Countries all around the world have been severely affected by the novel coronavirus, popularly known as COVID-19, causing hundreds of thousands of unfortunate deaths without having found a widespread immunization method. It is extremely crucial for healthcare officials and the Governments of various countries to be able to differentiate the possible coronavirus strains to provide appropriate care and treatment. This paper proposes an effective way to set apart coronavirus strains into classes such as severe acute respiratory syndrome (SARS), acute respiratory distress syndrome (ARDS), and COVID-19 using machine learning algorithms and recurrent neural networks (RNN). Natural language processing (NLP) techniques were performed on textual clinical reports to structure the data and improve its usability for the model. Bag of words model and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were some of the few processes involved in extracting the prominent symptoms associated with each variant. The same has fed to various machine learning algorithms include multinomial naive Bayes (NB), logistic regression (LR), decision tree (DT), support vector machines (SVMs), and random forest (RF). Moreover, two more algorithms namely bagging, and gradient boost, are used for classification purposes. The reasons for choosing them are to observe their performance while classifying the proposed...

Digital computing using silicon chips has transformed nearly every aspect of modern life and enabled the remarkable growth of artificial intelligence. But as AI models scale up, that growth comes with increasing demands for energy, water and computing infrastructure. At the same time, many emerging applications — from satellites and robots to distributed sensors — need to process information where it is generated, often with limited power and connectivity. UCLA helped blaze the trail for a complementary computing approach that could one day work alongside silicon-based digital computers to mitigate these challenges. In this conception, software and hardware are no longer separate. There is no neural network software executed on the hardware. Rather, the hardware itself is the neural network, where computation occurs directly within the structure of a self-organizing material that’s allowed to establish physical connections measured on the scale of billionths of a meter. Over the last 15 years, researchers have uncovered rich collective behavior in these systems that can be harnessed to process complex information in real time with low power demands. The advantages could help advance physical AI — systems in which computation and learning are embedded directly into the physical hardware that senses and interacts...

Unberath, M. et al. The impact of machine learning on 2D/3D registration for image-guided interventions: a systematic review and perspective. Front. Robot. AI 8, 716007 (2021). Yip, M. et al. Artificial intelligence meets medical robotics. Science 381, 141–146 (2023). Penney, G. P. et al. A comparison of similarity measures for use in 2D/3D medical image registration. IEEE Trans. Med. Imaging 17, 586–595 (1998). Knaan, D. & Joskowicz, L. Effective intensity-based 2D/3D rigid registration between fluoroscopic X-ray and CT. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Ellis, R. E. & Peters, T. M.) 351–358 (Springer, 2003). Grupp, R. B. et al. Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration. Int. J. Comput. Assist. Radiol. Surg. 15, 759–769 (2020). Grimm, M., Esteban, J., Unberath, M. & Navab, N. Pose-dependent weights and domain randomization for fully automatic X-ray to CT registration. IEEE Trans. Med. Imaging 40, 2221–2232 (2021). Mahesh, M., Ansari, A. J. & Mettler, F. A. Jr Patient exposure from radiologic and nuclear medicine procedures in the United States and worldwide: 2009–2018. Radiology 307, e221263 (2022). Cornelis, F. H., Dzaye, O., Schoellnast, H. & Solomon, S. B. Imaging of interventional therapies in...

Hepatitis infection presents a global health threat with significant morbidity and mortality implications. Early and precise survival prediction plays an essential role in guiding clinical decisions and enhancing patient outcomes. Electronic health records (EHRs) contain extensive information regarding the medical history of patients. The increasing volume of data in EHRs means they are becoming more valuable for data-driven healthcare research. With the rapid growth of EHR data, sophisticated analytical techniques are progressively required to capture meaningful patterns and support predictive modeling. Recent advances in deep learning (DL) techniques have demonstrated the capability to learn feature representations from data and enhance model performance across diverse fields. In healthcare, DL techniques have also been successfully implemented for clinical event prediction, disease classification, and EHR-based analysis, accomplishing extraordinary results. These developments emphasize the ability of DL systems to enhance survival prediction in hepatitis patients. Therefore, this study presents an Ensemble Deep Learning Framework for Survival Prediction of Hepatitis Patients (EDLF-SPHP). The proposed model aims to accurately predict survival outcomes in hepatitis patients by leveraging EHR data. To achieve this, the proposed framework first performs preprocessing, where the input EHR data is prepared for analysis through data normalization, missing data handling, and target value...

Everyone tells you to start with PyTorch, then hands you a wall of theory. This walkthrough skips it. In about twenty minutes you train a real network to read handwritten digits, and every number you see was measured, not estimated.

