Data-driven Automated Analysis Pipeline for High-energy Synchrotron Beamline 2-D
XRD Datasets
Pawan K. Tripathi
Department of Materials Science and Engineering, Case Western Reserve University,Cleveland OH, USA. Materials Data Science for Stockpile Stewardship: Center of
Excellence, Cleveland, OH, USA
High energy 2-D X-ray diffraction (XRD) diffractograms obtained at synchrotron
facilities play an important role in determining materials properties associated with crystal
structure such as crystalline phases, texture, chemistry, etc. However, the data produced at
third-generation synchrotron sources is enormously massive and difficult to analyze. In thecurrent work, we are developing a comprehensive pipeline for automated analysis ofbeamline 2-D XRD patterns using state-of-the-art data science approaches. The pipeline
involves developing standard practices for the FAIRification of the data to make themfindable, usable, interoperable, and reusable. It further involves the use of distributed
computing environment, image processing, and deep learning techniques to help create anefficient and accurate solution capable of processing large amounts of image data. Herein,
we analyzed patterns of a Ti-6Al-4V (Ti-64) alloy that was heat treated throughout thecapture of the diffraction patterns at Advanced Photon Source (APS) facility. A convolutional
neural network (CNN) was designed to predict the titanium beta phase volume percentage
during temperature fluctuations. Images were pre-processed to remove bias in the XRD
patterns and prevent loss of information. The 2-D XRD patterns were input directly into theCNN model instead of traditional 1-D XRD peak patterns. The model was trained using anexperimental dataset that contains 3,012 XRD images and was tested with another 1,102
experimental XRD images, achieving a mean square error (MSE) of 0.076%.

What artificial intelligence can learn from first language acquisition
Naho Orita
Faculty of Science and Engineering, Waseda University
This talk will show a series of research in the field of first language acquisition anddiscuss whether and how these findings could help improve "artificial intelligence" that could
potentially learn a natural language like a human child.

Machine learning applications in 3D modeling
Junye Wu
Graduate Program in Data Science, GSIS, Tohoku UniversityThis talk will introduce new machine learning applications in 3D modeling with live
demonstrations using open-source software Blender. 4 add-ons will be focused:
1. AI render: Feed rendering and text prompt to generate image using Stable
Diffusion.
2. DMT-mesh: Generate 3D mesh with text or image based on Pint-E.
3. Dream Texture: Generate/Project Texture with Stable Diffusion.
4. DeepBump: Generate physics-based rendering (PBR) material.

The origin and longevity of information on Earth.
Anthony M. PooleProfessor, School of Biological Sciences, University of Auckland, New ZealandAll life on Earth uses a common system for storing genetic information. Informationis stored in DNA as genes. Genes can be read to generate two products: RNA and proteins.
Proteins are encoded by a universal genetic code, indicating that all life shares a common
evolutionary origin, and some RNAs are central to the machinery for reading the geneticcode. Scientists have developed methods to read information in genes, and, more recently,methods for synthesising artifical genes have become common. It is even possible to store
digital data in DNA polymers.
In this seminar, I will address three broad questions.
1. How did DNA originate?
I will examine how DNA came to become the primary material for genetic storage, and I will
present some of my group’s work on this topic which shows that DNA carries a faint but
discernible signal of its own origins. I will also present ongoing experimental work aimed atunderstanding how DNA systems originated.
2. How has evolution changed information processing in biological systems?
One fascinating feature of some systems is that not all the information stored in DNA isencoded simply within DNA. Some must be processed to remove unnecessary packets andsome even require editing and correction before the genetic code can be deciphered. I will
examine what we know about how such systems evolve, and present results showing that
we can evolve such systems in the laboratory.
3. How might DNA storage systems evolve in the digital era?
Humans are now generating enormous amounts of information that may soon rival theinformation stored as DNA in the biosphere. However, our approach to digital data storage
has been more functional than archival. Some have noted that, because of digital
obsolescence, future historians will know more about the beginning of the 20th century thanthe beginning of the 21st century. Thus, there are major challenges for the long-term storageof digital data. In contrast, genetic information is incredibly long-lived. In the last part of mytalk, I will consider how we can create biology-inspired DNA storage systems that could bereadable over vastly longer timescales than our present stems.