Go-Back-N ARQ (Automatic Repeat reQuest) is an error control protocol used in computer networks and data communications. It is a type of sliding window protocol that allows multiple frames to be sent before needing an acknowledgment for the first frame, which increases the efficiency of data transmission. ### Key Features of Go-Back-N ARQ: 1. **Sliding Window Protocol**: The protocol utilizes a sliding window to manage the sequence of frames being sent.

Locally testable code refers to a concept in software development and programming that emphasizes the ability to verify or "test" components of code independently and in isolation from the rest of the system. The goal of locally testable code is to ensure that individual parts of the program can be tested without requiring the entire application to be executed or without needing extensive setups or dependencies.

A Message Authentication Code (MAC) is a cryptographic checksum on data that provides integrity and authenticity assurances on a message. It is designed to protect both the message content from being altered and the sender's identity from being impersonated. ### Key Features of a MAC: 1. **Integrity**: A MAC helps to ensure that the message has not been altered in transit. If even a single bit of the message changes, the MAC will also change, allowing the recipient to detect the alteration.

Reed–Muller codes are a family of error-correcting codes that are used in digital communication and data storage to detect and correct errors in transmitted or stored data. They are particularly known for their simple decoding algorithms and their good performance in terms of error correction capabilities.

The Srivastava code is a method of encoding the decimal digits of numbers into a binary format for efficient transmission and storage in digital systems. It is particularly used in applications like data compression, telecommunications, and digital signal processing.

The Bellman-Ford algorithm is an efficient algorithm used to find the shortest paths from a single source vertex to all other vertices in a graph. It is particularly useful for graphs that may contain edges with negative weights, making it more versatile than Dijkstra's algorithm, which only works with non-negative weights.

A. Aneesh is a name that may refer to various individuals, but one notable figure is A. Aneesh, an academic known for his work in the fields of sociology and anthropology. He is recognized for his research on globalization, technology, and contemporary social issues.

The Financial Crimes Enforcement Network (FinCEN) is an agency of the U.S. Department of the Treasury that was established in 1990. Its primary mission is to combat financial crimes, including money laundering, terrorist financing, and other forms of illicit financial activities.

NeXtProt is a comprehensive knowledge database focused on human proteins. It provides detailed information about the protein-coding genes in the human genome, including their sequences, functions, localization, interactions, and involvement in various biological processes and diseases.

The Robodebt scheme, officially known as the Income Compliance Program, was a controversial program implemented by the Australian government aimed at identifying and recovering overpaid welfare benefits. The scheme used an automated data-matching system to compare income reported by welfare recipients with income data held by the Australian Taxation Office (ATO). If discrepancies were found, recipients could be issued a debt notice, requiring them to repay what was perceived to be overpaid support.

The A* search algorithm is a popular and efficient pathfinding and graph traversal algorithm used in computer science and artificial intelligence. It is commonly utilized in various applications, including route navigation, game development, and robotics. The algorithm combines features of both Dijkstra's algorithm and Greedy Best-First Search, allowing it to efficiently find the least-cost path to a target node.

3-opt is an optimization algorithm commonly used in the context of solving the Traveling Salesman Problem (TSP) and other routing problems. It is a local search improvement technique that refines a given tour (a sequence of vertices) by exploring small changes to reduce the overall tour length. The algorithm works by considering all possible ways to remove three edges from the tour and reconnect the resulting segments in a different way to create a new tour.

A Graph Neural Network (GNN) is a type of neural network specifically designed to work with data represented as graphs. Graphs are mathematical structures consisting of nodes (or vertices) connected by edges, which can represent various types of relationships between entities. Common applications for GNNs include social networks, molecular chemistry, recommendation systems, and knowledge graphs. ### Key Features of Graph Neural Networks: 1. **Graph Structure**: Unlike traditional neural networks that operate on grid-like data (e.g.

Lexicographic breadth-first search (Lex-BFS) is a specific order of traversal used in graph theory, particularly for directed and undirected graphs. It operates similar to a standard breadth-first search (BFS), but incorporates a lexicographic ordering to determine the order in which nodes are explored. ### Key Concepts: 1. **BFS Overview**: In a standard BFS, nodes are explored level by level, starting from a given source node.

Incremental learning is a machine learning paradigm where the model is trained continuously as new data arrives, rather than being trained on a fixed dataset all at once. This approach allows the system to learn from new information in a manner that is efficient and presents a number of advantages, such as: 1. **Adaptability**: The model can adapt to changes in the environment or data distribution over time without needing to be retrained from scratch.

Kernel Principal Component Analysis (KPCA) is a non-linear extension of Principal Component Analysis (PCA) that uses kernel methods to transform data into a higher-dimensional space. This transformation allows for the extraction of principal components that can capture complex, non-linear relationships in the data.

Minimum Redundancy Feature Selection (MRMR) is a feature selection method used primarily in machine learning and data mining to select a subset of relevant features from a larger set while minimizing redundancy among those features. The goal is to identify the most informative features that contribute to the predictive power of the model without introducing unnecessary overlap among the selected features. ### Key Concepts: 1. **Relevance**: Features that have a strong relationship with the target variable are considered relevant.

Mixture of Experts (MoE) is a machine learning architecture designed to improve model performance by leveraging multiple sub-models, or "experts," each specialized in different aspects of the data. The idea is to use a gating mechanism to dynamically select which expert(s) to utilize for a given input, allowing the model to adaptively allocate resources based on the complexity of the task at hand.

Online machine learning is a type of machine learning where the model is trained incrementally as new data becomes available, rather than being trained on a fixed dataset all at once (batch learning). This approach is particularly useful in scenarios where data arrives in a continuous stream, allowing the model to adapt and update itself continuously.

State–action–reward–state–action (SARSA) is an algorithm used in reinforcement learning for training agents to make decisions in environments modeled as Markov Decision Processes (MDPs). SARSA is an on-policy method, meaning that it learns the value of the policy being followed by the agent. The components of SARSA can be broken down as follows: 1. **State (S)**: This represents the current state of the environment in which the agent operates.