当前的量子点（QD）设备的自动传动方法在显示出一些成功的同时，缺乏对数据可靠性的评估。当自主系统处理嘈杂或低质量数据时，这会导致意外的失败。在这项工作中，我们为QD设备的强大自动调整提供了一个框架，该QD设备将机器学习（ML）状态分类器与数据质量控制模块结合在一起。数据质量控制模块充当“守门人”系统，确保只有国家分类器处理可靠的数据。较低的数据质量会导致设备重新校准或终止。为了训练两个ML系统，我们通过结合QD实验的典型合成噪声来增强QD仿真。我们确认，在状态分类器的训练中包含合成噪声可以显着提高性能，在测试实验数据时，准确性为95.0（9）％。然后，我们通过表明状态分类器的性能随着预期的数据质量而恶化，从而验证数据质量控制模块的功能。我们的结果为嘈杂的QD设备的自动调整建立了强大而灵活的ML框架。

There has been great recent advancement in human-computer chat. However, proper evaluation currently requires human judgements that produce notoriously high-variance metrics due to their inherent subjectivity. Furthermore, there is little standardization in the methods and labels used for evaluation, with an overall lack of work to compare and assess the validity of various evaluation approaches. As a consequence, existing evaluation results likely leave an incomplete picture of the strengths and weaknesses of open-domain chatbots. We aim towards a dimensional evaluation of human-computer chat that can reliably measure several distinct aspects of chat quality. To this end, we present our novel human evaluation method that quantifies the rate of several quality-related chatbot behaviors. Our results demonstrate our method to be more suitable for dimensional chat evaluation than alternative likert-style or comparative methods. We then use our validated method and existing methods to evaluate four open-domain chat models from the recent literature.

The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations due to differences in hardware and acquisition parameters. In recent years, MR harmonization using image synthesis with disentanglement has been proposed to compensate for the undesired contrast variations. Despite the success of existing methods, we argue that three major improvements can be made. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both Tw-weighted and T2-weighted images must be available), which limits their applicability. Third, existing methods generally are sensitive to imaging artifacts. In this paper, we present a novel approach, Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), to address these three issues. We first propose an anatomy fusion module that enables HACA3 to respect the anatomical differences between MR contrasts. HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts. Experiments show that HACA3 achieves state-of-the-art performance under multiple image quality metrics. We also demonstrate the applicability of HACA3 on downstream tasks with diverse MR datasets acquired from 21 sites with different field strengths, scanner platforms, and acquisition protocols.

Purpose: Trans-oral robotic surgery (TORS) using the da Vinci surgical robot is a new minimally-invasive surgery method to treat oropharyngeal tumors, but it is a challenging operation. Augmented reality (AR) based on intra-operative ultrasound (US) has the potential to enhance the visualization of the anatomy and cancerous tumors to provide additional tools for decision-making in surgery. Methods: We propose and carry out preliminary evaluations of a US-guided AR system for TORS, with the transducer placed on the neck for a transcervical view. Firstly, we perform a novel MRI-transcervical 3D US registration study. Secondly, we develop a US-robot calibration method with an optical tracker and an AR system to display the anatomy mesh model in the real-time endoscope images inside the surgeon console. Results: Our AR system reaches a mean projection error of 26.81 and 27.85 pixels for the projection from the US to stereo cameras in a water bath experiment. The average target registration error for MRI to 3D US is 8.90 mm for the 3D US transducer and 5.85 mm for freehand 3D US, and the average distance between the vessel centerlines is 2.32 mm. Conclusion: We demonstrate the first proof-of-concept transcervical US-guided AR system for TORS and the feasibility of trans-cervical 3D US-MRI registration. Our results show that trans-cervical 3D US is a promising technique for TORS image guidance.

