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Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state-of-the-art results, high computational demands due to multi-step inference limited its use in many scenarios. In this paper, we show that the perceived inefficiency was caused by a flaw in the inference pipeline that has so far gone unnoticed. The fixed model performs comparably to the best previously reported configuration while being more than 200$\times$ faster. To optimize for downstream task performance, we perform end-to-end fine-tuning on top of the single-step model with task-specific losses and get a deterministic model that outperforms all other diffusion-based depth and normal estimation models on common zero-shot benchmarks. We surprisingly find that this fine-tuning protocol also works directly on Stable Diffusion and achieves comparable performance to current state-of-the-art diffusion-based depth and normal estimation models, calling into question some of the conclusions drawn from prior works., Comment: Project page: https://vision.rwth-aachen.de/diffusion-e2e-ft

We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point clouds. Given a language instruction and a point cloud of the scene, our framework initiates a hierarchical planning procedure, whereby a language model generates a high-level plan and a sampling-based planner produces constraint-satisfying continuous parameters for manipulation primitives sequenced according to the high-level plan. Key to our approach is the use of a relational dynamics model as a unifying interface between the continuous and symbolic representations of states and actions, thus facilitating language-driven planning from high-dimensional perceptual input such as point clouds. Whereas previous relational dynamics models require training on datasets of multi-step manipulation scenarios that align with the intended test scenarios, Points2Plans uses only single-step simulated training data while generalizing zero-shot to a variable number of steps during real-world evaluations. We evaluate our approach on tasks involving geometric reasoning, multi-object interactions, and occluded object reasoning in both simulated and real-world settings. Results demonstrate that Points2Plans offers strong generalization to unseen long-horizon tasks in the real world, where it solves over 85% of evaluated tasks while the next best baseline solves only 50%. Qualitative demonstrations of our approach operating on a mobile manipulator platform are made available at sites.google.com/stanford.edu/points2plans., Comment: Under review

Soft interfaces are ubiquitous in nature, governing quintessential hydrodynamics functions, like lubrication, stability and cargo transport. It is shown here how a magnetic force field at a magnetic-nonmagnetic fluid interface results in an ultra-soft interface with nonlinear elasticity and tunable viscous shear properties. The balance between magnetic pressure, viscous stress and Laplace pressure results in a deformed and stable liquid-in-liquid tube with apparent elasticity in the range 2 kPa -10 kPa, possibly extended by a proper choice of liquid properties. Such highly deformable liquid-liquid interfaces of arbitrary shape with vanishing viscous shear open doors to unique microfluidic phenomena, biomaterial flows and complex biosystems mimicking., Comment: 7 pages , 5 figures. arXiv admin note: text overlap with arXiv:2310.14280

Adding game elements to higher education is an increasingly common practice. As a result, many recent empirical studies focus on studying the effectiveness of gamified or game-based educational experiences. The findings of these studies are very diverse, showing both positive and negative effects, and thus calling for comparative meta-studies. In this paper we review and analyze different studies, aiming to summarise and evaluate controlled experiments conducted within different scientific disciplines. We focus on the clarity of non-experimental conditions' descriptions and show that in most cases a. educational methods used in control groups' activities are poorly described, b. educational materials used in control groups' activities are often unclear, and c. the starting conditions are unclear. We also noticed that studies in the fields of computer science and engineering, in general, report results more clearly than in other fields. Based on the above finding, we conclude with a few recommendations for the execution of future empirical studies of games in education for the sake of allowing a more structured comparison., Comment: draft

Prior research has explored potential applications of video games in programming education to elicit computational thinking skills. However, existing approaches are often either too general, not taking into account the diversity of genres and mechanisms between video games, or too narrow, selecting tools that were specifically designed for educational purposes. In this paper we propose a more fundamental approach, defining beneficial connections between individual design patterns present in video games and computational thinking skills. We argue that video games have the capacity to elicit these skills and even to potentially train them. This could be an effective method to solidify a conceptual base which would make programming education more effective., Comment: draft

A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and generality, along with limited or no hardware safety guarantees. In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy trained entirely in simulation that combines reinforcement learning, geometric fabrics, and teacher-student distillation. We address key challenges in joint arm-hand policy learning, such as high-dimensional observation and action spaces, the sim2real gap, collision avoidance, and hardware constraints. DextrAH-G enables a 23 motor arm-hand robot to safely and continuously grasp and transport a large variety of objects at high speed using multi-modal inputs including depth images, allowing generalization across object geometry. Videos at https://sites.google.com/view/dextrah-g.

