Outlier Detection with Cluster Catch Digraphs: A Novel Approach for High-Dimensional and Arbitrary-Shaped Clusters
The paper introduces a novel family of outlier detection algorithms based on Cluster Catch Digraphs (CCDs) that are designed to address the challenges of high dimensionality and varying cluster shapes, which deteriorate the performance of most traditional outlier detection methods.
사기꾼 로봇을 상대하는 경찰관들
사기꾼 로봇 버전의 경찰관과 도둑 게임에서 필요한 경찰관의 최소 수를 조사하였다.
Cheating Robot Cops and Robber: Analyzing a Pursuit-Evasion Game on Graphs
The cheating robot number of a graph is the minimum number of cops needed to capture a robber who can react to the cops' moves in a pursuit-evasion game.
頭頸部がんに対するプロトン線治療計画の自動化：ポリシーグラジエントベースの深層強化学習の活用
深層強化学習を用いて、頭頸部がんに対するプロトン線治療計画を自動的に生成することができる。
Algorithmic Methods for Constructing Hamiltonian Cycles in Complete Graphs and Solving the Traveling Salesman Problem
This work presents an algorithmic approach to constructing Hamiltonian cycles in complete graphs and solving the Traveling Salesman Problem.
쿼터니언 좌불변 EKF와 잡음 공분산 조정을 이용한 강건한 자세 추정
쿼터니언 좌불변 EKF와 반복적 기대-최대화(EM) 알고리즘을 통해 프로세스 및 측정 잡음 공분산을 효과적으로 추정할 수 있다. 이를 통해 자세 추정 정확도와 초기 매개변수 오류에 대한 강건성을 향상시킬 수 있다.
Evaluating the Effectiveness of Chain-of-Thought Prompting: Insights from a Quantitative Meta-Analysis and Experiments Across Models and Datasets
Chain-of-thought prompting primarily helps on tasks involving mathematical, logical, or algorithmic reasoning, with limited benefits on other types of reasoning tasks.
LifeGPT: A Topology-Agnostic Transformer Model for Accurately Simulating Conway's Game of Life
A decoder-only transformer model, LifeGPT, can accurately simulate the dynamics of Conway's Game of Life on a toroidal grid without any prior knowledge of the grid size or boundary conditions.
Enhancing Transformer Models with Möbius Attention for Improved Linguistic Pattern Capture
Möbius Attention, a novel attention mechanism based on Möbius transformations, enhances the expressivity of Transformer models by enabling them to capture more intricate linguistic patterns and relationships between tokens.
Local-Global Representation Alignment for Robust Time Series Classification
The proposed LogoRA framework employs a two-branch encoder to extract both local and global representations from time series data, and utilizes various alignment strategies to learn domain-invariant features for robust time series classification across different domains.
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