Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
District Data Labs Workshop
Current Workshop: August 23, 2014
Previous Workshops:
- April 5, 2014
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
Larry will discuss what data science means in general, and more specifically at Udemy. He will describe some key data science frameworks, and what it means for them to be agile. He will also discuss ideally what it would mean to be a data scientist at Udemy.
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
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Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
Easing the Secondary Production Process During Design: The design stage is where the smart decisions are made in additive manufacturing — not only on ways to increase clinical outcomes and additive processes, but also to ease post-processing. In these areas, the process can be made efficient and cost-effective. Receive tips and tricks on orientation, supports, stock material and design features.
Enhancing Design through Engineering and Manufacturing Collaboration: Unlike conventional methods, additive manufacturing requires design and manufacturing teams to work closely together very early in the design phase. In a world where the pace of innovation is nearly vertical, it’s imperative to understand this concept early to make additive manufacturing work for you. This discussion lends a high-level overview of best practices when working with internal or external suppliers, and shows examples of the use of additive manufacturing to create innovative medical device designs.
Golang for Data Analytics Applications is a suitable choice because of its standard official libraries which enable easy data parsing, sorting, analyzing and visualizing.
Golang for Data Analytics Applications is a suitable choice because of its standard official libraries which enable easy data parsing, sorting, analyzing and visualizing.
Do you want to travel to galaxy?
We can make real for you!
Using VR technology and special NASA datas.
This project is made for NASA #spaceapps challenge.
Automating fetal heart monitor using machine learningTamjid Rayhan
This is a webinar held by the IEEE student branch of University of Chittagong. This talks about how a beginner can gain expert level knowledge in Machine learning and deep learning using online resources. It focuses on how the presentar solved a biomedical engineering problem using Machine learning. Also gives reference to many interesting references to advices given by the leaders of Machine learning field.
ML Products have become a prolific and integral part of taking the insights of Data Science from theory to reality. Oddly though, the path from conception to implementation is often unclear with seemingly few similar examples to work from. The result is often a sea of agony between sliding deadlines, heroic efforts of people working though unforeseen challenges and haphazard innovation. Each time a beautiful model makes its impact on the business bottom line, something worked. In this talk we present the ML Playbook. It pulls together the best aspects from a variety of successful ML Product launches into a cohesive strategy to Plan, Build, Test, Learn, and Release ML Products. We'll demonstrate the ML Playbook in action with the story of launching an alert monitoring product for the world's most powerful jet engines, the GE90-115B.
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...Spark Summit
BigDL is a distributed deep Learning framework built for Big Data platform using Apache Spark. It combines the benefits of “high performance computing” and “Big Data” architecture, providing native support for deep learning functionalities in Spark, orders of magnitude speedup than out-of-box open source DL frameworks (e.g., Caffe/Torch) wrt single node performance (by leveraging Intel MKL), and the scale-out of deep learning workloads based on the Spark architecture. We’ll also share how our users adopt BigDL for their deep learning applications (such as image recognition, object detection, NLP, etc.), which allows them to use their Big Data (e.g., Apache Hadoop and Spark) platform as the unified data analytics platform for data storage, data processing and mining, feature engineering, traditional (non-deep) machine learning, and deep learning workloads.
Many companies have invested time and money into building sophisticated data pipelines that can move massive amounts of data, often in real time. However, for the analyst or data scientist who builds offline models, integrating their analyses into these pipelines for operational purposes can pose a challenge.
In this slide deck, we will discuss some key technologies and workflows companies can leverage to build end-to-end solutions for automating statistical and machine learning solutions: from collection and storage to analysis and real-time predictions.
Similar to Machine Learning for Molecular Graph Representations and Geometries (20)
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
A machine-learning view on heterogeneous catalyst design and discoveryIchigaku Takigawa
Telluride Workshop on Computational Materials Chemistry, Telluride, Colorado, USA, July 1, 2021.
https://research.chem.ucr.edu/groups/jiang/Telluride_Workshop.html
https://www.telluridescience.org/meetings/workshop-details?wid=901
https://www.telluridescience.org/meetings/workshop-details?wid=945
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Machine Learning for Molecular Graph Representations and Geometries
1. Machine Learning
for Molecular Graph Representations and Geometries
Ichigaku Takigawa
https://itakigawa.github.io/
December 1st, 2021
2. TAKIGAWA Ichigaku
https://itakigawa.github.io
• 1995-2004 Hokkaido Univ (Grad School. Engineering)
2004 PhD Computer Science
• 2005-2011 Kyoto Univ (Inst. Chemical Research)
Bioinformatics Center, Assist. Prof.
Grad School Pharmaceutical Sciences, Assit. Prof.
• 2012-2018 Hokkaido Univ (Grad School. Info Sci & Tech)
Large-Scale Knowledge Processing Lab, Assoc. Prof.
2015-2018 JST PRESTO for Materials Informatics
• 2019- RIKEN Center for AI Project
2019- Hokkaido Univ
(Inst. Chemical Reaction Design & Discovery)
I belong to a joint research team based at Kyoto
with RIKEN AIP and Kyoto Univ CiRA,
working on stem cell biology.
Hi, I am a machine-learning researcher
3. TAKIGAWA Ichigaku
https://itakigawa.github.io
• 1995-2004 Hokkaido Univ (Grad School. Engineering)
2004 PhD Computer Science
• 2005-2011 Kyoto Univ (Inst. Chemical Research)
Bioinformatics Center, Assist. Prof.
Grad School Pharmaceutical Sciences, Assit. Prof.
• 2012-2018 Hokkaido Univ (Grad School. Info Sci & Tech)
Large-Scale Knowledge Processing Lab, Assoc. Prof.
2015-2018 JST PRESTO for Materials Informatics
• 2019- RIKEN Center for AI Project
2019- Hokkaido Univ
(Inst. Chemical Reaction Design & Discovery)
I belong to a joint research team based at Kyoto
with RIKEN AIP and Kyoto Univ CiRA,
working on stem cell biology.
But also, I am a machine-learning user
4. This talk
1. ML in a nutshell
2. The dark side: Modern aspects of ML
3. The light side: Deep learning for molecules
4. Challenges
Machine Learning (ML) for Molecules
May the ML Force be with you…