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Given a set $A$ and an abelian group $B$ with operators in $A$, we introduce the Ore group extension $B[x ; \delta_B , \sigma_B]$ as the additive group $B[x]$, with $A[x]$ as a set of operators, the action of $A[x]$ on $B[x]$ being defined by mimicking the multiplication used in the classical case where $A$ and $B$ are the same ring. We derive generalizations of Vandermonde's and Leibniz's identities for this construction, and they are then used to establish associativity criteria. Additionally, we prove a version of Hilbert's basis theorem for this structure, under the assumption that the action of $A$ on $B$ is what we call weakly $s$-unital. Finally, we apply these results to the case where $B$ is a left module over a ring $A$, and specifically to the case where $A$ and $B$ coincide with a non-associative ring which is left distributive but not necessarily right distributive.

Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural networks. We propose a protocol to identify closure and conduct experiments using simple visual stimuli with progressively removed edge sections. We evaluate well-known networks on their ability to classify incomplete polygons. Our findings reveal a performance degradation as the edge removal percentage increases, indicating that current models heavily rely on complete edge information for accurate classification. The data used in our study is available on Github., Comment: Published at the ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024

We study the stellar, neutral gas content within halos over a halo mass range $10^{10} \text{ to } 10^{15.5} \text{M}_\odot$ and hot X-ray gas content over a halo mass range $10^{12.8} \text{ to } 10^{15.5} \text{M}_\odot$ in the local universe. We combine various empirical datasets of stellar, \HI\ and X-ray observations of galaxies, groups and clusters to establish fundamental baryonic mass vs halo mass scaling relations. These scaling relations are combined with halo mass function to obtain the baryon densities of stars, neutral gas and hot gas ($T>10^6 \text{K}$), as a function of halo mass. We calculate the contributions of the individual baryonic components to the cosmic baryon fraction. Cosmic stellar mass density ($\Omega_\text{star}=2.09^{+0.21}_{-0.18} \times 10^{-3}$), cosmic HI mass density ($\Omega_\text{HI}=0.49^{+0.25}_{-0.12} \times 10^{-3}$) and cosmic neutral gas mass density ($\Omega_\text{neutral gas}=0.71^{+0.39}_{-0.18} \times 10^{-3}$) estimates are consistent with previous more direct method measurements of these values, thereby establishing the veracity of our method. We also give an estimate of the cosmic hot plasma density ($\Omega_\text{hot gas}=2.58^{+2.1}_{-0.66} \times 10^{-3}$)., Comment: Accepted for publication in MNRAS. Comments are welcome

We use modern dimensionally-reduced effective field theory methods, with careful attention to scale hierarchies, to analyze and catalog the types of first-order electroweak phase transitions that are possible in the Standard Model Effective Field Theory (SMEFT). Our calculations lay the necessary groundwork to perform gauge invariant, properly resummed perturbative expansions, and therefore address many of the theoretical problems with phase transition calculations. We find three types of configurations of the scalar potential that allow for a first-order phase transition, namely tree-level barriers, radiative barriers, or radiative symmetry breaking through the Coleman-Weinberg mechanism. We also find versions of these with significant supercooling. We perform a global likelihood scan over the Wilson coefficients of SMEFT operators involving only the Higgs field, to identify parameter regions that exhibit these first-order phase transitions and are consistent with experimental and theoretical constraints. We comment on the possibilities for electroweak baryogenesis within the SMEFT, and roughly estimate if the gravitational wave spectra generated by the phase transitions are detectable., Comment: 52 pages, 2 figures

We perform an experimental two-color high-order harmonic generation study in argon with the fundamental of an ytterbium ultrashort pulse laser and its second harmonic. The intensity of the second harmonic and its phase relative to the fundamental are varied, in a large range compared to earlier works, while keeping the total intensity constant. We extract the optimum values for the relative phase and ratio of the two colors which lead to a maximum yield enhancement for each harmonic order in the extreme ultraviolet spectrum. Within the semi-classical three-step model, the yield maximum can be associated with a flat electron return time vs. return energy distribution. An analysis of different distributions allows to predict the required relative two-color phase and ratio for a given harmonic order, total laser intensity, fundamental wavelength, and ionization potential.

The analysis of neural power spectra plays a crucial role in understanding brain function and dysfunction. While recent efforts have led to the development of methods for decomposing spectral data, challenges remain in performing statistical analysis and group-level comparisons. Here, we introduce Bayesian Spectral Decomposition (BSD), a Bayesian framework for analysing neural spectral power. BSD allows for the specification, inversion, comparison, and analysis of parametric models of neural spectra, addressing limitations of existing methods. We first establish the face validity of BSD on simulated data and show how it outperforms an established method (\fooof{}) for peak detection on artificial spectral data. We then demonstrate the efficacy of BSD on a group-level study of EEG spectra in 204 healthy subjects from the LEMON dataset. Our results not only highlight the effectiveness of BSD in model selection and parameter estimation, but also illustrate how BSD enables straightforward group-level regression of the effect of continuous covariates such as age. By using Bayesian inference techniques, BSD provides a robust framework for studying neural spectral data and their relationship to brain function and dysfunction., Comment: 18 pages

