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In this paper, we investigate offline reinforcement learning (RL) with the goal of training a single robust policy that generalizes effectively across environments with unseen dynamics. We propose a novel approach, Trajectory Encoding Augmentation (TEA), which extends the state space by integrating latent representations of environmental dynamics obtained from sequence encoders, such as AutoEncoders. Our findings show that incorporating these encodings with TEA improves the transferability of a single policy to novel environments with new dynamics, surpassing methods that rely solely on unmodified states. These results indicate that TEA captures critical, environment-specific characteristics, enabling RL agents to generalize effectively across dynamic conditions.

Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the practices followed when researching, teaching, and reviewing SE studies that apply ML. Method: We analyzed SE researchers familiar with ML or who authored SE articles using ML, along with the articles themselves. We examined practices, SE tasks addressed with ML, challenges faced, and reviewers' and educators' perspectives using grounded theory coding and qualitative analysis. Results: We found diverse practices focusing on data collection, model training, and evaluation. Some recommended practices (e.g., hyperparameter tuning) appeared in less than 20\% of literature. Common challenges involve data handling, model evaluation (incl. non-functional properties), and involving human expertise in evaluation. Hands-on activities are common in education, though traditional methods persist. Conclusion: Despite accepted practices in applying ML to SE, significant gaps remain. By enhancing guidelines, adopting diverse teaching methods, and emphasizing underrepresented practices, the SE community can bridge these gaps and advance the field., Comment: under review

The dispersion measure (DM) of fast radio bursts (FRBs) is sensitive to the electron distribution in the Universe, making it a promising probe of cosmology and astrophysical processes such as baryonic feedback. However, cosmological analyses of FRBs require knowledge of the contribution to the observed DM coming from the FRB host. The size and distribution of this contribution is still uncertain, thus significantly limiting current cosmological FRB analyses. In this study, we extend the baryonification (BCM) approach to derive a physically-motivated, analytic model for predicting the host contribution to FRB DMs. By focusing on the statistical properties of FRB host DMs, we find that our simple model is able to reproduce the probability distribution function (PDF) of host halo DMs measured from the CAMELS suite of hydrodynamic simulations, as well as their mass- and redshift dependence. Furthermore, we demonstrate that our model allows for self-consistent predictions of the host DM PDF and the matter power spectrum suppression due to baryonic effects, as observed in these simulations, making it promising for modelling host-DM-related systematics in FRB analyses. In general, we find that the shape of the host DM PDF is determined by the interplay between the FRB and gas distributions in halos. Our findings indicate that more compact FRB profiles require shallower gas profiles (and vice versa) in order to match the observed DM distributions in hydrodynamic simulations. Furthermore, the analytic model presented here shows that the shape of the host DM PDF is highly sensitive to the parameters of the BCM. This suggests that this observable could be used as an interesting test bed for baryonic processes, complementing other probes due to its sensitivity to feedback on galactic scales. We further discuss the main limitations of our analysis, and point out potential avenues for future work., Comment: 13 pages, 12 figures, to be submitted to OJA, comments welcome

The lack of a formal model of events hinders interoperability in distributed event-based systems. In this paper, we present a formal model of events, called Event-Model-F. The model is based on the foundational ontology DOLCE+DnS Ultralite (DUL) and provides comprehensive support to represent time and space, objects and persons, as well as mereological, causal, and correlative relationships between events. In addition, the Event-Model-F provides a flexible means for event composition, modeling event causality and event correlation, and representing different interpretations of the same event. The Event-Model-F is developed following the pattern-oriented approach of DUL, is modularized in different ontologies, and can be easily extended by domain specific ontologies., Comment: Reprint of KCAP 2009 paper with republished ontologies

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current state of the art, SafeSlinger, and our newly designed protocol, PairSonic, which extends trust from physical encounters to spontaneous online communication. Our lab study (N=45) demonstrates PairSonic's superior usability, automating the tedious verification tasks from previous approaches via an acoustic out-of-band channel. Although participants significantly preferred our system, minimizing user effort surprisingly decreased the perceived security for some users, who associated security with complexity. We discuss user perceptions of the different protocol components and identify remaining usability barriers for CSCW application scenarios., Comment: 44 pages, 8 figures, the dataset is available at https://doi.org/10.5281/zenodo.13324112 and a demo video is available at https://www.youtube.com/watch?v=e1AMYDLWN0E and further project information is at https://fputz.net/pairsonic

Securely exchanging contact information is essential for establishing trustworthy communication channels that facilitate effective online collaboration. However, current methods are neither user-friendly nor scalable for large groups of users. In response, we introduce PairSonic, a novel group pairing protocol that extends trust from physical encounters to online communication. PairSonic simplifies the pairing process by automating the tedious verification tasks of previous methods through an acoustic out-of-band channel using smartphones' built-in hardware. Our protocol not only facilitates connecting users for computer-supported collaboration, but also provides a more user-friendly and scalable solution to the authentication ceremonies currently used in end-to-end encrypted messengers like Signal or WhatsApp. PairSonic is available as open-source software: https://github.com/seemoo-lab/pairsonic, Comment: 3 pages, 1 figure, the source code is available at https://github.com/seemoo-lab/pairsonic and a demo video is available at https://www.youtube.com/watch?v=e1AMYDLWN0E and further project information is at https://fputz.net/pairsonic