We prove an analog of the Deligne conjecture for prestacks. We show that given a prestack $\mathbb A$, its Gerstenhaber--Schack complex $\mathbf{C}_{\mathsf{GS}}(\mathbb A)$ is naturally an $E_2$-algebra. This structure generalises both the known $\mathsf{L}_\infty$-algebra structure on $\mathbf{C}_{\mathsf{GS}}(\mathbb A)$, as well as the Gerstenhaber algebra structure on its cohomology $\mathbf{H}_{\mathsf{GS}}(\mathbb A)$. The main ingredient is the proof of a conjecture of Hawkins \cite{hawkins}, stating that the homology of the dg operad $\mathsf{Quilt}$ has vanishing homology in positive degrees. As a corollary, $\Quilt$ is quasi-isomorphic to the operad $\mathsf{Brace}$ encoding brace algebras. In addition, we improve the $L_\infty$-structure on $\Quilt$ be showing that it originates from a $\mathsf{PreLie}_\infty$-structure lifting the $\mathsf{PreLie}$-structure on $\mathsf{Brace}$ in homology., Comment: Comments are welcome

Autonomous vehicles require a precise understanding of their environment to navigate safely. Reliable identification of unknown objects, especially those that are absent during training, such as wild animals, is critical due to their potential to cause serious accidents. Significant progress in semantic segmentation of anomalies has been driven by the availability of out-of-distribution (OOD) benchmarks. However, a comprehensive understanding of scene dynamics requires the segmentation of individual objects, and thus the segmentation of instances is essential. Development in this area has been lagging, largely due to the lack of dedicated benchmarks. To address this gap, we have extended the most commonly used anomaly segmentation benchmarks to include the instance segmentation task. Our evaluation of anomaly instance segmentation methods shows that this challenge remains an unsolved problem. The benchmark website and the competition page can be found at: https://vision.rwth-aachen.de/oodis ., Comment: Accepted at the VAND 2.0 Workshop at CVPR 2024. Project page: https://vision.rwth-aachen.de/oodis

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes., Comment: 40 pages, 2 figures, 3 tables

In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the functional correspondences between object parts, we conceptualize functional actions, such as pouring and constrained placing, as combinations of different correspondence constraints. CPM comprises a collection of composable diffusion models, where each model captures a different inter-object correspondence. These diffusion models can generate parameters for manipulation skills based on the specific object parts. Leveraging part-based correspondences coupled with the task decomposition into distinct constraints enables strong generalization to novel objects and object categories. We validate our approach in both simulated and real-world scenarios, demonstrating its effectiveness in achieving robust and generalized manipulation capabilities., Comment: Presented at CoRL 2023. For videos and additional results, see our website: https://cpmcorl2023.github.io/

Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often exhibiting sharp declines in capability even for minor differences. In this work, we examine robustness to a suite of these types of visual changes for RGB-D and point cloud based visual control policies. To perform these experiments on both model-free and model-based reinforcement learners, we introduce a novel Point Cloud World Model (PCWM) and point cloud based control policies. Our experiments show that policies that explicitly encode point clouds are significantly more robust than their RGB-D counterparts. Further, we find our proposed PCWM significantly outperforms prior works in terms of sample efficiency during training. Taken together, these results suggest reasoning about the 3D scene through point clouds can improve performance, reduce learning time, and increase robustness for robotic learners. Project Webpage: https://pvskand.github.io/projects/PCWM, Comment: Accepted at International Conference on Robotics and Automation, 2024

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this problem do not capture accurate uncertainty, which is required to subsequently produce safe motion plans. In this paper, we cast the representation of 3D tabletop scenes as a multi-class classification problem. To tackle this, we introduce V-PRISM, a framework and method for robustly creating probabilistic 3D segmentation maps of tabletop scenes. Our maps contain both occupancy estimates, segmentation information, and principled uncertainty measures. We evaluate the robustness of our method in (1) procedurally generated scenes using open-source object datasets, and (2) real-world tabletop data collected from a depth camera. Our experiments show that our approach outperforms alternative continuous reconstruction approaches that do not explicitly reason about objects in a multi-class formulation.

