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The significant growth in the aquaculture industry over the last few decades encourages new technological and robotic solutions to help improve the efficiency and safety of production. In sea-based farming of Atlantic salmon in Norway, Unmanned Underwater Vehicles (UUVs) are already being used for inspection tasks. While new methods, systems and concepts for sub-sea operations are continuously being developed, these systems generally does not take into account how their presence might impact the fish. This abstract presents an experimental study on how underwater robotic operations at fish farms in Norway can affect farmed Atlantic salmon, and how the fish behaviour changes when exposed to the robot. The abstract provides an overview of the case study, the methods of analysis, and some preliminary results., Comment: 5 pages, 3 figures, accepted to IEEE IROS Workshop on Autonomous Robotic Systems in Aquaculture: Research Challenges and Industry Needs

In the context of developing new vehicle concepts, especially autonomous vehicles with novel seating arrangements and occupant activities, predicting occupant motion can be a tool for ensuring safety and comfort. In this study, a data-driven surrogate contact model integrated into an optimal control framework to predict human occupant behavior during driving maneuvers is presented. High-fidelity finite element simulations are utilized to generate a dataset of interaction forces and moments for various human body configurations and velocities. To automate the generation of training data, an active learning approach is introduced, which iteratively queries the high-fidelity finite element simulation for an additional dataset. The feasibility and effectiveness of the proposed method are demonstrated through a case study of a head interaction with an automotive headrest, showing promising results in accurately replicating contact forces and moments while reducing manual effort., Comment: Submitted for publication in the Proceedings in Applied Mathematics and Mechanics

In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e. pine needles, grass). Large eddy simulations (LES) are a common way of numerically representing turbulence. The Smagorinsky model (1963) serves as one of the most studied sub-grid scale representations in LES. In this investigation, the Smagorinsky model was implemented in HIGRAD/FIRETEC, LANL's coupled fire-atmosphere model. The Smagorinsky turbulent kinetic energy (TKE) was compared to FIRETEC's 1.5-order TKE eddy-viscosity subgrid-scale model, known as the Linn turbulence model. This was done in simulations of flow over flat terrain with a homogeneous, cuboidal canopy in the center of the domain. Examinations of the modeled vertical TKE profile and turbulent statistics at the leading edge, and throughout the canopy, show that the Smagorinsky model provides comparable results to that of the original closure model posed in FIRETEC., Comment: 24 pages, 10 figures

Vorticity is a key characteristic of flow patterns that determine wildland fire behavior, frontal evolution, and wind-canopy interaction. Investigating the role of vorticity in the flow fields around vegetation can help us better understand fire-atmosphere feedback and the influences of vegetation on this feedback. In modeling vorticity, ``perhaps the greatest knowledge gap exists in understanding which terms in the vorticity equation dominate [...] (and) when one or the other might dominate" (Potter, 2012). In this study, we investigate the role of vorticity in boundary layer dynamics and canopy/forest edge effects using HIGRAD/FIRETEC, a three-dimensional, two-phase transport model that conserves mass, momentum, energy, and chemical species. A vorticity transport equation was derived and discretized. Simulations were performed over a cuboidal homogeneous canopy surrounded by surface vegetation. This derivation led to the discovery of a drag tilting and stretching term, which shows that gradients in the aerodynamic drag of the vegetation, tied to heterogeneities in surface area-to-volume ratio, play an important role in the generation of vorticity. Results from the vorticity budget analysis show that this term contributes significantly in the areas where these gradients are present, namely the edges of the canopy., Comment: 24 pages, 10 figures

Cover times quantify the speed of exhaustive search. In this work, we compute exactly the mean cover time associated with a one-dimensional Brownian search under exponentially distributed resetting. We also approximate the moments of cover times of a wide range of stochastic search processes in $d$-dimensional continuous space and on an arbitrary discrete network under frequent stochastic resetting. These results hold for a large class of resetting time distributions and search processes including diffusion and Markov jump processes., Comment: 13 pages, 1 figure

We introduce an efficient computational framework for solving a class of multi-marginal martingale optimal transport problems, which includes many robust pricing problems of large financial interest. Such problems are typically computationally challenging due to the martingale constraint, however, by extending the state space we can identify them with problems that exhibit a certain sequential martingale structure. Our method exploits such structures in combination with entropic regularisation, enabling fast computation of optimal solutions and allowing us to solve problems with a large number of marginals. We demonstrate the method by using it for computing robust price bounds for different options, such as lookback options and Asian options.