Context: The small- to intermediate-mass ($M <0.8 M_\odot$), most metal-poor stars that formed in the infancy of the Universe are still shining today in the sky. They are very rare, but their discovery and investigation brings new knowledge on the formation of the first stellar generations. Aims: SDSS J102915.14+172927.9 is one of the most metal-poor star known to date. Since no carbon can be detected in its spectrum, a careful upper limit is important, both to classify this star and to distinguish it from the carbon-enhanced stars that represent the majority at these metallicities. Methods: We undertook a new observational campaign to acquire high-resolution UVES spectra. The new spectra were combined with archival spectra in order to increase the signal-to-noise ratio. From the combined spectrum, we derived abundances for seven elements (Mg, Si, Ca, Ti, Fe, Ni, and a tentative Li) and five significant upper limits (C, Na, Al, Sr, and Ba). Results: The star has a carbon abundance A(C) <4.68 and therefore is not enhanced in carbon, at variance with the majority of the stars at this Fe regime, which typically show A(C)> 6.0. A feature compatible with the Li doublet at 670.7 nm is tentatively detected. Conclusions: The upper limit on carbon implies $Z<1.915 \times 10^{-6}$, more than 20 times lower than the most iron-poor star known. Therefore, the gas cloud out of which the star was formed did not cool via atomic lines but probably through dust. Fragmentation of the primordial cloud is another possibility for the formation of a star with a metallicity this low.

Finite-dimensional Reedy algebras form a ring-theoretic analogue of Reedy categories and were recently proved to be quasi-hereditary. We identify Reedy algebras as quasi-hereditary algebras admitting a triangular (or Poincar\'e-Birkhoff-Witt type) decomposition into the tensor product of two oppositely directed subalgebras over a common semisimple subalgebra. This exhibits homological and representation-theoretic structure of the ingredients of the Reedy decomposition and it allows to give a characterisation of Reedy algebras in terms of idempotent ideals occurring in heredity chains, providing an analogue for Reedy algebras of a result of Dlab and Ringel on quasi-hereditary algebras., Comment: 12 pages

A famous conjecture of Chowla on the least primes in arithmetic progressions implies that the abscissa of convergence of the Weil representation zeta function for a procyclic group $G$ only depends on the set $S$ of primes dividing the order of $G$ and that it agrees with the abscissa of the Dedekind zeta function of $\mathbb{Z}[p^{-1}\mid p \not\in S]$. Here we show that these consequences hold unconditionally for random procyclic groups in a suitable model. As a corollary, every real number $1 \leq \beta \leq 2$ is the Weil abscissa of some procyclic group., Comment: 9 pages, comments welcome

The effect of columnar grain boundaries on the fracture toughness was investigated using micro-cantilever fracture testing with a bridge notch, and a unique hard coating consisting of two distinct microstructures: one with columnar grains and another with an epitaxial layer. The bridge-failure sequence qualitatively demonstrated the lower fracture toughness at the columnar-grained structure. Quantitatively, the load drops measured at bridge-failure also revealed a significant decrease in fracture toughness due to grain boundaries. Specifically, the fracture toughness decreased by around 30%, from 4.1 +/- 0.4 MPa m1/2 for epitaxial microstructure to 3.0 +/- 0.3 MPa m1/2 for columnar-grained structure. The fracture toughness of columnar-grained structure is 3.0 +/- 0.2 MPa m1/2 perpendicular to the growth direction higher than 2.7 +/- 0.1 MPa m1/2 along it. These findings suggest that future optimization of hard coatings should focus on grain boundary toughening, and the present toolbox proposes suitable techniques for such microstructure optimization., Comment: 4 figures

Field Programmable Gate Arrays (FPGAs) are known for their reprogrammability that allows for post-manufacture circuitry changes. Nowadays, they are integral to a variety of systems including high-security applications such as aerospace and military systems. However, this reprogrammability also introduces significant security challenges, as bitstream manipulation can directly alter hardware circuits. Malicious manipulations may lead to leakage of secret data and the implementation of hardware Trojans. In this paper, we present a comprehensive framework for manipulating bitstreams with minimal reverse engineering, thereby exposing the potential risks associated with inadequate bitstream protection. Our methodology does not require a complete understanding of proprietary bitstream formats or a fully reverse-engineered target design. Instead, it enables precise modifications by inserting pre-synthesized circuits into existing bitstreams. This novel approach is demonstrated through a semi-automated framework consisting of five steps: (1) partial bitstream reverse engineering, (2) designing the modification, (3) placing and (4) routing the modification into the existing circuit, and (5) merging of the modification with the original bitstream. We validate our framework through four practical case studies on the OpenTitan design synthesized for Xilinx 7-Series FPGAs. While current protections such as bitstream authentication and encryption often fall short, our work highlights and discusses the urgency of developing effective countermeasures. We recommend using FPGAs as trust anchors only when bitstream manipulation attacks can be reliably excluded.

Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes and locations, incorporating scattered radiation simulation, and including Poisson noise, the pipeline enables the expansion of limited datasets for training deep neural networks. We validate the proposed pipeline by a qualitative and quantitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.