Most speech enhancement (SE) models learn a point estimate, and do not make use of uncertainty estimation in the learning process. In this paper, we show that modeling heteroscedastic uncertainty by minimizing a multivariate Gaussian negative log-likelihood (NLL) improves SE performance at no extra cost. During training, our approach augments a model learning complex spectral mapping with a temporary submodel to predict the covariance of the enhancement error at each time-frequency bin. Due to unrestricted heteroscedastic uncertainty, the covariance introduces an undersampling effect, detrimental to SE performance. To mitigate undersampling, our approach inflates the uncertainty lower bound and weights each loss component with their uncertainty, effectively compensating severely undersampled components with more penalties. Our multivariate setting reveals common covariance assumptions such as scalar and diagonal matrices. By weakening these assumptions, we show that the NLL achieves superior performance compared to popular losses including the mean squared error (MSE), mean absolute error (MAE), and scale-invariant signal-to-distortion ratio (SI-SDR).

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

赤道等离子体气泡（EPB）是低密度血浆的羽毛，它们从F层的底部升至Exosphere。 EPB是无线电波闪烁的已知原因，可以降低与航天器的通信。我们构建了一个随机的森林回归剂，以预测和预测IBI处理器在船上检测到的EPB [0-1]的可能性。我们使用从2014年到2021年的8年群数据，并将数据从时间序列转换为5维空间，该空间包括纬度，经度，MLT，年份和年度。我们还增加了KP，F10.7厘米和太阳风速。关于地理位置，当地时间，季节和太阳活动的EPB的观察主要与现有工作一致，而链接的地磁活动尚不清楚。该预测的精度为88％，并且在EPB特异性时空尺度上的性能很好。这证明了XGBoost方法能够成功捕获群EPB的气候和每日变异性。由于电离层内的局部和随机特征，捕获每日方差长期以来一直逃避研究人员。我们利用Shapley值来解释该模型并深入了解EPB的物理学。我们发现，随着太阳能速度的增加，EPB的概率降低。我们还确定了EPB概率周围的尖峰。这两个见解直接源自XGBoost和Shapley技术。

机器学习潜力是分子模拟的重要工具，但是由于缺乏高质量数据集来训练它们的发展，它们的开发阻碍了它们。我们描述了Spice数据集，这是一种新的量子化学数据集，用于训练与模拟与蛋白质相互作用的药物样的小分子相关的潜在。它包含超过110万个小分子，二聚体，二肽和溶剂化氨基酸的构象。它包括15个元素，带电和未充电的分子以及广泛的共价和非共价相互作用。它提供了在{\ omega} b97m-d3（bj）/def2-tzVPPD理论水平以及其他有用的数量（例如多极矩和键阶）上计算出的力和能量。我们在其上训练一组机器学习潜力，并证明它们可以在化学空间的广泛区域中实现化学精度。它可以作为创建可转移的，准备使用潜在功能用于分子模拟的宝贵资源。

我们提出了对使用Rademacher和Vapnik-Chervonenkis边界学习有条件的价值（VAR）和预期短缺的两步方法的非反应收敛分析。我们的VAR方法扩展到了一次学习的问题，该问题对应于不同的分数水平。这导致基于神经网络分位数和最小二乘回归的有效学习方案。引入了一个后验蒙特卡洛（非巢）程序，以估计地面真相和ES的距离，而无需访问后者。使用高斯玩具模型中的数值实验和财务案例研究中的目标是学习动态初始边缘的情况。

开发有效的自动分类器将真实来源与工件分开，对于宽场光学调查的瞬时随访至关重要。在图像差异过程之后，从减法伪像的瞬态检测鉴定是此类分类器的关键步骤，称为真实 - 博格斯分类问题。我们将自我监督的机器学习模型，深入的自组织地图（DESOM）应用于这个“真实的模拟”分类问题。 DESOM结合了自动编码器和一个自组织图以执行聚类，以根据其维度降低的表示形式来区分真实和虚假的检测。我们使用32x32归一化检测缩略图作为底部的输入。我们展示了不同的模型训练方法，并发现我们的最佳DESOM分类器显示出6.6％的检测率，假阳性率为1.5％。 Desom提供了一种更细微的方法来微调决策边界，以确定与其他类型的分类器（例如在神经网络或决策树上构建的）结合使用时可能进行的实际检测。我们还讨论了DESOM及其局限性的其他潜在用法。