediction, offering new avenues for the development of epitope-based vaccines and immunotherapies targeting IL-13-mediated disorders. The successful identification of IL-13-inducing epitopes paves the way for novel therapeutic interventions against allergic diseases, inflammatory conditions, and potentially severe viral infections like COVID-19. © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Exploring Security Threats by Hardware-Faults in Approximate Arithmetic Computing

Exploring Security Threats by Hardware-Faults in Approximate...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Morgana M. A. Da Rosa Eduardo A. C. Da Costa Rafael I. Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Approximate computing (AxC) emerged as a design alternative to boost design efficiency by leveraging the inherent error resiliency of many applications. Recent literature shows attackers could exploit some AxC mechanisms to create new attack surfaces. This work proposes integrity checking and exclusive logic-based methods detect attacks by hardware faults on AxC systems. During ASIC-based synthesis, we investigate the security vulnerabilities of the Approximate Parallel Prefix Adder (AxPPA). We compare the expected output of the designer with the output netlists of ASIC-based synthesis. We propose a verification methodology consisting of a golden model (GM) described in a Simulink model and the DUT represented at the netlist level in a standard cell library after logic synthesis. Experimental results show that the AxPPA has an error range of 1.263 × 10 −5 to 0.0098 concerning the designer’s expected output under ASIC-based synthesis attacks.

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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part Approximate Parallel Prefix Adder

Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part A...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Morgana M. A. Rosa Eduardo Costa Rafael Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work proposes an accuracy-driven evaluation model of an approximate adder (AxA) called an approximate parallel prefix adder (AxPPA). It explores the significance of approximate computing (AxC) in optimizing performance and energy efficiency in computer systems by accepting minor accuracy trade-offs. We examine the AxPPA through a meticulous error analysis model and an automated analysis framework that comprehensively evaluates area and delay based on the fundamental equations of parallel prefix adders (PPAs). Additionally, it emphasizes the importance of lower-part approximation in adders, effectively addressing the elongation of carry chains and enhancing overall efficiency. The choice of approximation method directly impacts computational accuracy, resulting in variations in the number of approximated bits and carry chain length for a given tolerance level. Conducting a thorough comparison of accuracy trade-offs requires a unique design and simulation for each unit and approximation level, which can be time-consuming, particularly in larger circuits. Therefore, the proposed approach facilitates the exploration of larger circuit design space encompassing multiple AxAs, significantly minimizing time investment. In our experiments, the accuracy-driven evaluation of the lower part approximation method in AxA took 71.268 milliseconds for 1 million random inputs, with a K -bits approximation ranging from 1 to 16. The results indicate that AxPPA exhibits a minor error magnitude among the explored literature AxAs, being approximately 50.97% more accurate on average.

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Enhancing the sustainability and effectiveness of non-governmental organizations: An integrated neutrosophic decision-making model for determining the most effective corporate governance strategies

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Emrah Ayhan Hakan Gökhan Gündoğdu Ahmet Aytekin Ahmet Furkan Karakaya Vladimir Simic Dragan Pamucar Department of Political Science and Public Administration Faculty of Economics and Administrative Sciences Anadolu University Tepebaşı 26470 Eskişehir Türkiye Department of Business Administration Hopa Faculty of Economics and Administrative Sciences Artvin Çoruh University Hopa 08100 Artvin Türkiye Public Administration Master Program Graduate Education Institute Anadolu University Tepebaşı 26470 Eskişehir Türkiye University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 11010 Belgrade Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer 34396 İstanbul Türkiye

This research explores the development of effective corporate governance strategies for non-governmental organizations (NGOs) by evaluating both critical decision criteria and innovative organizational models. Based on an extensive literature review, seven governance criteria were identified: credibility and non-profit orientation, regular information flow and transparency, level of institutionalization, frequency of leadership turnover, internal and external stakeholder relations, annual budget, and ideological or religious motivation. In addition, five innovative organizational models were selected as strategic alternatives: continuous learning organization, technology-oriented policy development, income diversification, pluralistic governance, and transformational leadership. The empirical data were gathered through interviews with 16 experts from academia, civil society, and public institutions in Türkiye. To address the complexity and uncertainty of decision-making processes, a hybrid type-2 neutrosophic number (T2NN) multi-criteria decision-making methodology is introduced. The T2NN-ranking comparison and T2NN-criteria importance through intercriteria correlation methods are used to determine the weight of criteria, while T2NN-stable preference ordering towards ideal solution is utilized to rank the alternative models. Results show that the level of institutionalization is the most significant criterion, and income diversification is the most effective organizational model for improving NGO sustainability. The study not only provides practical guidance for NGO leaders but also introduces a robust methodological approach applicable to governance challenges under uncertain conditions.

