CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most work has focused on cooperation and collaboration between agents, little work explores competition, another important mechanism that fosters the development of society and economy. In this paper, we seek to examine the competition behaviors in LLM-based agents. We first propose a general framework to study the competition between agents. Then, we implement a practical competitive environment using GPT-4 to simulate a virtual town with two types of agents, including restaurant agents and customer agents. Specifically, restaurant agents compete with each other to attract more customers, where the competition fosters them to transform, such as cultivating new operating strategies. The results of our experiments reveal several interesting findings ranging from social learning to Matthew Effect, which aligns well with existing sociological and economic theories. We believe that competition between agents deserves further investigation to help us understand society better. The code will be released soon.Comment: Technical report; 21 page

A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality

The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications, enabling the realization of precision medicine, intelligent healthcare, and telemedicine in the era of digitalization and intelligence. However, the IoMT faces various challenges, including sustainable power supply, human adaptability of sensors and the intelligence of sensors. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD). By further integrating deep learning-assisted data analytics, we actualized an intelligent healthcare monitoring system for the surveillance and interaction of PD patients, which includes location/trajectory tracking, heart monitoring and identity recognition. This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients conditions. This monitoring system is cost-effective, easily fabricated, highly sensitive, and intelligent, consequently underscores the immense potential of human body sensing technology in a Health 4.0 society

ELM of ELM-WD: An extremely low mass hot donor star discovered in LAMOST survey

The Extremely Low Mass White Dwarfs (ELM WDs) and pre-ELM WDs are helium core white dwarfs with mass $<\sim 0.3M_{\odot}$. They are formed in close binaries and have lost over half of their initial masses via Common Envelope (CE) ejection or stable Roche Lobe Over Flow (RLOF). Both evolution simulations and observations show that a lower mass limit for ELM WDs exists at $\approx0.14M_{\odot}$. Here we report the discovery of an extremely low mass ELM WD, ID70904216 in LAMOST survey, that may be lower than the ELM WD mass limit. Based on LAMOST and P200 spectroscopic observations, ID70904216 shows orbital period $P_{orb} =$ 0.219658 days and radial velocity semi-amplitude $K1=317.33km/s$, which gives the mass function of 0.73$M_{\odot}$, indicating the companion is a compact star. The low resolution spectra shows a F type star with $T_{\rm eff} \sim 7361K$ without emission features. The temperature is consistent with that derived from SED fitting($7440K$) and multi-color light curve solution($7400K$). The optical light curves, in ZTF g, r and i bands and Catalina V band, show ellipsoidal variability with amplitudes $\approx30\%$, suggesting that the visible companion is heavily tidal distorted. Combining with the distance from Gaia survey, the WD code modeling estimates that the mass of the visible star is $M1=0.08^{+0.06}_{-0.03}M_{\odot}$, and the mass of the invisible star is $M2=0.94^{+0.45}_{-0.10}M_{\odot}$. The radius of the visible donor is $R=0.29\pm0.01R_{\odot}$. The inclination angle is constrained between 60$^{\circ}$ and 90$^{\circ}$. The observations indicate the system is a pre-ELM WD + WD/NS binary system with an extremely low mass hot donor below the $0.14M_{\odot}$ theoretical limit.Comment: 16 pages, 10 figure

Orbital parameters for an ELM white dwarf with a white dwarf companion: LAMOST J033847.06+413424.2

Double white dwarf systems are of great astrophysical importance in the field of gravitational wave and Type Ia supernova. While the binary fraction of CO core white dwarf is about a few percents, the extremely low mass white dwarfs are all thought to be within binary systems. In this work, we report the orbital solution of a double degenerate system: J033847.06+413424.24, an extremely low mass He core white dwarf orbiting a CO core white dwarf. With LAMOST and P200, time domain spectroscopic observations have been made and spectral atmosphere parameters are estimated to be $T_{\rm eff}\sim22500$ K and log $g\sim5.6$ dex. Combining Gaia parallax, 3D extinction, and evolution tracks, we estimate a radius of $\sim0.12$ $R_{\odot}$ and a mass of $\sim0.22$ $M_{\odot}$. With the 37 single exposure spectra, the radial velocities are measured and the orbital parameters are estimated to be $P=0.1253132(1)$ days, $K1=289\pm4$ km/s and $V_{sys}=-41\pm3$ km/s. The radial velocity based system ephemeris is also provided. The light curves from several photometric surveys show no orbital modulation. The orbital solution suggests that the invisible companion has a minimum mass of about 0.60 $M_{\odot}$ and is $\sim0.79$ $M_{\odot}$ for an inclination of $60.0^{\circ}$, indicating most probably a CO core white dwarf. The system is expected to merge in about 1 Gyr. With present period and distance ($\sim596$ pc) it can not irradiate strong enough gravitational wave for LISA. More double degenerate systems are expected to be discovered and parameterized as the LAMOST survey goes on.Comment: 12 pages, 11 figure

Single-cell atlas reveals different immune environments between stable and vulnerable atherosclerotic plaques

IntroductionRegardless of the degree of stenosis, vulnerable plaque is an important cause of ischemic stroke and thrombotic complications. The changes of the immune microenvironment within plaques seem to be an important factor affecting the characteristics of the plaque. However, the differences of immune microenvironment between stable and vulnerable plaques were remained unknown.MethodsIn this study, RNA-sequencing was performed on superficial temporal arteries from 5 traumatic patients and plaques from 3 atherosclerotic patients to preliminary identify the key immune response processes in plaques. Mass cytometry (CyTOF) technology was used to explore differences in immune composition between 9 vulnerable plaques and 12 stable plaques. Finally, immunofluorescence technique was used to validate our findings in the previous analysis.ResultsOur results showed that more CD86+CD68+ M1 pro-inflammatory macrophages were found in vulnerable plaques, while CD4+T memory cells were mainly found in stable plaques. In addition, a CD11c+ subset of CD4+T cells with higher IFN-r secretion was found within the vulnerable plaque. In two subsets of B cells, CD19+CD20-B cells in vulnerable plaques secreted more TNF-a and IL-6, while CD19-CD20+B cells expressed more PD-1 molecules.ConclusionIn conclusion, our study suggested that M1-like macrophages are the major cell subset affecting plaque stability, while functional B cells may also contribute to plaque stability