量子物理实验产生有趣的现象，例如干扰或纠缠，这些现象是许多未来量子技术的核心特性。量子实验的设置结构与其纠缠特性之间的复杂关系对于量子光学的基本研究至关重要，但很难直观地理解。我们提出了量子光学实验的深层生成模型，其中在量子光学实验设置的数据集中对变异自动编码器进行了训练。在一系列计算实验中，我们研究了我们的量子光学变量自动编码器（Qovae）的学识渊博表示及其对量子光学世界的内部理解。我们证明Qovae学习了量子光学实验的可解释表示以及实验结构与纠缠之间的关系。我们显示，Qovae能够为高度纠缠的量子状态生成具有匹配其训练数据的特定分布的新型实验。 Qovae可以学会生成特定的纠缠状态，并有效地搜索产生高度纠缠量子状态的实验空间。重要的是，我们能够解释Qovae如何结构其潜在空间，从而找到可以从量子物理学来解释的好奇模式。结果表明，我们如何在复杂的科学领域中使用和理解深层生成模型的内部表示。 Qovae和我们调查的见解可以立即应用于其他物理系统。

Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it. As such, whether we soothe or flame an emotion is not inconsequential. Recent work in conversational AI has focused on responding empathetically to users, validating and soothing their emotions without a real basis. This AI-aided emotional regulation can have negative consequences for users and society, tending towards a one-noted happiness defined as only the absence of "negative" emotions. We argue that we must carefully consider whether and how to respond to users' emotions.

This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions.

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community.

We analyze the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several particularities such as the concentric circular ring shape of the objects and high precision requirements due to which existing methods don't perform sufficiently well. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD.

We present AI-SDC, an integrated suite of open source Python tools to facilitate Statistical Disclosure Control (SDC) of Machine Learning (ML) models trained on confidential data prior to public release. AI-SDC combines (i) a SafeModel package that extends commonly used ML models to provide ante-hoc SDC by assessing the vulnerability of disclosure posed by the training regime; and (ii) an Attacks package that provides post-hoc SDC by rigorously assessing the empirical disclosure risk of a model through a variety of simulated attacks after training. The AI-SDC code and documentation are available under an MIT license at https://github.com/AI-SDC/AI-SDC.

The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-solving skills at younger ages. Conversational AI systems have started maturing to a point where they could play a significant role in helping students learn fundamental math concepts. This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education. The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions. We discuss our efforts to improve the SDS pipeline built for math learning, for which we explore utilizing MathBERT representations for potential enhancement to the Natural Language Understanding (NLU) module. We perform an end-to-end evaluation using real-world deployment outputs from the Automatic Speech Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to understand how error propagation affects the overall performance in real-world scenarios.

本文介绍了预测媒体难忘性的Mediaeval 2021，这是今年第4版的任务，因为短期和长期视频难忘性的预测仍然是一个具有挑战性的任务。在2021年，使用两个视频数据集：第一，TRECVID 2019视频到文本数据集的子集;其次，Memento10K数据集是为了提供探索交叉数据集泛化的机会。另外，介绍了基于脑电图（EEG）的预测导频子任务。在本文中，我们概述了任务的主要方面，并描述了参与者提交的数据集，评估指标和要求。

使用公共可用链路的集合，平均每周6秒的视频剪辑，每次，1,275用户多次手动注释每个视频，以指示视频的长期和短期难忘性。注释作为在线记忆游戏的一部分，并测量了参与者在显示视频的集合时先前召回过视频的能力。在前几分钟内看到的视频进行识别任务，以进行短期令人难忘，以便在前24到72小时内进行长期难忘。数据包括每个视频的每个识别的反应时间。与每个视频相关联是文本描述（标题）以及应用于从每个视频中提取的3帧的图像级别功能集合（开始，中间和结束）。还提供了视频级功能。数据集在视频难忘任务中使用，作为2020年的Mediaeval基准的一部分。