多养殖养殖具有环境优势，但比单一养殖需要更修剪。我们介绍用于自动修剪的新型硬件和算法。自主系统使用高架摄像头从物理规模的花园测试床中收集数据，利用学识渊博的植物表型卷积神经网络和边界磁盘跟踪算法来评估单个植物分布并每天估算花园的状态。从这个花园状态下，Alphagardensim选择植物自主修剪。训练有素的神经网络检测并靶向工厂上的特定修发点。实验评估了两种与农业机器人龙门系统兼容的定制设计的修剪工具，并通过受控算法进行了自主削减。我们提出了四个60天的花园周期的结果。结果表明，该系统可以自主实现0.94个归一化的植物多样性，并在修剪剪切的同时保持平均冠层覆盖率为0.84，到周期结束时。有关代码，视频和数据集，请参见https：//sites.google.com/berkeley.edu/pruningpolyculture。

本文展示了alphaRARDEN：一个自治的多种植花园，在1.5米×3.0米的物理测试平台中撒上和灌溉生物植物。alphanArden使用架空相机和传感器来跟踪植物分布和土壤水分。我们模拟个体植物生长和平面动态，以培训选择行动以最大化叶片覆盖和多样性的政策。对于自主修剪，alphanarden使用两个定制的修剪工具和训练有素的神经网络来检测紫杉角。我们为四个60天的花园周期提供了结果。结果表明，alphaRARARDEN可以自主地实现0.96个归一化多样性，在循环峰值期间保持0.86的平均冠层覆盖率。可以在https://github.com/berkeleyautomation/alpharden找到代码，数据集和补充材料。

Single-cell reference atlases are large-scale, cell-level maps that capture cellular heterogeneity within an organ using single cell genomics. Given their size and cellular diversity, these atlases serve as high-quality training data for the transfer of cell type labels to new datasets. Such label transfer, however, must be robust to domain shifts in gene expression due to measurement technique, lab specifics and more general batch effects. This requires methods that provide uncertainty estimates on the cell type predictions to ensure correct interpretation. Here, for the first time, we introduce uncertainty quantification methods for cell type classification on single-cell reference atlases. We benchmark four model classes and show that currently used models lack calibration, robustness, and actionable uncertainty scores. Furthermore, we demonstrate how models that quantify uncertainty are better suited to detect unseen cell types in the setting of atlas-level cell type transfer.

来自不同摄像头设备的光学相干断层扫描（OCT）成像会导致挑战域的变化，并可能导致机器学习模型的精度严重下降。在这项工作中，我们引入了基于单数值分解（SVDNA）的最小噪声适应方法，以克服视网膜OCT成像中三个不同设备制造商的目标域之间的域间隙。我们的方法利用噪声结构的差异成功地弥合了不同OCT设备之间的域间隙，并将样式从未标记的目标域图像转移到可用手动注释的源图像。我们演示了该方法尽管简单，但如何比较甚至胜过最先进的无监督域适应方法，用于在公共OCT数据集中进行语义细分。 SVDNA可以将仅几行代码集成到任何网络的增强管道中，这些网络与许多最新的域适应方法形成鲜明对比，这些方法通常需要更改基础模型体系结构或训练单独的样式转移模型。 SVDNA的完整代码实现可在https://github.com/valentinkoch/svdna上获得。

我们描述了一种新型有损压缩方法，称为DIFFC，该方法基于无条件扩散生成模型。与依靠转换编码和量化来限制传输信息的现代压缩方案不同，DIFFC依赖于高斯噪声损坏的像素的有效通信。我们实施了概念证明，并发现尽管缺乏编码器变换，但它的工作原理表现出色，超过了Imagenet 64x64上最先进的生成压缩方法。 DIFFC仅使用单个模型在任意比特率上编码和DENOISE损坏的像素。该方法进一步提供了对渐进编码的支持，即从部分位流进行解码。我们执行速率分析，以更深入地了解其性能，为多元高斯数据以及一般分布的初始结果提供分析结果。此外，我们表明，基于流动的重建可以比祖先采样在高比特率上获得3 dB的增长。

Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.

2 Lambda Labs 3 Twitter Figure 1. HoloGAN learns to separate pose from identity (shape and appearance) only from unlabelled 2D images without sacrificing the visual fidelity of the generated images. All results shown here are sampled from HoloGAN for the same identities in each row but in different poses.

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.