Miracle-dz/UniFAR-FTUs huggingface dataset:
A Parametric Study of the SASI Comparing General Relativistic and Nonrelativistic Treatments*

We present numerical results from a parameter study of the standing accretion shock instability (SASI), investigating the impact of general relativity (GR) on the dynamics. Using GR hydrodynamics with GR gravity, and nonrelativistic (NR) hydrodynamics with Newtonian gravity, in an idealized model setting, we vary the initial radius of the shock, and by varying its mass and radius in concert, the proto-neutron star compactness. We investigate four compactnesses expected in a post-bounce core-collapse supernova (CCSN). We find that GR leads to a longer SASI oscillation period, with ratios between the GR and NR cases as large as 1.29 for the highest-compactness suite. We also find that GR leads to a slower SASI growth rate, with ratios between the GR and NR cases as low as 0.47 for the highest-compactness suite. We discuss implications of our results for CCSN simulations.
Statistical Modelling and Estimation of Space-Time Extremes

This thesis deals with extremal spatial and space-time stochastic processes. Such processes find applications in many areas of environmental analysis with focus on the assessment of rare and extreme events in space and/or time. We consider regularly varying processes and their subclass of heavy tailed max-stable processes which are characterised by extremal dependence functions. We develop new parametric models for these functions and we introduce new inference methods to fit the models to real data.
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection

Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video. A significant challenge in TVG is the low"Semantic Noise Ratio (SNR)", which results in worse performance with lower SNR. Prior works have addressed this challenge using sophisticated techniques. In this paper, we propose a no-frills TVG model that consists of two core modules, namely multi-scale neighboring attention and zoom-in boundary detection. The multi-scale neighboring attention restricts each video token to only aggregate visual contexts from its neighbor, enabling the extraction of the most distinguishing information with multi-scale feature hierarchies from high-ratio noises. The zoom-in boundary detection then focuses on local-wise discrimination of the selected top candidates for fine-grained grounding adjustment. With an end-to-end training strategy, our model achieves competitive performance on different TVG benchmarks, while also having the advantage of faster inference speed and lighter model parameters, thanks to its lightweight architecture.
Label Calibration for Semantic Segmentation Under Domain Shift

Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabilities. The proposed adaptation procedure is fast, comes almost for free in terms of computational resources and leads to considerable performance improvements. We demonstrate the benefits of such label calibration on the highly-practical synthetic-to-real semantic segmentation problem.
Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect"paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks.
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics

The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this theory--practice gap by characterizing the large-sample statistical asymptotics of SGAs via a joint step-size--sample-size scaling limit. We show that iterate averaging with a large fixed step size is robust to the choice of tuning parameters and asymptotically has covariance proportional to that of the MLE sampling distribution. We also prove a Bernstein--von Mises-like theorem to guide tuning, including for generalized posteriors that are robust to model misspecification. Numerical experiments validate our results and recommendations in realistic finite-sample regimes. Our work lays the foundation for a systematic analysis of other stochastic gradient Markov chain Monte Carlo algorithms for a wide range of models.
Caustic formation in a non-Gaussian model for turbulent aerosols

Caustics in the dynamics of heavy particles in turbulence accelerate particle collisions. The rate $\mathscr{J}$ at which these singularities form depends sensitively on the Stokes number St, the non-dimensional inertia parameter. Exact results for this sensitive dependence have been obtained using Gaussian statistical models for turbulent aerosols. However, direct numerical simulations of heavy particles in turbulence yield much larger caustic-formation rates than predicted by the Gaussian theory. In order to understand possible mechanisms explaining this difference, we analyse a non-Gaussian statistical model for caustic formation in the limit of small St. We show that at small St, $\mathscr{J}$ depends sensitively on the tails of the distribution of Lagrangian fluid-velocity gradients. This explains why different authors obtained different St-dependencies of $\mathscr{J}$ in numerical-simulation studies. The most-likely gradient fluctuation that induces caustics at small St, by contrast, is the same in the non-Gaussian and Gaussian models. Direct-numerical simulation results for particles in turbulence show that the optimal fluctuation is similar, but not identical, to that obtained by the model calculations.
JWST's TEMPLATES for Star Formation: The First Resolved Gas-phase Metallicity Maps of Dust-obscured Star-forming Galaxies at z ∼ 4

