在本文中，我们介绍了一个专家注册的数据集，用于检测上市公司提出的现实环境主张。我们训练和发布基线模型，用于使用此新数据集检测环境主张。我们进一步预测了数据集的潜在应用：我们使用微调模型来检测2012年至2020年之间每季度收入电话的回答部分中提出的环境主张 - 我们发现自从巴黎协议中的《巴黎协定》中的环境要求稳步增加2015。

近年来，大型的语言模型（LM）彻底改变了自然语言处理（NLP）的领域。但是，虽然对通用语言进行了预测，但已证明对通用语言非常有效，但已经观察到利基语言会带来问题。特别是，与气候相关的文本包括常见LM无法准确表示的特定语言。我们认为，当今LMS的这种缺点限制了现代NLP对与气候相关文本的文本处理的广泛领域的适用性。作为一种补救措施，我们提出了Climatebert，这是一种基于变压器的语言模型，该模型在超过160万段的气候相关文本中进一步审议，这些文本涉及各种来源，例如普通新闻，研究文章和公司的气候报告。我们发现，在蒙版语言模型目标上，ClimateBertleads提高了46％的改善，这反过来又导致各种与气候相关的下游任务（如文本分类，情感分析和事实检查）的错误率降低了3.57％至35.71％。

Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required class when generating new images. In the case of images from Imaging Atmospheric Cherenkov Telescopes (IACTs), an important property is the total brightness of all image pixels (image size), which is in direct correlation with the energy of primary particles. We used a cGAN technique to generate images similar to whose obtained in the TAIGA-IACT experiment. As a training set, we used a set of two-dimensional images generated using the TAIGA Monte Carlo simulation software. We artificiallly divided the training set into 10 classes, sorting images by size and defining the boundaries of the classes so that the same number of images fall into each class. These classes were used while training our network. The paper shows that for each class, the size distribution of the generated images is close to normal with the mean value located approximately in the middle of the corresponding class. We also show that for the generated images, the total image size distribution obtained by summing the distributions over all classes is close to the original distribution of the training set. The results obtained will be useful for more accurate generation of realistic synthetic images similar to the ones taken by IACTs.

期刊影响因素（JIF）通常等同于期刊质量和提交给该期刊的论文的同行评审质量。我们通过分析提交给1,644家医学和生命科学期刊的10,000个同行评审报告，研究了同行评审与JIF的内容之间的关联。两名研究人员手工编码了2,000个句子的随机样本。然后，我们训练了机器学习模型，以将所有187,240个句子分类为贡献或不为内容类别做出贡献。我们研究了JIF DICILES定义的十组期刊与使用线性混合效应模型的同行评审的内容之间的关联，并调整了评论的长度。 JIF的范围为0.21至74.70。同行评审长度从最低（单词中位数185）增加到JIF组（387个单词）。分配给不同内容类别的句子的比例甚至在JIF组中也有很大变化。为了彻底，与最低的JIF组相比，关于“材料和方法”的句子在最高的JIF期刊中更为普遍（7.8个百分点； 95％CI 4.9至10.7％）。 “演示和报告”的趋势朝相反的方向发展，最高的JIF期刊对此类内容的重视程度较小（差异-8.9％； 95％CI -11.3至-6.5％）。为了有助于，对更高的JIF期刊的评论更少关注“建议和解决方案”，而提供的示例少于较低的影响因素期刊。对于其他内容类别而言，没有，或者只有很小的差异。总之，在讨论使用的方法时，在提出解决方案和提供示例方面，在讨论所使用的方法但较小的帮助时，较高的JIF期刊的同行评审往往更为透彻。差异是适度的，可变性很高，表明JIF是对单个手稿的同伴评论质量的不良预测指标。

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.

Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation. Methods: We present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level. Results: We validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below 1{\deg}. We further illustrate how TAToo may be used in a surgical navigation setting. Conclusion: We present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.

Selecting the number of topics in LDA models is considered to be a difficult task, for which alternative approaches have been proposed. The performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be implemented to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for several alternative settings, varying with respect to the number of topics, the number of documents and the size of documents in the corpora. Performance is measured using different criteria which take into account the correct number of topics, but also whether the relevant topics from the DGPs are identified. Practical recommendations for LDA model selection in applications are derived.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.