Pediatric heart diseases present a broad spectrum of congenital and acquired diseases. More complex congenital malformations require a differentiated and multimodal decision-making process, usually including echocardiography as a central imaging method. Artificial intelligence (AI) offers considerable promise for clinicians by facilitating automated interpretation of pediatric echocardiography data. However, adapting AI technologies for pediatric echocardiography analysis has challenges such as limited public data availability, data privacy, and AI model transparency. Recently, researchers have focused on disruptive technologies, such as federated learning (FL) and explainable AI (XAI), to improve automatic diagnostic and decision support workflows. This study offers a comprehensive overview of the limitations and opportunities of AI in pediatric echocardiography, emphasizing the synergistic workflow and role of XAI and FL, identifying research gaps, and exploring potential future developments. Additionally, three relevant clinical use cases demonstrate the functionality of XAI and FL with a focus on (i) view recognition, (ii) disease classification, (iii) segmentation of cardiac structures, and (iv) quantitative assessment of cardiac function., Comment: This article is planned for submission to Frontiers Journal

We present the canonical analysis of different versions of unimodular gravity defined in the Pleba\'nski formalism, based on a (generally complex) SO(3) spin connection and set of (self-dual) two-forms. As in the metric formulation of unimodular gravity, one can study either a theory with fixed volume form or work in a parametrised formalism in which the cosmological constant becomes a dynamical field, constrained to be constant by the field equations. In the first case, the Hamiltonian density contains a part which is not constrained to vanish, but rather constrained to be constant, again as in the metric formulation. We also discuss reality conditions and challenges in extracting Lorentzian solutions., Comment: 10 pages, two-column revtex

Dirac operators and Dirac cohomology for Lie superalgebras of Riemannian type, as introduced by Huang and Pand\v{z}i\'{c}, serve as a powerful framework for studying unitarizable supermodules. This paper explores the relationships among the Dirac operator, Dirac cohomology, and unitarizable supermodules specifically within the context of the basic classical Lie superalgebras of type $A(m\vert n)$. The first part examines the structural properties of Dirac cohomology and unitarizable supermodules, including how the Dirac operator captures unitarity, a Dirac inequality, and the uniqueness of the supermodule determined by its Dirac cohomology. Additionally, we calculate the Dirac cohomology for unitarizable simple supermodules. The second part focuses on applications: we give a novel characterization of unitarity, relate Dirac cohomology to nilpotent Lie superalgebra cohomology, derive a decomposition of formal characters, and introduce a Dirac index., Comment: 42 pages

This study employs numerical simulations to explore the relationship between the dynamical instability of planetary systems and the uniformity of planetary masses within the system, quantified by the Gini index. Our findings reveal a significant correlation between system stability and mass uniformity. Specifically, planetary systems with higher mass uniformity demonstrate increased stability, particularly when they are distant from first-order mean motion resonances (MMRs). In general, for non-resonant planetary systems with a constant total mass, non-equal mass systems are less stable than equal mass systems for a given spacing in units of mutual Hill radius. This instability may arise from the equipartition of the total random energy, which can lead to higher eccentricities in smaller planets, ultimately destabilizing the system. This work suggests that the observed mass uniformity within multi-planet systems detected by \textit{Kepler} may result from a combination of survival bias and ongoing dynamical evolution processes., Comment: 9 pages, 5 figures, accepted to The Astronomical Journal

We study the existence and the distribution of "long" chains in the Weihrauch degrees, mostly focusing on chains with uncountable cofinality. We characterize when such chains have an upper bound and prove that there are no cofinal chains (of any order type) in the Weihrauch degrees. Furthermore, we show that the existence of coinitial sequences of non-zero degrees is equivalent to $\mathrm{CH}$. Finally, we explore the extendibility of antichains, providing some necessary conditions for maximality.

The ability to concentrate sound energy with a tunable focal point is essential for a wide range of acoustic applications, offering precise control over the location and intensity of sound pressure maxima. However, conventional acoustic metalenses are typically passive, with fixed focal positions, limiting their versatility. A significant obstacle in achieving tunable sound wave focusing lies in the complexity of precise and programmable adjustments, which often require intricate mechanical or electronic systems. In this study, we present a theoretical and experimental investigation of a reconfigurable acoustic metalens based on a bistable origami design. The metalens comprises eight flexible origami units, each capable of switching between two stable equilibrium states, enabling local modulation of sound waves through two distinct reflection phases. The metalens can be locked into specific symmetric or asymmetric configurations by manually tailoring the origami units to settle either of the two states. Each configuration generates a unique phase profile, focusing sound energy at a specific point. This concept allows the focal spot to be dynamically reconfigured both on and off-axis. Furthermore, the approach introduces a simple yet effective mechanism for tuning sound energy concentration, offering a solution for flexible acoustic manipulation.

Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shape of the LLF's optimized remap function, the characteristics of the style transfer can be inferred and reliability of the algorithm can be ensured. Moreover, we enable the LLF to capture complex X-ray style features by replacing the remap function with a Multi-Layer Perceptron (MLP) and adding a trainable normalization layer. We demonstrate the effectiveness of the proposed method by transforming unprocessed mammographic X-ray images into images that match the style of target mammograms and achieve a Structural Similarity Index (SSIM) of 0.94 compared to 0.82 of the baseline LLF style transfer method from Aubry et al.