We present the first spatially resolved maps of gas-phase metallicity for two dust-obscured star-forming galaxies at z ∼ 4, from the JWST TEMPLATES Early Release Science program, derived from NIRSpec integral field unit spectroscopy of the Hα and [N ii] emission lines. Empirical optical line calibrations are used to determine that the sources are globally enriched to near-solar levels. While one source shows elevated [N ii]/Hα ratios and broad Hα emission consistent with the presence of an active galactic nucleus in a ≳1 kpc region, we argue that both systems have already undergone significant metal enrichment as a result of their extremely high star formation rates. Utilizing Atacama Large Millimeter/submillimeter Array rest-frame 380 μm continuum and [Ci](3P2–3P1) line maps we compare the spatial variation of the metallicity and gas-to-dust ratio in the two galaxies, finding the two properties to be anticorrelated on highly resolved spatial scales, consistent with various literature studies of z ∼ 0 galaxies. The data are indicative of the enormous potential of JWST to probe the enrichment of the interstellar medium on ∼kpc scales in extremely dust-obscured systems at z ∼ 4 and beyond.
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge

In this perspective paper, we argue that the dominant paradigm in anomaly detection cannot scale indefinitely and will eventually hit fundamental limits. This is due to the a no free lunch principle for anomaly detection. These limitations can be overcome when there are strong tasks priors, as is the case for many industrial tasks. When such priors do not exists, the task is much harder for anomaly detection. We pose two such tasks as grand challenges for anomaly detection: i) scientific discovery by anomaly detection ii) a"mini-grand"challenge of detecting the most anomalous image in the ImageNet dataset. We believe new anomaly detection tools and ideas would need to be developed to overcome these challenges.
Conditional expectation network for SHAP

A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.
Layer thickness crossover of type-II multiferroic magnetism in NiI2

The discovery of atomically thin van der Waals ferroelectric and magnetic materials encourages the exploration of 2D multiferroics, which holds the promise to understand fascinating magnetoelectric interactions and fabricate advanced spintronic devices. In addition to building a heterostructure consisting of ferroelectric and magnetic ingredients, thinning down layered multiferroics of spin origin such as NiI2 becomes a natural route to realize 2D multiferroicity. However, the layer-dependent behavior, widely known in the community of 2D materials, necessitates a rigorous scrutiny of the multiferroic order in the few-layer limit. Here, we interrogate the layer thickness crossover of helimagnetism in NiI2 that drives the ferroelectricity and thereby type-II multiferroicity. By using wavelength-dependent polarization-resolved optical second harmonic generation (SHG) to probe the ferroic symmetry, we find that the SHG arises from the inversion-symmetry-breaking magnetic order, not previously assumed ferroelectricity. This magnetism-induced SHG is only observed in bilayer or thicker layers, and vanishes in monolayer, suggesting the critical role of interlayer exchange interaction in breaking the degeneracy of geometrically frustrated spin structures in triangular lattice and stabilizing the type-II multiferroic magnetism in few-layers. While the helimagnetic transition temperature is layer dependent, the few-layer NiI2 exhibits another thickness evolution and reaches the bulk-like behavior in trilayer, indicated by the intermediate centrosymmetric antiferromagnetic state as revealed in Raman spectroscopy. Our work therefore highlights the magnetic contribution to SHG and Raman spectroscopy in reduced dimension and guides the optical study of 2D multiferroics.
Assessing the Use of AutoML for Data-Driven Software Engineering

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.
Data-integrated uncertainty quantification for the performance prediction of iced airfoils

