- The document contains the results of an experiment with multiple data points plotted as dots across various x and y-axis values.
- There are a large number of data points densely plotted in the graph, with some outliers at the edges.
- The data points are recorded measurements from an experiment, but no other context is provided about the experiment, variables, or what is being measured.
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
1) Machine learning can help rationalize the "experience and intuition" of chemical research by finding patterns and exceptions from large amounts of chemical data to predict new materials and phenomena.
2) While in theory chemical structures and properties can be described by Schrodinger's equation, it is impossible to solve for realistic systems, requiring approximations. Machine learning may help address this challenge.
3) Chemists have successfully created compounds with desired properties through "experience and intuition", which involves inductive reasoning from experiments rather than purely deductive logic, incorporating serendipitous findings.
This document discusses clustering and anomaly detection in data science. It introduces the concept of clustering, which is grouping a set of data into clusters so that data within each cluster are more similar to each other than data in other clusters. The k-means clustering algorithm is described in detail, which works by iteratively assigning data to the closest cluster centroid and updating the centroids. Other clustering algorithms like k-medoids and hierarchical clustering are also briefly mentioned. The document then discusses how anomaly detection, which identifies outliers in data that differ from expected patterns, can be performed based on measuring distances between data points. Examples applications of anomaly detection are provided.
- The document contains the results of an experiment with multiple data points plotted as dots across various x and y-axis values.
- There are a large number of data points densely plotted in the graph, with some outliers at the edges.
- The data points are recorded measurements from an experiment, but no other context is provided about the experiment, variables, or what is being measured.
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
1) Machine learning can help rationalize the "experience and intuition" of chemical research by finding patterns and exceptions from large amounts of chemical data to predict new materials and phenomena.
2) While in theory chemical structures and properties can be described by Schrodinger's equation, it is impossible to solve for realistic systems, requiring approximations. Machine learning may help address this challenge.
3) Chemists have successfully created compounds with desired properties through "experience and intuition", which involves inductive reasoning from experiments rather than purely deductive logic, incorporating serendipitous findings.
This document discusses clustering and anomaly detection in data science. It introduces the concept of clustering, which is grouping a set of data into clusters so that data within each cluster are more similar to each other than data in other clusters. The k-means clustering algorithm is described in detail, which works by iteratively assigning data to the closest cluster centroid and updating the centroids. Other clustering algorithms like k-medoids and hierarchical clustering are also briefly mentioned. The document then discusses how anomaly detection, which identifies outliers in data that differ from expected patterns, can be performed based on measuring distances between data points. Examples applications of anomaly detection are provided.
This presentation vividly describes what is going on in the IT industry with the advent of cloud technology, focusing in Google App Engine. Then reviews competitors’ moves both in terms of background technology and the service capitalization.
It was revealed that major Japanese companies such as Fujitsu and NTT Data are introducing Microsoft Azure in their data centers, but Oracle and IBM are the technically strong chasers. So far there are very few moves
for Japanese IT companies to implement their own cloud products.
IBM's zEnterprise product line has potential to replace and wipe out container-based data centers, but their pricing policy unknown. Oracle's next hardware strategy unknown.
In Japan, tailoring open-source softwares auch as Cassandra and Hadoop found at newly rising companies, IIJ, and Rakuten.
CTFとは、世界的に有名な旗取り合戦(Capture The Flag)のことで、セキュリティ技術を競うコンテストの総称です。出題ジャンルは、暗号、バイナリ、ネットワーク、Web、プログラミングなど多岐に渡り、クイズ形式の問題の謎を解いたり、実験ネットワーク内で疑似的な攻防戦を行ったりするものです。セキュリティだけでなくプログラミングに関する知見も問われ、攻撃技術、防御技術、解析技術、暗号の知見、ネットワーク技術など、広範な知識と経験が必要となっています。CTFは総合的な問題解決力を磨く最適な競技と言えるでしょう。
CTFには既に20年近い歴史があり、ラスベガスのDEFCONでCTFが開催されたことをきっかけに、今やヨーロッパやアジア、オセアニアや南米など、各国で頻繁に競技会が開催されています。国際大会も多く、元祖DEFCONを筆頭に、マレーシアHack in the BOXや、韓国CODEGATEなど、世界中からチームがCTFに参戦して熱戦を繰り広げています。
日本では2000年代の前半に「運動会」、それに続く「セキュリティスタジアム」が開催されましたが、現在に至るまで数年の空白期間があり、普及啓発や人材育成という面で世界に後れを取っているのが現状です。先日JNSA賞を受賞した日本チームsutegoma2の孤軍奮闘のおかげで昨年ようやくDEFCON CTFの世界予選を2位で突破しましたが、残念ながら本選ではふるわず、まだまだ世界とは大きな格差があると言わざるを得ません。
そんな中、今年SECCON CTF実行委員会を発足し、日本全体のセキュリティ技術の底上げと人材発掘をはかる目的で、国内でも本格的にCTF競技会を開催することとしました。
14. Neural Network Potential (NNP)とは
原子座標 エネルギー、力
原子座標からエネルギー・力を求める際にDFTで行っていた複雑な電子状態
計算が不要
→ 原子座標を入力すれば瞬時にエネルギー、力を算出することができる技術
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O
H
H
Neural Network
15. DFT計算を高速化する技術:Neural Network Potential
[5] J. S. Smith, O. Isayev, and A. E. Roitberg, Chem. Sci. 8, 3192 (2017).
メリット:高速
•量子化学計算手法(DFT)と比べ
圧倒的に高速
デメリット:
•精度面 ― 精度の評価が難しい
•教師データ取得が必要
–学習するためのデータ収集として、結局DFT計算が必要
–取得したデータの周辺しか予測できない
15