连续控制设置中的复杂顺序任务通常需要代理在其状态空间中成功遍历一组“窄段”。通过以样本有效的方式解决具有稀疏奖励的这些任务对现代钢筋（RL）构成了挑战，由于问题的相关的长地平性，并且在学习期间缺乏充足的正信号。已应用各种工具来解决这一挑战。当可用时，大型演示可以指导代理探索。后威尔同时释放不需要额外的信息来源。然而，现有的战略基于任务不可行的目标分布探索，这可以使长地平线的解决方案不切实际。在这项工作中，我们扩展了后视可释放的机制，以指导沿着一小组成功示范所暗示的特定任务特定分布的探索。我们评估了四个复杂，单身和双臂，机器人操纵任务的方法，对抗强合适的基线。该方法需要较少的演示来解决所有任务，并且达到明显更高的整体性能作为任务复杂性增加。最后，我们研究了提出的解决方案对输入表示质量和示范人数的鲁棒性。

强化学习（RL）原则上可以让机器人自动适应新任务，但是当前的RL方法需要大量的试验来实现这一目标。在本文中，我们通过元学习的框架来快速适应新任务，该框架利用过去的任务学习适应了对工业插入任务的特定关注。快速适应至关重要，因为大量的机器人试验可能会损害硬件件。另外，在不同的插入应用之间的经验中，有效的适应性也可以在很大程度上彼此利用。在这种情况下，我们在应用元学习时解决了两个具体的挑战。首先，传统的元元算法需要冗长的在线元训练。 We show that this can be replaced with appropriately chosen offline data, resulting in an offline meta-RL method that only requires demonstrations and trials from each of the prior tasks, without the need to run costly meta-RL procedures online.其次，元RL方法可能无法推广到与元训练时间时看到的新任务太大的任务，这在高成功率至关重要的工业应用中构成了特定的挑战。我们通过将上下文元学习与直接在线填充结合结合来解决这一问题：如果新任务与先前数据中看到的任务相似，则可以立即适应上下文的元学习者，如果它太不同，它会逐渐通过Finetuning适应。我们表明，我们的方法能够快速适应各种不同的插入任务，成功率为100％仅使用从头开始学习任务所需的样本的一小部分。实验视频和详细信息可从https://sites.google.com/view/offline-metarl-insertion获得。

Correct scoring of a driver's risk is of great significance to auto insurance companies. While the current tools used in this field have been proven in practice to be quite efficient and beneficial, we argue that there is still a lot of room for development and improvement in the auto insurance risk estimation process. To this end, we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding). This enables us to visually represent the complex structure of the risk as a two-dimensional surface, while still preserving the properties of the local region in the features space. The obtained results, which are based on real insurance data, reveal a clear contrast between the high and low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer. Due to the visual accessibility of the portfolio in this approach, we argue that this framework could be advantageous to the auto insurer, both as a main risk prediction tool and as an additional validation stage in other approaches.

$ $With recent advances in CNNs, exceptional improvements have been made in semantic segmentation of high resolution images in terms of accuracy and latency. However, challenges still remain in detecting objects in crowded scenes, large scale variations, partial occlusion, and distortions, while still maintaining mobility and latency. We introduce a fast and efficient convolutional neural network, ASBU-Net, for semantic segmentation of high resolution images that addresses these problems and uses no novelty layers for ease of quantization and embedded hardware support. ASBU-Net is based on a new feature extraction module, atrous space bender layer (ASBL), which is efficient in terms of computation and memory. The ASB layers form a building block that is used to make ASBNet. Since this network does not use any special layers it can be easily implemented, quantized and deployed on FPGAs and other hardware with limited memory. We present experiments on resource and accuracy trade-offs and show strong performance compared to other popular models.

