带有变异自动编码器（VAE）的学习分解表示通常归因于损失的正则化部分。在这项工作中，我们强调了数据与损失的重建项之间的相互作用，这是VAE中解散的主要贡献者。我们注意到，标准化的基准数据集的构建方式有利于学习似乎是分解的表示形式。我们设计了一个直观的对抗数据集，该数据集利用这种机制破坏了现有的最新分解框架。最后，我们提供了一种解决方案，可以通过修改重建损失来实现分离，从而影响VAES如何感知数据点之间的距离。

这项研究礼物活动融合的理念引入现有的姿势估计架构，以提高他们的预测能力。这是由上升现代机器学习架构发现更高层次的概念，并相信活动上下文的信息姿态估计的问题，一个有用的片段动机。要分析这个概念，我们利用现有的深度学习建筑和一个额外的1x1卷积保险丝活动信息到模型中增加它。我们在一个共同的姿势估计数据集进行评价和比较，并表现出性能改善了我们的基础模型，特别是在罕见的姿势和上通常难以关节。此外，我们执行烧蚀分析表明，性能的提升事实上确实从活动信息绘制。

放射线学使用定量医学成像特征来预测临床结果。目前，在新的临床应用中，必须通过启发式试验和纠正过程手动完成各种可用选项的最佳放射组方法。在这项研究中，我们提出了一个框架，以自动优化每个应用程序的放射线工作流程的构建。为此，我们将放射线学作为模块化工作流程，并为每个组件包含大量的常见算法。为了优化每个应用程序的工作流程，我们使用随机搜索和结合使用自动化机器学习。我们在十二个不同的临床应用中评估我们的方法，从而在曲线下导致以下区域：1）脂肪肉瘤（0.83）； 2）脱粘型纤维瘤病（0.82）; 3）原发性肝肿瘤（0.80）; 4）胃肠道肿瘤（0.77）； 5）结直肠肝转移（0.61）; 6）黑色素瘤转移（0.45）; 7）肝细胞癌（0.75）; 8）肠系膜纤维化（0.80）; 9）前列腺癌（0.72）； 10）神经胶质瘤（0.71）; 11）阿尔茨海默氏病（0.87）;和12）头颈癌（0.84）。我们表明，我们的框架具有比较人类专家的竞争性能，优于放射线基线，并且表现相似或优于贝叶斯优化和更高级的合奏方法。最后，我们的方法完全自动优化了放射线工作流的构建，从而简化了在新应用程序中对放射线生物标志物的搜索。为了促进可重复性和未来的研究，我们公开发布了六个数据集，框架的软件实施以及重现这项研究的代码。

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in Generative Adversarial Networks (GANs), data synthesis, and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.

我们探索使用大型预用语言模型作为少量语义解析器。语义解析中的目标是给定自然语言输入的结构化含义表示。但是，培训语言模型以生成自然语言。为了弥合差距，我们使用语言模型来解释进入一个类似于英语的受控的子宫内的输入，可以自动映射到目标含义表示表示。我们的结果表明，只有少量的数据和较少的代码转换为类似英语的代表，我们为快速启动语义解析器的蓝图导致了对多个社区任务的令人惊讶的有效性能，大大超过基线方法也在相同的限制上培训数据。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.