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.

本文着重于基于雷达的同时定位和映射（SLAM）中的有效地标管理。必须进行地标管理，以保持相对于平台姿势估计的估计地标的一致地图。当面对从相同地标和/或动态环境的多个检测到地标可以更改的地标和/或动态环境时，此任务尤其重要。雷达数据的另一个挑战是存在错误检测。因此，我们为Radar Slam Landmark Management提出了一个简单而有效的规则解决方案。假设我们的解决方案中有几个步骤：需要检测并包括新的地标，需要识别和删除虚假地标，并且需要维护地图中注册的地标的一致性。为了说明我们的解决方案，我们在包含固定和固定地标的环境中运行扩展的Kalman Filter Slam算法。我们的仿真结果表明，即使面对虚假检测和来自同一地标的多次检测，提出的解决方案也能够可靠地管理地标。

由于气候变化，预计蚊子栖息地范围会扩大。这项研究旨在通过分析蚊子幼虫的首选生态条件来鉴定未来的蚊子栖息地。在与大气记录和幼虫观测值组装在一起的数据集之后，训练了神经网络，以预测来自生态输入的幼虫计数。时间序列预测是对这些变量进行的，气候预测将传递到初始深度学习模型中，以产生特定于位置的幼虫丰度预测。结果支持区域生态系统驱动的蚊子扩散变化的概念，高海拔地区特别探讨了蚊子侵扰的易感性增加。

每年大约有6,800次自然灾害发生，由于气候变化的影响，这一令人震惊的数量继续增长。改善自然灾害反应的有效方法包括执行变更检测，地图对准和视觉辅助导航，以允许节省时间的救生援助。当前的软件仅在地面高于地面九十度的NADIR图像上发挥最佳功能。无法概括倾斜的图像增加了计算图像的地心姿势的需求，这是其在重力方面的空间取向。这项深入学习调查提出了三个卷积模型，以使用5,923个Nadir和斜红，绿色和蓝色（RGB）卫星图像预测地心的姿势。第一个模型是一种自动编码器，将256 x 256 x 3图像凝结到32 x 32 x 16潜在空间表示形式，证明了从数据中学习有用功能的能力。第二个模型是U-NET完全卷积网络，其SKIP连接用于预测每个图像的相应像素级掩码。该模型在测试数据上实现了0.335米的中值绝对偏差为0.335米，R2为0.865。之后，将高程面膜与RGB图像串联以形成馈入第三个模型的四通道输入，该输入预测了每个图像的旋转角度和比例，即其地理为中心姿势的组件。这种深度卷积神经网络在测试数据上达到了0.943的R2，大大优于研究人员设计的先前模型。本研究中建立的高准确软件有助于制定和导航程序，以加速救灾并挽救人类的生命。

目前，由精确的径向速度（RV）观察结果受到恒星活性引入的虚假RV信号的限制。我们表明，诸如线性回归和神经网络之类的机器学习技术可以有效地从RV观测中删除活动信号（由于星形/张图引起的）。先前的工作着重于使用高斯工艺回归等建模技术仔细地过滤活性信号（例如Haywood等人，2014年）。取而代之的是，我们仅使用对光谱线平均形状的更改进行系统地删除活动信号，也没有有关收集观测值的信息。我们对模拟数据（使用SOAP 2.0软件生成； Dumusque等人，2014年生成）和从Harps-N太阳能望远镜（Dumusque等，2015; Phillips等人2015; 2016; Collier训练）培训了机器学习模型。 Cameron等人2019）。我们发现，这些技术可以从模拟数据（将RV散射从82 cm/s提高到3 cm/s）以及从HARPS-N太阳能望远镜中几乎每天进行的600多种真实观察结果来预测和消除恒星活动（将RV散射从82 cm/s提高到3 cm/s）。 （将RV散射从1.753 m/s提高到1.039 m/s，提高了约1.7倍）。将来，这些或类似的技术可能会从太阳系以外的恒星观察中去除活动信号，并最终有助于检测到阳光状恒星周围可居住的区域质量系外行星。

学习捕获文本表对齐对于文本到SQL等任务至关重要。一个模型需要正确识别对列和值的自然语言引用，并在给定的数据库架构中将其扎根。在本文中，我们为文本到SQL提出了一个新颖的弱监督结构接地预处理框架（strug），可以有效地学习基于平行的文本表语料库来捕获文本表对齐。我们确定了一组新的预测任务：列接地，价值接地和列值映射，并利用它们为文本表编码预处理。此外，为了评估更现实的文本表对齐设置下的不同方法，我们基于蜘蛛dev设置的新评估集蜘蛛现实化，并明确提及已删除的列名，并采用八个现有的文本到SQL数据集以进行交叉 - 数据库评估。在所有设置中，Strug对Bert-Large都有显着改善。与现有的预训练方法（例如Grappa）相比，Strug在蜘蛛方面的性能相似，并且在更现实的集合上都优于所有基线。蜘蛛现实的数据集可从https://doi.org/10.5281/zenodo.5205322获得。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

Remote sensing imagery provides comprehensive views of the Earth, where different sensors collect complementary data at different spatial scales. Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pretrains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a $5.0\%$ non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a $0.9$ mIoU to $3.8$ mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.

With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data movement for these millions of model parameters causes a growing imbalance known as the memory wall. Neuromorphic computing is an emerging paradigm that confronts this imbalance by performing computations directly in analog memories. On the software side, the sequential Backpropagation algorithm prevents efficient parallelization and thus fast convergence. A novel method, Direct Feedback Alignment, resolves inherent layer dependencies by directly passing the error from the output to each layer. At the intersection of hardware/software co-design, there is a demand for developing algorithms that are tolerable to hardware nonidealities. Therefore, this work explores the interrelationship of implementing bio-plausible learning in-situ on neuromorphic hardware, emphasizing energy, area, and latency constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip. To the best of our knowledge, this work is the first to compare the impact of different learning algorithms on Compute-In-Memory-based hardware and vice versa. The best results achieved for accuracy remain Backpropagation-based, notably when facing hardware imperfections. Direct Feedback Alignment, on the other hand, allows for significant speedup due to parallelization, reducing training time by a factor approaching N for N-layered networks.

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.