在本文中，我们通过将歧管学习步骤纳入鉴别器来改善生成的对抗网络。我们考虑了基于位置约束的线性和子空间的歧管，以及地方约束的非线性歧管。在我们的设计中，歧管学习和编码步骤与鉴别器的层交织在一起，其目标是将中间特征表示吸引到歧管上。我们自适应地平衡特征表示和歧管视图之间的差异，这代表了在歧管上的去噪和精炼歧管之间的折衷。我们得出结论，由于它们的不均匀密度和平滑度，地区限制的非线性歧管具有上部的线性歧管。我们对不同最近最新的基线显示出实质性的改进。

为了获取3D注释，我们仅限于受控环境或合成数据集，导致我们到3D数据集，其概括为现实世界方案。为了在半监督3D手形状和姿势估计的上下文中解决这个问题，我们提出了姿势对齐网络，以将标记帧传播到附近的稀疏注释视频中的附近未标记帧的3D注释。我们表明，在标记 - 未标记的帧对对对准监控允许我们提高姿态估计精度。此外，我们表明所提出的姿势对齐网络可以有效地传播在不良稀疏的视频上的注释而无需微调。

在最近的文献中，在最近的文献中已经过度研究了不确定性估计，通常可以被归类为炼体不确定性和认知不确定性。在当前的炼拉内不确定性估计框架中，往往忽略了炼拉线性的不确定性是数据的固有属性，只能用一个无偏见的Oracle模型正确估计。由于在大多数情况下，Oracle模型无法访问，我们提出了一个新的采样和选择策略，在火车时间近似甲骨文模型以实现炼梯不确定性估计。此外，我们在基于双头的异源型梯级不确定性估计框架中显示了一种琐碎的解决方案，并引入了新的不确定性一致性损失，以避免它。对于认知不确定性估算，我们认为条件潜在变量模型中的内部变量是模拟预测分布的另一个认识性的不确定性，并探索了关于隐藏的真实模型的有限知识。我们验证了我们对密集预测任务的观察，即伪装对象检测。我们的研究结果表明，我们的解决方案实现了准确的确定性结果和可靠的不确定性估算。

在在下游决策取决于预测概率的安全关键应用中，校准神经网络是最重要的。测量校准误差相当于比较两个实证分布。在这项工作中，我们引入了由经典Kolmogorov-Smirnov（KS）统计测试的自由校准措施，其中主要思想是比较各自的累积概率分布。由此，通过通过Quidsime使用可微分函数来近似经验累积分布，我们获得重新校准函数，将网络输出映射到实际（校准的）类分配概率。使用停滞校准组进行脊柱拟合，并在看不见的测试集上评估所获得的重新校准功能。我们测试了我们对各种图像分类数据集的现有校准方法的方法，并且我们的样条键的重新校准方法始终如一地优于KS错误的现有方法以及其他常用的校准措施。我们的代码可在https://github.com/kartikgupta-at-anu/spline-calibration获得。

牛顿方法和Adagrad等高级优化算法受益于二阶导数或二阶统计，以实现更好的下降方向和更快的收敛速率。在他们的心中，这种算法需要计算矩阵的矩阵的反平方根或反平方根，其大小是搜索空间维度的二次。对于高维搜索空间，平方根的矩阵反转或反转变为压倒性的，进而需要近似方法。在这项工作中，我们提出了一种新的矩阵近似方法，该方法将矩阵分为块，并将每个块代表一个或两个数字。该方法允许有效地计算矩阵逆和逆平方根。我们将我们的方法应用于Adagrad，以培训深层神经网络。实验表明与对角线近似相比令人鼓舞的结果。

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.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.