The development of superconducting qubit technology has shown great potential for the construction of practical quantum computers. As the complexity of quantum processors continues to grow, the need for stringent fabrication tolerances becomes increasingly critical. Utilizing advanced industrial fabrication processes could facilitate the necessary level of fabrication control to support the continued scaling of quantum processors. However, these industrial processes are currently not optimized to produce high coherence devices, nor are they a priori compatible with the commonly used approaches to make superconducting qubits. In this work, we demonstrate for the first time superconducting transmon qubits manufactured in a 300 mm CMOS pilot line, using industrial fabrication methods, with resulting relaxation and coherence times already exceeding 100 microseconds. We show across-wafer, large-scale statistics studies of coherence, yield, variability, and aging that confirm the validity of our approach. The presented industry-scale fabrication process, using exclusively optical lithography and reactive ion etching, shows performance and yield similar to the conventional laboratory-style techniques utilizing metal lift-off, angled evaporation, and electron-beam writing. Moreover, it offers potential for further upscaling by including three-dimensional integration and additional process optimization using advanced metrology and judicious choice of processing parameters and splits. This result marks the advent of more reliable, large-scale, truly CMOS-compatible fabrication of superconducting quantum computing processors., Comment: main text: 7 pages, 4 figures. bibliography: 55 references. supplement: 8 sections, 8 figures, 2 tables

Flows are omnipresent and govern the dynamics of plasma. Solar tornadoes are a class of apparently rotating prominences, that might be formed by thermal instability. In spectroscopic studies on thermal instability background flow is commonly neglected. We here determine the effect of background flow on thermal instability in cylindrical magnetic field configurations as the influence of various parameters on the MHD spectrum. We investigate discrete thermal modes. In an analytical study, we extend upon the literature by including a generic background flow in a cylindrical coordinate system. The non-adiabatic MHD equations are linearised, Fourier-analysed, and are examined to understand how a background flow changes the continua. An approximate expression for discrete thermal modes is derived using a WKB analysis. The analytical results are then verified for a benchmark equilibrium using the eigenvalue code Legolas. The eigenfunctions of discrete thermal modes are visualised in 2D and 3D. The thermal continuum is Doppler-shifted due to the background flow, just like the slow and Alfv\'en continua. Discrete modes are altered because the governing equations contain flow-related terms. An approximate expression to predict the appearance of discrete thermal modes based on the equilibrium parameters is derived. All analytical expressions match the numerical results. The distribution of the density perturbations of the discrete thermal modes is not a singular condensation. 3D visualisation of the total velocity field shows that the helical field is heavily influenced by the radial velocity perturbation. We derived analytic expressions for non-adiabatic MHD modes of a cylindrical equilibrium with background flow and verified them using a coronal equilibrium. However, the equations are valid for and can be applied in other astrophysical environments., Comment: 19 pages, 22 figures, accepted for publication by A&A

Ferrofluids kept in place by permanent magnet quadrupoles can act as liquid walls to surround a second non-magnetic inside, resulting in a liquid fluidic channel with diameter size ranging from mm down to less than 10 micrometer. Micro particle tracking velocimetry (micro PTV) experiments and modeling show that near ideal plug flow is possible in such liquid-in-liquid channels due to the reduced friction at the walls. The measured fluids velocity profiles agree with the predictions of a hydrodynamic model of cylindrical symmetry with a minimal set of hypotheses. By introducing symmetry breaking elements in the system, we show how unique velocity and flow properties can be obtained. Our liquid-in-liquid confinement opens new possibilities for < 10 micrometer-sized microfluidics with low pressures and low shear, with flow characteristics not attainable in comparable solid-wall devices., Comment: 12 pages 7 figures