We study the superconducting critical fields of two-dimensional electron systems at (111)$\mathrm{LaAlO_3/KTaO_3}$ interfaces as a function of electrostatic back-gating. Our work reveals inplane critical fields of unprecedented magnitudes at oxide interfaces. By comparing the critical fields in-plane and out-of-plane we discover an extremely anisotropic superconductor with an effective thickness below 1 nm and a 12-fold violation of the Chandrasekhar-Clogston paramagnetic limit. The analysis of magneto-transport indicates that the enhancement of the critical fields is due to an exceptionally thin superconducting layer and to a paramagnetic susceptibility suppressed by spin-orbit scattering.

The increasing importance of supply chain security for digital devices -- from consumer electronics to critical infrastructure -- has created a high demand for skilled cybersecurity experts. These experts use Hardware Reverse Engineering (HRE) as a crucial technique to ensure trust in digital semiconductors. Recently, the US and EU have provided substantial funding to educate this cybersecurity-ready semiconductor workforce, but success depends on the widespread availability of academic training programs. In this paper, we investigate the current state of education in hardware security and HRE to identify efficient approaches for establishing effective HRE training programs. Through a systematic literature review, we uncover 13 relevant courses, including eight with accompanying academic publications. We identify common topics, threat models, key pedagogical features, and course evaluation methods. We find that most hardware security courses do not prioritize HRE, making HRE training scarce. While the predominant course structure of lectures paired with hands-on projects appears to be largely effective, we observe a lack of standardized evaluation methods and limited reliability of student self-assessment surveys. Our results suggest several possible improvements to HRE education and offer recommendations for developing new training courses. We advocate for the integration of HRE education into curriculum guidelines to meet the growing societal and industry demand for HRE experts., Comment: 7 pages, 1 figure, 2 tables. To be published in Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1. For supplementary materials, see https://osf.io/dt8ne/

We investigate the tomography of unknown unitary quantum processes within the framework of a finite-dimensional Wigner-type representation. This representation provides a rich visualization of quantum operators by depicting them as shapes assembled as a linear combination of spherical harmonics. These shapes can be experimentally tomographed using a scanning-based phase-space tomography approach. However, so far, this approach was limited to $\textit{known}$ target processes and only provided information about the controlled version of the process rather than the process itself. To overcome this limitation, we introduce a general protocol to extend Wigner tomography to $\textit{unknown}$ unitary processes. This new method enables experimental tomography by combining a set of experiments with classical post-processing algorithms introduced herein to reconstruct the unknown process. We also demonstrate the tomography approach experimentally on IBM quantum devices and present the specific calibration circuits required for quantifying undesired errors in the measurement outcomes of these demonstrations.

In the logical framework introduced by Grohe and Tur\'an (TOCS 2004) for Boolean classification problems, the instances to classify are tuples from a logical structure, and Boolean classifiers are described by parametric models based on logical formulas. This is a specific scenario for supervised passive learning, where classifiers should be learned based on labelled examples. Existing results in this scenario focus on Boolean classification. This paper presents learnability results beyond Boolean classification. We focus on multiclass classification problems where the task is to assign input tuples to arbitrary integers. To represent such integer-valued classifiers, we use aggregate queries specified by an extension of first-order logic with counting terms called FOC1. Our main result shows the following: given a database of polylogarithmic degree, within quasi-linear time, we can build an index structure that makes it possible to learn FOC1-definable integer-valued classifiers in time polylogarithmic in the size of the database and polynomial in the number of training examples., Comment: To appear at ICDT 2025

Recently discovered high-T$_c$ superconductivity in pressurized bilayer nickelate La$_3$Ni$_2$O$_7$ (La-327) is believed to be driven by the non-phononic repulsive interaction. Depending on the strength of the interlayer repulsion, the symmetry of the superconducting order parameter is expected to be either $d$-wave or sign-changing bonding-antibonding $s_{\pm}$-wave. Unfortunately, due to the need of high pressure to reach superconducting phase, conventional spectroscopic probes to validate the symmetry of the order parameter are hard to use. Here, we study the effect of the point-like non-magnetic impurities on the superconducting state of La-327 and show that $s_{\pm}$-wave and $d$-wave symmetries show a very different behavior as a function of impurity concentration, which can be studied experimentally by irradiating the La-327 samples by electrons prior applying the pressure. While $d-$wave superconducting state will be conventionally suppressed, the $s_{\pm}$-wave state shows more subtle behavior, depending on the asymmetry between bonding and antibonding subspaces. For the electronic structure, predicted to realize in La-327, the $s_{\pm}-$wave state will be robust against complete suppression and the transition temperature, $T_c$ demonstrates a transition from convex to concave behavior, indicating a crossover from $s_{\pm}$-wave to $s_{++}$-wave symmetry as a function of impurity concentration. We further analyze the sensitivity of the obtained results with respect to the potential electronic structure modification., Comment: 9 pages, 2 figures + supplemental material (4 pages, 4 figures)

