健壮的学习是科学机器学习（SCIML）的重要问题。文献中有几篇关于该主题的作品。但是，对方法的需求不断增加，可以同时考虑SCIML模型识别中涉及的所有不同不确定性组成部分。因此，这项工作提出了一种对SCIML的不确定性评估的综合方法，该方法还考虑了识别过程中涉及的几种不确定性来源。提出的方法中考虑的不确定性是缺乏理论和因果模型，对数据腐败或不完美的敏感性以及计算工作。因此，可以为SCIML领域中的不确定性感知模型提供总体策略。该方法通过案例研究验证，开发了用于聚合反应器的软传感器。结果表明，已识别的软传感器对于不确定性是可靠的，并以所提出的方法的一致性证实。

科学机器学习（SCIML）是对几个不同应用领域的兴趣越来越多的领域。在优化上下文中，基于SCIML的工具使得能够开发更有效的优化方法。但是，必须谨慎评估和执行实施优化的SCIML工具。这项工作提出了稳健性测试的推论，该测试通过表明其结果尊重通用近似值定理，从而确保了基于多物理的基于SCIML的优化的鲁棒性。该测试应用于一种新方法的框架，该方法在一系列基准测试中进行了评估，以说明其一致性。此外，将提出的方法论结果与可行优化的可行区域进行了比较，这需要更高的计算工作。因此，这项工作为保证在多目标优化中应用SCIML工具的稳健性测试提供了比存在的替代方案要低的计算努力。

味道是遵循社会趋势和行为的风味行业的焦点。新调味剂和分子的研究和开发在该领域至关重要。另一方面，自然风味的发展在现代社会中起着至关重要的作用。鉴于此，目前的工作提出了一个基于科学机器学习的新颖框架，以在风味工程和行业中解决新的问题。因此，这项工作带来了一种创新的方法来设计新的自然风味分子。评估了有关合成可及性，原子数以及与天然或伪天然产物的相似性的分子。

本文提出了我们为在葡萄牙语中自发和准备的语音和语音情感识别的共享任务自动语音识别（SE＆R 2022）的共同任务自动语音识别的努力。挑战的目的是考虑葡萄牙语的ASR研究，考虑到不同方言的准备和自发语音。我们的方法包括在域特异性方法中微调ASR模型，应用增益归一化和选择性噪声插入。提出的方法比可用的4个曲目中的3个曲目中提供的强大基线改进了

我们提出了一个新的框架，在增强的自然语言（TANL）之间的翻译，解决了许多结构化预测语言任务，包括联合实体和关系提取，嵌套命名实体识别，关系分类，语义角色标记，事件提取，COREREFED分辨率和对话状态追踪。通过培训特定于特定于任务的鉴别分类器来说，我们将其作为一种在增强的自然语言之间的翻译任务，而不是通过培训问题，而不是解决问题，而是可以轻松提取任务相关信息。我们的方法可以匹配或优于所有任务的特定于任务特定模型，特别是在联合实体和关系提取（Conll04，Ade，NYT和ACE2005数据集）上实现了新的最先进的结果，与关系分类（偶尔和默示）和语义角色标签（Conll-2005和Conll-2012）。我们在使用相同的架构和超参数的同时为所有任务使用相同的架构和超级参数，甚至在培训单个模型时同时解决所有任务（多任务学习）。最后，我们表明，由于更好地利用标签语义，我们的框架也可以显着提高低资源制度的性能。

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-toword structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results

This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents. The system, called Visconde, uses a three-step pipeline to perform the task: decompose, retrieve, and aggregate. The first step decomposes the question into simpler questions using a few-shot large language model (LLM). Then, a state-of-the-art search engine is used to retrieve candidate passages from a large collection for each decomposed question. In the final step, we use the LLM in a few-shot setting to aggregate the contents of the passages into the final answer. The system is evaluated on three datasets: IIRC, Qasper, and StrategyQA. Results suggest that current retrievers are the main bottleneck and that readers are already performing at the human level as long as relevant passages are provided. The system is also shown to be more effective when the model is induced to give explanations before answering a question. Code is available at \url{https://github.com/neuralmind-ai/visconde}.

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.