The importance of faster drug development has never been more evident than in present time when the whole world is struggling to cope up with the COVID-19 pandemic. At times when timely development of effective drugs ...
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The importance of faster drug development has never been more evident than in present time when the whole world is struggling to cope up with the COVID-19 pandemic. At times when timely development of effective drugs ...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
The importance of faster drug development has never been more evident than in present time when the whole world is struggling to cope up with the COVID-19 pandemic. At times when timely development of effective drugs and treatment plans could potentially save millions of lives, drug repurposing is one area of medicine that has garnered much of research interest. Apart from experimental drug repurposing studies that happen within wet labs, lot many new quantitative methods have been proposed in the literature. In this paper, one such quantitative methods for drug repurposing is implemented and evaluated. DruSiLa (DRUg in-SIlico LAboratory) is an in-silico drug repurposing method that leverages disease similarity measures to quantitatively rank existing drugs for their potential therapeutic efficacy against novel diseases. The proposed method makes use of available, manually curated, and open datasets on diseases, their genetic origins, and disease-related patho-phenotypes. DruSiLa evaluates pairwise disease similarity scores of any given target disease to each known disease in our dataset. Such similarity scores are then propagated through disease-drug associations, and aggregated at drug nodes to rank them for their predicted effectiveness against the target disease.
In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-dr...
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This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates f...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.
We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational ...
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The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well...
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The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational ...
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In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) met...
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In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) metaheuristic solely or coupled with the Hooke-Jeeves (HJ) local search algorithm. The evaluated versions of ACO are based on a discretization procedure to deal with the continuous domain design variables together with different pheromone evaporation and deposit strategies and also on the frequency of calling the local search algorithm. The damage estimation is evaluated using noiseless and noisy synthetic experimental data assuming a damage configuration throughout the structure. The reported results show the hybrid method as the best choice when both rank-based pheromone deposit and a new heuristic information based on the search history are used.
The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news ...
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ISBN:
(纸本)9789897580390
The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news programs, to send and/or receive emails, to access interactive applications and also other contents. In this context, a high demand of requests from users is expected. Therefore, from the content provider's point of view, the determination of transmission parameters is needed in order to ensure the best quality of transmission to every user. The aforementioned identification problem is modelled as an optimization problem and a solution procedure based on metaheuristic techniques is proposed. Genetic Algorithm and Tabu Search metaheuristics are employed separately and coupled in a hybrid scheme to define the best transmission policy, optimizing the transmission parameters, such as audio and video transmission rates. Based on the experimental results, the hybrid algorithm has produced better solutions which meet the quality requirements.
The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news ...
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The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news programs, to send and/or receive emails, to access interactive applications and also other contents. In this context, a high demand of requests from users is expected. Therefore, from the content provider's point of view, the determination of transmission parameters is needed in order to ensure the best quality of transmission to every user. The aforementioned identification problem is modelled as an optimization problem and a solution procedure based on metaheuristic techniques is proposed. Genetic Algorithm and Tabu Search metaheuristics are employed separately and coupled in a hybrid scheme to define the best transmission policy, optimizing the transmission parameters, such as audio and video transmission rates. Based on the experimental results, the hybrid algorithm has produced better solutions which meet the quality requirements.
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