In recent years, multilingual pre-trained language models have gained prominence due to their remarkable performance on numerous downstream Natural Language Processing tasks (NLP). However, pre-training these large multilingual language models requires a lot of training data, which is not available for African Languages. Active learning is a semi-supervised learning algorithm, in which a model consistently and dynamically learns to identify the most beneficial samples to train itself on, in order to achieve better optimization and performance on downstream tasks. Furthermore, active learning effectively and practically addresses real-world data scarcity. Despite all its benefits, active learning, in the context of NLP and especially multilingual language models pretraining, has received little consideration. In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework. Pretrained on a dataset significantly (14x) smaller than existing baselines, AfroLM outperforms many multilingual pretrained language models (AfriBERTa, XLMR-base, mBERT) on various NLP downstream tasks (NER, text classification, and sentiment analysis). Additional out-of-domain sentiment analysis experiments show that \textbf{AfroLM} is able to generalize well across various domains. We release the code source, and our datasets used in our framework at https://github.com/bonaventuredossou/MLM_AL.

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

我们提出了一种基于事件的降雪算法，称为EBSNOR。我们开发了一种技术，可以使用基于事件的相机数据来测量像素上雪花的停留时间，该数据用于进行Neyman-Pearson假设测试，以将事件流分为雪花和背景事件。在一个名为udayton22ebsnow的新数据集上验证了拟议的EBSNOR的有效性，该数据集由前面事件的摄像机组成，该相机在汽车中驾驶雪中，并在周围车辆周围手动注释的边界盒。在定性上，Ebsnor正确地标识了与雪花相对应的事件；并且在定量上，EBSNOR预处理的事件数据改善了基于事件的CAR检测算法的性能。

我们提出了一个开放域的社交聊天机器人Chirpy Cardinal。为了既有信息又有信息，我们的机器人以一种真实的，情感上的方式与用户聊天。通过将受控的神经产生与脚手架，手写的对话整合在一起，我们让用户和机器人都轮流推动对话，从而产生引人入胜且流利的体验。Chirpy Cardinal部署在Alexa奖Socialbot Grand Challenge的第四次迭代中，每天处理数千次对话，在9个机器人中排名第二，平均用户评级为3.58/5。

随着来自太阳的电流能通过大气传播，它受到辐射转移效应的影响，包括吸收，发射和散射。建模这些影响对于地球和大气的科学遥感测量至关重要。例如，高光谱图像是一种数字图像的一种形式，这些形式是在像素中收集了许多（通常数百个）光的光之长。在传感器处测得的光量是发射阳光，大气辐射转移以及地面上材料的反射率的结果，所有这些材料的每个波长都因多种物理现象而变化。因此，地面光谱或大气成分的测量需要分离每个波长这些不同的贡献。在本文中，我们创建了一种类似于将大气的自动编码器视为“噪声”和地面反射率的自动编码器，每次频谱的真相。我们通过通过Modtran（http://modtran.spectral.com/modtran \ _home）从实验室测量中获取光谱的随机样品，从而产生数十万个训练样品，并通过变化大气输入来增加大气影响。理想情况下，这一过程可以创建一个自动编码器，该自动编码器将在高光谱图像中分离大气效应和地面反射率，这是一个称为大气补偿的过程，这是困难且耗时的，需要启发式近似值，物理量的估计和物理建模。尽管我们方法的准确性不如该领域的其他方法那样好，但这是将深入学习物理原理的不断增长领域应用于高光谱图像和遥感中的大气补偿领域的重要第一步。

Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally-segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC=0.893, real-world AUC=0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas.

在本文中，我们提出了一种与渔业相关数据的方法，该方法使我们能够通过多个可以利用众包接口的培训和生产循环在数据集上迭代标记的图像数据集。我们将算法及其结果介绍在使用海底自动水下车辆收集的两组单独的图像数据上。第一个数据集由2,026个完全未标记的图像组成，而第二个数据集由21,968张图像组成，这些图像由专家注释。我们的结果表明，使用小子集进行培训，并迭代以构建较大的标记数据，从而使我们能够收敛到带有少量迭代的完全注释数据集。即使在专家标记的数据集的情况下，该方法论的单个迭代也通过发现与鱼层相关的鱼类相关标签的其他复杂示例，也很小，或者被与水下图像相关的对比度限制所掩盖，从而改善了标签。

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.