在本文中，我们提出了一种评估为策略的长期绩效提供了现实预期的自主交易策略的方法。此方法解决此方法解决了许多陷阱，目前甚至经历过多种软件开发人员和研究人员，更不用说购买这些产品的客户。我们展示了将我们的方法应用于几种着名的自主交易策略的结果，用于管理各种金融资产选择。结果表明，许多这些公布的策略远远不可靠的金融投资车辆。我们的方法暴露了建立可靠，长期策略的困难，并提供了一种通过建立最小期间和测试执行要求来选择最有前途的潜在策略的手段。有许多开发人员可以创建软件，以自主购买和销售金融资产，其中一些人在使用历史价格系列（通常称为Resolties）时仿真时具有很大的性能。尽管如此，当这些策略用于实际市场（或在培训或评估中使用的数据）时，它们通常会非常糟糕。该方法可用于评估潜在的策略。通过这种方式，该方法有助于判断您是否真的有一个很好的交易策略，或者您只是愚弄自己。

在人工智能区域中已经在人工智能区域进行了自主交易机器人。已经测试了许多AI技术，用于建立能够交易金融资产的自主代理。这些举措包括传统的神经网络，模糊逻辑，加固学习，而且还有更新的方法，如深神经网络和深度加强学习。许多开发人员声称在使用历史价格系列执行时，在模拟执行时，可以成功创建具有良好性能的机器人。然而，当这些机器人在真正的市场中使用时，通常它们在风险方面存在糟糕的表现并返回。在本文中，我们提出了一个名为MT5SE的开源框架，有助于开发，重新击退，实时测试和自主交易者的实际运作。我们使用MT5SE构建并测试了几个交易者。结果表明它可能有助于开发更好的交易者。此外，我们讨论了许多研究中使用的简单架构，并提出了一种替代的多层架构。这种架构将投资组合经理（PM）分开了两个主要问题：价格预测和资本分配。超过达到高精度，PM应该在正确的时候增加利润并减少损失。此外，价格预测高度依赖于资产的性质和历史，而资本分配仅依赖于分析师的预测性能和资产的相关性。最后，我们讨论了该地区的一些有前途的技术。

当使用基于视觉的方法对被占用和空的空地之间的单个停车位进行分类时，人类专家通常需要注释位置，并标记包含目标停车场中收集的图像的训练集，以微调系统。我们建议研究三种注释类型（多边形，边界框和固定尺寸的正方形），提供停车位的不同数据表示。理由是阐明手工艺注释精度和模型性能之间的最佳权衡。我们还调查了在目标停车场微调预训练型号所需的带注释的停车位数。使用PKLOT数据集使用的实验表明，使用低精度注释（例如固定尺寸的正方形），可以将模型用少于1,000个标记的样品微调到目标停车场。

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).

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.

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。

关于使用ML模型的一个基本问题涉及其对提高决策透明度的预测的解释。尽管已经出现了几种可解释性方法，但已经确定了有关其解释可靠性的一些差距。例如，大多数方法都是不稳定的（这意味着它们在数据中提供了截然不同的解释），并且不能很好地应对无关的功能（即与标签无关的功能）。本文介绍了两种新的可解释性方法，即Varimp和Supclus，它们通过使用局部回归拟合的加权距离来克服这些问题，以考虑可变重要性。 Varimp生成了每个实例的解释，可以应用于具有更复杂关系的数据集，而Supclus解释了具有类似说明的实例集群，并且可以应用于可以找到群集的较简单数据集。我们将我们的方法与最先进的方法进行了比较，并表明它可以根据几个指标产生更好的解释，尤其是在具有无关特征的高维问题中，以及特征与目标之间的关系是非线性的。