Airfoil icing is a severe safety hazard in aviation and causes power losses on wind turbines. The precise shape of the ice formation is subject to large uncertainties, so uncertainty quantification (UQ) is needed for a reliable prediction of its effects. In this study, we aim to establish a reliable estimate of the effect of icing on airfoil performance through UQ. We use a series of experimentally measured wind tunnel ice shapes as input data. Principal component analysis is employed to construct a set of linearly uncorrelated geometric modes from the data, which serves as random input to the UQ simulation. For uncertainty propagation, non-intrusive polynomial chaos expansion (NIPC), multi-level Monte Carlo (MLMC) and multi-fidelity Monte Carlo control variate (MFMC) methods are employed and compared. As a baseline model, large eddy simulations (LES) are carried out using the discontinuous Galerkin flow solver FLEXI. UQ simulations are carried out with the in-house framework PoUnce. Its focus is on a high level of automation and efficiency considerations in a high performance computing environment. Due to the high number of samples, the simulation tool chain of the baseline model is completely automatized, including a new structured boundary layer grid generator for highly irregular domain shapes. The results show that forces on the airfoil vary considerably due to the uncertain ice shape. All three methods prove to be suited to predict mean and standard deviation. In the Monte Carlo techniques, the choice and performance of low-fidelity models is shown to be decisive for estimator variance reduction. The MFMC method performs best in this study. To our knowledge, there are no UQ studies of iced airfoils based on LES, let alone with advanced UQ methods such as MLMC or MFMC. The present study thus represents a leap in accuracy and level of detail for this application.
Invariant Aggregator for Defending against Federated Backdoor Attacks

Federated learning enables training high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vulnerable to various adversarial attacks in the presence of malicious clients. Despite the theoretical and empirical success in defending against attacks that aim to degrade models' utility, defense against backdoor attacks that increase model accuracy on backdoor samples exclusively without hurting the utility on other samples remains challenging. To this end, we first analyze the failure modes of existing defenses over a flat loss landscape, which is common for well-designed neural networks such as Resnet (He et al., 2015) but is often overlooked by previous works. Then, we propose an invariant aggregator that redirects the aggregated update to invariant directions that are generally useful via selectively masking out the update elements that favor few and possibly malicious clients. Theoretical results suggest that our approach provably mitigates backdoor attacks and remains effective over flat loss landscapes. Empirical results on three datasets with different modalities and varying numbers of clients further demonstrate that our approach mitigates a broad class of backdoor attacks with a negligible cost on the model utility.
Growth strains cause vascular browning and cavities in ´Nicoter´ apples

‘Nicoter’ apples (Malus × domestica Borkh.) occasionally develop a disorder referred to as vascular browning. Symptomatic fruit are perceived as being of low quality. The objective was to identify the mechanistic basis of this disorder. The frequency of symptomatic ‘Nicoter’ apples differed between growing sites and increased with delayed harvest. Typical symptoms are tissue browning and cavities in the ray parenchyma of the calyx region, and occasionally also of the stem end. Cavity size is positively correlated with the extent of tissue browning. Cavities were oriented radially in the direction of the bisecting line between the radii connecting the calyx/pedicel axis to the sepal and petal bundles. Microscopy revealed cell wall fragments in the cavities indicating physical rupture of cell walls. Immunolabelling of cell wall epitopes offered no evidence for separation of cells along cell walls. The growth pattern in ‘Nicoter’ is similar to that in its parents ‘Gala’ and ‘Braeburn’. Allometric analyses revealed no differences in growth in fruit length among the three cultivars. However, the allometric analyses of growth in diameter revealed a marked increase in the distance between the surface of the calyx cavity and the vascular bundle in ‘Nicoter’, that was absent in ‘Braeburn’ and ‘Gala’. This increase displaced the petal bundles in the ray parenchyma outwards and subjected the tissue between the petal and sepal bundles to tangential strain. Rupture of cells results in tissue browning and cavity formation. A timely harvest is a practicable countermeasure for decreasing the incidence of vascular browning.
Types of kindergarten and their relationship with parental and children’s socio-demographic characteristics in Denmark