Producing code of good quality is an essential skill in software development. Code quality is an aspect of software quality that concerns the directly observable properties of code, such as decomposition, modularization, and code flow. Code quality can often be improved by means of code refactoring -- an internal change made to code that does not alter its observable behavior. According to the ACM/IEEE-CS/AAAI Computer Science Curricula 2023, code refactoring and code quality are core topics in software engineering education. However, studies show that students often produce code with persistent quality issues. Therefore, it is important to understand what problems students experience when trying to identify and fix code quality issues. In a prior study, we identified a number of student misconceptions in method-level code refactoring. In this paper, we present the findings from a think-aloud study conducted to investigate what students think when working on method-level refactoring exercises. We use grounded theory to identify and classify student reasoning. As a result of the analysis, we identify a set of eight reasons given by students to refactor code, which either concerns the presence of code quality issues, the improvement of software quality attributes, or code semantics. We also analyze which quality issues are identified by students, and to which reasonings these quality issues are related. We found that experienced students reason more often about code quality attributes rather than pointing at a problem they see in the code. Students were able to remove code quality issues in most cases. However, they often overlooked particular issues, such as the presence of a method with multiple responsibilities or the use of a less suitable loop structure.

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures

In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrographs (namely Gemini-S/GPI and VLT/SPHERE-IFS) that we calibrated homogeneously, and in which we injected a handful of synthetic planetary signals (ground truth) to be characterized by the data challenge participants. The tasks of the participants consist of (1) extracting the precise astrometry of each injected planetary signals, and (2) extracting the precise spectro-photometry of each injected planetary signal. Additionally, the participants may provide the 1-sigma uncertainties on their estimation for further analyses. When available, the participants can also provide the posterior distribution used to estimate the position/spectrum and uncertainties. The data are permanently available on a Zenodo repository and the participants can submit their results through the EvalAI platform. The EvalAI submission platform opened on April 2022 and closed on the 31st of May 2024. In total, we received 4 valid submissions for the astrometry estimation and 4 valid submissions for the spectrophotometry (each submission, corresponding to one pipeline, has been submitted by a unique participant). In this communication, we present an analysis and interpretation of the results., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, Adaptive Optics Systems IX, Paper 13097-40

We investigate the electronic structure of high-density layers of Sb dopants in a silicon host, so-called Si:Sb $\delta$-layers. We show that, in spite of the known challenges in producing highly confined Sb $\delta$-layers, sufficient confinement is created such that the lowest conduction band states ($\Gamma$ states, studied in depth in other silicon $\delta$-layers), become occupied and can be observed using angle-resolved photoemission spectroscopy. The electronic structure of the Si:Sb $\delta$-layers closely resembles that of Si:P systems, where the observed conduction band is near-parabolic and slightly anisotropic in the $\mathbf{k}_\parallel$ plane. The observed $\Gamma$ state extends ~ 1 nm in the out-of-plane direction, which is slightly wider than the 1/3 monolayer thick dopant distribution. This is caused by a small segregation of the dopant layer, which is nevertheless minimal when comparing with earlier published attempts. Our results serve to demonstrate that Sb is still a feasible dopant alternative for use in the semiconductor $\delta$-layer platform, providing similar electronic functionality to Si:P systems. Additionally, it has the advantages of being less expensive, more controllable, safer to handle, and more compatible with industrial patterning techniques. Si:Sb is therefore a viable platform for emerging quantum device applications.

Context. Transient sky astronomy is entering a new era with the advent of the SVOM mission (Space Variable Objects Monitor), which was successfully launched on the 26th of June, 2024. The primary goal of SVOM is to monitor the hard X-ray sky searching for gamma-ray bursts (GRBs). On top of its on-board follow-up capabilities, SVOM will be backed by its ground segment composed of several facilities, of which the near-infrared imager CAGIRE. Mounted on the robotic telescope COLIBRI, it will be a unique instrument, able to perform fast follow-up of GRB afterglows in J and H bands, an ideal combination to catch high-redshift (z>6) and/or obscured GRBs. Aims. This paper aims at estimating the performances of CAGIRE for GRB near-infrared afterglow detection based on the characteristics of the detector and the specificities of the COLIBRI telescope. Quickly fading GRB afterglows pose challenges that should be addressed by adapting observing strategies to the capabilities of CAGIRE. Methods. We use an end-to-end image simulator to produce realistic CAGIRE images, taking into account results from the characterization of the ALFA detector used by CAGIRE. We implemented a GRB afterglow generator that simulates infrared lightcurves and spectra based on published observation of distant GRBs (z>6). Results. We retrieved the photometry of 9 GRB afterglows in various scenarios covered by CAGIRE. Catching afterglows as early as two minutes after burst allows the identification of a nIR counterpart in the brightest 4 events. When artificially redshifted even further away, these events remain detectable by CAGIRE up to z=9.6 in J band, and z=13.3 in H band, indicating the potential of CAGIRE to be a pioneer in the identification of the most distant GRBs to date., Comment: 8 pages, 5 figures, 3 tables, accepted for publication in A&A

This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations. Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness.

