许多现实世界中的问题都包含多个目标和代理，其中目标之间存在权衡。解决此类问题的关键是利用代理之间存在的稀疏依赖性结构。例如，在风电场控制中，在最大化功率和最大程度地减少对系统组件的压力之间存在权衡。涡轮机之间的依赖性是由于唤醒效应而产生的。我们将这种稀疏依赖性模拟为多目标配位图（MO-COG）。在多目标强化学习实用程序功能通常用于对用户偏好而不是目标建模，这可能是未知的。在这种情况下，必须计算一组最佳策略。哪些策略是最佳的，取决于哪些最佳标准适用。如果用户的效用函数是从策略的多个执行中得出的，则必须优化标识的预期收益（SER）。如果用户的效用是从策略的单个执行中得出的，则必须优化预期的标量回报（ESR）标准。例如，风电场受到必须始终遵守的限制和法规，因此必须优化ESR标准。对于Mo-COG，最新的算法只能计算一组SER标准的最佳策略，而ESR标准进行了研究。要计算在ESR标准下（也称为ESR集合）下的一组最佳策略，必须维护回报上的分布。因此，为了计算MO-COGS的ESR标准下的一组最佳策略，我们提出了一种新型的分布多目标变量消除（DMOVE）算法。我们在逼真的风电场模拟中评估了DMOVE。鉴于实际风电场设置中的回报是连续的，我们使用称为Real-NVP的模型来学习连续的返回分布来计算ESR集合。

在许多实际情况下，用户的实用程序来自策略的单个执行。在这种情况下，要应用多目标增强学习，必须优化收益的预期效用。存在各种方案，其中用户对目标（也称为实用程序功能）的偏好是未知或难以指定的。在这种情况下，必须学习一组最佳政策。但是，多目标增强学习社区必须最大程度地忽略了必须最大程度地提高预期效用的设置，结果，一组最佳解决方案尚未定义。在本文中，我们通过提出一阶随机优势作为建立解决方案集以最大化预期效用的标准来应对这一挑战。我们还提出了一种新的优势标准，称为预期标量回报（ESR）优势，该标准率扩展了一阶随机优势，以允许在实践中学习一组最佳策略。然后，我们定义一个称为ESR集的新解决方案概念，该概念是ESR主导的一组策略。最后，我们定义了一种新的多目标分布表格增强学习（MOT-DRL）算法，以在多目标多臂强盗设置中学习设置的ESR。

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Micro-CT images of the renal arteries of intact rat kidneys, which had their vasculature injected with the contrast agent polymer Microfil, were characterized. Measurement of inter-branch segment properties and the hierarchical structure of the vessel trees were computed by an automated algorithmic approach. The perfusion territories of the different kidneys, as well as the local diameters of the segmented vasculature were mapped onto the representative structures and visually explored. Various parameters were compared in order to outline key geometrical properties, properties which were shown to not have a wide range of inter-specimen variation. It is shown that the fractal scaling in non-symmetric branching reveals itself differently, than in symmetric branching (e.g., in the lung the mean bronchial diameters at each generation are closely related). Also, perfused tissue is shown to have very little inter-specimen variation and therefore could be used in future studies related to characterizing various disease states of tissues and organs based on vascular branching geometry.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

In a wide variety of fields, analysis of images involves defining a region and measuring its inherent properties. Such measurements include a region's surface area, curvature, volume, average gray and/or color scale, and so on. Furthermore, the subsequent subdivision of these regions is sometimes performed. These subdivisions are then used to measure local information, at even finer scales. However, simple griding or manual editing methods are typically used to subdivide a region into smaller units. The resulting subdivisions can therefore either not relate well to the actual shape or property of the region being studied (i.e., gridding methods), or be time consuming and based on user subjectivity (i.e., manual methods). The method discussed in this work extracts subdivisional units based on a region's general shape information. We present the results of applying our method to the medical image analysis of nested regions-of-interest of myocardial wall, where the subdivisions are used to study temporal and/or spatial heterogeneity of myocardial perfusion. This method is of particular interest for creating subdivision regions-of-interest (SROIs) when no variable intensity or other criteria within a region need be used to separate a particular region into subunits.

This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous division and was further improved for the 2022 event, both of which resulting in first place finishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-defined track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.

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.

Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel estimation approaches have demonstrated unprecedented performance at handling unknown degradations. Nonetheless, a number of limitations constrain their efficacy when used by downstream SR models. Specifically, this family of methods yields i) excessive inference time due to long per-image adaptation times and ii) inferior image fidelity due to kernel mismatch. In this work, we introduce a learning-to-learn approach that meta-learns from the information contained in a distribution of images, thereby enabling significantly faster adaptation to new images with substantially improved performance in both kernel estimation and image fidelity. Specifically, we meta-train a kernel-generating GAN, named MetaKernelGAN, on a range of tasks, such that when a new image is presented, the generator starts from an informed kernel estimate and the discriminator starts with a strong capability to distinguish between patch distributions. Compared with state-of-the-art methods, our experiments show that MetaKernelGAN better estimates the magnitude and covariance of the kernel, leading to state-of-the-art blind SR results within a similar computational regime when combined with a non-blind SR model. Through supervised learning of an unsupervised learner, our method maintains the generalizability of the unsupervised learner, improves the optimization stability of kernel estimation, and hence image adaptation, and leads to a faster inference with a speedup between 14.24 to 102.1x over existing methods.