对经济决策中情绪的作用，越来越感兴趣。然而，大多数关于该主题的研究都集中在积极和消极的效果上。定罪叙事理论（CNT）在真实世界决策中的核心逼近和避免情绪（驱动行动），并认为它更好地捕捉到金融市场中的情绪。这项研究介绍了心理学和机器学习，介绍了在基本含义的基本和负面情绪中区分方法和避免的新技术。它通过比较以前构造的单词列表来实现这一点，以捕获文本数据中的这些概念，横跨大量的语义功能。结果表明，特别是避免良好地定义为单独的情绪，这是评价/认知和自然的行动导向。根据这些功能炼制避免词列表改善了宏观经济模型，表明他们捕捉到避免的本质，并在驾驶真实的经济决策方面发挥着至关重要的作用。

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.

我们在GPU上实现了一种信任区域方法，用于使用称为JAX的新的深度学习Python库，用于非线性最小二乘曲线曲线拟合问题。我们的开源软件包JaxFit适用于无约束和约束曲线拟合问题，并允许仅在Python中定义拟合功能 - 而无需对GPU或CUDA编程的任何专业知识。由于JaxFit在GPU上运行，尽管非常易于使用，但它比基于CPU的库甚至其他基于GPU的库快得多。此外，由于JAX的深度学习基础，Jaxfit的信任区域算法中的Jacobian是通过自动分化计算的，而不是使用衍生近似值或要求用户定义拟合函数的部分导数。

对于任何负责满足人类价值观或偏好的人工智能而言，平衡多个竞争和冲突目标是一项重要任务。冲突既是由于具有竞争价值的个体之间的错位而引起的，也是一个人之间的冲突价值体系之间的不对准。从规避损失的原则开始，我们设计了一组软目标决策的软最大化功能。在一组先前开发的环境中，板凳标记了这些功能，我们发现一种新的方法特别是“分裂功能exp-log averver over over over over over”（SFELLA），学习的速度比最先进的阈值对准目标方法\引用{vamplew_potential的_2021}对其进行了测试的四个任务中的三个，并在学习后达到了相同的最佳性能。 SFELLA还显示出相对鲁棒性的改善，以抵抗客观量表的变化，这可能突出了涉及环境动态分布变化的优势。必须从预印本中省略进一步的工作，但是在最终发布的版本中，我们将进一步将SFELLA与多目标奖励指数（更多）方法进行比较，表明SFELLA在简单的先前描述的觅食任务中的性能类似，但是，在经纪人工作时没有耗尽的新资源的经过修改的觅食环境中，SFELLA收集了更多的新资源，而在旧资源方面几乎没有成本。总体而言，我们发现SFELLA对于避免有时以阈值方法出现的问题而有用，并且在保留其保守的，避开逆转损失的激励结构的同时，比更多的奖励响应响应。

语言是协调问题的强大解决方案：他们提供了稳定的，有关我们所说的单词如何对应于我们头脑中的信仰和意图的共同期望。然而，在变量和非静止社会环境中的语言使用需要语言表征来灵活：旧词在飞行中获取新的临时或合作伙伴特定含义。在本文中，我们介绍了柴（通过推理的连续分层适应），一个分层贝叶斯的协调理论和会议组织，旨在在这两个基本观察之间调和长期张力。我们认为，沟通的中央计算问题不仅仅是传输，如在经典配方中，而是在多个时间尺度上持续学习和适应。合作伙伴特定的共同点迅速出现在数型互动中的社会推论中，而社群范围内的社会公约是稳定的前锋，这些前锋已经抽象出与多个合作伙伴的互动。我们展示了新的实证数据，展示了我们的模型为多个现象提供了对先前账户挑战的计算基础：（1）与同一合作伙伴的重复互动的更有效的参考表达的融合（2）将合作伙伴特定的共同基础转移到陌生人，并（3）交际范围的影响最终会形成。

在本文中，我们介绍了四种突出的恶意软件检测工具的科学评估，以帮助组织提出两个主要问题：基于ML的工具在多大程度上对以前和从未见过的文件进行了准确的分类？是否值得购买网络级恶意软件检测器？为了识别弱点，我们针对各种文件类型的总计3,536个文件（2,554或72 \％恶意，982或28 \％良性）测试了每个工具，包括数百个恶意零日，polyglots和apt-style-style style文件，在多个协议上交付。我们介绍了有关检测时间和准确性的统计结果，请考虑互补分析（一起使用多个工具），并提供了近期成本效益评估程序的两种新颖应用。尽管基于ML的工具在检测零日文件和可执行文件方面更有效，但基于签名的工具仍然是总体上更好的选择。两种基于网络的工具都与任何一种主机工具配对时都可以进行大量（模拟）节省，但两者在HTTP或SMTP以外的协议上都显示出较差的检测率。我们的结果表明，所有四个工具都具有几乎完美的精度但令人震惊的召回率，尤其是在可执行文件和Office文件以外的文件类型上 - 未检测到37％的恶意软件，包括所有Polyglot文件。给出了研究人员的优先事项，并给出了最终用户的外卖。

Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays [1]. NumPy is the primary array programming library for the Python language [2,3,4,5]. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves [6] and the first imaging of a black hole [7].Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

Cataloging the complex behaviors of dynamical systems can be challenging, even when they are well-described by a simple mechanistic model. If such a system is of limited analytical tractability, brute force simulation is often the only resort. We present an alternative, optimization-driven approach using tools from machine learning. We apply this approach to a novel, fully-optimizable, reaction-diffusion model which incorporates complex chemical reaction networks (termed "Dense Reaction-Diffusion Network" or "Dense RDN"). This allows us to systematically identify new states and behaviors, including pattern formation, dissipation-maximizing nonequilibrium states, and replication-like dynamical structures.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.