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Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors. Our approach employs a multi-resolution graph neural network that operates on a prior geometric template which is deformed to fit object boundaries of interest. We show how different templates may be used for the different surface meshing targets, and introduce a novel masked autoencoder pretraining strategy for 3D spherical data. The proposed method outperforms nnUNet in a one-shot setting for segmentation of the pericardium, left ventricle (LV) cavity and the LV myocardium. Similarly, the method outperforms other lumen segmentation operating on multi-planar reformatted images. Results further indicate that mesh quality is on par with or improves upon marching cubes post-processing of voxel mask predictions, while remaining flexible in the choice of mesh triangulation prior, thus paving the way for more accurate and topologically consistent 3D medical object surface meshing., Comment: Submitted to Medical Image Analysis

We extend the formalism for the low-energy analysis of the $\gamma p\rightarrow K^{+}\pi\Sigma$ photoproduction reaction to include the strangeness $S=-1$ meson-baryon p-wave final-state interaction with total angular momentum $J=\frac{3}{2}\,$. As an application, and a check of our method, we evaluate contributions due to the exchange of the $\Sigma^{\ast}(1385)$ resonance as derived from a chiral Lagrangian., Comment: 25 pages, 6 figures

This article introduces the analytical approach of practice mapping, using vector embeddings of network actions and interactions to map commonalities and disjunctures in the practices of social media users, as a framework for methodological advancement beyond the limitations of conventional network analysis and visualisation. In particular, the methodological framework we outline here has the potential to incorporate multiple distinct modes of interaction into a single practice map, can be further enriched with account-level attributes such as information gleaned from textual analysis, profile information, available demographic details, and other features, and can be applied even to a cross-platform analysis of communicative patterns and practices. The article presents practice mapping as an analytical framework and outlines its key methodological considerations. Given its prominence in past social media research, we draw on examples and data from the platform formerly known as Twitter in order to enable experienced scholars to translate their approaches to a practice mapping paradigm more easily, but point out how data from other platforms may be used in equivalent ways in practice mapping studies. We illustrate the utility of the approach by applying it to a dataset where the application of conventional network analysis and visualisation approaches has produced few meaningful insights.

In this paper we study the problem of estimating the migration direction of cells based on a single image. To the best of our knowledge, there is only one related work that uses a classification CNN for four classes (quadrants). This approach does not allow detailed directional resolution. We solve the single image estimation problem using deep circular regression with special attention to cycle-sensitive methods. On two databases we achieve an average accuracy of $\sim$17 degrees, which is a significant improvement over the previous work.

Mechanical loss in amorphous solids at low frequencies is commonly attributed to thermally activated transitions of isolated two-level systems (TLS) that come in resonance with a mechanical wave. Using atomistic modeling of amorphous silicon, we observe instead that the inherent structures that constitute the TLS form a sparsely connected network with thermodynamic pathways between states. An analytically tractable theory for mechanical loss of the full network is derived from a nonequilibrium thermodynamic perspective. We show that the connected network model predicts mechanical loss with distinct temperature and frequency profiles when compared to the isolated TLS model. This not only calls into question the validity of the TLS model, but also gives us many new avenues and properties to analyze for the targeted design of low mechanical loss materials for applications in gravitational wave detectors.

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 quest for room-temperature ductile ceramics has been repeatedly fueled by hopes for large-scale applications but so far has been not successful. Recent demonstrations of enhanced functional properties in ceramics through judicious dislocation imprint, however, have been sparking renewed interest in dislocation plasticity in brittle ceramics. Here, we propose a facile approach using room-temperature mechanically seeded mobile dislocations with a density of ~10^14/m^2 to significantly improve the room-temperature plasticity of ceramics with a large plastic strain beyond ~30%. The seeded mobile dislocations trigger profuse dislocation multiplication via cross slip and motion. Hence, they offer an avenue to suppress brittle fracture and harvest plasticity in ceramics without any additional high-temperature process. We employ both in situ nano-/micromechanical deformation and ex situ bulk deformation to bridge the length scales. This finding tackles the pressing bottleneck of dislocation engineering in ceramics for achieving ductile ceramics and harvesting both versatile mechanical and functional properties.

Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system. Our approach shows the ability to integrate large-scale loop closures, while limiting necessary reintegration. Furthermore, we verify the scalability of our approach by demonstrating successful building-scale mapping taking multiple loop closures into account during the optimization, and show that our method outperforms existing state-of-the-art approaches on large scenes in terms of quality and runtime. Our code is available at https://kth-rpl.github.io/neural_graph_mapping/., Comment: Project page: https://kth-rpl.github.io/neural_graph_mapping/

Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user's current situation. Unfortunately, today's cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on high-level percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.

Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.

In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.

Autonomous agents rely on sensor data to construct representations of their environment, essential for predicting future events and planning their own actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in dynamic environments, where efficiently inferring the state of the environment based on sensor readings from different times is still an open problem. This work focuses on inferring the state of the dynamic part of the environment, i.e., where dynamic objects might be, based on previous observations and constraints on their dynamics. We formalize the problem and introduce Transitional Grid Maps (TGMs), an efficient analytical solution. TGMs are based on a set of novel assumptions that hold in many practical scenarios. They significantly reduce the complexity of the problem, enabling continuous prediction and updating of the entire dynamic map based on the known static map (see Fig.1), differentiating them from other alternatives. We compare our approach with a state-of-the-art particle filter, obtaining more prudent predictions in occluded scenarios and on-par results on unoccluded tracking.

The maximal degree of monomials belonging to the unique minimal system of monomial generators of the canonical module $\omega(K[{\mathcal P}])$ of the toric ring $K[{\mathcal P}]$ defined by a lattice polytope ${\mathcal P}$ will be studied. It is shown that if ${\mathcal P}$ possesses an interior lattice point, then the maximal degree is at most ${\rm dim} {\mathcal P} - 1$, and that this bound is the best possible in general.

This study investigates the determinants for the uptake of institutional and subject repository Open Access (OA) in the university landscape of Germany and considers three factors: the disciplinary profile of universities, their OA infrastructures and services and large transformative agreements. The uptake of OA as well as the determinants are measured by combining several data sources (incl. Web of Science, Unpaywall, an authority file of standardised German affiliation information, the ISSN-Gold-OA 4.0 list, and lists of publications covered by transformative agreements). For universities OA infrastructures and services, a structured data collection was created by harvesting different sources of information and by manual online search. To determine the explanatory power of the different factors, a series of regression analyses was performed for different periods and for both institutional as well as subject repository OA. As a result of the regression analyses, the most determining factor for the explanation of differences in the uptake of both repository OA-types turned out to be the disciplinary profile, whereas all variables that capture local infrastructural support and services for OA turned out to be non-significant. The outcome of the regression analyses is contextualised by an interview study conducted with 20 OA officers of German universities. The contextualisation provides hints that the original function of institutional repositories, offering a channel for secondary publishing is vanishing, while a new function of aggregation of metadata and full texts is becoming of increasing importance., Comment: 26 pages, 7 tables. arXiv admin note: text overlap with arXiv:2209.12505

Model checking has found a role in the engineering of reactive systems. However, model checkers are still strongly limited by the size of the system description they can check. Here we present a technique in which a system is simplified prior to model checking by the application of abstraction rules. The rules can greatly reduce the state space of a system description and help in understanding why a system satisfies a property. We illustrate the use of the technique on examples, including Dekker's mutual exclusion algorithm., Comment: 11 pages, 0 figures

We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space. Because representation learning is used, concepts can be identified even if they do not appear in the training data set. However, reference EEG data must exist for each such concept. We also show an application of the method to the problem of information retrieval. In neural information retrieval, EEG data is captured while a user recalls the contents of a document, and a list of links to predicted documents is produced., Comment: 18 pages, 18 figures

We experimentally study collective decay of an extended disordered ensemble of $N$ atoms inside a hollow-core fiber. We observe up to $300$-fold enhanced decay rates, strong optical bursts and a coherent ringing. Due to inhomogeneities limiting the synchronization of atoms, the data does not show the typical scaling with $N$. We show that an effective number of collective emitters can be determined to recover the $N$ scaling known to homogeneous ensembles over a large parameter range. This provides physical insight into the limits of collective decay and allows for its optimization in extended ensembles as used, e.g., in quantum optics, precision time-keeping or waveguide QED.

Service providers commonly provide only a fixed catalog of services to their clients. Both clients and service providers can benefit from service negotiation, in which a client makes a query for a specific service, and the provider counters with an offer. The query could include parameters that control the performance, reliability, and function of the service. However, a problem with service negotiation is that it can be expensive for a service provider to support. In this paper we define a formal negotiation policy language that enables automated service negotiation. In the model supported by the language, service providers can recursively obtain the services they need from sub-providers. The queries made by clients, and the offers returned from service providers, are expressed in quantifier-free first-order logic. Quantifier elimination is used to transform constraints between providers and sub-providers. The pattern of interaction between clients and service providers is defined in process algebra. We show a correctness property of our language: if sub-providers respond positively to queries, then so does the provider itself., Comment: 17 pages, 7 figures

Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries.