Humanitarian negotiations in conflict zones, called \emph{frontline negotiation}, are often highly adversarial, complex, and high-risk. Several best-practices have emerged over the years that help negotiators extract insights from large datasets to navigate nuanced and rapidly evolving scenarios. Recent advances in large language models (LLMs) have sparked interest in the potential for AI to aid decision making in frontline negotiation. Through in-depth interviews with 13 experienced frontline negotiators, we identified their needs for AI-assisted case analysis and creativity support, as well as concerns surrounding confidentiality and model bias. We further explored the potential for AI augmentation of three standard tools used in frontline negotiation planning. We evaluated the quality and stability of our ChatGPT-based negotiation tools in the context of two real cases. Our findings highlight the potential for LLMs to enhance humanitarian negotiations and underscore the need for careful ethical and practical considerations.

The following is a write up of the progress of modeling data from the K12 organization \cite{Riedman_2023}. Data was characterized and investigated for statistically significant factors. The incident data was spilt into three sets: the entire set of incidents, incidents from 1966 - 2017, and incidents from 2018 - 2023. This was done in an attempt to discern key factors for the acceleration of incidents over the last several years. The data set was cleaned and processed primarily through RStudio. The individual factors were studied and subjected to statistical analysis where appropriate. As it turns out, there are differences between media portrayals of shooters and actual shooters. Then, multiple regression techniques were performed then followed by ANOVA of the models to determine statistically significant independent variables and their influence on casualties. Thus far, linear regression and negative binomial regression have been attempted. Further refining of the methods will be necessary for Poisson regression and logistic regression to be viably attempted. At this point in time a common theme among each of the models is the presence of targeted attacks affecting casualties. Further study can lead to improved safe guarding strategies to eliminate or minimize casualties. Further, increased understanding of shooter demographics can also lead to outreach and prevention programs.

Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environment in which the galaxies are located and their interactions with other galaxies. The volume of present and future data on galaxies is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI based techniques like machine learning and deep learning for finding morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) data release DR18. We use a catalog by Buta (2017) of ringed galaxies from the SDSS to train the network, reaching good accuracy and recall, and generate a catalog of 29420 galaxies of which 4855 have ring like structures with prediction confidence exceeding 90 percent. Using a catalog of barred galaxy images identified by Abraham et. al. (2018) using deep learning techniques, we identify a set of 2087 galaxies with bars as well as rings. The catalog should be very useful in understanding the origin of these important morphological structures. As an example of the usefulness of the catalog, we explore the environments and star formation characteristics of ring galaxies in our sample., Comment: 22 pages, 15 figures, 1 Table. Accepted for publication in The Astrophysical Journal (ApJ)

Many physical phenomena are modeled as stochastic searchers looking for targets. In these models, the probability that a searcher finds a particular target, its so-called hitting probability, is often of considerable interest. In this work we determine hitting probabilities for stochastic search processes conditioned on being faster than a random short time. Such times have been used to model stochastic resetting or stochastic inactivation. These results apply to any search process, diffusive or otherwise, whose unconditional short-time behavior can be adequately approximated, which we characterize for broad classes of stochastic search. We illustrate these results in several examples and show that the conditional hitting probabilities depend predominantly on the relative geodesic lengths between the initial position of the searcher and the targets. Finally, we apply these results to a canonical evidence accumulation model for decision making., Comment: 26 pages, 5 figures

The present work aims at describing hysteresis behaviour arising from cyclic bending experiments on cables by means of the Preisach operator. Pure bending experiments conducted in previous work show that slender structures such as electric cables behave inelastically and open hysteresis loops arise, with noticeable difference between the first load cycle and the following ones. The Preisach operator plays an important role in describing the input-output relation in hysteresis behaviours and it can be expressed as a superposition of relay operators. Here, we utilise data collected from pure bending experiments for a first approach. We introduce a mathematical formulation of the problem, and starting from the curvature of the cable specimen, we recursively define the Preisach plane for this specific case. Therefore, we derive a suitable kernel function in a way that the integration of such function over the Preisach plane results in the bending moment of the specimen., Comment: arXiv admin note: substantial text overlap with arXiv:2403.03531

This contribution aims at introducing first steps to develop hysteresis operator type inelastic constitutive laws for Cosserat rods for the simulation of cables composed of complex interior components. Motivated by the basic elements of Cosserat rod theory, we develop a specific approach to constitutive modelling adapted for this application. Afterwards, we describe the hysteretical behaviour arising from cyclic bending experiments on cables by means of the Preisach operator. As shown in pure bending experiments, slender structures such as electric cables behave inelastically, and open hysteresis loops arise with noticeable difference between the first load cycle and the following ones. The Preisach operator plays an important role in describing the input-output relation in hysteresis behaviours, and it can be expressed as a superposition of relay operators. Hence, a mathematical formulation of the problem is introduced, and a first attempt is made to determine the hysteresis behaviour that describes the relation between curvature and bending moment. Therefore, a suitable kernel function is identified in a way that its integration over the Preisach plane results in the bending moment of the specimen, and a comparison between different kernel functions is performed.

