21世纪的现代旅游面临着许多挑战。这些挑战之一是太空有限地区的游客数量迅速增长，例如历史城市中心，博物馆或地理瓶颈，例如狭窄的山谷。在这种情况下，对特定领域内的旅游量和旅游流程的正确准确预测对于游客管理任务，例如游客流量控制和预防人满为患至关重要。静态流量控制方法，例如限制对热点或使用常规低级控制器的访问，无法解决问题。在本文中，我们通过使用旅游区提供的可用粒状数据，并将结果与​​Arima进行比较，并将结果与​​Arima进行比较经典统计方法。我们的结果表明，与Arima方法相比，深度学习模型可以产生更好的预测，同时具有更快的推理时间和能够结合其他输入功能。

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

Common disabilities like stroke and spinal cord injuries may cause loss of motor function in hands. They can be treated with robot assisted rehabilitation techniques, like continuously opening and closing the hand with help of a robot, in a cheaper, and less time consuming manner than traditional methods. Hand exoskeletons are developed to assist rehabilitation, but their bulky nature brings with it certain challenges. As soft robots use elastomeric and fabric elements rather than heavy links, and operate with pneumatic, hydraulic or tendon based rather than traditional rotary or linear motors, soft hand exoskeletons are deemed a better option in relation to rehabilitation.

Modal verbs (e.g., "can", "should", or "must") occur highly frequently in scientific articles. Decoding their function is not straightforward: they are often used for hedging, but they may also denote abilities and restrictions. Understanding their meaning is important for various NLP tasks such as writing assistance or accurate information extraction from scientific text. To foster research on the usage of modals in this genre, we introduce the MIST (Modals In Scientific Text) dataset, which contains 3737 modal instances in five scientific domains annotated for their semantic, pragmatic, or rhetorical function. We systematically evaluate a set of competitive neural architectures on MIST. Transfer experiments reveal that leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST. Our corpus analysis provides evidence that scientific communities differ in their usage of modal verbs, yet, classifiers trained on scientific data generalize to some extent to unseen scientific domains.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

Recent developments in the methods of explainable AI (XAI) methods allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton-proton collisions at the Large Hadron Collider (LHC). We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how feature correlations impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. By incorporating observations from the interpretability studies, we obtain state-of-the-art top tagging performance from augmented implementation of existing network

机器学习（ML）为生物处理工程的发展做出了重大贡献，但其应用仍然有限，阻碍了生物过程自动化的巨大潜力。用于模型构建自动化的ML可以看作是引入另一种抽象水平的一种方式，将专家的人类集中在生物过程开发的最认知任务中。首先，概率编程用于预测模型的自动构建。其次，机器学习会通过计划实验来测试假设并进行调查以收集信息性数据来自动评估替代决策，以收集基于模型预测不确定性的模型选择的信息数据。这篇评论提供了有关生物处理开发中基于ML的自动化的全面概述。一方面，生物技术和生物工程社区应意识到现有ML解决方案在生物技术和生物制药中的应用的限制。另一方面，必须确定缺失的链接，以使ML和人工智能（AI）解决方案轻松实施在有价值的生物社区解决方案中。我们总结了几个重要的生物处理系统的ML实施，并提出了两个至关重要的挑战，这些挑战仍然是生物技术自动化的瓶颈，并减少了生物技术开发的不确定性。没有一个合适的程序；但是，这项综述应有助于确定结合生物技术和ML领域的潜在自动化。

多实施学习（MIL）被广泛用于对病理整体幻灯片图像（WSIS）的计算机辅助解释，以解决缺乏像素或贴片的注释。通常，这种方法直接应用“自然图像驱动”的MIL算法，该算法忽略了WSIS的多尺度（即金字塔）性质。现成的MIL算法通常部署在单个WSIS（例如20x放大倍率）上，而人类病理学家通常以多尺度的方式汇总全球和局部模式（例如，通过放大不同大型）。在这项研究中，我们提出了一种新型的跨尺度注意机制，以明确地将尺度间相互作用汇总到单个MIL网络的克罗恩病（CD）（CD），这是炎症性肠病的一种形式。本文的贡献是两个方面：（1）提出了一种跨尺度注意机制，以从不同分辨率的多尺度相互作用汇总特征； （2）生成差异多尺度注意的可视化，以定位可解释的病变模式。通过训练来自20名CD患者的约250,000 H＆E染色的上升结肠（AC）斑块，在不同尺度上训练30个健康对照样品，我们的方法在曲线下（AUC）得分为0.8924，与基线模型相比达到0.8924。官方实施可在https://github.com/hrlblab/cs-mil上公开获得。

唇裂是一种先天性异常，需要专家手术修复。外科医生必须具有丰富的经验和理论知识才能进行手术，并且已经提出了人工智能（AI）方法来指导外科医生改善手术结局。如果可以使用AI来预测修复的唇唇的外观，那么外科医生可以将其用作辅助手术技术来调整其手术技术并改善结果。为了在保护患者隐私时探索这个想法的可行性，我们提出了一种基于深度学习的图像镶嵌方法，该方法能够覆盖唇裂，并产生唇彩，而无需裂缝。我们的实验是在两个现实世界中的裂口数据集上进行的，并由专家cleft唇外科医生评估，以证明该方法的可行性。

对于许多任务，基于变压器的体系结构已经实现了最新的结果，从而导致实践从使用特定于任务的架构到预先训练的语言模型的微调。持续的趋势包括具有越来越多的数据和参数的培训模型，这需要大量资源。它导致了强有力的搜索，以提高基于仅针对英语评估的算法和硬件改进的算法和硬件改进。这引发了有关其可用性的疑问，当应用于小规模的学习问题时，对于资源不足的语言任务，有限的培训数据可用。缺乏适当尺寸的语料库是应用数据驱动和转移学习的方法的障碍。在本文中，我们建立了致力于基于变压器模型的可用性的最新努力，并建议评估这些改进的法语表现，而法语的效果很少。我们通过通过数据增强，超参数优化和跨语性转移来调查各种培训策略来解决与数据稀缺有关的不稳定。我们还为法国弗拉伯特（Fralbert）引入了一种新的紧凑型模型，该模型在低资源环境中被证明具有竞争力。