paper_authors: Yi Zhu, Mahsa Abdollahi, Ségolène Maucourt, Nico Coallier, Heitor R. Guimarães, Pierre Giovenazzo, Tiago H. Falk
for: 这个论文是为了提供多种现象特征测量和蜂群学专家标注的蜂巢数据集，以便更广泛的分析。
methods: 该论文使用了多种感知器和温度传感器收集数据，并对数据进行预处理和分析。
results: 论文提供了蜂巢数据集的分布和感知器数据的视觉化，并基于感知器数据分析和机器学习实现了冬季死亡预测、蜂巢人口估计和活蜂后繁殖的应用。Abstract
We present a longitudinal multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were continuously collected between May-2020 and April-2021 from 53 hives located at two apiaries in Qu\'ebec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis. We first summarize the data collection procedure, sensor data pre-processing steps, and data composition. We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns. Lastly, we showcase several hive monitoring applications based on sensor data analysis and machine learning, such as winter mortality prediction, hive population estimation, and the presence of an active and laying queen.
摘要
我们提供了一个长期多感器数据集，从加拿大魁北克省五十三个坛巢中收集到的蜜蜂（Apis mellifera）的数据。数据从2020年5月到2021年4月连续收集，来自两个 apiary 地点。感器数据包括声音特征、温度和相对湿度。现象特征测量包括坛巢人口、卵、幼虫和蛹数量、Varroa destructor 感染水平、防御和卫生行为、蜜产量和冬季死亡率。我们的研究是 amongst the first 提供了蜜蜂生物学专家 annotated 多种现象特征测量，使得更广泛的分析 scope 可行。我们首先介绍数据采集过程、感器数据预处理步骤和数据结构。然后我们提供现象特征数据分布的概述以及感器数据模式的视觉化。最后，我们展示了基于感器数据分析和机器学习的坛巢监测应用，如冬季死亡预测、坛巢人口估计和活跃和繁殖 queen 存在。
Retrieval Augmented Generation of Symbolic Music with LLMs
paper_authors: Nicolas Jonason, Luca Casini, Carl Thomé, Bob L. T. Sturm
for: 用于音乐生成
methods: 使用检索系统选择相关示例
results: 在对用户进行对话时，音乐生成初步结果显示投入效果，特别是考虑到实现的可能性。I hope that helps! Let me know if you have any other questions.Abstract
We explore the use of large language models (LLMs) for music generation using a retrieval system to select relevant examples. We find promising initial results for music generation in a dialogue with the user, especially considering the ease with which such a system can be implemented. The code is available online.
摘要
我们研究使用大型自然语言模型（LLM） для музыкаль生成，使用检索系统选择相关的示例。我们发现在与用户对话中进行音乐生成初果很有前途，特别是考虑到这种系统的实现非常容易。代码在线上可用。Note: "音乐生成" (yīn yuè chàng jì) is a term used in China to refer to the generation of music using machine learning or other computational methods.