Digital Assyriology: Using artificial intelligence to unlock an ancient lingua franca

Using artificial intelligence to unlock an ancient lingua franca

The ancient writing system of cuneiform was used to record millennia of human history, but relatively few of the hundreds of thousands of known cuneiform texts have yet been translated and made available to researchers and the public alike. The Babylonian Engine project, led by Dr Shai Gordin of Ariel University, Israel, has developed two tools – Atrahasis and Akkademia […]

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Using Artificial Intelligence to improve prediction and prevention of violence

Drs Gary Chaimowitz and Mini Mamak of McMaster University and St. Joseph’s Healthcare, Hamilton, Canada have developed the Electronic Hamilton Anatomy of Risk Management (eHARM) platform. It is an innovative risk assessment and management tool for violence and aggression which can be used in a variety of mental health areas, including but not limited to forensic psychiatry. The validity of […]

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Ripeta: Enhancing scientific integrity

scientists in a laboratory

Ripeta is an automated tool designed with a simple but vital objective: to enhance the credibility, reproducibility, and trustworthiness of scientific research papers. Its algorithms use several important metrics to assess the integrity of a paper, stemming the tide of untrustworthy science one paper at a time. Research Outreach caught up with Dr Leslie McIntosh, Ripeta’s founder and CEO, to […]

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ILLMO: A new platform for interactive statistics

Jean-Bernard Martens, a Full Professor on Visual Interaction with the Department of Industrial Design of the Eindhoven University of Technology, Netherlands, has developed ILLMO (Interactive Log Likelihood MOdeling), a statistical modelling tool that provides an interactive environment offering an intuitive and interactive approach to statistics. Scientists and researchers, including those who are not specialists in statistics, can access modern statistical […]

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Predicting treatment resistance in schizophrenia patients

Predicting treatment resistance in schizophrenia patients

Schizophrenia is a severe and often debilitating mental disorder. While antipsychotic medications aim to alleviate the symptoms, about a third of patients with schizophrenia are treatment-resistant. Dr Olesya Ajnakina, a Research Fellow at King’s College London, combines state-of-the-art methods in statistical analyses, such as machine learning, to develop prediction models aiming to identify patients at risk of developing treatment-resistant schizophrenia, […]

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How did the data propagate? Automated Optical Path Monitoring

Optical fibre cables facilitate high-speed data transfer.

With the development of 5G, our world might seem more wireless than ever. However, lurking behind this and facilitating all high-speed data transfer are kilometres and kilometres of optical fibres. The backbone network for all communication, wireless or otherwise, is this sprawling network of fibres. For reliable communications and internet access, the world’s expanse of fibre optic cables must work […]

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Optimising Particle Accelerators with Adaptive Machine Learning

Alexander Scheinker introduces new techniques based on machine learning.

Machine learning has become a staple of research into many of today’s most cutting-edge technologies. Until now, however, it has not been widely considered as a useful tool for online optimisation of the performance of particle accelerators. Through his research, Dr Alexander Scheinker at the Los Alamos National Laboratory in New Mexico, USA, introduces new techniques based on machine learning, […]

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Unsupervised feature extraction applied to bioinformatics

Unsupervised feature extraction applied to bioinformatics

In his new book, Professor Y-h Taguchi, from Chuo University, Tokyo, Japan, takes two classical mathematical techniques, principal component analysis and tensor decomposition, and demonstrates how they can be used to perform feature selection in his cutting-edge research. Both unsupervised learning methods are applied to carry out feature extraction in a wide range of ‘large p small n’ problems. This […]

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Protein structure prediction with machine learning

Research-Outreach-Shuichiro-Makigaki

Shuichiro Makigaki and Dr Takashi Ishida, from the Department of Computer Science at Tokyo Institute of Technology, are developing a new sequence alignment generation model that employs machine learning and dynamic programming to predict protein structures. This novel methodology can also be applied to homology detection which is fundamental to bioinformatics. A protein’s function is dictated by its three-dimensional structure. If the structure is […]

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Reconstructing astronomical images with machine learning

Reconstructing astronomical images with machine learning

Much of what we know about how our universe works has been learnt by analysing the astronomical signals captured from the sky. However, these signals will inevitably have some noise associated with them – so how can astronomers be sure that their observations of strange, unexpected signals reflect reality? Edward Higson at the University of Cambridge and his colleagues have […]

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