Students' ability to ask curious questions is a crucial skill that improves their learning processes. To train this skill, previous research has used a conversational agent that propose specific cues to prompt children's curiosity during learning. Despite showing pedagogical efficiency, this method is still limited since it relies on generating the said prompts by hand for each educational resource, which can be a very long and costly process. In this context, we leverage the advances in the natural language processing field and explore using a large language model (GPT-3) to automate the generation of this agent's curiosity-prompting cues to help children ask more and deeper questions. We then used this study to investigate a different curiosity-prompting behavior for the agent. The study was conducted with 75 students aged between 9 and 10. They either interacted with a hand-crafted conversational agent that proposes "closed" manually-extracted cues leading to predefined questions, a GPT-3-driven one that proposes the same type of cues, or a GPT-3-driven one that proposes "open" cues that can lead to several possible questions. Results showed a similar question-asking performance between children who had the two "closed" agents, but a significantly better one for participants with the "open" agent. Our first results suggest the validity of using GPT-3 to facilitate the implementation of curiosity-stimulating learning technologies. In a second step, we also show that GPT-3 can be efficient in proposing the relevant open cues that leave children with more autonomy to express their curiosity.

近年来，大型语言模型（LLMS）在自然语言产生中表现出了令人印象深刻的实力。提高发电多样性的一种常见做法是从模型中采样多个输出。但是，缺乏一种简单且可靠的方式来从这些随机样品中选择最佳输出。作为一个案例研究，在问题产生的背景下，我们提出了两种基于迅速的方法，以从一组LLM生成的候选人中选择高质量问题。我们的方法在1）限制下起作用，一个黑框（不可修改）问题生成模型和2）缺乏访问人类宣传的参考文献 - 这两者都是现实世界中LLMS的现实局限性。通过自动和人类评估，我们从经验上证明，我们的方法可以有效地选择比贪婪的生成更高质量的问题。

Consider $n$ points independently sampled from a density $p$ of class $\mathcal{C}^2$ on a smooth compact $d$-dimensional sub-manifold $\mathcal{M}$ of $\mathbb{R}^m$, and consider the generator of a random walk visiting these points according to a transition kernel $K$. We study the almost sure uniform convergence of this operator to the diffusive Laplace-Beltrami operator when $n$ tends to infinity. This work extends known results of the past 15 years. In particular, our result does not require the kernel $K$ to be continuous, which covers the cases of walks exploring $k$NN-random and geometric graphs, and convergence rates are given. The distance between the random walk generator and the limiting operator is separated into several terms: a statistical term, related to the law of large numbers, is treated with concentration tools and an approximation term that we control with tools from differential geometry. The convergence of $k$NN Laplacians is detailed.

Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: they can use the length differences between texts of different classes as a prominent classification feature. This has the effect of producing models that are brittle and fragile to concept drift, can provide misleading performances and are trivially explainable regardless of text content. This paper illustrates the problem using synthetic and real-world data and provides a simple solution using weight decay regularization.

Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.

本文考虑了在线配置器通常使用的一组替代方案中学习用户偏好的任务。在许多设置中，学习者在过去的互动过程中只有一组选定的替代方案。Fargier等。[2018]提出了一种在这种环境中学习用户偏好模型的方法，该模型对先前选择的替代方案进行了排名尽可能高；以及在这种情况下学习的算法，是一种特定的偏好模型：词典偏好树（LP-Trees）。在本文中，我们研究了与这种方法相关的复杂性理论问题。我们对学习LP-Tree的样本复杂性给出了上限，这在属性数量上是对数。我们还证明，计算最小化经验风险的LP树当仅限于线性LP-Trees的类别时，可以在多项式时间内完成。

推荐系统已被广泛用于各种领域，例如音乐，电影，电子购物。等等。在大多避免数字化之后，由于流行病而最近达到了技术转折点，使在线销售显着增长，并提供定量的定量性。有关艺术家和艺术品的在线数据。在这项工作中，我们提出了一个基于内容的推荐系统，依靠艺术品和艺术家的上下文元数据的图像。我们收集和注释的艺术品提供了高级和特定于艺术的信息，以创建一个完全独特的数据库，该数据库用于培训我们的模型。有了这些信息，我们在艺术品之间构建了一个接近图。同样，我们使用NLP技术来表征艺术家的实践，并从展览和其他活动历史中提取信息，以在艺术家之间创建近距离图。图形分析的力量使我们能够基于艺术品和艺术家的视觉和上下文信息的结合提供艺术品推荐系统。经过一组艺术专家的评估，与他们的专业评估相比，我们的平均最终评分为75％。

我们提出了一种无标记的性能捕获方法，该方法从稀疏采样的未跟踪3D点云的稀疏采样序列中计算随时间变形的参与者变形的时间相干4D表示。我们的方法通过以前的时空运动来进行潜在优化。最近，已经引入了任务通用运动先验，并提出了基于单个潜在代码的人类运动的连贯表示，并具有简短序列和给定时间对应关系的令人鼓舞的结果。将这些方法扩展到没有对应的较长序列几乎是直接的。一种潜在代码证明，由于可能的倒置姿势配件，因此对长期可变性的编码效率低下，而潜在空间优化将非常容易受到错误的本地最小值。我们通过学习一个运动来解决这两个问题，该动作将4D人体运动序列编码为一系列潜在的原语，而不是一个潜在的代码。我们还提出了一个附加的映射编码器，该编码器将点云直接投入到学习的潜在空间中，以在推理时提供潜在表示的良好初始化。我们从潜在空间进行的时间解码是隐式和连续的，可以通过时间分辨率提供灵活性。我们通过实验表明我们的方法优于最先进的运动先验。

在本文中，引入了传输分配系统灵活性市场，其中系统运营商（SOS）共同采购不同系统的灵活性，以满足他们使用公共市场的需求（平衡和拥堵管理）。然后，这种共同的市场是作为一个合作游戏，旨在识别参与SOS之间联合采购灵活性的成本稳定有效地分配，以激励其合作。然后在数学上证明了这场比赛的核心的非空虚，暗示了游戏的稳定性以及SOS之间的合作自然而然的激励。然后引入了几种成本分配机制，同时表征了它们的数学特性。专注于互连系统的数值结果（由IEEE 14总线传输系统和MATPower 18-Bus，69总线和141母线分布系统组成）展示了系统范围内灵活性采购成本的合作诱导的降低，在各种成本分配方法下识别不同的SOS所承受的不同成本。

休息状态功能磁共振成像（FMRI）是一种强大的成像技术，用于研究UTETO脑功能的功能发展。然而，胎儿的不可预测和过度运动具有有限的临床应用，因为它导致可以系统地改变了功能连接模式的大量信号波动。以前的研究专注于在大胎儿头部运动的情况下的运动参数的准确估计，并在每个时间点使用3D单步插值方法来恢复无动态的FMRI图像。这并不保证重建的图像对应于给定获取的数据的FMRI时间序列的最小错误表示。在这里，我们提出了一种基于胎儿FMRI散射切片的四维迭代重建的新技术。在一组真正的临床FMRI胎儿上定量评估所提出的方法的准确性。结果表明与传统的3D插值方法相比，重建质量的改进。