MD4 and MD5 are seminal cryptographic hash functions proposed in early 1990s. MD4 consists of 48 steps and produces a 128-bit hash given a message of arbitrary finite size. MD5 is a more secure 64-step extension of MD4. Both MD4 and MD5 are vulnerable to practical collision attacks, yet it is still not realistic to invert them, i.e. to find a message given a hash. In 2007, the 39-step version of MD4 was inverted via reducing to SAT and applying a CDCL solver along with the so-called Dobbertin's constraints. As for MD5, in 2012 its 28-step version was inverted via a CDCL solver for one specified hash without adding any additional constraints. In this study, Cube-and-Conquer (a combination of CDCL and lookahead) is applied to invert step-reduced versions of MD4 and MD5. For this purpose, two algorithms are proposed. The first one generates inversion problems for MD4 by gradually modifying the Dobbertin's constraints. The second algorithm tries the cubing phase of Cube-and-Conquer with different cutoff thresholds to find the one with minimal runtime estimation of the conquer phase. This algorithm operates in two modes: (i) estimating the hardness of an arbitrary given formula; (ii) incomplete SAT-solving of a given satisfiable formula. While the first algorithm is focused on inverting step-reduced MD4, the second one is not area-specific and so is applicable to a variety of classes of hard SAT instances. In this study, for the first time in history, 40-, 41-, 42-, and 43-step MD4 are inverted via the first algorithm and the estimating mode of the second algorithm. 28-step MD5 is inverted for four hashes via the incomplete SAT-solving mode of the second algorithm. For three hashes out of them this is done for the first time.

Accurate mapping of forests is critical for forest management and carbon stocks monitoring. Deep learning is becoming more popular in Earth Observation (EO), however, the availability of reference data limits its potential in wide-area forest mapping. To overcome those limitations, here we introduce contrastive regression into EO based forest mapping and develop a novel semisupervised regression framework for wall-to-wall mapping of continuous forest variables. It combines supervised contrastive regression loss and semi-supervised Cross-Pseudo Regression loss. The framework is demonstrated over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery for mapping forest tree height. Achieved prediction accuracies are strongly better compared to using vanilla UNet or traditional regression models, with relative RMSE of 15.1% on stand level. We expect that developed framework can be used for modeling other forest variables and EO datasets.

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.

自动临床标题生成问题被称为建议模型，将额叶X射线扫描与放射学记录中的结构化患者信息结合在一起。我们将两种语言模型结合在一起，即表演 - 泰尔和GPT-3，以生成全面和描述性的放射学记录。这些模型的建议组合产生了文本摘要，其中包含有关发现的病理，其位置以及将每个病理定位在原始X射线扫描中的每个病理的2D热图。提出的模型在两个医学数据集（Open-I，Mimic-CXR和通用MS-Coco）上进行了测试。用自然语言评估指标测量的结果证明了它们对胸部X射线图像字幕的有效适用性。

大多数从功能磁共振成像（fMRI）数据估算大脑功能连接性的方法依赖于计算统计依赖性的某些度量，或者更一般地，单变量代表性的时间序列（ROIS）（ROI）由多个Voxels组成。但是，总结ROI的多个时间序列具有其平均值或第一个主成分（1pc）可能导致信息丢失，例如，1PC仅解释了神经元活动的多变量信号的一小部分。我们建议在不使用代表性时间序列的情况下直接比较ROI，并根据Wasserstein距离定义了ROI之间的新的多元连通性量度，不一定由相同数量的体素组成。我们在自闭症筛查任务上评估了拟议的Wasserstein功能连接度量，证明了其优越性优于常用单变量和多元功能连通性测量。

使用高斯混合模型（GMM）的变异推断能够学习可侵入性目标分布的高度扣除但多模式的近似值。 GMM与最多数百个维度的问题设置特别相关，例如机器人技术，用于对轨迹或联合分布进行建模。这项工作着重于基于GMM的两种非常有效的方法，这些方法既采用独立的自然梯度更新来为单个组件和权重的分类分布。我们首次表明，尽管它们的实际实现和理论保证有所不同，但他们的派生更新是等效的。我们确定了几种设计选择，可以区分两种方法，即在样本选择，自然梯度估计，步骤适应以及信任区域是否得到强制或适应的组件数量方面。我们对这些设计选择进行广泛的消融，并表明它们强烈影响了优化的效率和学习分布的可变性。基于我们的见解，我们提出了对广义框架的新颖实例化，该实例将一阶自然梯度估计与信任区域和组件适应相结合，并且在我们所有实验中都显着优于以前的两种方法。