Ultra-long gamma-ray bursts (ULGRBs) are characterized by exceptionally long-duration central engine activities, with characteristic timescales exceeding 1000 seconds. We present ground-based optical afterglow observations of the ultra-long gamma-ray burst GRB 211024B, detected by \textit{Swift}. Its X-ray light curve exhibits a characteristic ``internal plateau" with a shallow decay phase lasting approximately $\sim 15$ ks, followed by a steep decline ($\alpha_{\rm drop}\sim-7.5$). Moreover, the early optical emission predicted by the late r-band optical afterglow is significantly higher than the observed value, indicating an external shock with energy injection. To explain these observations, we propose a magnetar central engine model. The magnetar collapse into a black hole due to spin-down or hyperaccretion, leading to the observed steep break in the X-ray light curve. The afterglow model fitting reveals that the afterglow injection luminosity varies with different assumptions of the circumburst medium density, implying different potential energy sources. For the interstellar medium (ISM) case with a fixed injection end time, the energy may originate from the magnetar's dipole radiation. However, in other scenarios, relativistic jets produced by the magnetar/black hole system could be the primary energy source., Comment: 18 pages, 7 figures, accepted by ApJ

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

A major challenge in gravitational-wave multi-messenger astrophysics is the imprecise localization of gravitational-wave compact binary mergers. We investigate the use of a method to include galaxy catalog information in performing parameter estimation of these events. We test its effectiveness with the gravitational-wave events GW170817, GW190425, and GW190814, as well as with simulated binary neutron star mergers. For GW170817, we recover the true host galaxy as the most probable galaxy after a straightforward mass reweighting, with significantly decreased localization area and volume. On the simulated sample, however, we do not find improvement compared to performing a simple galaxy catalog crossmatch with a regular gravitational wave localization. Future investigations into sampling methods may yield improvements that increase the viability of this method., Comment: 13 pages, 6 figures

In this contribution to the VIEWS 2023 prediction challenge, we propose using an observed Markov model for making predictions of densities of fatalities from armed conflicts. The observed Markov model can be conceptualized as a two-stage model. The first stage involves a standard Markov model, where the latent states are pre-defined based on domain knowledge about conflict states. The second stage is a set of regression models conditional on the latent Markov-states which predict the number of fatalities. In the VIEWS 2023/24 prediction competition, we use a random forest classifier for modeling the transitions between the latent Markov states and a quantile regression forest to model the fatalities conditional on the latent states. For the predictions, we dynamically simulate latent state paths and randomly draw fatalities for each country-month from the conditional distribution of fatalities given the latent states. Interim evaluation of out-of-sample performance indicates that the observed Markov model produces well-calibrated forecasts which outperform the benchmark models and are among the top performing models across the evaluation metrics.

Integrating floating photovoltaic (FPV) installations into offshore wind farms has been proposed as a major opportunity to scale up offshore renewable energy generation. The interaction between these hybrid wind-solar farms and the atmospheric boundary layer (ABL) is for the first time addressed in the present study. Idealized large-eddy simulations (LES) are used to investigate the flow through both isolated wind and solar farms, as well as combined wind-solar farms, in varying configurations and under different atmospheric conditions. When the FPV modules are arranged in long strips parallel to the flow direction, secondary motions arise due to the temperature difference between the warm FPV modules and colder sea surface, significantly affecting the horizontal distribution of mean wind speed in the ABL. Associated downdrafts between the strips increase entrainment of high-speed momentum from above, thereby increasing the wind speed at these locations. LES of hybrid wind-solar farms reveals that this is beneficial for wind turbines when they are located between the FPV arrays, leading, for the cases that we considered, to a farm-averaged power increase of up to 30% compared to an isolated wind farm. It is shown that the ratio between inertial and buoyancy forces, quantified by a heterogeneity Richardson number $Ri_h$, plays a crucial role in the formation of secondary flows and the resulting wind-farm power enhancement. We further show that no significant power gains, but also no losses emerge in situations when the solar panels are aligned perpendicular to the flow. Although the present results suggest a large potential for wind-solar hybridization, future research should focus on a wider range of scenarios, obtained from a realistic distribution of wind directions and speeds to quantify the potential beneficial impact on real annual power production.

This paper introduces "Synthetic Interlocutors" for ethnographic research. Synthetic Interlocutors are chatbots ingested with ethnographic textual material (interviews and observations) by using Retrieval Augmented Generation (RAG). We integrated an open-source large language model with ethnographic data from three projects to explore two questions: Can RAG digest ethnographic material and act as ethnographic interlocutor? And, if so, can Synthetic Interlocutors prolong encounters with the field and extend our analysis? Through reflections on the process of building our Synthetic Interlocutors and an experimental collaborative workshop, we suggest that RAG can digest ethnographic materials, and it might lead to prolonged, yet uneasy ethnographic encounters that allowed us to partially recreate and re-visit fieldwork interactions while facilitating opportunities for novel analytic insights. Synthetic Interlocutors can produce collaborative, ambiguous and serendipitous moments.