In Danish outdoor kindergartens, children are spending most of the day outdoors often in forests or similar nature environments. These children are assumed to be healthier than children attending conventional kindergartens, however, factors related to choosing a specific type of kindergarten may explain the differences. To better understand this, we aimed to investigate parents reasons for choosing either outdoor or conventional kindergartens based on a mixed-method participatory Concept Mapping approach, and further if parental socio-demographics and early child characteristics differed prior to enrolling children to either type of kindergarten using a cohort register-based approach. Parents of children attending outdoor kindergartens (n = 23) weighed reasons such as “physical setting, outdoor life, and freedom of movement” high, whereas “a good first impression of the kindergarten” was an important reason for parents choosing a conventional kindergarten (n = 22). In the register-based approach, 2434 and 2643 children attended outdoor or conventional kindergartens, respectively. The parents choosing outdoor kindergartens as well as their children differed according to most investigated characteristics, including origin (maternal non-Western: 4.2% vs. 21.9%, p < .0001), educational level (maternal long education: 45.6% vs. 33.0%, p < .0001), prematurity (5.1% vs. 7.1%, p = 0.004) and sex (females: 43.5% vs. 48.6%, p = <0.0013). In conclusion, parental reasons for choosing kindergarten as well as parental socio-demographics differed substantially among kindergarten type. These differences might cause selection bias if not considering when comparing health outcomes among children attending different kinds of kindergartens.
“Help! I need some music!”: Analysing music discourse & depression on Reddit

Individuals choose varying music listening strategies to fulfill particular mood-regulation goals. However, ineffective musical choices and a lack of cognizance of the effects thereof can be detrimental to their well-being and may lead to adverse outcomes like anxiety or depression. In our study, we use the social media platform Reddit to perform a large-scale analysis to unearth the several music-mediated mood-regulation goals that individuals opt for in the context of depression. A mixed-methods approach involving natural language processing techniques followed by qualitative analysis was performed on all music-related posts to identify the various music-listening strategies and group them into healthy and unhealthy associations. Analysis of the music content (acoustic features and lyrical themes) accompanying healthy and unhealthy associations showed significant differences. Individuals resorting to unhealthy strategies gravitate towards low-valence tracks. Moreover, lyrical themes associated with unhealthy strategies incorporated tracks with low optimism, high blame, and high self-reference. Our findings demonstrate that being mindful of the objectives of using music, the subsequent effects thereof, and aligning both for well-being outcomes is imperative for comprehensive understanding of the effectiveness of music.
Media coverage, fake news, and the diffusion of xenophobic violence: A fine-grained county-level analysis of the geographic and temporal patterns of arson attacks during the German refugee crisis 2015–2017

Over the year of 2015, about 800.000 refugees arrived in Germany, a number which equals around one percent of the total population. This migration process was labelled the refugee crisis and was accompanied by a contested debate. On the one hand, there was a widespread willingness to voluntarily help arriving refugees, on the other hand, the number of xenophobic attacks against refugees drastically increased. Our paper will focus on a specific form of xenophobic violence with a strong symbolic meaning: We analyze how arson attacks against collective accommodation facilities spread. Using a comprehensive web chronicle, we collected temporal and spatial data about arson attacks perpetrated on accommodations or facilities for refugees in Germany between 2015 and 2017. We counted 251 attacks, assigned each incident location to its county, merged county characteristics such as population size, proportion of foreigners, right-wing party support, and—going beyond previous research—added geographically coded media data from two digital archives. Besides newspaper contents of a popular nation-wide tabloid, we use a data base that covers local fake news on refugees. Based on these data, we constructed a balanced panel data set with the counties as geographical units and periods of 14 days as the time dimension. Results indicate that social contagion drives the diffusion process of arson attacks. Spatial proximity of previous attacks increased the propensity of attacks in the neighboring counties. Attacks were more likely to occur in counties with larger populations and fewer foreigners. While local newspaper coverage did not impact the diffusion of xenophobic attacks, fake news were relevant–but only in East Germany. We also considered two particularly salient threatening events that received nation-wide media attention, namely Merkel’s “border opening” on the 5th of September 2015 and the sexual assaults occurring during New Year’s 2015/16 in Cologne. Both were followed by temporary increases in violence.