An $r$-regular graph is an $r$-graph, if every odd set of vertices is connected to its complement by at least $r$ edges. Seymour [On multicolourings of cubic graphs, and conjectures of Fulkerson and Tutte.~\emph{Proc.~London Math.~Soc.}~(3), 38(3): 423-460, 1979] conjectured (1) that every planar $r$-graph is $r$-edge colorable and (2) that every $r$-graph has $2r$ perfect matchings such that every edge is contained in precisely two of them. We study several variants of these conjectures. A $(t,r)$-PM is a multiset of $t \cdot r$ perfect matchings of an $r$-graph $G$ such that every edge is in precisely $t$ of them. We show that the following statements are equivalent for every $t, r \geq 1$: 1. Every planar $r$-graph has a $(t,r)$-PM. 2. Every $K_5$-minor-free $r$-graph has a $(t,r)$-PM. 3. Every $K_{3,3}$-minor-free $r$-graph has a $(t,r)$-PM. 4. Every $r$-graph whose underlying simple graph has crossing number at most $1$ has a $(t,r)$-PM., Comment: 11 pages, 1 figure

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce a novel general-purpose model for battery degradation prediction and synthesis, DiffBatt. Leveraging an innovative combination of conditional and unconditional diffusion models with classifier-free guidance and transformer architecture, DiffBatt achieves high expressivity and scalability. DiffBatt operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation. The performance of the model excels in prediction tasks while also enabling the generation of synthetic degradation curves, facilitating enhanced model training by data augmentation. In the remaining useful life prediction task, DiffBatt provides accurate results with a mean RMSE of 196 cycles across all datasets, outperforming all other models and demonstrating superior generalizability. This work represents an important step towards developing foundational models for battery degradation., Comment: 15 pages, 6 figures

Deep neural networks are susceptible to adversarial attacks and common corruptions, which undermine their robustness. In order to enhance model resilience against such challenges, Adversarial Training (AT) has emerged as a prominent solution. Nevertheless, adversarial robustness is often attained at the expense of model fairness during AT, i.e., disparity in class-wise robustness of the model. While distinctive classes become more robust towards such adversaries, hard to detect classes suffer. Recently, research has focused on improving model fairness specifically for perturbed images, overlooking the accuracy of the most likely non-perturbed data. Additionally, despite their robustness against the adversaries encountered during model training, state-of-the-art adversarial trained models have difficulty maintaining robustness and fairness when confronted with diverse adversarial threats or common corruptions. In this work, we address the above concerns by introducing a novel approach called Fair Targeted Adversarial Training (FAIR-TAT). We show that using targeted adversarial attacks for adversarial training (instead of untargeted attacks) can allow for more favorable trade-offs with respect to adversarial fairness. Empirical results validate the efficacy of our approach.

The attractive interaction in conventional BCS superconductors is provided by a bosonic mode. However, the pairing glue of most unconventional superconductors is unknown. The effect of electron-boson coupling is therefore extensively studied in these materials. A key signature are dispersion kinks that can be observed in the spectral function as abrupt changes in velocity and lifetime of quasiparticles. Here, we show the existence of two kinks in the unconventional iron-based superconductor RbFe$_2$As$_2$ using angle-resolved photoemission spectroscopy (ARPES) and dynamical mean field theory (DMFT). In addition, we observe the formation of a Hubbard band multiplet due to the combination of Coulomb interaction and Hund's rule coupling in this multiorbital systems. We demonstrate that the two dispersion kinks are a consequence of these strong many-body interactions. This interpretation is in line with a growing number of theoretical predictions for kinks in various general models of correlated materials. Our results provide a unifying link between iron-based superconductors and different classes of correlated, unconventional superconductors such as cuprates and heavy-fermion materials.

Consider a Jordan curve on the Riemann sphere passing through $n \ge 3$ given points. We show that in each relative isotopy class of such curves, there exists a unique curve that minimizes the Loewner energy. These curves have the property that each arc between two consecutive points is a hyperbolic geodesic in the domain bounded by the other arcs. This geodesic property lets us define a complex projective structure on the punctured sphere. We show that its associated quadratic differential has simple poles, whose residues (accessory parameters) are given by the Wirtinger derivatives of the minimal Loewner energy, a result resembling Polyakov's conjecture for the Fuchsian projective structure that was later proven by Takhtajan and Zograf. Finally, we show that the projective structures we obtain are related to the Fuchsian projective structures via a $\pi$-grafting., Comment: 37 pages, 7 figures

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarise new developments, essential features, and ongoing improvements within the Sherpa 3 release series. Physics improvements include higher-order electroweak corrections, simulations of photoproduction and hard diffraction at NLO QCD, heavy-flavour matching in NLO multijet merging, spin-polarised cross section calculations, and a new model of colour reconnections. In addition, the modelling of hadronisation, the underlying event and QED effects in both production and decay has been improved, and the overall event generation efficiency has been enhanced., Comment: We proudly present the new major release of the Sherpa MC generator

Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the $\mathcal{SROI}^-$ description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the $\mathcal{ALCOIR}$ description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL from tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.

Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness, and computational efficiency during the fine-tuning process. We introduce GeoLoRA, a novel approach that addresses these limitations by leveraging dynamical low-rank approximation theory. GeoLoRA requires only a single backpropagation pass over the small-rank adapters, significantly reducing computational cost as compared to similar dynamical low-rank training methods and making it faster than popular baselines such as AdaLoRA. This allows GeoLoRA to efficiently adapt the allocated parameter budget across the model, achieving smaller low-rank adapters compared to heuristic methods like AdaLoRA and LoRA, while maintaining critical convergence, descent, and error-bound theoretical guarantees. The resulting method is not only more efficient but also more robust to varying hyperparameter settings. We demonstrate the effectiveness of GeoLoRA on several state-of-the-art benchmarks, showing that it outperforms existing methods in both accuracy and computational efficiency.

Recent research has focused on literary machine translation (MT) as a new challenge in MT. However, the evaluation of literary MT remains an open problem. We contribute to this ongoing discussion by introducing LITEVAL-CORPUS, a paragraph-level parallel corpus comprising multiple verified human translations and outputs from 9 MT systems, which totals over 2k paragraphs and includes 13k annotated sentences across four language pairs, costing 4.5k Euro. This corpus enables us to (i) examine the consistency and adequacy of multiple annotation schemes, (ii) compare evaluations by students and professionals, and (iii) assess the effectiveness of LLM-based metrics. We find that Multidimensional Quality Metrics (MQM), as the de facto standard in non-literary human MT evaluation, is inadequate for literary translation: While Best-Worst Scaling (BWS) with students and Scalar Quality Metric (SQM) with professional translators prefer human translations at rates of ~82% and ~94%, respectively, MQM with student annotators prefers human professional translations over the translations of the best-performing LLMs in only ~42% of cases. While automatic metrics generally show a moderate correlation with human MQM and SQM, they struggle to accurately identify human translations, with rates of at most ~20%. Our overall evaluation indicates that human professional translations consistently outperform LLM translations, where even the most recent LLMs tend to produce more literal and less diverse translations compared to human translations. However, newer LLMs such as GPT-4o perform substantially better than older ones.

We examine the speed of different boarding methods in a proposed Flying Wing aircraft design with four aisles using an agent-based model. We study the effect of various passenger movement variables on the boarding process. We evaluate the impact of these factors on the boarding time when the boarding process runs sequentially and in parallel with the aisles of the Flying Wing layout. Then, we analyze the impact of an increase in the number of aisles on the relative speed of all boarding methods and conclude that methods utilizing boarding of the separate aisles simultaneously (parallel boarding) converge to the fastest boarding time given by the Steffen method. With parallel boarding of the aisles the relative advantage of the Steffen method compared to Windows-Middle-Aisle (WMA) or Back-to-front boarding decreases, from being 1.6-2.1 times as fast to being approximately equal for our fiducial Flying Wing seating arrangement. Standard methods such as Back-to-front or WMA are about twice as fast to board a four-aisle Flying Wing plane, compared to a single-aisle aircraft with the same number of passengers. We also investigate the difference between the optimal approach to parallel boarding, where consecutive passengers always enter separate aisles, and a less optimal but more practical approach. The practical approach is only up to 1.06 times slower than the optimal, meaning that the advantages of parallel boarding can be utilized without resorting to impractical boarding methods. Hence, the introduction of multiple aisles into aircraft seating design offers the prospect of significantly decreasing the boarding time for passengers, without the introduction of inconvenient boarding methods., Comment: 25 pages, 9 figures

We experimentally study the breakup of water-glycerol liquid bridges on non-conductive surfaces and find that spontaneous charge deposition at the receding contact line, slide electrification, can have a substantial influence. Electrostatic forces slow down the dynamics during, and cause spontaneous motion of satellite drops after the bridge breakup. We show that our experimental observations align with slide electrification theory. Our findings demonstrate that slide electrification plays an important role in dewetting beyond drop-related scenarios.

Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing data. In this paper, we propose Knowledge-Guided Visual representation learning (KGV), a distribution-based learning approach leveraging multi-modal prior knowledge, to improve generalization under distribution shift. We use prior knowledge from two distinct modalities: 1) a knowledge graph (KG) with hierarchical and association relationships; and 2) generated synthetic images of visual elements semantically represented in the KG. The respective embeddings are generated from the given modalities in a common latent space, i.e., visual embeddings from original and synthetic images as well as knowledge graph embeddings (KGEs). These embeddings are aligned via a novel variant of translation-based KGE methods, where the node and relation embeddings of the KG are modeled as Gaussian distributions and translations respectively. We claim that incorporating multi-model prior knowledge enables more regularized learning of image representations. Thus, the models are able to better generalize across different data distributions. We evaluate KGV on different image classification tasks with major or minor distribution shifts, namely road sign classification across datasets from Germany, China, and Russia, image classification with the mini-ImageNet dataset and its variants, as well as the DVM-CAR dataset. The results demonstrate that KGV consistently exhibits higher accuracy and data efficiency than the baselines across all experiments.

Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to the true underlying generative factors of the data. Here, we deepen this connection and show that SSL can perform system identification in latent space. We propose DynCL, a framework to uncover linear, switching linear and non-linear dynamics under a non-linear observation model, give theoretical guarantees and validate them empirically.