We present a sample of 1956 individual stellar clumps at redshift 0.73, reflecting the rapid increase towards the peak of the cosmic star formation history. We conclude that the results achieved over the studied redshift range are in agreement with expectation of in-situ clump formation scenario from large-scale disk fragmentation., Comment: Submitted to MNRAS, comments welcome

Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping. This work introduces a new probabilistic method to detect and mitigate the effect of degeneracies in point-to-plane error minimization. The noise on the Hessian of the point-to-plane optimization problem is characterized by the noise on points and surface normals used in its construction. We exploit this characterization to quantify the probability of a direction being degenerate. The degeneracy-detection procedure is used in a new real-time degeneracy-aware iterative closest point algorithm for LiDAR registration, in which we smoothly attenuate updates in degenerate directions. The method's parameters are selected based on the noise characteristics provided in the LiDAR's datasheet. We validate the approach in four real-world experiments, demonstrating that it outperforms state-of-the-art methods at detecting and mitigating the adverse effects of degeneracies. For the benefit of the community, we release the code for the method at: github.com/ntnu-arl/drpm., Comment: 8 pages, 5 figures, accepted by IEEE Robotics and Automation Letters (IEEE RAL). Supplementary video: https://www.youtube.com/watch?v=bKnHs_wwnXs. Code: https://github.com/ntnu-arl/drpm

Technological progress related to astronomical observatories such as the recently launched James Webb Space Telescope (JWST) allows searching for signs of life beyond our Solar System, namely in the form of unambiguous biosignature gases in exoplanetary atmospheres. The tentative assignment of a $1-2.4\sigma$ spectral feature observed with JWST in the atmosphere of exoplanet K2-18b to the biosignature gas dimethyl sulfide (DMS; sum formula C$_2$H$_6$S) raised hopes that, although controversial, a second genesis had been found. Terrestrial atmospheric DMS is exclusively stemming from marine biological activity and no natural abiotic source has been identified - neither on Earth nor in space. Therefore, DMS is considered a robust biosignature. Since comets possess a pristine inventory of complex organic molecules of abiotic origin, we have searched high-resolution mass spectra collected at comet 67P/Churyumov-Gerasimenko, target of the European Space Agency's Rosetta mission, for the signatures of DMS. Previous work reported the presence of a C2H6S signal when the comet was near its equinox but distinction of DMS from its structural isomer ethanethiol remained elusive. Here we reassess these and evaluate additional data. Based on differences in the electron ionization induced fragmentation pattern of the two isomers, we show that DMS is significantly better compatible with the observations. Deviations between expected and observed signal intensities for DMS are $<1\sigma$, while for ethanethiol they are $2-4\sigma$. The local abundance of DMS relative to methanol deduced from these data is (0.13$\pm$0.04)%. Our results provide the first evidence for the existence of an abiotic synthetic pathway to DMS in pristine cometary matter and hence motivate more detailed studies of the sulfur chemistry in such matter and its analogs. [...], Comment: 11 pages, 4 figures

The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature. To address this fundamental challenge, we propose a novel approach, Neuroplastic Expansion (NE), inspired by cortical expansion in cognitive science. NE maintains learnability and adaptability throughout the entire training process by dynamically growing the network from a smaller initial size to its full dimension. Our method is designed with three key components: (1) elastic neuron generation based on potential gradients, (2) dormant neuron pruning to optimize network expressivity, and (3) neuron consolidation via experience review to strike a balance in the plasticity-stability dilemma. Extensive experiments demonstrate that NE effectively mitigates plasticity loss and outperforms state-of-the-art methods across various tasks in MuJoCo and DeepMind Control Suite environments. NE enables more adaptive learning in complex, dynamic environments, which represents a crucial step towards transitioning deep reinforcement learning from static, one-time training paradigms to more flexible, continually adapting models.

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 700 workflows registered., Comment: 30 pages, 4 figures

Generative models are transforming creative domains such as music generation, with inference-time strategies like Classifier-Free Guidance (CFG) playing a crucial role. However, CFG doubles inference cost while limiting originality and diversity across generated contents. In this paper, we introduce diversity-rewarded CFG distillation, a novel finetuning procedure that distills the strengths of CFG while addressing its limitations. Our approach optimises two training objectives: (1) a distillation objective, encouraging the model alone (without CFG) to imitate the CFG-augmented predictions, and (2) an RL objective with a diversity reward, promoting the generation of diverse outputs for a given prompt. By finetuning, we learn model weights with the ability to generate high-quality and diverse outputs, without any inference overhead. This also unlocks the potential of weight-based model merging strategies: by interpolating between the weights of two models (the first focusing on quality, the second on diversity), we can control the quality-diversity trade-off at deployment time, and even further boost performance. We conduct extensive experiments on the MusicLM (Agostinelli et al., 2023) text-to-music generative model, where our approach surpasses CFG in terms of quality-diversity Pareto optimality. According to human evaluators, our finetuned-then-merged model generates samples with higher quality-diversity than the base model augmented with CFG. Explore our generations at https://google-research.github.io/seanet/musiclm/diverse_music/.