The simulation of inelastic effects in flexible slender technical devices has become of increasing interest in the past years. Different approaches have been considered depending on the effects relevant for the specific application. Recently, a mixed stress strain driven computational homogenisation has been proposed to model the dissipative nonlinear bending response of spiral strands subjected to axial force. In this study, we propose two different approaches, namely a rheological model and a databased greybox model, to predict the cyclic response of these strands using only their monotonic response. In the first approach, a system of so-called bending springs and sliders is used to model different contributions to the bending stiffness of the strands. The data-based approach makes use of mathematical tools called hysteresis operators. The Prandtl-Ishlinskii operator plays a relevant role in modelling the input-output relation in phenomena showing hysteretic behaviour and can be expressed as a weighted superposition of elementary stop operators. Comparing the two approaches leads to a better understanding and an explicit physical interpretation of the parameters of a specific class of hysteresis operator models.

Undefined behavior in C often causes devastating security vulnerabilities. One practical mitigation is compartmentalization, which allows developers to structure large programs into mutually distrustful compartments with clearly specified privileges and interactions. In this paper we introduce SECOMP, a compiler for compartmentalized C code that comes with machine-checked proofs guaranteeing that the scope of undefined behavior is restricted to the compartments that encounter it and become dynamically compromised. These guarantees are formalized as the preservation of safety properties against adversarial contexts, a secure compilation criterion similar to full abstraction, and this is the first time such a strong criterion is proven for a mainstream programming language. To achieve this we extend the languages of the CompCert verified C compiler with isolated compartments that can only interact via procedure calls and returns, as specified by cross-compartment interfaces. We adapt the passes and optimizations of CompCert as well as their correctness proofs to this compartment-aware setting. We then use compiler correctness as an ingredient in a larger secure compilation proof that involves several proof engineering novelties, needed to scale formally secure compilation up to a C compiler., Comment: CCS'24 camera ready version, extended with appendices and a few more references

Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in large groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased, and hence make better choices. We obtain asymptotic expressions in the large population limit that quantify how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in non-trivial ways to determine the reliability of decisions in a group., Comment: 12 pages, 4 figures

When observing the atmospheres of transiting exoplanets using high-resolution spectroscopy, one aims to detect well-resolved spectral features with high signal-to-noise ratios (SNR) as is possible today with modern spectrographs. However, obtaining such high-quality observations comes with a trade-off: a lower cadence of fewer, longer exposures across the transit collects more photons thanks to reduced overheads, enhancing the SNR of each observation, while a higher cadence of several, shorter exposures minimises spectral feature smearing due to the continuously changing radial velocity of the planet. Considering that maximising SNR and minimising smearing are both beneficial to analysis, there is a need to establish where the optimal compromise lies. In this work, we model real transit events based on targets as they would be observed with VLT/CRIRES+ at Paranal Observatory. Creating four hypothetical scenarios, we simulate each observation across 100 realisations of the same transit event in order to vary the time resolution only. We remove telluric and stellar lines using the SYSREM algorithm and analyse them through cross-correlation with model templates, measuring how successfully each time resolution and case detects the planetary signal. We demonstrate that there is a continuous change in the detection significance based on time resolutions, and that the function of this significance has clear maxima. The strength and location of this maxima varies on e.g. planet system parameters, instrumentation, and no. of removal iterations. We discuss why observers should therefore take several factors into account, using a strategy akin to the 'exposure triangle' from traditional photography where a balance must be struck by considering the full context of the observation. Our method is robust and may be employed by observers to estimate best observational strategies for other targets., Comment: 16 pages, 8 figures, 2 tables. Accepted 13 Dec 2023 for publication in A&A

