RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: \url{https://github.com/tencent-ailab/RLogist}.Comment: accepted by AAAI 202

Application of spherical harmonics analysis on LBS particles and LBS fragments

This paper applies surface parameterization and spherical harmonics analysis to the characterization of particle shapes of Leighton Buzzard sand (LBS) particles and LBS fragments obtained from X-ray micro-tomography (μCT). The rotation, transition and scale independent spherical coefficients were obtained. The relationship between spherical coefficients and shape parameters of form, roundness and compactness was investigated. The coefficients of degree one determine the principal dimensions of an ellipsoid, which has a similar aspect ratio with the original surface. The coefficients of higher degree characterise more details by increasing the percentage of higher and lower mean curvature on the reconstructed surface. As the spherical degree increases, the reconstructed surface tend to have lower particle roundness, sphericity and convexity, and higher aspect ratio

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.Comment: AAAI 202

A simple method for particle shape generation with spherical harmonics

The increasing interest in particle shape influence on granular mechanics necessitates a fast and robust particle shape generation method. We describe a new approach based on rotation-invariant spherical harmonic (SH) analysis. The core of this method is to construct morphology features at various length scales and superimpose them together to form the overall morphology. This method uses four rotation-invariant SH factors to construct SH coefficient matrices. We quantify particle shape at form, roundness, and compactness to establish the linkage between SH factors and traditional shape parameters. It is found that SH factors effectively control particle features at different scales. This method has a great potential to facilitate the research on granular mechanics considering particle shape effects.National Science Foundation of ChinaResearch Grant Council of HKSARShenzhen Basic Research Grant2021-11-12 JG: PDF replaced with correct pape