Single photon emitters with internal spin are leading contenders for developing quantum repeater networks, enabling long-range entanglement distribution for transformational technologies in communications and sensing. However, scaling beyond current few-node networks will require radical improvements to quantum link efficiencies and fidelities. Solid-state emitters are particularly promising due to their crystalline environment, enabling nanophotonic integration and providing spins for memory and processing. However, inherent spatial and temporal variations in host crystals give rise to static shifts and dynamic fluctuations in optical transition frequencies, posing formidable challenges in establishing large-scale, multipartite entanglement. Here, we introduce a scalable approach to quantum networking that utilizes frequency erasing photon detection in conjunction with adaptive, real-time quantum control. This enables frequency multiplexed entanglement distribution that is also insensitive to deleterious optical frequency fluctuations. Single rare-earth ions are an ideal platform for implementing this protocol due to their long spin coherence, narrow optical inhomogeneous distributions, and long photon lifetimes. Using two 171Yb:YVO4 ions in remote nanophotonic cavities we herald bipartite entanglement and probabilistically teleport quantum states. Then, we extend this protocol to include a third ion and prepare a tripartite W state: a useful input for advanced quantum networking applications. Our results provide a practical route to overcoming universal limitations imposed by non-uniformity and instability in solid-state emitters, whilst also showcasing single rare-earth ions as a scalable platform for the future quantum internet.

This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. Different encoding methods, including one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa and also on the model architecture used. We make our code publicly available on GitHub., Comment: 17 pages, 3 figures, the code is available at: https://github.com/paddyOnGithub/ordinal_regression

Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and desired placement locations to ensure success. While several works have examined this joint pick-and-place problem, none have fully leveraged recent learning-based approaches for multi-fingered grasp planning. We present a modular algorithm for joint pick and place planning that can make use of state of the art grasp classifiers for planning multi-fingered grasps for novel objects from partial view point clouds. We demonstrate our joint pick and place formulation with several costs associated with different placement tasks. Experiments on pick and place tasks with cluttered scenes using a physical robot show that our joint inference method is more successful than a sequential pick then place approach, while also achieving better placement configurations., Comment: 8 pages, 14 figures, IEEE RA-L

Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segmentate the cytoplasm. Material & Methods: We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist and Cellpose. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test. Results: Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath $19.46/0.91\%$; StarDist $45.33/2.32\%$; Cellpose $31.85/5.61\%$). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard ($\bar{D} = 0.15$) outperforming QuPath ($\bar{D} = 0.22$), StarDist ($\bar{D} = 0.25$) and Cellpose ($\bar{D} = 0.23$). Conclusion: Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Prestacks are algebro-geometric objects whose defining relations are far from quadratic. Indeed, they are cubic and quartic, and moreover inhomogeneous. We show that box operads, a rectangular type of operads introduced in arXiv:2305.20036, constitute the correct framework to encode them and resolve their relations up to homotopy. Our first main result is a Koszul duality theory for box operads, extending the duality for (nonsymmetric) operads. In this new theory, the classical restriction of being quadratic is replaced by the notion of being $\textit{thin-quadratic}$, a condition referring to a particular class of ``thin'' operations. Our main case of interest is the box operad $Lax$ encoding lax prestacks as algebras. We show that $Lax$ is not Koszul by explicitly computing its Koszul complex. We then go on to remedy the situation by suitably restricting the Koszul dual box cooperad $Lax^{\antishriek}$ to obtain our second main result: we establish a minimal (in particular cofibrant) model $Lax_{\infty}$ for the box operad $Lax$ encoding lax prestacks. This sheds new light on Markl's question on the existence of a cofibrant model for the operad encoding presheaves of algebras from arXiv:math/0103052. Indeed, we answer the parallel question with presheaves viewed as prestacks, and prestacks considered as algebras over a box operad, in the positive., Comment: 45 pages, lots of pictures; All comments are welcome!

The computational resources required to solve the full 3D inversion of time-domain electromagnetic data are immense. To overcome the time-consuming 3D simulations, we construct a surrogate model, more precisely, a data-driven statistical model to replace the 3D simulations. It is trained on 3D data and predicts the approximate output much faster. We construct a surrogate model that predicts the discrepancy between a 1D subsurface model and a deviation of the 1D assumption. The latter response is fastly computable with a semi-analytical 1D forward model. We exemplify the approach on a two-layered case. The results are encouraging even with few training samples. Given the computational cost related to the 3D simulations, there are limitations in the number of training samples that can be generated. In addition, certain applications require a wide range of parameters to be sampled, such as the electrical conductivity parameters in a saltwater intrusion case. The challenge of this work is achieving the best possible accuracy with only a few thousand samples. We propose to view the performance in terms of learning gain, representing the gain from the surrogate model whilst still acknowledging a residual discrepancy. Our works open new avenues for effectively simulating 3D TDEM data.