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An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet Transform for ECG Data Compression

An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet T...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Arthur Cardozo Morgana M. A. Rosa Rafael Soares Eduardo Costa Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work proposes an ultra-low-energy ECG data compression with VLSI DHWT-based (discrete Haar wavelet transform) architecture to enable storage and transmission in resource-constrained environments. We present original, pruned, and approximate DHWT (ODHWT, PDHWT, and AxDHWT, respectively) hardware architectures for ECG data compression at ultra-high energy efficiency. Our best proposal employing the AxDHWT hardware architecture requires just five additions only. Using a PDHWT technique to improve energy efficiency observes the evolution of the signal-to-noise ratio and the ultimate impact on the ECG data compression application. The DHWT-based configurations architecture proposal achieves a minimum compression ratio of 0.125 (i.e., 1/8) and a PRD (percent root difference) <1.34. We implemented the VLSI architectures in 65nm CMOS technology in maximum and fixed frequencies and two different supply voltages. The AxDHWT occupies a die area of 0.64mm 2 . The measured total power is $0.534\mu {\mathrm {W}}$, the higher energy-savings among the ECG data compression architectures.

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies

New Energy-Efficient 3-2 and 4-2 Approximate Adder Compresso...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Rodrigo Lopes Leonardo Antonietti Morgana M. A. Rosa Eduardo Costa Rafael Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Addition units are present across many computational kernels inherent in various error-tolerant applications, including machine learning, signal, image, and video processing. Notably, adder compressors are the target when high speed and low power are the main design concerns. This work introduces energy-efficient structures of 3-2 approximate adder compressors (Ax3-2) and three distinct versions of 4-2 approximate adder compressors (Ax4-2). We compared our proposed Ax3-2 and Ax4-2 compressors with state-of-the-art energy-efficient approximate adder compressors (AxACs). Both Ax3-2 and Ax4-2 models passed through rigorous tests as standalone units. Furthermore, we integrated the Ax4-2 models into an essential application kernel appearing in video coding, i.e., the Sum Squared Differences (SSD) video accelerator. We introduce Ax3-2 and Ax4-2 that yield a new Pareto front concerning both energy-quality and area-quality trade-offs, thereby demonstrating a marked improvement over the prevailing state-of-the-art energy-efficient AxACs.

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Goal-oriented inference of environment from redundant observations

arXiv

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arXiv 2023年

作者： Takahashi, Kazuki Fukai, Tomoki Sakai, Yutaka Takekawa, Takashi Informatics Program Graduate School of Engineering Kogakuin University of Technology and Engineering Japan Neural Coding and Brain Computing Unit Okinawa Institute of Science and Technology Japan Brain Science Institute Tamagawa University Japan

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related"core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Utilizing Battery Energy Storage Systems to Enhance the Integration of Photovoltaic Systems for Damping Oscillations

Utilizing Battery Energy Storage Systems to Enhance the Inte...

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IEEE Southeastcon

作者： Ilham Akbar Ariesa Budi Zakaria Mayur Basu Eduard Muliadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

As the integration of Variable Renewable Energy (VRE) generators into power systems increases, there is a potential decrease in overall system reliability, particularly in terms of stability. This research suggests the integration of a Battery Energy Storage System (BESS) into a power system with Variable Renewable Energy sources, such as a utility-scale Photovoltaic (PV) plant, to accelerate the damping of post-fault oscillations following a short-circuit disturbance. The objective is for a Power Oscillation Damping (POD) within the PV plant to address post-fault oscillations within a specific timeframe. The POD in the PV plant is designed to supply additional active and reactive power, responding to the system's demands after a disturbance. The addition of BESS to the system is anticipated to enhance the time required for the system to achieve a stable state, resulting in quicker system stabilization. Simulations conducted through PSCAD software can effectively depict the post-fault conditions of short-circuit disturbances, considering the impact of the integrated BESS.

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Reducing Dependency on Imported Electricity: Utilizing Renewable Energy and Minimizing Fossil Fuel Power Plant Operations in West Kalimantan

Reducing Dependency on Imported Electricity: Utilizing Renew...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Ilham Akbar Ariesa Budi Zakaria Eduard Muljadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper discusses West Kalimantan's dependency on imported electricity and fossil fuels and advocates for integrating renewable energy sources, especially solar power. With abundant solar resources and its equatorial location, West Kalimantan presents a significant solar PV generation opportunity. However, its dependence on imported electricity from the ASEAN Grid initiative poses risks to energy security. By promoting solar power as an alternative energy source and reducing reliance on fossil fuels, the paper aims to improve energy independence and sustainability. Integrating solar PV requires assessing frequency stability to ensure system resilience. Moreover, the interconnection between the two grids enhances system resilience and facilitates renewable energy integration. In summary, integrating solar PV power plants into the West Kalimantan system aims to decrease dependence on electricity imports from Sarawak while preserving the system's frequency stability.

关键词： Photovoltaic systems Renewable energy sources Fault tolerance Electricity Power system stability Fossil fuels Security Sustainable development Thermal stability Research and development

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