We develop the novel method of artificial barriers for scalar stochastic differential equations (SDEs) and use it to construct boundary-preserving numerical schemes for strong approximations of scalar SDEs, possibly with non-globally Lipschitz drift and diffusion coefficients, whose state-space is either bounded or half-bounded. The idea of artificial barriers is to augment the SDE with artificial barriers outside the state-space to not change the solution process, and then apply a boundary-preserving numerical scheme to the resulting reflected SDE (RSDE). This enables us to construct boundary-preserving numerical schemes with the same strong convergence rate as the strong convergence rate of the numerical scheme for the corresponding RSDE. Based on the method of artificial barriers, we construct two boundary-preserving schemes that we call the Artificial Barrier Euler-Maruyama (ABEM) scheme and the Artificial Barrier Euler-Peano (ABEP) scheme. We provide numerical experiments for the ABEM scheme and the numerical results agree with the obtained theoretical results., Comment: 32 pages, 6 figures

This paper addresses a significant gap in explainable AI: the necessity of interpreting epistemic uncertainty in model explanations. Although current methods mainly focus on explaining predictions, with some including uncertainty, they fail to provide guidance on how to reduce the inherent uncertainty in these predictions. To overcome this challenge, we introduce new types of explanations that specifically target epistemic uncertainty. These include ensured explanations, which highlight feature modifications that can reduce uncertainty, and categorisation of uncertain explanations counter-potential, semi-potential, and super-potential which explore alternative scenarios. Our work emphasises that epistemic uncertainty adds a crucial dimension to explanation quality, demanding evaluation based not only on prediction probability but also on uncertainty reduction. We introduce a new metric, ensured ranking, designed to help users identify the most reliable explanations by balancing trade-offs between uncertainty, probability, and competing alternative explanations. Furthermore, we extend the Calibrated Explanations method, incorporating tools that visualise how changes in feature values impact epistemic uncertainty. This enhancement provides deeper insights into model behaviour, promoting increased interpretability and appropriate trust in scenarios involving uncertain predictions., Comment: 35 pages, 11 figures, journal

Context: A new generation of instruments (e.g., JWST, ELTs, PLATO, Ariel) is providing atmospheric spectra and mass/radius measurements for large exoplanet populations, challenging planetary models used to interpret these findings. Aims: We develop a new model, the Heat Atmosphere Density Evolution Solver (HADES), by coupling an atmosphere and interior model self-consistently and comparing its results to observed data. Methods: Atmospheric calculations are performed under radiative-convective equilibrium, while the interior relies on recent ab initio equations of state. We ensure continuity in the thermal, gravity, and molecular mass profiles between models. Results: The model is applied to the known exoplanet database to characterize intrinsic thermal properties. We find that intrinsic temperatures (T$_{int}$) of 200-400 K, increasing with equilibrium temperature, are needed to explain radius inflation in hot Jupiters. Additionally, we perform atmosphere-interior retrievals using observed spectra and measured parameters for WASP-39 b and 51 Eridani b. For WASP-39 b, spectroscopic data breaks degeneracies in metallicity and Tint, deriving high values: Z = 14.79$^{+1.80}_{-1.91}$ x Solar and T$_{int} = 297.39^{+8.95}_{-16.9}$ K. For 51 Eridani b, we show the importance of using self-consistent models with radius as a constrained parameter, deriving a planet mass M$_{p} = 3.13^{+0.05}_{-0.04}$ M$_{J}$ and a core mass M$_{core} = 31.86^{+0.32}_{-0.18}$ M$_{E}$, suggesting formation via core accretion with a "hot start." Conclusions: Self-consistent atmosphere-interior models can efficiently break degeneracies in the structure of transiting and directly imaged exoplanets, offering new insights into exoplanet formation and evolution., Comment: 20 pages, 10 Figures + 2 Figures in appendix, Accepted for publication in Astronomy & Astrophysics

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity, reproducibility, and democratized access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative's FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present our recommendations with commentary that reflects our discussions and justifies our choices and adaptations. Like the software and data principles on which they are based, these are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guide rails for adoption and fodder for discussion. Workflows are becoming more prevalent as documented, automated instruments for data analysis, data collection, AI-based predictions, and simulations. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community., Comment: 17 pages, 1 figure, 1 table

Considering turbulence is crucial to understanding clouds. However, covering all scales involved in the turbulent mixing of clouds with their environment is computationally challenging, urging the development of simpler models to represent some of the processes involved. By using full direct numerical simulations as a reference, this study compares several statistical approaches for representing small-scale turbulent mixing. All models use a comparable Lagrangian representation of cloud microphysics, and simulate the same cases of cloud-edge mixing, covering different ambient humidities and turbulence intensities. It is demonstrated that all statistical models represent the evolution of thermodynamics successfully, but not all models capture the changes in cloud microphysics (cloud droplet number concentration, droplet mean radius, and spectral width). Implications of these results for using the presented models as subgrid-scale schemes are discussed.

