随机旋转的Cholesky（RPCholesky）是一种用于计算N X N阳性半芬酸矩阵（PSD）矩阵的等级K近似的天然算法。RPCholesky只需几行代码就可以实现。它仅需要（k+1）n进入评估，o（k^2 n）其他算术操作。本文对其实验和理论行为进行了首次认真研究。从经验上讲，rpcholesky匹配或改善了低级别PSD近似的替代算法的性能。此外，RPCholesky可证明达到了近乎最佳的近似保证。该算法的简单性，有效性和鲁棒性强烈支持其在科学计算和机器学习应用中的使用。

变异蒙特卡洛（VMC）是一种计算地面波形的方法，由于引入基于神经网络的波函数参数化，最近它变得更加强大。但是，有效地训练神经波函数以收敛到最小能量仍然是一个困难的问题。在这项工作中，我们分析了VMC中使用的优化和采样方法，并引入了改动以提高其性能。首先，基于理论融合分析在无噪声的环境中，我们激励了一种新的优化器，我们称之为雷利 - 高斯 - 纽顿方法，该方法可以改善梯度下降和自然梯度下降，以实现超线性收敛，而计算成本小于两倍。其次，为了在存在随机噪声的情况下实现这种有利的比较，我们分析了采样误差对VMC参数更新的影响，并在实验上证明可以通过平行回火方法将其降低。特别是，我们证明，当采样器在配置空间的亚稳态区域之间移动时，可以使RGN具有强大的能量尖峰。最后，将理论付诸实践，我们将增强的优化和采样方法应用于大型晶格的横向场和XXZ模型，在仅200个参数更新后，以高度准确性地产生了基态能量估计。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

As causal inference becomes more widespread the importance of having good tools to test for causal effects increases. In this work we focus on the problem of testing for causal effects that manifest in a difference in distribution for treatment and control. We build on work applying kernel methods to causality, considering the previously introduced Counterfactual Mean Embedding framework (\textsc{CfME}). We improve on this by proposing the \emph{Doubly Robust Counterfactual Mean Embedding} (\textsc{DR-CfME}), which has better theoretical properties than its predecessor by leveraging semiparametric theory. This leads us to propose new kernel based test statistics for distributional effects which are based upon doubly robust estimators of treatment effects. We propose two test statistics, one which is a direct improvement on previous work and one which can be applied even when the support of the treatment arm is a subset of that of the control arm. We demonstrate the validity of our methods on simulated and real-world data, as well as giving an application in off-policy evaluation.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

使用相对比心脏磁共振成像（PC-CMR）进行的流量分析可以量化用于评估心血管功能的重要参数。该分析的重要部分是鉴定正确的CMR视图和质量控制（QC），以检测可能影响流量定量的伪像。我们提出了一个新型的基于深度学习的框架，用于对完整CMR扫描的流量进行完全自动化的分析，该框架首先使用两个顺序卷积神经网络进行这些视图选择和QC步骤，然后进行自动主动脉和肺动脉分段，以实现对量化的量化。钥匙流参数。对于观察分类和QC，获得了0.958和0.914的精度值。对于细分，骰子分数为$> $ 0.969，而平淡的altman情节表示手动和自动峰流量值之间的一致性很高。此外，我们在外部验证数据集上测试了管道，结果表明管道的鲁棒性。这项工作是使用由986例病例组成的多生临床数据进行的，表明在临床环境中使用该管道的潜力。

神经肌肉疾病，例如脊柱肌肉萎缩（SMA）和Duchenne肌肉营养不良症（DMD），导致6,000名儿童中有1例的渐进性肌肉变性和运动功能丧失。传统的上肢运动功能评估不能定量测量患者的性能，这使得很难跟踪进度的增量变化。评估神经肌肉疾病儿童的运动功能特别具有挑战性，因为他们在实验过程中可能会紧张或兴奋，或者简直太年轻而无法遵循精确的说明。这些挑战转化为混杂因素，例如执行臂卷曲的不同部分较慢或更快（相位变异性），从而影响评估的运动质量。本文使用曲线注册和形状分析来暂时对齐轨迹，同时提取平均参考形状。距这种平均形状的距离用于评估运动质量。所提出的指标是混杂因素（例如相位变异性）的不变性，同时提出了几种临床相关的见解。首先，控制和患者人群的功能分数在统计上存在显着差异（p $ = $ 0.0213 $ \ le $ 0.05）。接下来，患者队列中的几名患者能够与健康队列进行运动，反之亦然。我们的指标是根据可穿戴设备计算的，与Brooke的分数有关（（P $ = $ 0.00063 $ \ le $ $ 0.05））以及基于功能测定法的电动机功能评估（（P $ = $ = $ 0.0006 $ \ le $ 0.05）） 。这些结果表明了日常生活中无处不在的运动质量评估的希望。