现代的天空调查正在产生大量的观测数据，这使经典方法的应用用于分类和分析对象具有挑战性和耗时的。但是，使用自动机器和深度学习方法可能会大大减轻此问题。我们提出了一种新的深度学习工具Ulisse，它从单个原型对象开始，能够识别具有相同形态和光度特性的对象，因此可以创建候选苏西亚列表。在这项工作中，我们专注于在斯隆数字天空调查的星系样本中应用方法来检测AGN候选物，因为光带中主动银河系核（AGN）的鉴定和分类仍然是外层术天文学的挑战性任务。乌里斯（Ulisse）旨在初步探索大型天空调查，直接使用从图像网数据集提取的功能来执行相似性搜索。该方法能够快速识别仅从给定原型的单个图像开始的候选人列表，而无需任何耗时的神经网络训练。我们的实验表明，乌里斯（Ulisse）能够根据宿主星系形态，颜色和中央核源的存在的结合来鉴定AGN候选物，检索效率从21％到65％（包括复合源）（包括复合源），这是基于宿主的候选者。随机猜测基线为12％。我们发现，与具有螺旋形或晚期特性的原型相反，Ulisse在早期型宿主星系中检索AGN最有效。根据这项工作中描述的结果，Ulisse可以是在当前和未来的广阔田野调查（例如欧几里得，LSST等）中选择不同类型的天体物理对象的有前途的工具，该工具每晚都针对数百万个来源。

白内障手术中的语义分割具有广泛的应用，可导致外科结果增强和降低临床风险。但是，在这些手术中分割不同相关结构的不同问题使得指定独特的网络非常具有挑战性。本文提出了一个语义分割网络，称为Deeppyramid，可以使用三个新颖性来应对这些挑战：（1）金字塔视图融合模块，该模块可在输入卷积中每个像素位置的周围区域中提供不同的角度的全球视图功能图； （2）一个可变形的金字塔接收模块，该模块可实现一个可适应感兴趣对象的几何变换的广泛可变形接收场； （3）专用的金字塔损失，可自适应监督多尺度语义特征图。结合在一起，我们表明这些模块可以有效地提高语义分割性能，尤其是在对象中透明度，可变形性，可伸缩性和钝边缘的情况下。我们证明我们的方法在最先进的级别上执行，并且优于许多现有方法，其利润率很高（与最佳竞争对手的方法相比，联合的交叉路口总体改善为3.66％）。

视觉问题回答（VQA）模型以图像和自然语言问题为输入，并推断出问题的答案。最近，由于患者参与和对临床医生的第二意见，医学成像中的VQA系统已广受欢迎。尽管大多数研究工作都集中在改善架构和克服与数据相关的限制上，但答案一致性仍被忽略，尽管它在建立可信赖的模型中起着至关重要的作用。在这项工作中，我们提出了一个新颖的损失功能和相应的培训程序，该程序允许将问题之间的关系纳入培训过程。具体而言，我们考虑了一种含义，即感知和推理问题之间的含义是已知的A-Priori。为了显示我们的方法的好处，我们将其评估在眼底成像中糖尿病性黄斑水肿（DME）分期的临床相关任务上。我们的实验表明，我们的方法的表现优于最先进的基线，这不仅是提高模型一致性，而且在整体模型的准确性方面。我们的代码和数据可在https://github.com/sergiotasconmorales/consistency_vqa上找到。

无监督的分销（U-OOD）检测最近引起了很多关注，因为它在关键任务系统中的重要性以及对其监督对方的更广泛的适用性。尽管注意力增加，U-OOD方法遭受了重要的缺点。通过对不同的基准和图像方式进行大规模评估，我们在这项工作中展示了最受欢迎的最先进的方法无法始终如一地始终基于Mahalanobis距离（Mahaad）的简单且相对未知的异常探测器。这些方法不一致的一个关键原因是缺乏U-OOD的正式描述。通过一个简单的思想实验，我们提出了基于培训数据集的不变性的U-OOD的表征。我们展示了这种表征如何在众所周置的Mahaad方法中体现在不知不觉中，从而解释了其质量。此外，我们的方法可用于解释U-OOD探测器的预测，并为评估未来U-OOD方法的良好实践提供见解。

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Deep neural networks (DNNs) are often used for text classification tasks as they usually achieve high levels of accuracy. However, DNNs can be computationally intensive with billions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize and to transfer to out-of-distribution (OOD) cases in practice. In this paper, we propose a non-parametric alternative to DNNs that's easy, light-weight and universal in text classification: a combination of a simple compressor like gzip with a $k$-nearest-neighbor classifier. Without any training, pre-training or fine-tuning, our method achieves results that are competitive with non-pretrained deep learning methods on six in-distributed datasets. It even outperforms BERT on all five OOD datasets, including four low-resource languages. Our method also performs particularly well in few-shot settings where labeled data are too scarce for DNNs to achieve a satisfying accuracy.

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.

Scoring rules promote rational and good decision making and predictions by models, this is increasingly important for automated procedures of `auto-ML'. The Brier score and Log loss are well-established scoring rules for classification and regression and possess the `strict properness' property that encourages optimal predictions. In this paper we survey proposed scoring rules for survival analysis, establish the first clear definition of `(strict) properness' for survival scoring rules, and determine which losses are proper and improper. We prove that commonly utilised scoring rules that are claimed to be proper are in fact improper. We further prove that under a strict set of assumptions a class of scoring rules is strictly proper for, what we term, `approximate' survival losses. We hope these findings encourage further research into robust validation of survival models and promote honest evaluation.

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

Extreme wildfires continue to be a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatio-temporal trends, with a view to understanding the impacts of global warming on fire activity. To this end, we analyse the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020, and identify high fire activity during this period in eastern Europe, Algeria, Italy and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land cover usage, and orography, for the domain. To model the complex relationships between the predictor variables and wildfires, we make use of a hybrid statistical deep-learning framework that allows us to disentangle the effects of vapour-pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects extreme wildfire spread. Furthermore, to gain insights into the effect of climate change on wildfire activity in the near future, we perturb VPD and temperature according to their observed trends and find evidence that global warming may lead to spatially non-uniform changes in wildfire activity.