We study the problem of evacuation planning for natural disasters, focusing on wildfire evacuations. By creating pre-planned evacuation routes that can be updated based on real-time data, we provide an easily adjustable approach to fire evacuation planning and implementation. Our method uses publicly available data and can be easily tailored for a particular region or circumstance. We formulate large-scale evacuations as maximum flow problems on time-expanded networks, in which we integrate fire information given in the form of a shapefile. An initial flow is found based on a predicted fire, and is then updated based on revised fire information received during the evacuation. We provide a proof on concept on three locations with historic deadly fires using data available through OpenStreetMaps, a basemap for a geographic information system (GIS), on a NetworkX Python script. The results validate viable running times and quality of information for application in practice.

Structure determination is a key application of XFELs and 4th generation synchrotron sources, particularly using the coherent and pulsed X-ray radiation from X-ray free-electron lasers (XFEL). Scientific interest focuses on understanding the physical, biological, and chemical properties of samples at the nanometer scale. The X-rays from XFELs enable Coherent X-ray Diffraction Imaging (CXDI), where coherent X-rays irradiate a sample, and a far-field diffraction pattern is captured by an imaging detector. By the nature of the underlying physics, the resolution, at which the sample can be probed with the CXDI technique, is limited by the wavelength of the X-ray radiation and the exposure time if a detector can record the diffraction pattern to very large scattering angles. The resolution that can be achieved under real experimental conditions, depends strongly on additional parameters. The Shannon pixel size, linked to the detector resolution, the coherent dose that can be deposited in the sample without changing its structure, the image contrast, and the signal-to-noise ratio of the detected scattered radiation at high $q$, i.e. at high scattering angles $2\Theta$, have a strong influence on the resolution. The signal-to-noise ratio at high $q$ defines the "effective" maximum solid angle in a specific experiment setup up to which a detector can efficiently detect a signal and in consequence determines the achievable resolution. The image contrast defines how well bright image features can be distinguished from dark ones. We present the preliminary results of our study on the influence of the PSF on SNR, image contrast, position resolution, and achievable sample resolution for different pixel sizes., Comment: 7 pages, 4 figures, submitted to the Journal of Physics: Conference Series (JPCS)

We simulate the charging of a single electrolyte-filled pore using the modified Poisson-Nernst-Planck and Navier-Stokes equations. We find that electroconvection, previously ignored in this context, can substantially speed up the charging dynamics. We derive an analytical model that describes the induced fluid velocity and the electric current arising due to convection. Our findings suggest that convection becomes significant beyond a certain threshold voltage that is an inherent electrolyte property.

Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA are not really complex, and the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks, most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models drops significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks, composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods.

McXtrace is an established Monte Carlo based ray-tracing tool to simulate synchrotron beamlines and X-ray laboratory instruments. This work explains and demonstrates the new capability of GPU-accelerated McXtrace ray-tracing simulations. The openACC implementation is presented, followed by a demonstration of the achieved speed-up factor for several types of instruments across different types of hardware. The instruments achieve speed-up factors around \SIrange{250}{600}{} dependent on the instrument complexity. Instruments requiring repeated memory access might require optimised memory access procedures to avoid severe penalties in the simulation time when using GPUs. The importance of reducing the simulations was demonstrated for an aviation security application by comparing the simulation time of a projection of an energy-dispersive X-ray computed tomography instrument.

Research on conspiracy theories and related content online has traditionally focused on textual data. To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approaches for analyzing multimodal online content. Our research contributes to this field by exploring the potential of multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels. Our work uses the BERTopic topic modeling approach in combination with CLIP for the analysis of textual and visual data. We analyze a corpus of ~40, 000 Telegram messages posted in October 2023 in 571 German-language Telegram channels known for disseminating conspiracy theories and other deceptive content. We explore the potentials and challenges of this approach for studying a medium-sized corpus of user-generated, text-image online content. We offer insights into the dominant topics across modalities, different text and image genres discovered during the analysis, quantitative inter-modal topic analyses, and a qualitative case study of textual, visual, and multimodal narrative strategies in the communication of conspiracy theories., Comment: 11 pages, 11 figures

LiV$_2$O$_4$ is a member of the so-called $3d$ heavy fermion compounds, with effective electron mass exceeding 60 times the free electron mass, comparable to $4f$ heavy fermion compounds. The origin of the strong electron correlation in combination with its metallic character have been a subject of intense theoretical and experimental discussion, with Kondo-like physics and Mott-physics being suggested as its physical origin. Here we report state-of-the art \textit{ab initio} Density Functional Theory + dynamical mean-field theory calculations for LiV$_2$O$_4$ for the full three orbital V $t_\mathrm{2g}$ manifold, and present temperature-dependent spectral properties. We map out the phase diagram for a representative 3-orbital model system as a function of doping and interaction strength, which indicates that LiV$_2$O$_4$ is located between two orbital-selective Mott phases, giving rise to a robust strongly correlated Hund's metal behavior. At low temperature we find the emergence of a strongly renormalized sharp quasi-particle peak a few meV above the Fermi level of V $a_\mathrm{1g}$ character, in agreement with experimental reports., Comment: 6 pages with 4 figures, supplemental 2 pages with 3 figures

