机器学习模型容易对远离培训分布的投入进行错误的预测。这阻碍了他们在自动驾驶汽车和医疗保健等安全至关重要应用中的部署。从单个数据点的训练分布转移的检测引起了人们的注意。已经提出了许多用于分发（OOD）检测的技术。但是在许多应用中，机器学习模型的输入形成了时间序列。时间序列数据中的OOD检测技术要么不利用序列中的时间关系，要么不提供任何检测保证。我们建议将偏离分布式时间均衡力偏差作为在时间序列数据中进行OOD检测的保形异常检测框架中的不符合度量度。导致提议的检测器编码，并保证在时间序列数据中进行虚假检测。我们通过在自动驾驶中实现计算机视觉数据集的最新结果来说明编码的功效。我们还表明，通过在生理步态感觉数据集上执行实验，可以将CODIT用于非视觉数据集中的OOD检测。代码，数据和训练有素的模型可在https://github.com/kaustubhsridhar/time-series-ood上找到。

对抗性训练（AT）及其变体在过去几年来改善对对抗性扰动和常见腐败的神经网络的鲁棒性方面取得了长足的进步。 AT及其变体的算法设计集中在指定的扰动强度$ \ epsilon $上，并且仅利用该$ \ epsilon $ -Robust模型的性能的反馈来改善算法。在这项工作中，我们专注于在$ \ epsilon $值的频谱上训练的模型。我们分析了三个观点：模型性能，中间特征精度和卷积滤波器灵敏度。在每种情况下，我们都会确定AT的替代改进，否则在单个$ \ epsilon $中并不明显。具体来说，我们发现，对于以某种强度$ \ delta $的pgd攻击，有一个型号以某种稍大的强度$ \ epsilon $，但没有更大的范围，可以概括它。因此，我们建议过度设计鲁棒性，我们建议以$ \ epsilon $略高于$ \ delta $的培训模型。其次，我们观察到（在各种$ \ epsilon $值中），鲁棒性对中间特征的精度，尤其是在第一层和第二层之后的精度高度敏感。因此，我们建议在防御措施中添加简单的量化，以提高可见和看不见的适应性攻击的准确性。第三，我们分析了增加$ \ epsilon $的每一层模型的卷积过滤器，并注意到第一和第二层的卷积过滤器可能完全负责放大输入扰动。我们通过在CIFAR-10和CIFAR-10-C数据集上使用Resnet和WideSnet模型进行实验，介绍我们的发现并证明我们的技术。

异常检测对于预防自动驾驶（例如自动驾驶）的安全性危险结果至关重要。鉴于它们的安全至关重要，这些应用程序受益于在异常检测中的各种错误中的可证明界限。为了在半监督的环境中实现这一目标，我们建议对异常检测算法的假阴性和假阳性检测率提供大致正确的（PAC）保证。我们的方法（PAC包）几乎可以围绕任何现有的半监督和无监督的异常检测方法，从而获得严格的保证。我们对各种异常检测器和数据集进行的实验表明，PAC包装是广泛有效的。

诸如深神经网络（DNN）之类的机器学习方法，尽管他们在不同域中取得了成功，但是众所周知，通常在训练分布之外的输入上具有高信心产生不正确的预测。在安全关键域中的DNN部署需要检测分配超出（OOD）数据，以便DNN可以避免对那些人进行预测。最近已经开发了许多方法，以便检测，但仍有改进余地。我们提出了新的方法IdeCode，利用了用于共形OOD检测的分销标准。它依赖于在电感共形异常检测框架中使用的新基础非符合性测量和新的聚合方法，从而保证了有界误报率。我们通过在图像和音频数据集上的实验中展示了IDecode的功效，获得了最先进的结果。我们还表明Idecode可以检测对抗性示例。

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。