背景：荧光血管造影表现出非常有希望的结果，可以通过允许外科医生选择最佳灌注组织来减少吻合泄漏。但是，由于存在不同外科医生之间的显着差异，因此对荧光信号的主观解释仍然阻碍了该技术的广泛应用。我们的目的是开发一种人工智能算法，以基于术中荧光血管造影数据将结肠组织分类为“灌注”或“不灌注”。方法：在第三纪转介中心的荧光血管造影视频数据集中对具有重新结构结构的分类模型进行了训练。与结肠的荧光和非荧光段相对应的框架用于训练分类算法。进行了使用训练集未使用的患者的框架进行验证，包括使用相同的设备和使用其他相机收集的数据收集的数据。计算了性能指标，并用于进一步分析输出。根据组织分类确定了决策边界。结果：卷积神经网络已成功地对790名患者进行了1790帧的培训，并在14例患者的24帧中进行了验证。训练集的准确性为100％，验证集为80％。训练集的召回和精度分别为100％和100％，验证集分别为68.8％和91.7％。结论：具有高度准确性的术中荧光血管造影的自动分类是可能的，并且允许自动决策边界识别。这将使外科医生能够标准化荧光血管造影技术。基于Web的应用程序可用于部署该算法。

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

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.

Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli. For a deeper understanding of this alignment, it is important to understand the alignment between the detailed processing of linguistic information by the human brain versus language models. In NLP, linguistic probing tasks have revealed a hierarchy of information processing in neural language models that progresses from simple to complex with an increase in depth. On the other hand, in neuroscience, the strongest alignment with high-level language brain regions has consistently been observed in the middle layers. These findings leave an open question as to what linguistic information actually underlies the observed alignment between brains and language models. We investigate this question via a direct approach, in which we eliminate information related to specific linguistic properties in the language model representations and observe how this intervention affects the alignment with fMRI brain recordings obtained while participants listened to a story. We investigate a range of linguistic properties (surface, syntactic and semantic) and find that the elimination of each one results in a significant decrease in brain alignment across all layers of a language model. These findings provide direct evidence for the role of specific linguistic information in the alignment between brain and language models, and opens new avenues for mapping the joint information processing in both systems.

This paper aims to provide an unsupervised modelling approach that allows for a more flexible representation of text embeddings. It jointly encodes the words and the paragraphs as individual matrices of arbitrary column dimension with unit Frobenius norm. The representation is also linguistically motivated with the introduction of a novel similarity metric. The proposed modelling and the novel similarity metric exploits the matrix structure of embeddings. We then go on to show that the same matrices can be reshaped into vectors of unit norm and transform our problem into an optimization problem over the spherical manifold. We exploit manifold optimization to efficiently train the matrix embeddings. We also quantitatively verify the quality of our text embeddings by showing that they demonstrate improved results in document classification, document clustering, and semantic textual similarity benchmark tests.

Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted in several ways and may have multiple distinct answers. In this paper, we address multi-answer retrieval which entails retrieving passages that can capture majority of the diverse answers to the question. We propose a re-ranking based approach using Determinantal point processes utilizing BERT as kernels. Our method jointly considers query-passage relevance and passage-passage correlation to retrieve passages that are both query-relevant and diverse. Results demonstrate that our re-ranking technique outperforms state-of-the-art method on the AmbigQA dataset.

多种业务场景需要从结构化输入数据中自动生成描述性的人类可读文本。因此，已经开发了针对各种下游任务的事实到文本的系统主要是由于相关数据集的高可用性。直到最近，提出了跨语言事实与文本（XF2T）的问题，该问题是针对多种语言的生成，以及一个数据集，Xalign的八种语言。但是，实际上XF2T生成问题没有严格的工作。我们使用另外四种语言的注释数据扩展了Xalign数据集：旁遮普语，马拉雅拉姆语，阿萨姆语和Oriya。我们在扩展的多语言数据集上使用基于变压器的流行文本生成模型进行了广泛的研究，我们称之为Xalignv2。此外，我们研究了不同文本生成策略的性能：预处理，事实感知的嵌入和结构意识的输入编码的多种变化。我们的广泛实验表明，使用具有结构意识的输入编码的事实感知的嵌入式的多语言MT5模型可以平均在十二种语言中获得最佳结果。我们将代码，数据集和模型公开可用，并希望这将有助于进一步在此关键领域进行进一步的研究。

在线新闻和信息来源是方便且可访问的方法来了解当前问题。例如，超过3亿人在全球Twitter上参与帖子，这提供了传播误导信息的可能性。在许多情况下，由于虚假新闻，已经犯了暴力犯罪。这项研究介绍了Covidmis20数据集（Covid-19误导2020数据集），该数据集由2月至2020年7月收集的1,375,592条推文组成。Covidmis20可以自动更新以获取最新新闻，并在以下网址公开，网址为：HTTPPS://GITHUB.COM./github.com./github.com。/一切guy/covidmis20。这项研究是使用BI-LSTM深度学习和合奏CNN+BI-GRU进行假新闻检测进行的。结果表明，测试精度分别为92.23％和90.56％，集合CNN+BI-GRU模型始终提供了比BI-LSTM模型更高的精度。

2022年，乌克兰遭受了入侵，随着时间的流逝和地理位置的急剧影响。本文研究了使用分析以及基于区域的网络模型对持续中断对交通行为的影响。该方法是一种数据驱动的方法，该方法利用了在进化算法框架内获得的旅行时间条件，该算法框架在基于流量分配的自动化过程中渗透了原始过程的需求值。由于实施的自动化，可以为多个城市近似众多的每日模型。本文与先前发表的核心方法的新颖性包括一项分析，以确保获得的数据合适，因为由于持续的破坏，某些数据源被禁用。此外，新颖性包括将分析与中断时间表的直接联系，以新的方式检查相互作用。最后，确定了特定的网络指标，这些指标特别适合概念化冲突中断对交通网络条件的影响。最终目的是建立过程，概念和分析，以促进快速量化冲突情景的交通影响的更广泛的活动。

我们提出了一种使用神经网络反馈控制器对封闭环控制系统进行状态空间探索的新技术。我们的方法涉及近似闭环动力学轨迹的灵敏度。使用这样的近似器和系统模拟器，我们提出了一种指导状态空间探索方法，该方法可以生成在指定时间访问目标状态附近的轨迹。我们提出了一个理论框架，该框架确定我们的方法将产生一系列轨迹，该轨迹将到达目标状态的合适邻居。我们通过不同配置的神经网络反馈控制器对各种系统进行彻底评估。我们的表现优于早期的状态空间探索技术，并在质量（解释性）和性能（收敛速度）方面取得了显着改善。最后，我们采用算法来伪造一类时间逻辑规范，评估其针对最先进的伪造工具的绩效，并表现出其在补充现有的伪造算法方面的潜力。