NishMath

Recent research highlights the mathematical principles underlying natural phenomena, including the spiral patterns observed in plant growth. A new book, "Do plants know math?", delves into the fascinating world of phyllotaxis, which is the arrangement of leaves on a plant stem. The book explores the connection between the positioning of leaves, scales on cones, and the patterning of flower heads with mathematical concepts like the Fibonacci sequence and divergence angles. These concepts are explained alongside other essential phyllotaxis terminology within the book, showcasing the technical nature of the subject.

In other mathematical developments, mathematicians are using quaternions to analyze spherical trigonometry. This involves an extension of complex numbers which are non-commutative. Quaternions have properties like associative multiplication and the existence of multiplicative inverses. The exploration of these mathematical constructs provide insights into rotations and relationships in space, adding another dimension to mathematical analysis. Additionally, basic mathematical concepts, such as place value and face value, are also being explored. Place value refers to the value of a digit based on its position in a number, while face value is simply the digit itself.


References :

• What's new: Quaternions and spherical trigonometry
• Terence Tao: In my most recent blog post I show how quaternions can be used to derive the equations of spherical trigonometry, and as an example derive the sunrise equation relating the time of sunrise or sunset to one's latitude and the declination of the Sun.

Classification:

• HashTags: AppliedMath PlantMath Quaternions
• Target: MathConcepts
• Product: AppliedMath
• Feature: Applications
• Type: Research
• Severity: Informative

The intersection of mathematics and technology is proving to be a hot topic, with articles exploring how mathematical concepts underpin many aspects of data science and programming. Key areas of focus include the essential math needed for programming, highlighting the importance of Boolean algebra, number systems, and linear algebra for creating efficient and complex code. Linear algebra, specifically the application of matrices, was noted as vital for data transformations, computer vision algorithms, and machine learning, enabling tasks such as vector operations, matrix transformations, and understanding data representation.

The relationship between data science and mathematics is described as complex but crucial, with mathematical tools being the foundation of data-driven decisions. Probability and statistics are also essential, acting as lenses to understand uncertainty and derive insights, covering descriptive statistics like mean, median, mode and the application of statistical models. Computer vision also relies on math concepts, with specific applications like optical character recognition using techniques like pattern recognition and deep learning. Optimization of computer vision models is also discussed, with a focus on making models smaller and faster using techniques like pruning and quantization.


References :

• dipjyotimetia.medium.com: Essential Mathematics for Better Programming
• medium.com: Data Science and Math: A Rollercoaster Relationship
• medium.com: Math for Datascience — Matrices
• aqsazafar81.medium.com: What Math is Required for Data Science?
• levelup.gitconnected.com: A Beginner-to-Upper Intermediate Data Science Roadmap for 2025 #2: Mathematics for Data Science
• medium.com: Day 2: Math Magic — Why Data Science Needs More Than a Calculator

Classification:

• HashTags: DataScience Programming AppliedMath
• Company: Medium
• Target: tech
• Product: datascience
• Feature: applications
• Malware: none
• Type: Research
• Severity: Medium