The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis w...
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As individuals age, the brain undergoes structural, functional and connectivity changes. Brain activity and anatomy do not always correspond, then evolving differently. In this regard, Independent Component Analysis (...
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The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis w...
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ISBN:
(数字)9798350381092
ISBN:
(纸本)9798350381108
The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis with kinematics parameters and pain sensation using Von Frey filaments in acute osteoarthritis knee (AOAK) model. This would allow us to have enough information to recommend or not recommend a new based treatment that can be compared to another successful treatment for osteoarthritis. Clinical Relevance — Therefore, with this study it is proposed to be able to analyze different compounds from opioids, steroids, and non-steroid anti-inflammatories (NSAIDs) like cannabinoid components, avoiding with that the dependance pathology and renal damage in patients treated with one of this kind of common treatments and thus be able to determine their viability on a in vivo model to recommend their study in instances closer to a clinical model.
Electrocardiograms (ECGs) are crucial for detecting cardiac diseases like atrial fibrillation (AF). Traditional analysis methods like fast Fourier transform (FFT) face challenges with increasing data complexity. This ...
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ISBN:
(数字)9798331541378
ISBN:
(纸本)9798331541385
Electrocardiograms (ECGs) are crucial for detecting cardiac diseases like atrial fibrillation (AF). Traditional analysis methods like fast Fourier transform (FFT) face challenges with increasing data complexity. This study uses Taipei Veterans General Hospital data to explore quantum Fourier transform (QFT) for ECG analysis. Results show that QFT effectively analyzes ECG signals, matching FFT performance while benefiting from quantum computing's efficiency.
作者:
Juan ZuluagaMichael CastilloDivya SyalAndres CalleNavid ShaghaghiDepartment of Bioengineering (BIOE)
Computer Science & Engineering (CSEN) Ethical Pragmatic & Intelligent Computing (EPIC) Laboratory in collaboration with the Healthcare Innovation & Design (HID) Program Information Systems & Analytics (ISA) and Mathematics & Computer Science (MCS) Santa Clara University Santa Clara California USA
Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein offici...
Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein officially presented the characteristics of it. Even though the TB epidemic has touched all corners of the world, Africa and Asia are the regions that currently suffer the worst consequences. The purpose of this study is to construct a model within the eVision forecasting environment, capable of forecasting the trend of Tuberculosis cases in India, as India is the country that accounts for the largest percentage of TB cases and deaths worldwide. And being able to make predictions for India may also lead to successful results for other regions in Asia and Africa. In order to do so, this study presents different test cases that show the effectiveness of the model, varying the number of steps for each one of the data sets created. It's important to note, that these data sets are combinations of data gathered from the states with the most TB cases in India in the last years, as well as the total data for India, and supplemental data from Google Trends, as a way to facilitate the machine learning model. Even though the final results were respectable compared to past research done on India and other countries, the model nevertheless has a limitation on the number of weeks ahead which the predictions are still considered to be good; with 7 weeks being the optimal result.
The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines tra...
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ISBN:
(数字)9798331541378
ISBN:
(纸本)9798331541385
The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines training and the quality of image reconstruction on the MINST digits and fashion datasets.
Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments fo...
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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.
Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning regarding computation time and maintaining prediction accuracy. We propose an innovative approach using qua...
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The prevalence of gaps and outliers within datasets presents substantial challenges, particularly in the realm of time series fore-casting and various other predictive machine learning (ML) tasks. This paper, introduc...
The prevalence of gaps and outliers within datasets presents substantial challenges, particularly in the realm of time series fore-casting and various other predictive machine learning (ML) tasks. This paper, introduces an effective technique for correcting gaps and outliers in data and validates the approach by applying it to datasets with outlier zones drawn from three diverse contexts. This innovative technique holds promising potential to enhance the performance of machine learning models by treating the data to alleviate the complications posed by these issues and in doing so contributes a valuable tool to the data science toolbox.
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