In the current scientific and political discourse surrounding the transformation of the scientific publication system, significant attention is focused on Diamond Open Access (OA). This article explores the potential and challenges of Diamond OA journals, using Germany as a case study. Two questions are addressed: first, the current role of such journals in the scientific publication system is determined through bibliometric analysis across various disciplines. Second, an investigation is conducted to assess the sustainability of Diamond OA journals and identify associated structural problems or potential breaking points. This investigation includes an in-depth expert interview study involving 20 editors of Diamond OA journals. The empirical results are presented using a landscape map that considers two dimensions: 'monetized and gift-based completion of tasks' and 'journal team size.' The bibliometric analysis reveals a substantial number of Diamond OA journals in the social sciences and humanities, but limited adoption in other fields. The model proves effective for small to mid-sized journals, but not for larger ones. Additionally, it was found that 23 Diamond OA journals have recently discontinued their operations. The expert interviews demonstrate the usefulness of the two dimensions in understanding key differences. Journals in two of the four quadrants of the map exemplify sustainable conditions, while the other two quadrants raise concerns about long-term stability. These concerns include limited funding leading to a lack of division of labor and an excessive burden on highly committed members. These findings underscore the need for the development of more sustainable funding models to ensure the success of Diamond OA journals., Comment: 29 pages, 8 figures

The paper presents a first time attempt on a combined fit of the $K^{-}p$ low-energy data and the $\pi\Sigma$ photoproduction mass spectra, performed without fixing the meson-baryon rescattering amplitudes to a specific $\pi\Sigma - \bar{K}N$ coupled channels model obtained from fitting exclusively the $K^{-}p$ data. The formalism adopted to describe the photoproduction process is based on chiral perturbation theory and employs a limited number of free parameters. The achieved description of the photoproduction mass distributions is not quite satisfactory, leaving a room for improving the photo-kernel construction, but still provides additional constraints on the positions of the $\Lambda(1405)$ poles. In particular, the presented models tend to limit the mass of the lower pole and yield a larger width of the $\bar{K}N$ related pole at a higher mass., Comment: 20 pages, 6 figures

Additive manufacturing technologies enable the production of complex and bioinspired shapes using magneto-responsive materials, which find diverse applications in soft robotics. Particularly, the development of composites with controlled gradients in mechanical properties offers new prospects for advancements in magneto-active materials. However, achieving such composites with gradients typically involves complex multi-material printing procedures. In this study, a single-step laser powder bed fusion (LPBF) process is proposed that enables precise local adjustments of the mechanical stiffness within magneto-active composites. By utilizing distinct laser parameters in specific regions of a composite containing thermoplastic polyurethane and atomized magnetic powder derived from hard magnetic Nd-Fe-B, the stiffness of the composite can be modified within the range of 2 to 22 MPa. Various magneto-responsive actuators with locally tailored stiffness are fabricated and their magnetic performance is investigated. The enhanced response exhibited by actuators with locally adjusted mechanical properties in comparison to their homogeneous counterparts with identical geometries is shown. As a demonstration of a biomedical application, a magnetically responsive stent with localized adjustment is presented with the ability to meet specific requirements in terms of geometry and local stiffness based on an individual's anatomy and disease condition. The proposed method presents an approach for creating functionally graded materials using LPBF, not only for magneto-active materials but also for several other structural and functional materials.

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine learning estimators combine outcome modeling with balancing weights - weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine the coefficients from the original outcome model and coefficients from an unpenalized ordinary least squares (OLS) fit on the same data. We see that, under certain choices of regularization parameters, the augmented estimator often collapses to the OLS estimator alone; this occurs for example in a re-analysis of the Lalonde 1986 dataset. We then extend these results to specific choices of outcome and weighting models. We first show that the augmented estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression. This holds numerically in finite samples and lays the groundwork for a novel analysis of undersmoothing and asymptotic rates of convergence. When the weighting model is instead lasso-penalized regression, we give closed-form expressions for special cases and demonstrate a ``double selection'' property. Our framework opens the black box on this increasingly popular class of estimators, bridges the gap between existing results on the semiparametric efficiency of undersmoothed and doubly robust estimators, and provides new insights into the performance of augmented balancing weights.