深层生成模型有可能从根本上改变我们创建高保真数字内容的方式，但通常很难控制。提示生成模型是一个有希望的最新发展，原则上，最终用户可以创造性地利用零击和几乎没有学习的学习来将新任务分配给AI Ad-Hoc，只需将其写下即可。但是，对于大多数最终用户而言，编写有效提示目前主要是试验和错误过程。为了解决这个问题，我们讨论了使用促使人类互动的新范式的交互式创意应用程序的关键机会和挑战。根据我们的分析，我们为支持提示的用户界面提出了四个设计目标。我们用混凝土UI设计草图说明了这些内容，重点是创意写作的用例。HCI和AI的研究社区可以将这些作为起点，以开发足够的用户界面，以供能够零和少数学习的模型。

我们提出了一个文本编辑器，以帮助用户计划，结构并反思其写作过程。它使用自动文本摘要提供了不断更新的段落摘要作为边缘注释。摘要级别范围从全文到选定的（中央）句子，一直到关键字的集合。为了了解用户在写作过程中如何与该系统进行交互，我们进行了两项用户研究（n = 4和n = 8），人们在其中写了有关给定主题和文章的分析文章。作为关键发现，这些摘要使用户对他们的写作有了外部视角，并帮助他们修改了草稿段落的内容和范围。人们进一步使用该工具快速获得文本概述，并制定了整合自动摘要中见解的策略。从更广泛的角度来看，这项工作探索并突出了为作家设计AI工具的价值，其自然语言处理（NLP）功能超出了直接文本生成和更正。

神经语言模型有可能支持人类写作。但是，关于其整合和对写作和产出的影响仍然存在问题。为了解决这个问题，我们设计并比较了两个用于写作的用户界面与移动设备上的AI，这些用户界面操纵主动性和控制级别：1）使用连续生成的文本编写，AI添加了逐字文字和用户转向。 2）编写建议，AI建议短语和用户从列表中选择。在监督的在线研究（n = 18）中，参与者使用了这些原型和无AI的基线。我们收集了触摸互动，关于灵感和作者的评分以及访谈数据。有了AI的建议，人们的写作不那么积极，但觉得他们是作者。连续生成的文本减少了这种感知的作者身份，但编辑行为增加了。在这两种设计中，AI都会增加文本长度，并被认为会影响措辞。我们的发现为UI设计决策对用户体验和共同创造系统的产出的影响增加了新的经验证据。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.

Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.