Peat, a crucial component in whisky production, imparts distinctive and
irreplaceable flavours to the final product. However, the extraction of peat
disrupts ancient ecosystems and releases significant amounts of carbon,
contributing to climate change. This paper aims to address this issue by
conducting a feasibility study on enhancing peat use efficiency in whisky
manufacturing through non-destructive analysis using hyperspectral imaging.
Results show that shot-wave infrared (SWIR) data is more effective for
analyzing peat samples and predicting total phenol levels, with accuracies up
to 99.81%.

通过使用高光谱成像技术进行非破坏性分析，本文旨在解决威士忌酿造中保持泥炭利用效率的问题。结果表明，短波红外数据对于分析泥炭样本和预测总酚水平更为有效，准确率高达 99.81%。

利用高光谱成像和机器学习进行非破坏性泥炭分析

Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine  Learning

Plastic waste entering the riverine harms local ecosystems leading to
negative ecological and economic impacts. Large parcels of plastic waste are
transported from inland to oceans leading to a global scale problem of floating
debris fields. In this context, efficient and automatized monitoring of
mismanaged plastic waste is paramount. To address this problem, we analyze the
feasibility of macro-plastic litter detection using computational imaging
approaches in river-like scenarios. We enable near-real-time tracking of
partially submerged plastics by using snapshot Visible-Shortwave Infrared
hyperspectral imaging. Our experiments indicate that imaging strategies
associated with machine learning classification approaches can lead to high
detection accuracy even in challenging scenarios, especially when leveraging
hyperspectral data and nonlinear classifiers. All code, data, and models are
available online:
this https URL

在本文中，我们通过计算成像方法分析了在河流情景下利用大气塑料垃圾的宏观可视 - 短波红外高光谱成像进行监测的可行性；我们的实验表明，结合机器学习分类方法和高光谱数据以及非线性分类器，可以在具有挑战性的情景中实现高精度的检测。

清洁河流的愿景：利用快照式高光谱成像技术检测大型塑料垃圾

A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging  to Detect Macro-Plastic Litter

This research project investigates Lenia, an artificial life platform that
simulates ecosystems of digital creatures. Lenia's ecosystem consists of
simple, artificial organisms that can move, consume, grow, and reproduce. The
platform is important as a tool for studying artificial life and evolution, as
it provides a scalable and flexible environment for creating a diverse range of
organisms with varying abilities and behaviors. Measuring complexity in Lenia
is a key aspect of the study, which identifies the metrics for measuring
long-term complex emerging behavior of rules, with the aim of evolving better
Lenia behaviors which are yet not discovered. The Genetic Algorithm uses
neighborhoods or kernels as genotype while keeping the rest of the parameters
of Lenia as fixed, for example growth function, to produce different behaviors
respective to the population and then measures fitness value to decide the
complexity of the resulting behavior. First, we use Variation over Time as a
fitness function where higher variance between the frames are rewarded. Second,
we use Auto-encoder based fitness where variation of the list of reconstruction
loss for the frames is rewarded. Third, we perform combined fitness where
higher variation of the pixel density of reconstructed frames is rewarded. All
three experiments are tweaked with pixel alive threshold and frames used.
Finally, after performing nine experiments of each fitness for 500 generations,
we pick configurations from all experiments such that there is a scope of
further evolution, and run it for 2500 generations. Results show that the
kernel's center of mass increases with a specific set of pixels and together
with borders the kernel try to achieve a Gaussian distribution.

对 Lenia 平台进行的人工生命研究，研究其生态系统，利用遗传算法识别复杂性，并找到未来演化的可能性。

捕捉 Lenia 中不断出现的复杂性

Capturing Emerging Complexity in Lenia

This chapter discusses the possibility of instilling a virtual world with
mechanisms for evolution and natural selection in order to generate rich
ecosystems of complex organisms in a process akin to biological evolution. Some
previous work in the area is described, and successes and failures are
discussed. The components of a more comprehensive framework for designing such
worlds are mapped out, including the design of the individual organisms, the
properties and dynamics of the environmental medium in which they are evolving,
and the representational relationship between organism and environment. Some of
the key issues discussed include how to allow organisms to evolve new
structures and functions with few restrictions, and how to create an
interconnectedness between organisms in order to generate drives for continuing
evolutionary activity.

本文讨论在虚拟世界中注入演化和自然选择机制，以生成复杂生物的丰富生态系统，包括设计个体生物、演化环境介质的特性和动态，以及个体与环境的表征关系。关键问题包括如何允许生物在少限制下演化新结构和功能，以及如何创造生物之间的相互联系以产生延续的进化活动。