We study two dimensional and three dimensional tropical subrepresentations of the regular representation $\mathbb{B}[G]$ of a finite group over the tropical booleans, utilizing the theory of group representations over a fixed idempotent semifield as developed by Giansiracusa--Manaker. In dimension two we completely classify all two dimensional tropical subrepresentations of $\mathbb{B}[G]$, provide an explicit characterization for the set of bases of the corresponding matroids, and show an equivalence with the subgroups of $G$. In dimension three we show such an equivalence no longer holds. Towards a classification in dimension three we give a collection of tropical subrepresentations corresponding to subgroups of index 2, and we show that in the special case of finite cyclic groups, one can find three dimensional tropical subrepresentations that do not correspond to subgroups in a similar way., Comment: 18 pages

Precise synthesis of graphene nanoribbons (GNRs) is of great interest to chemists and materials scientists because of their unique opto-electronic properties and potential applications in carbon-based nanoelectronics and spintronics. In addition to the tunable edge structure and width, introducing curvature in GNRs is a powerful structural feature for their chemi-physical property modification. Here, we report an efficient solution synthesis of the first pyrene-based GNR (PyGNR) with curved geometry via one-pot K-region oxidation and Scholl cyclization of its corresponding well-soluble tetrahydropyrene-based polyphenylene precursor. The efficient A2B2-type Suzuki polymerization and subsequent Scholl reaction furnishes up to 35 nm long curved GNRs bearing cove- and armchair-edges. The construction of model compound, as a cutout of PyGNR, from a tetrahydropyrene-based oligophenylene precursor proves the concept and efficiency of the one-pot K-region oxidation and Scholl cyclization, which is clearly revealed by single crystal X-ray diffraction analysis. The structure and optical properties of PyGNR are investigated by Raman, FT-IR, solid-state NMR and UV-Vis analysis with the support of DFT calculations. PyGNR shows the absorption maximum at 680 nm, exhibiting a narrow optical bandgap of 1.4 eV, qualifying as a low-bandgap GNR. Moreover, THz spectroscopy on PyGNR estimates its macroscopic charge mobility of 3.6 cm2/Vs, outperforming other curved GNRs reported via conventional Scholl reaction.

Elongated particles in dense systems often exhibit alignment due to volume exclusion interactions, leading to packing configurations. Traditional models of collective dynamics typically impose this alignment phenomenologically, neglecting the influence of volume exclusion on particle positions. In this paper, we derive nematic alignment from an anisotropic repulsive potential, focusing on a Gaussian-type potential and first-order dynamics for the particles. By analyzing larger particle systems and performing a hydrodynamic limit, we uncover the effects of anisotropy on both particle density and direction. Our findings reveal that while particle density evolves independently of direction, anisotropy slows down nonlinear diffusion. The direction dynamics are affected by the particles' position and involve complex transport and diffusion processes, with different behaviors for oblate and prolate particles. The key to obtaining these results lies in recent advancements in Generalized Collision Invariants offered by Degond, Frouvelle and Liu (KRM 2022)., Comment: 42 pages, 10 figures

We use the dispersion measure (DM) of localised Fast Radio Bursts (FRBs) to constrain cosmological and host galaxy parameters using simulation-based inference (SBI) for the first time. By simulating the large-scale structure of the electron density with the Generator for Large-Scale Structure (GLASS), we generate log-normal realisations of the free electron density field, accurately capturing the correlations between different FRBs. For the host galaxy contribution, we rigorously test various models, including log-normal, truncated Gaussian and Gamma distributions, while modelling the Milky Way component using pulsar data. Through these simulations, we employ the truncated sequential neural posterior estimation method to obtain the posterior. Using current observational data, we successfully recover the amplitude of the DM-redshift relation, consistent with Planck, while also fitting both the mean host contribution and its shape. Notably, we find no clear preference for a specific model of the host galaxy contribution. Although SBI may not yet be strictly necessary for FRB inference, this work lays the groundwork for the future, as the increasing volume of FRB data will demand precise modelling of both the host and large-scale structure components. Our modular simulation pipeline offers flexibility, allowing for easy integration of improved models as they become available, ensuring scalability and adaptability for upcoming analyses using FRBs. The pipeline is made publicly available under https://github.com/koustav-konar/FastNeuralBurst., Comment: Submitted to OJA, 14 pages, 10 figures, comments welcome

In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and evaluation of item recommendation models becomes computationally expensive. To lower this cost, it has become common to sample negative items. However, the recommendation quality can suffer from biases introduced by traditional negative sampling mechanisms. In this work, we demonstrate the benefits from correcting the bias introduced by sampling of negatives. We first provide sampled batch version of the well-studied WARP and LambdaRank methods. Then, we present how these methods can benefit from improved ranking estimates. Finally, we evaluate the recommendation quality as a result of correcting rank estimates and demonstrate that WARP and LambdaRank can be learned efficiently with negative sampling and our proposed correction technique.

Deep learning is actively being used in biometrics to develop efficient identification and verification systems. Handwritten signatures are a common subset of biometric data for authentication purposes. Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. While most GAN techniques create a strong signature verifier, which is the discriminator, there is a need to focus more on the quality of forgeries generated by the generator model. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems. We use CycleGANs infused with Inception model-like blocks with attention heads as the generator and a variation of the SigCNN model as the base Discriminator. We train our model with a new technique that results in 80% to 100% success in signature spoofing. Additionally, we create a custom evaluation technique to act as a goodness measure of the generated forgeries. Our work advocates generator-focused GAN architectures for spoofing data quality that aid in a better understanding of biometric data generation and evaluation.