有限的角度X射线断层扫描重建是一个不良反问题一般。特别是当投影角度有限并且在光子限制条件下进行测量时，来自经典算法的重建，例如过滤的反光，可能导致由于缺失的问题而获取伪影。为了获得令人满意的重建结果，通常在重建算法中结合在重建算法中的令人满意的重建结果，例如总变化最小化和非局部图像相似度。在这项工作中，我们介绍了深度神经网络，以确定并应用重建过程的先前分配。我们的神经网络直接从合成训练样本中学习。因此，神经网络获得了对我们对重建感兴趣的对象类的特定的先前分配。特别是，我们使用了具有3D卷积层和3D注意图层的深生成的模型，这些层在来自DubBed电路库的3D合成集成电路（IC）数据上培训。我们证明，当投影角度和光子预算受到限制时，来自我们深度生成模型的前沿可以显着提高合成数据的IC重建质量，而与最大似然估计相比。使用电路库的合成IC数据训练深度生成模型说明了从机器学习之前学到的学习功能。我们预计，如果使用实验数据再现过程，机器学习的优势将持续存在。机器学习在有限角X射线断层扫描的优点可以进一步能够在低光子纳米级成像中实现应用。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

对于放射科医生和深度学习算法而言，MRI的早期前列腺癌检测和分期是极具挑战性的任务，但是向大型和多样化数据集学习的潜力仍然是提高其内部和整个诊所的概括能力的有希望的途径。为了对原型阶段算法进行此项启用，其中大多数现有研究仍然存在，在本文中，我们引入了一个灵活的联合学习框架，用于跨站点培训，验证和评估深前列腺癌检测算法。我们的方法利用了模型体系结构和数据的抽象表示，该表示允许使用NVFlare联合学习框架对未打磨的原型深度学习模型进行培训。我们的结果表明，使用专门的神经网络模型以及在加利福尼亚大学两家研究医院收集的专门神经网络模型以及不同的前列腺活检数据的前列腺癌检测和分类精度的提高，这证明了我们方法在适应不同数据集并改善MR-Biomarker发现的方法方面的功效。我们开源的FLTOOLS系统可以很容易地适应其他深度学习项目进行医学成像。

基于采样的推理技术是现代宇宙学数据分析的核心;然而，这些方法与维度不良，通常需要近似或顽固的可能性。在本文中，我们描述了截短的边际神经比率估计（TMNRE）（即所谓的基于模拟的推断的新方法）自然避免了这些问题，提高了$（i）$效率，$（ii）$可扩展性和$ （iii）推断后的后续后续的可信度。使用宇宙微波背景（CMB）的测量，我们表明TMNRE可以使用比传统马尔可夫链蒙特卡罗（MCMC）方法更少模拟器呼叫的数量级来实现融合的后海后。值得注意的是，所需数量的样本有效地独立于滋扰参数的数量。此外，称为\ MEMPH {本地摊销}的属性允许对基于采样的方法无法访问的严格统计一致性检查的性能。 TMNRE承诺成为宇宙学数据分析的强大工具，特别是在扩展宇宙学的背景下，其中传统的基于采样的推理方法所需的时间级数融合可以大大超过$ \ Lambda $ CDM等简单宇宙学模型的时间。为了执行这些计算，我们使用开源代码\ texttt {swyft}来使用TMNRE的实现。

学习排名 - 制作特定于查询的项目的排名列表以及一组监督项目 - 是一个普遍兴趣的问题。我们认为的设置是没有分析描述构成良好排名的设置。取而代之的是，我们有一个包含（目标项目，有趣的项目集）对的表示和监督信息的集合。我们在仿真中进行了分析证明，在实际数据示例中，当监督与“这几个相似的项目相似”时，通过使用整数线性程序组合表示来进行排名是有效的。尽管这项提名任务是相当普遍的，但对于特异性，我们从图表中的顶点提名的角度介绍了我们的方法论。本文描述的方法是模型不可知论。

Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a F1-score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datasets. I am pleased to introduce the EmoGator dataset, which consists of 32,040 samples from 365 speakers, 16.91 hours of audio; each sample classified into one of 30 distinct emotion categories by the speaker. Several different approaches to construct classifiers to identify emotion categories will be discussed, and directions for future research will be suggested. Data set is available for download from https://github.com/fredbuhl/EmoGator.