本文旨在提出和应用机器学习方法，以使用其组件的历史回报数据来分析交易所交易基金（ETF）的回报方向，从而通过交易算法有助于制定投资策略决策。从方法论方面，除了算法误差指标外，还使用来自巴西和美国市场的标准数据集应用了回归和分类模型。在研究结果方面，它们进行了分析并将其与NA \“ Ive”预测和购买和持有技术在同一时期获得的收益进行了比较。就风险和回报而言，模型的性能大多要比控制指标重点是线性回归模型和通过逻辑回归的分类模型，支持向量机（使用LinearsVC模型），高斯天真的贝叶斯和K-Nearest邻居，在某些数据集中，在某些数据集中，回报超过了两次，并且夏普比率高达购买和持有控制模型的比率四倍。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

先前的工作表明，深-RL可以应用于无地图导航，包括混合无人驾驶空中水下车辆（Huauvs）的中等过渡。本文介绍了基于最先进的演员批评算法的新方法，以解决Huauv的导航和中型过渡问题。我们表明，具有复发性神经网络的双重评论家Deep-RL可以使用仅范围数据和相对定位来改善Huauvs的导航性能。我们的深-RL方法通过通过不同的模拟场景对学习的扎实概括，实现了更好的导航和过渡能力，表现优于先前的方法。

深钢筋学习中的确定性和随机技术已成为改善运动控制和各种机器人的决策任务的有前途的解决方案。先前的工作表明，这些深-RL算法通常可以应用于一般的移动机器人的无MAP导航。但是，他们倾向于使用简单的传感策略，因为已经证明它们在高维状态空间（例如基于图像的传感的空间）方面的性能不佳。本文在执行移动机器人无地图导航的任务时，对两种深-RL技术 - 深确定性政策梯度（DDPG）和软参与者（SAC）进行了比较分析。我们的目标是通过展示神经网络体系结构如何影响学习本身的贡献，并根据每种方法的航空移动机器人导航的时间和距离提出定量结果。总体而言，我们对六个不同体系结构的分析强调了随机方法（SAC）更好地使用更深的体系结构，而恰恰相反发生在确定性方法（DDPG）中。

强化学习（RL）通过原始像素成像和连续的控制任务在视频游戏中表现出了令人印象深刻的表现。但是，RL的性能较差，例如原始像素图像，例如原始像素图像。人们普遍认为，基于物理状态的RL策略（例如激光传感器测量值）比像素学习相比会产生更有效的样品结果。这项工作提出了一种新方法，该方法从深度地图估算中提取信息，以教授RL代理以执行无人机导航（UAV）的无地图导航。我们提出了深度模仿的对比度无监督的优先表示（DEPTH-CUPRL），该表示具有优先重播记忆的估算图像的深度。我们使用RL和对比度学习的组合，根据图像的RL问题引发。从无人驾驶汽车（UAV）对结果的分析中，可以得出结论，我们的深度cuprl方法在无MAP导航能力中对决策和优于最先进的像素的方法有效。

本文介绍了一种新型深度加强基于基于深度加强学习的3D Fapless导航系统（无人机）。我们提出了一个简单的学习系统，而不是使用一种简单的学习系统，该系统仅使用来自距离传感器的一些稀疏范围数据来训练学习代理。我们基于我们对两种最先进的双重评论家深度RL模型的方法：双延迟深度确定性政策梯度（TD3）和软演员 - 评论家（SAC）。我们表明，我们的两种方法可以基于深度确定性政策梯度（DDPG）技术和Bug2算法来胜过一种方法。此外，我们基于经常性神经网络（RNNS）的新的深度RL结构优于用于执行移动机器人的FAPLESS导航的当前结构。总体而言，我们得出结论，基于双重评论评价的深度RL方法与经常性神经网络（RNNS）更适合进行熔化的导航和避免无人机。

This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

近年来，出于计算机视觉目的，将图像传输到远程服务器的传输急剧增加。在许多应用程序（例如监视）中，图像主要是用于自动分析的，并且很少被人类看到。在这种情况下，使用传统的压缩在比特率方面效率低下，这可能是由于关注基于人类的失真指标。因此，重要的是创建特定的图像编码方法，以供人类和机器联合使用。创建这种编解码器的机器侧的一种方法是在深神经网络中执行某些中间层执行机器任务的功能匹配。在这项工作中，我们探讨了用于培训人类和机器可学习的编解码器时所使用的层选择的效果。我们证明，使用数据处理不平等，从速率延伸的意义上讲，更深层的匹配特征是可取的。接下来，我们通过重新培训现有的可扩展人机编码模型来从经验上确认我们的发现。在我们的实验中，我们显示了这种可扩展模型的人类和机器方面的权衡，并讨论了在这方面使用更深层进行训练的好处。