Boundary representation (B-rep) is the primary method of representing modeled objects in most geometric kernels, including our C3D Modeler kernel. The core algorithms that edit models, such as applying fillet operations, performing cutting operations, and obtaining flat projections require the precision of B-rep representations. The rapidly growing variety of 3D data in polygonal formats makes the task of model transformations from polygons into boundary representation increasingly relevant. As a result, we developed a new SDK, C3D B-Shaper, which is part of our C3D Toolkit.

Many programmers struggle when using formal methods to solve problems within their programs, as those methods, while effective, can be unreasonably complex. To understand why this happens, let’s use the model checking method to solve a relatively easy puzzle:

Conditions

You’re in a hallway with seven doors on one side leading to seven rooms. A cat is hiding in one of these rooms. Your task is to catch the cat. Opening a door takes one step. If you guess the correct door, you catch the cat. If you do not guess the correct door, the cat runs to the next room.

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Quick links

— The Road to Version 12
— First, Some Math
— The Calculus of Uncertainty
— Classic Math, Elementary and Advanced
— More with Polygons
— Computing with Polyhedra
— Euclid-Style Geometry Made Computable
— Going Super-Symbolic with Axiomatic Theories
— The n-Body Problem
— Language Extensions & Conveniences
— More Machine Learning Superfunctions
— The Latest in Neural Networks
— Computing with Images
— Speech Recognition & More with Audio
— Natural Language Processing
— Computational Chemistry
— Geographic Computing Extended
— Lots of Little Visualization Enhancements
— Tightening Knowledgebase Integration
— Integrating Big Data from External Databases
— RDF, SPARQL and All That
— Numerical Optimization
— Nonlinear Finite Element Analysis
— New, Sophisticated Compiler
— Calling Python & Other Languages
— More for the Wolfram “Super Shell”
— Puppeting a Web Browser
— Standalone Microcontrollers
— Calling the Wolfram Language from Python & Other Places
— Linking to the Unity Universe
— Simulated Environments for Machine Learning
— Blockchain (and CryptoKitty) Computation
— And Ordinary Crypto as Well
— Connecting to Financial Data Feeds
— Software Engineering & Platform Updates
— And a Lot Else…

The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.

Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).

The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.

Anyone, who has ever been interested in stocks or cryptocurrencies has seen these additional lines on charts. You may have heard the opinions that they don’t work. But they greatly improve my trading ability, while displaying alot of important data. But how are they actually works? And to whom it can be useful?

You should definitely read this if:

1. You use them in day trading
2. You are planning to write a trading bot
3. If you want to implement a trading strategy or indicators by yourself

Balanced ternary

I am working on a computer architecture principles lectures for our university; and as an assignment I'd like to propose to my students to build a simple programmable machine working in ternary. The main reason is fun: as a lecturer I must bring a bit of entertainment, otherwise I won't be listened to. Besides, it is important for historic reasons. Any further «why?!» questions will be answered «Because I can».

This page describes the very basics, it won't go beyond a simple ternary adder (and its hardware implementation). Stay tuned for more.

I chose the balanced ternary system: every trit represents one of three possible states, -1, 0 or 1. A very extensive description of this system may be found here.

Software component developers tend to be far removed from the end users of the products in which their components are employed. Recently, however, we connected directly with a KOMPAS-3D MCAD user to solve an issue involving mold design. It seems that 3D models were being exported incorrectly to data exchange formats like STP, X_T, and SAT. The cause, unhappily for us, turned out to be in our С3D Modeler geometric modeling kernel. Here is how we solved the problem, quickly.

The problem statement

I was surfing the Internet the other day and a rather curious thing caught my mind: the Mendocino motor. It’s an extremely low-friction bearing rotor: the original one had a glass cylinder hanging on two needles, but the modern ones use magnetic suspension. It’s a brushless engine: the rotor has solar batteries attached to it, which generate current for the coils wrapped around the rotor. The rotor spins in a fixed magnetic field, the solar batteries getting exposed to the light source one after the other. It’s a rather elegant solution that’s very possible to recreate at home.

Here’s the video that explains how it works (in Russian):


But this video had another curiosity even stronger than the engine itself. In the video description Dmitry Korzhevsky writes: “You CAN’T replace the side support with a magnet! Don’t ask me about this anymore!”

This is a short article about understanding time series and main characteristics behind that.

Problem statement

We have time-series data with daily and weekly regularity. We want to find the way how to model this data in an optimal way.

There are a lot of different articles on Kalman filter, but it is difficult to find the one which contains an explanation, where all filtering formulas come from. I think that without understanding of that this science becomes completely non understandable. In this article I will try to explain everything in a simple way.

Kalman filter is very powerful tool for filtering of different kinds of data. The main idea behind this that one should use an information about the physical process. For example, if you are filtering data from a car’s speedometer then its inertia give you a right to treat a big speed deviation as a measuring error. Kalman filter is also interesting by the fact that in some way it is the best filter. We will discuss precisely what does it mean. In the end of the article I will show how it is possible to simplify the formulas.

When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? How are you going to apply all this inversions, transpositions, eigenvector and eigenvalues for practical purposes?

Well, if you study linear algebra with the purpose of doing machine learning, this is the answer for you.

In brief, you can use linear algebra for machine learning on 3 different levels:

• application of a model to data;
• training the model;
• understanding how it works or why it does not work.