The authors declare no competing interests. Ethics All procedures performed in the study followed the ethical standards of the institutional research committee of the University of Hail and the 1964 Helsinki Declaration and its later amendments. Ethical considerations This study relies exclusively on secondary data aggregated from national time series on energy consumption, climate indicators, and CO₂ emissions, obtained from official statistical repositories. No individual-level records, personally identifiable information, or survey data were collected, and no experimental interventions were conducted. In accordance with the institutional research committee of the University of Hail guidelines, analyses based solely on aggregated, anonymized, and publicly available data do not constitute research on humans and therefore did not require formal ethical approval or informed consent. An AI-powered writing tool was used in some parts of the language editing; all scientific content, analyses, conclusions, and verification were developed and verified by the authors. Consent to Participate This study did not involve any human participants, and no individual-level data were collected or analyzed. Therefore, obtaining a Consent to Participate declaration was not applicable. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in...

Observations increasingly reveal the coupled radiative, chemical, and dynamical processes that shape exoplanet atmospheres.

Mechanistic Interpretability Research Knowledge Base + Notes - tegridydev/Mechanistic-Interpretability

The use of nanofluids has attracted significant attention due to their improved thermal performance compared with conventional heat transfer fluids. Because nanoparticles are on the nanometer scale, problems such as channel blockage and severe abrasion can be reduced. Among these, Ferrofluid containing Fe3O4 nanoparticles is of particular interest to researchers due to its unique properties, including favorable magnetic properties and environmental friendliness. In this study, an artificial neural network model is developed to predict the thermal behavior of Fe3O4/Water Ferro-nanofluid, and the effective factors, such as volume fraction (φ = 0.05% to 4.0%) and temperature (T = 24 °C to 50 °C), on thermal conductivity and dynamic viscosity are investigated. The performance of the ANN is validated by comparing the obtained results. The results show that when the \(\:\varphi\:\) changes from \(\:\varphi\:\) = 0.05% to 4.0%, the dynamic viscosity increases from 0 cP to 0.77 cP. The results showed that thermal conductivity increased with increasing nanoparticle concentration, while dynamic viscosity decreased with increasing temperature. In contrast, viscosity increased at higher nanoparticle volume fractions due to stronger particle interactions within the base fluid. The ANN models showed reasonable overall performance in estimating thermal conductivity, with correlation coefficients of 0.98 and errors...

Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019). Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proc. 2020 CHI Conference on Human Factors in Computing Systems 102 (Association for Computing Machinery, 2020). Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit. Health 2, e489–e492 (2020). Zech, J. R. et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15, e1002683 (2018). Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750 (2021). Caruana, R. et al. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In Proc. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1721–1730 (Association for Computing Machinery, 2015). Ambrosino, R., Buchanan, B. G., Cooper, G. F. & Fine, M. J. The use of misclassification costs...

Multipartite entanglement detection goes beyond simply identifying the presence of entanglement; it also involves determining its detailed structure. Specifically, any \({{{\mathcal{R}}}}_{1}| \cdots | {{{\mathcal{R}}}}_{j}\)-separable state can be decomposed in the form \(\hat{\rho }={\sum }_{\gamma }{p}_{\gamma }{\hat{\rho }}_{{{{\mathcal{R}}}}_{1}}^{(\gamma )}\otimes \cdots \otimes {\hat{\rho }}_{{{{\mathcal{R}}}}_{j}}^{(\gamma )}\), where \({{{\mathcal{R}}}}_{i}\) describes an ensemble of modes and \({\hat{\rho }}_{{{{\mathcal{R}}}}_{i}}^{(\gamma )}\) denotes the reduced density matrix of \({\hat{\rho }}^{(\gamma )}\) on the subsystem \({{{\mathcal{R}}}}_{i}\) (ref. 34). In the following task, we classify cases with the same number of modes in each subsystem \({{{\mathcal{R}}}}_{i}\) as the same multipartition class and label them by \({{\mathcal{S}}}\). For example, states with separability A∣BC, B∣AC and C∣AB belong to the multipartition class \({{\mathcal{S}}}=1\otimes 2\). Quantum state set construction We train the neural network on a broad set of random, mixed CV states, including all Gaussian states and most experimentally achievable non-Gaussian states. As illustrated in Fig. 1a, we start with the simplest multipartite case: tripartite states. To collect balanced samples for all possible classes of states with different multipartitions \({{\mathcal{S}}}\) in a random way, we first generate a large number of m-mode (m ≤ 3) seed states \({\hat{\sigma }}_{m}\). Then, we use these seed states to separately construct each class. For example,...