Here are my solutions to problems. I also include my "class notes", at the bottom of the page. They fill in many details not made explicit in the book but which helped my understanding.The Notes include solutions to a few additional problems.

This is a repository with the code for the EMNLP 2020 paper "Information-Theoretic Probing with Minimum Description Length"

Codebase to accompany the paper A Look Inside the Black Box: Using Graph-Theoretical Descriptors to Interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the Global and Local Minimum Energy Structures of Neutral Water Clusters.

🚀 The first learned approach to the Range Minimum Query (RMQ) problem, providing robust theoretical guarantees and novel space-time trade-offs.

Quaternion Series QM companion to "Quantum Mechanics: The Theoretical Minimum"

The exercises are all part of a typical application theme, namely tracking, navigation and SLAM: • Bayesian estimation applied to beacon based measurement systems • Kinematic and dynamic models for tracking • Tracking based on discrete Kalman filtering for linear-Gaussian systems • Tracking with extended Kalman filtering in nonlinear systems • Tracking with particle filtering in nonlinear systems • Slam As such the exercises cover the following theoretical subjects: 1. Fundamentals of parameter estimation; static and scalar case 2. Unbiased linear minimum mean square estimation; static and scalar case 3. Unbiased linear minimum mean square estimation; static and vectorial case 4. Propagation of uncertainty in Gaussian-linear systems; prediction 5. Discrete Kalman filtering 6. Extended Kalman filtering 7. Particle filtering 8. SLAM

My own solutions to problems in Susskind's Quantum Mechanics: The Theoretical Minimum using Julia

The data of article "Minimum Hop Path Model for LEO Mega Constellation Networks with Inter-Layer Satellite Links: A Theoretical Analytic Approach"

The theoretical minimum physics exercises and notes

Minimum-cost maximum-flow. This is an adapted version of the Edmonds-Karp relabelling mcmf algorithm [1], originally implemented by Igor Naverniouk. This adapted version allocates memory dynamically in order to use memory as needed. [1] J. Edmonds, R.M. Karp, "Theoretical Improvements in Algorithmic Efficieincy for Network Flow Problems," J. ACM, vol. 19, pp. 248-264, 1972.

No description available

A special tool for learning theoretical minimums for exams and other kinds of works!

No description available

No description available

Implement a Minimum Risk Bayes Decision Theoretic classifier and use it to classify the test examples in the provided datasets of Iris flowers.

A performance benchmarking test designed to calculate the overhead of the Zeebe engine and thus the theoretical minimum end-to-end processing time of a process.

Implemented Minimum Risk Bayes Decision Theoretic classifier,logistic regression classifier, Principal Component Analysis,Expectation-Maximization algorithms and used it to classify the test examples in the provided datasets of Iris flowers.

Entropy Calculation: Automatically calculates the theoretical Shannon Entropy (minimum required bits per symbol) of the input data. Near-Perfect Compression: Demonstrates how Range Coding encodes an entire input string into a single, high-precision fractional number, achieving compression ratios extremely close to the calculated entropy limit.

This work considers active deanonymization of bipartite networks. The scenario arises naturally in evaluating privacy in various applications such as social networks, mobility networks, and medical databases. For instance, in active deanonymization of social networks, an anonymous victim is targeted by an attacker (e.g. the victim visits the attacker's website), and the attacker queries her group memberships (e.g. by querying the browser history) to deanonymize her. In this work, the fundamental limits of privacy, in terms of the minimum number of queries necessary for deanonymization, is investigated. The bipartite network is generated based on linear and sublinear preferential attachment, and the stochastic block model. The victim's identity is chosen randomly based on a distribution modeling the users' risk of being the victim (e.g. probability of visiting the website). An attack algorithm is proposed which builds upon techniques from communication with feedback, and its performance, in terms of expected number of queries, is analyzed. Simulation results are provided to verify the theoretical derivations. In this project, we provide several simulations of synthesized and real-world attacks to verify the theoretical results presented in the paper and gain further intuition regarding the users’ privacy risks under such attack scenarios. For detailed problem formulation you can visit the following paper: https://arxiv.org/abs/2106.04766

This project focuses on network resilience to perturbation of edge weight. Other than connectivity, many network applications nowadays rely upon some measure of network distance between a pair of connected nodes. In these systems, a metric related to network functionality is associated to each edge. A pair of nodes only being functional if the weighted, shortest-path distance between the pair is below a given threshold \texttt{T}. Consequently, a natural question is on which degree the change of edge weights can damage the network functionality? With this motivation, we study a new problem, \textit{Quality of Service Degradation}: given a set of pairs, find a minimum budget to increase the edge weights which ensures the distance between each pair exceeds $\mathtt{T}$. We introduce four algorithms with theoretical performance guarantees for this problem. Each of them has its own strength in trade-off between effectiveness and running time, which are illustrated both in theory and comprehensive experimental evaluation

Misc

my work as an amateur/professional undergrad student...

A theoretical minimum guideline for Computer Science, including necessary maths & physics

Interview Questions

No description available

No description available

Notebooks with simple physics exercises, inspired by the "Theoretical minimum" series by Leonard Susskind.

No description available

No description available

Working through examples from "The Theoretical Minimum" in Jupyter notebook.