Laser beam shaping offers remarkable possibilities to control and optimise process stability and tailor material properties and structure in laser-based welding and additive manufacturing. However, little is known about the influence of laser beam shaping on the complex melt-pool behaviour, solidified melt-track bead profile and microstructural grain morphology in laser material processing. A simulation-based approach is utilised in the present work to study the effects of laser beam intensity profile and angle of incidence on the melt-pool behaviour in conduction-mode laser melting of stainless steel 316L plates. The present high-fidelity physics-based computational model accounts for crucial physical phenomena in laser material processing such as complex laser-matter interaction, solidification and melting, heat and fluid flow dynamics, and free-surface oscillations. Experiments were carried out using different laser beam shapes and the validity of the numerical predictions is demonstrated. The results indicate that for identical processing parameters, reshaping the laser beam leads to notable changes in the thermal and fluid flow fields in the melt pool, affecting the melt-track bead profile and solidification microstructure. The columnar-to-equiaxed transition is discussed for different laser-intensity profiles., Comment: 27 pages, 10 figures

The Rayleigh-Plateau instability (RPI) is a classical hydrodynamics phenomenon that prevents a jet of liquid to flow indefinitely within air or another liquid. Here, we show how adding a magnetic force field makes possible its suppression. Enclosing the jet in a ferrofluid held by magnetic forces allows flow focusing without sheath flow, which completely avoids dripping failure at small flow rates and provides conditional stability for a continuous fluid jet. Highly deformable liquid interfaces withstanding spatial and time varying flow conditions within a large parameter space can be realized., Comment: 14 pages , 4 figures

Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulation reasoning and planning framework. We propose DOOM and LOOM, which leverage transformer relational dynamics to encode the history of trajectories given partial-view point clouds and an object discovery and tracking engine. Our approaches can perform multiple challenging tasks including reasoning with occluded objects, novel objects appearance, and object reappearance. Throughout our extensive simulation and real-world experiments, we find that our approaches perform well in terms of different numbers of objects and different numbers of distractor actions. Furthermore, we show our approaches outperform an implicit memory baseline., Comment: Presented at IEEE Conference on Robotics and Automation (ICRA) 2024. Website: https://sites.google.com/view/rdmemory

Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method's effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical, real-world applications., Comment: Submitted to IEEE Conference on Robotics and Automation (ICRA) 2024. 8 pages, 11 figures

The quality of software produced by students is often poor. How to teach students to develop good quality software has long been a topic in computer science education and research. We must conclude that we still do not have a good answer to this question. Specifications are necessary to determine the correctness of software, to develop error-free software and to write complete tests. Several attempts have been made to teach students to write specifications before writing code. So far, that has not proven to be very successful: Students do not like to write a specification and do not see the benefits of writing specifications. In this paper we focus on the use of informal specifications. Instead of teaching students how to write specifications, we teach them how to use informal specifications to develop correct software. The results were surprising: the number of errors in software and the completeness of tests both improved considerably and, most importantly, students really appreciate the specifications. We think that if students appreciate specification, we have a key to teach them how to specify and to appreciate its value., Comment: 14 pages

A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding anomalous objects on the road. This task, called anomaly segmentation, can be a stepping stone to safe and reliable autonomous driving. Current approaches tackle anomaly segmentation by assigning an anomaly score to each pixel and by grouping anomalous regions using simple heuristics. However, pixel grouping is a limiting factor when it comes to evaluating the segmentation performance of individual anomalous objects. To address the issue of grouping multiple anomaly instances into one, we propose an approach that produces accurate anomaly instance masks. Our approach centers on an out-of-distribution segmentation model for identifying uncertain regions and a strong generalist segmentation model for anomaly instances segmentation. We investigate ways to use uncertain regions to guide such a segmentation model to perform segmentation of anomalous instances. By incorporating strong object priors from a generalist model we additionally improve the per-pixel anomaly segmentation performance. Our approach outperforms current pixel-level anomaly segmentation methods, achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the RoadAnomaly validation sets respectively. Project page: https://vision.rwth-aachen.de/ugains, Comment: Accepted for publication at GCPR 2023; Project page at https://vision.rwth-aachen.de/ugains