Understanding how gas flows into galactic centres, fuels the AGN, and is in turn expelled back through feedback processes is of great importance to appreciate the role AGN play in the growth and evolution of galaxies. We use the MUSE-AO optical spectra of the inner 7".5x 7".5 of the nearby Seyfert 1 galaxy NGC 4593 to characterise its ionised gas kinematics. We fit single-Gaussian components to the [O iii]$\lambda$ 5007 and [N ii]$\lambda$ 6583 emission lines, and double-Gaussian components to H$\alpha$ and H$\beta$ to determine the main ionisation mechanism of the gas. To determine the kinematics of the ionised gas, we fit double-Gaussian components to the [O iii]$\lambda$ 5007 line. Based on the stellar kinematic maps, we confirm the presence of a rotating disc. For the ionised gas, we find high-velocity dispersion values of up to $\mathrm{200-250\,km\,s^{-1}}$ that show that part of the gas is highly perturbed. The dominant ionisation mechanism of the gas is AGN photoionisation, which reaches the highest values within the innermost 4arcsec (680pc) diameter of the galaxy. At larger radii, the emission line ratios correspond to values in the composite region of the BPT diagram. The broad-component of [O iii]$\lambda$ 5007 shows blue-shifted velocities on the east side of the central 2arcsec (340pc), which spatially coincide with a region of high velocity-dispersion. This confirms the presence of outflowing gas. We estimate a mass outflow rate and kinetic power of ${\dot{M}}\geq {0.048 \,M_\odot\,{{\rm yr}^{-1}} }$ and ${\dot{E}_{\text{kin}}} \geq 4.09 \times 10^{39} \, \text{erg} \, \text{s}^{-1}$. The derived mass outflow rate is consistent with that expected from empirical relations between mass outflow rate and AGN luminosity for a low-luminosity AGN such as NGC 4593., Comment: Accepted for publication in A&A

The non-stoichiometric Fe$_2$P-type (FeMnP$_{0.5}$Si$_{0.5}$)$_{1-x}$(FeV)$_{x}$ alloys ( $x=0, 0.01$, $0.02$, and $0.03$) have been investigated as potential candidates for magnetic refrigeration near room temperature. The magnetic ordering temperature decreases with increasing FeV concentration, $x$, which can be ascribed to decreased ferromagnetic coupling strength between the magnetic atoms. The strong magnetoelastic coupling in these alloys results in large values of the isothermal entropy change ($\Delta S_M$); $15.7$ J/kgK, at $2$ T magnetic field for the $x = 0$ alloy. $\Delta S_M$ decreases with increasing $x$. Results from M{\"o}ssbauer spectroscopy reveal that the average hyperfine field (in the ferromagnetic state) and average center shift (in the paramagnetic state) have the same decreasing trend as $\Delta S_M$. The thermal hysteresis ($\Delta T_{hyst}$) of the magnetic phase transition decreases with increasing $x$, while the mechanical stability of the alloys improves due to the reduced lattice volume change across the magnetoelastic phase transition. The adiabatic temperature change $\Delta T_{ad}$, which highly depends on $\Delta T_{hyst}$, is $1.7$ K at $1.9$ T applied field for the $x = 0.02$ alloy.

Parametric timed automata (PTAs) extend the concept of timed automata, by allowing timing delays not only specified by concrete values but also by parameters, allowing the analysis of systems with uncertainty regarding timing behaviors. The full execution-time opacity is defined as the problem in which an attacker must never be able to deduce whether some private location was visited, by only observing the execution time. The problem of full ET-opacity emptiness (i.e., the emptiness over the parameter valuations for which full execution-time opacity is satisfied) is known to be undecidable for general PTAs. We therefore focus here on one-clock PTAs with integer-valued parameters over dense time. We show that the full ET-opacity emptiness is undecidable for a sufficiently large number of parameters, but is decidable for a single parameter, and exact synthesis can be effectively achieved. Our proofs rely on a novel construction as well as on variants of Presburger arithmetics. We finally prove an additional decidability result on an existential variant of execution-time opacity., Comment: This is the author (and extended) version of the manuscript of the same name published in the proceedings of the 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024)

The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reasons behind their effectiveness remain largely unknown. In this work we provide an in-depth analysis identifying the key factors driving this performance gain. We discover the surprising result that tokenizing the encoder output, rather than the use of multiple experts, is what is behind the efficacy of SoftMoEs. Indeed, we demonstrate that even with an appropriately scaled single expert, we are able to maintain the performance gains, largely thanks to tokenization.