In recent years, certain luminous extragalactic optical transients have been observed to last only a few days. Their short observed duration implies a different powering mechanism from the most common luminous extragalactic transients (supernovae) whose timescale is weeks. Some short-duration transients, most notably AT2018cow, display blue optical colours and bright radio and X-ray emission. Several AT2018cow-like transients have shown hints of a long-lived embedded energy source, such as X-ray variability, prolonged ultraviolet emission, a tentative X-ray quasiperiodic oscillation, and large energies coupled to fast (but subrelativistic) radio-emitting ejecta. Here we report observations of minutes-duration optical flares in the aftermath of an AT2018cow-like transient, AT2022tsd (the "Tasmanian Devil"). The flares occur over a period of months, are highly energetic, and are likely nonthermal, implying that they arise from a near-relativistic outflow or jet. Our observations confirm that in some AT2018cow-like transients the embedded energy source is a compact object, either a magnetar or an accreting black hole., Comment: 79 pages, 3 figures (main text) + 7 figures (extended data) + 2 figures (supplementary information). Published online in Nature on 15 November 2023

Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain problems and may help tackle this complex life science challenge. We analyze the resource requirements for simulating protein folding on a quantum computer, assessing this problem's feasibility in the current and near-future technological landscape. We calculate the minimum number of qubits, interactions, and two-qubit gates necessary to build a heuristic quantum algorithm with the specific information of a folding problem. Particularly, we focus on the resources needed to build quantum operations based on the Hamiltonian linked to the protein folding models for a given amino acid count. Such operations are a fundamental component of these quantum algorithms, guiding the evolution of the quantum state for efficient computations. Specifically, we study course-grained folding models on the lattice and the fixed backbone side-chain conformation model and assess their compatibility with the constraints of existing quantum hardware given different bit-encodings. We conclude that the number of qubits required falls within current technological capabilities. However, the limiting factor is the high number of interactions in the Hamiltonian, resulting in a quantum gate count unavailable today., Comment: 18 pages, 9 figures

BaNi$_2$As$_2$ is a structural analog of the pnictide superconductor BaFe$_2$As$_2$, which, like the iron-based superconductors, hosts a variety of ordered phases including charge density waves (CDWs), electronic nematicity, and superconductivity. Upon isovalent Sr substitution on the Ba site, the charge and nematic orders are suppressed, followed by a sixfold enhancement of the superconducting transition temperature ($T_c$). To understand the mechanisms responsible for enhancement of $T_c$, we present high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements of the Ba$_{1-x}$Sr$_{x}$Ni$_2$As$_2$ series, which agree well with our density functional theory (DFT) calculations throughout the substitution range. Analysis of our ARPES-validated DFT results indicates a Lifshitz transition and reasonably nested electron and hole Fermi pockets near optimal substitution where $T_c$ is maximum. These nested pockets host Ni $d_{xz}$/$d_{yz}$ orbital compositions, which we associate with the enhancement of nematic fluctuations, revealing unexpected connections to the iron-pnictide superconductors. This gives credence to a scenario in which nematic fluctuations drive an enhanced $T_c$., Comment: 19 pages, 12 figures, 1 table

The origin of Uranus and Neptune has long been challenging to explain, due to the large orbital distances from the Sun. After a planetary embryo has been formed, the main accretion processes are likely pebble, gas and planetesimal accretion. Previous studies of Uranus and Neptune formation typically don't consider all three processes; and furthermore, do not investigate how the formation of the outer planet impacts the inner planet. In this paper we study the concurrent formation of Uranus and Neptune via both pebble, gas and planetesimal accretion. We use a dust-evolution model to predict the size and mass flux of pebbles, and derive our own fit for gas accretion. We do not include migration, but consider a wide range of formation locations between 12 and 40au. If the planetary embryos form at the same time and with the same mass, our formation model with an evolving dust population is unable to produce Uranus and Neptune analogues. This is because the mass difference between the planets and the H-He mass fractions become too high. However, if the outer planetary embryo forms earlier and/or more massive than the inner embryo, the two planets do form in a few instances when the disk is metal-rich and dissipates after a few Myr. Furthermore, our study suggests that in-situ formation is rather unlikely. Nethertheless, giant impacts and/or migration could potentially aid in the formation, and future studies including these processes could bring us one step closer to understanding how Uranus and Neptune formed., Comment: Accepted for publication in MNRAS

We explore the use of machine-learning techniques to detect quantum speedup in random walks on graphs. Specifically, we investigate the performance of three different neural-network architectures (variations on fully connected and convolutional neural networks) for identifying linear, cyclic, and random graphs that yield quantum speedups in terms of the hitting time for reaching a target node after starting in another node of the graph. Our results indicate that carefully building the data set for training can improve the performance of the neural networks, but all architectures we test struggle to classify large random graphs and generalize from training on one graph size to testing on another. If classification accuracy can be improved further, valuable insights about quantum advantage may be gleaned from these neural networks, not only for random walks, but more generally for quantum computing and quantum transport., Comment: 15 pages, 8 figures

We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing., Comment: accepted at ISWC'23