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How quantum entanglement really works and why we accept its weirdness – New Scientist
Entanglement is a key part ofquantum computing Bartlomiej K. Wroblewski/Alamy While scientists generally try to find sensible explanations for weird phenomena, quantum entanglement has them tied in knots. This link between subatomic particles, in which they appear to instantly influence one another no matter how far apart, defies our understanding of space and time
Quantum Leap: Atom Interference and a Breakthrough in Boson Sampling – SciTechDaily
La Nia Sea Surface Height, December 1, 2021 This coupling of the atmosphere and ocean alters atmospheric circulation and jet streams in ways that intensify rainfall in some regions and bring drought to others. For the second year in a row, the cooler sister to El Nio showed up at the winter party in the Eastern Pacific.
Why are computational chemists making up their data? – Chemistry World
For scientists, faking or making up data has obvious connotations and, thanks to some high-profile cases of scientific misconduct, theyre generally not positive ones. Chemists may, for example, be aware of a 2022 case in which a respected journal retracted two papers by a Japanese chemistry group that were found to contain manipulated or fabricated data.
Inexpensive microplastic monitoring through porous materials and machine learning – EurekAlert
image: Inexpensive microplastic monitoring through porous materials and machine learning Credit: Reiko Matsushita Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates. Details of the method, developed by researchers at Nagoya University with collaborators at the National Institute for Materials Sciences in Japan and others, are published in the journal Nature Communications. Detecting and identifying microplastics in water samples is essential for environmental monitoring but is challenging due in part to the structural similarity of microplastics with natural organic compounds derived from biofilms, algae, and decaying organic matter.
Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight … – Nature.com
The aim of this research is to develop precise and dependable machine learning models for the prediction of SW (Water Saturation) using three DL and three SL techniques: LSTM, GRU, RNN, SVM, KNN and DT. These models were trained on an extensive dataset comprising various types of log data. The findings of our investigation illustrate the efficacy of data-driven machine learning models in SW prediction, underscoring their potential for a wide range of practical applications.
AI is helping NASA pinpoint the most violent explosions in the universe – Earth.com
The dawn of artificial intelligence (AI) has ushered in a transformative era, promising to reshape every facet of our lives.Now, AI has moved off-world, helping NASA scientists unlock the secrets of the cosmos, including the location of gamma ray bursts (GRBs). This exciting intersection of technology and astronomy is now a reality,thanks to recent research led by Maria Dainotti,a visiting professor at UNLVs Nevada Center for Astrophysics.
From density functional theory to machine learning predictive models for electrical properties of spinel oxides … – Nature.com
In the following section, we will give details about the methodologies that make up our proposed workflow for predicting the conductivity and band gap of spinel oxide materials: DFT for band structure calculation, principal layer, Greens functions, Landauer formalism for electric conductivity, and machine learning and band structure fitting to tight binding Hamiltonian. All DFT calculations were done using the Vienna ab-initio simulation package (VASP)25 with the PerdewBurkeErnzerhof (PBE) functional26.
Automated Machine Learning (AutoML) Market Size to Exhibit a CAGR of 44.6% By 2028 – openPR
Automated Machine Learning (AutoML) Market Download Report Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=193686230 The AutoML market has been expanding rapidly in recent years, driven by the increasing demand for machine learning solutions across a variety of industries. AutoML tools offer a range of functionalities, such as automating feature engineering, hyperparameter tuning, model selection, and deployment.
A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients | Scientific … – Nature.com
In this multicenter study, we designed a prediction model based on ML to accurately assessment liver fibrosis stages of CHB patients. Compared with traditional statistical models such as APRI or FIB-4, and ML model demonstrated significant improvements and was easy to process, which also suggested the great potential of ML in the field of noninvasive liver fibrosis evaluation
Focus on Humanity in the Age of the AI Revolution – InformationWeek
The arms race for the next AI breakthrough is upon us -- and for good reason. While the news cycles are quick to point to fears around job displacement, bias, security risks, and weaponization, there is reason to be optimistic about this technology if we proceed carefully. We are stepping into the future of an AI-enabled world, and the competitive landscape for AI innovation should have only one bias in mind: a bias towards humanity.