Dwarf galaxies hold the key to crucial frontiers of astrophysics, however, their faintness renders spectroscopy challenging. Here we present the JWST Cycle 2 survey, All the Little Things (ALT, PID 3516), which is designed to seek late-forming Pop III stars and the drivers of reionization at $z\sim6-7$. ALT has acquired the deepest NIRCam grism spectroscopy yet (7-27 hr), at JWST's most sensitive wavelengths (3-4 $\mu$m), covering the powerful lensing cluster Abell 2744. Over the same 30 arcmin$^2$, ALT's ultra-deep F070W+F090W imaging ($\sim$30 mag) enables selection of very faint sources at $z>6$. We demonstrate the success of ALT's novel ``butterfly" mosaic to solve spectral confusion and contamination, and introduce the ``Allegro" method for emission line identification. By collecting spectra for every source in the field of view, ALT has measured precise ($R\sim1600$) redshifts for 1630 sources at $z=0.2-8.5$. This includes one of the largest samples of distant dwarf galaxies: [1015, 475, 50] sources less massive than the SMC, Fornax, and Sculptor with $\log(M_{*}/M_{\odot})<$[8.5, 7.5, 6.5]. We showcase ALT's discovery space with: (i) spatially resolved spectra of lensed clumps in galaxies as faint as $M_{\rm{UV}}\sim-15$; (ii) large-scale clustering -- overdensities at $z$=[2.50, 2.58, 3.97, 4.30, 5.66, 5.77, 6.33] hosting massive galaxies with striking Balmer breaks; (iii) small-scale clustering -- a system of satellites around a Milky Way analog at $z\sim6$; (iv) spectroscopically confirmed multiple images that help constrain the lensing model underlying all science in this legacy field; (v) sensitive star-formation maps based on dust-insensitive tracers such as Pa$\alpha$; (vi) direct spectroscopic discovery of rare sources such as AGN with ionized outflows. These results provide a powerful proof of concept for how grism surveys maximize the potential of strong lensing fields., Comment: Submitted to the Open Journal of Astrophysics. Figs. 6 and 10 illustrate the quality of the spectra and imaging, while Fig. 12 summarizes the yield of the survey. Comments warmly welcomed and greatly appreciated. The ALT DR1 catalog is available at https://zenodo.org/records/13871850

State-of-the-art LLMs are powered by scaling -- scaling model size, dataset size and cluster size. It is economically infeasible to extensively tune hyperparameter for the largest runs. Instead, approximately optimal hyperparameters must be inferred or \textit{transferred} from smaller experiments. Hyperparameter transfer across model sizes has been studied in Yang et al. However, hyperparameter transfer across dataset size -- or token horizon -- has not been studied yet. To remedy this we conduct a large scale empirical study on how optimal learning rate (LR) depends on token horizon in LLM training. We first demonstrate that the optimal LR changes significantly with token horizon -- longer training necessitates smaller LR. Secondly we demonstrate the the optimal LR follows a scaling law, and that the optimal LR for longer horizons can be accurately estimated from shorter horizons via such scaling laws. We also provide a rule-of-thumb for transferring LR across token horizons with zero overhead over current practices. Lastly we provide evidence that LLama-1 used too high LR, and estimate the performance hit from this. We thus argue that hyperparameter transfer across data size is an important and overlooked component of LLM training.

Radiation pressure from Lyman-$\alpha$ (Ly$\alpha$) scattering is a potentially dominant form of early stellar feedback, capable of injecting up to $\sim 100 \, \times$ more momentum into the interstellar medium (ISM) than UV continuum radiation pressure and stellar winds. Ly$\alpha$ feedback is particularly strong in dust-poor environments and is thus especially important during the formation of the first stars and galaxies. As upcoming galaxy formation simulations incorporate Ly$\alpha$ feedback, it is crucial to consider processes that can limit it to avoid placing $\Lambda$CDM in apparent tension with recent \textit{JWST} observations indicating efficient star formation at Cosmic Dawn. We study Ly$\alpha$ feedback using a novel analytical Ly$\alpha$ radiative transfer solution that includes the effects of continuum absorption, gas velocity gradients, Ly$\alpha$ destruction (e.g. by $2p \rightarrow 2s$ transitions), ISM turbulence, and atomic recoil. We verify our solution for uniform clouds using extensive Monte Carlo radiative transfer (MCRT) tests, and resolve a previous discrepancy between analytical and MCRT predictions. We then study the sensitivity of Ly$\alpha$ feedback to the aforementioned effects. While these can dampen Ly$\alpha$ feedback by a factor $\lesssim \textrm{few} \times 10$, we find it remains $\gtrsim 5 - 100 \, \times$ stronger than direct radiation pressure and therefore cannot be neglected. We provide an accurate fit for the Ly$\alpha$ force multiplier $M_{\rm F}$, suitable for implementation in subgrid models for galaxy formation simulations. Our findings highlight the critical role of Ly$\alpha$ feedback in regulating star formation at Cosmic Dawn, and underscore the necessity of incorporating it into simulations to accurately model early galaxy evolution., Comment: Comments welcome! 42 pages (31 pages main text, the rest an extensive Appendix + references), 20 figures

We obtain distribution-free bounds for various fundamental quantities used in probability theory by solving optimization problems that search for extreme distributions among all distributions with the same mean and dispersion. These sharpest possible bounds depend only on the mean and dispersion of the driving random variable. We solve the optimization problems by a novel yet elementary technique that reduces the set of all candidate solutions to two-point distributions. We consider a general dispersion measure, with variance, mean absolute deviation and power moments as special cases. We apply the bounds to robust newsvendor stocking and monopoly pricing, generalizing foundational mean-variance works. This shows how pricing and order decisions respond to increased demand uncertainty, including scenarios where dispersion information allows for heavy-tailed demand distributions.

Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some non-trivial temporal correlation structure, which renders the SDE non-Markovian; a phenomenon commonly known as ``colored'' noise. Thus, an important objective is to develop effective tools for mathematically and numerically studying (possibly non-Markovian) SDEs. In this report, we formalise a mathematical theory for analysing and numerically studying SDEs based on so-called `generalised coordinates of motion'. Like the theory of rough paths, we analyse SDEs pathwise for any given realisation of the noise, not solely probabilistically. Like the established theory of Markovian realisation, we realise non-Markovian SDEs as a Markov process in an extended space. Unlike the established theory of Markovian realisation however, the Markovian realisations here are accurate on short timescales and may be exact globally in time, when flows and fluctuations are analytic. This theory is exact for SDEs with analytic flows and fluctuations, and is approximate when flows and fluctuations are differentiable. It provides useful analysis tools, which we employ to solve linear SDEs with analytic fluctuations. It may also be useful for studying rougher SDEs, as these may be identified as the limit of smoother ones. This theory supplies effective, computationally straightforward methods for simulation, filtering and control of SDEs; amongst others, we re-derive generalised Bayesian filtering, a state-of-the-art method for time-series analysis. Looking forward, this report suggests that generalised coordinates have far-reaching applications throughout stochastic differential equations., Comment: 38 pages of main, 45 pages including TOC, Appendix and references

In this work, a 3.5 GS/s voltage-controlled oscillator (VCO) analog-to-digital converter (ADC) using multi-stage noise shaping (MASH) is presented. This 28nm CMOS ADC achieves second-order noise shaping in an easily-scalable, open-loop configuration. A key enabler of the high-bandwidth MASH VCO ADC is the use of a multi-bit estimated error signal. With an OSR of 16, an SNDR of 67 dB and DR of 68 dB are achieved in 109.375 MHz bandwidth. The full-custom pseudo-analog circuits consume 9 mW, while the automatically generated digital circuits consume another 24 mW. A $\mathbf{FoM_{DR} = 163}$ dB and core area of $\mathbf{0.017\,\mathbf{mm}^2}$ are obtained., Comment: 14 pages, 29 figures. Author's version. IEEE Transactions on Circuits and Systems I: Regular Papers

The aim of this paper is to propose an adaptation of the well known adaptive conformal inference (ACI) algorithm to achieve finite-sample coverage guarantees in multi-step ahead time-series forecasting in the online setting. ACI dynamically adjusts significance levels, and comes with finite-sample guarantees on coverage, even for non-exchangeable data. Our multi-step ahead ACI procedure inherits these guarantees at each prediction step, as well as for the overall error rate. The multi-step ahead ACI algorithm can be used with different target error and learning rates at different prediction steps, which is illustrated in our numerical examples, where we employ a version of the confromalised ridge regression algorithm, adapted to multi-input multi-output forecasting. The examples serve to show how the method works in practice, illustrating the effect of variable target error and learning rates for different prediction steps, which suggests that a balance may be struck between efficiency (interval width) and coverage.t, Comment: 14 pages, 3 figures

Conformal prediction (CP) is a robust framework for distribution-free uncertainty quantification, but it requires exchangeable data to ensure valid prediction sets at a user-specified significance level. When this assumption is violated, as in time-series or other structured data, the validity guarantees of CP no longer hold. Adaptive conformal inference (ACI) was introduced to address this limitation by adjusting the significance level dynamically, ensuring finite-sample coverage guarantees even for non-exchangeable data. In this paper, we show that ACI does not require the use of conformal predictors; instead, it can be implemented with the more general confidence predictors, which are computationally simpler and still maintain the crucial property of nested prediction sets. Through experiments on synthetic and real-world data, we demonstrate that confidence predictors can perform comparably to, or even better than, conformal predictors, particularly in terms of computational efficiency. These findings suggest that confidence predictors represent a viable and efficient alternative to conformal predictors in non-exchangeable data settings, although further studies are needed to identify when one method is superior., Comment: 34 pages, 5 figures. Updated version after feedback

This study explores the formation and implications of mini-active galactic nuclei (mAGN) disks around intermediate-mass black holes (IMBHs) embedded in gas-rich globular/nuclear clusters (GCs). We examine the parameter space for stable mAGN disks, considering the influence of IMBH mass, disk radius, and gas density on disk stability. The dynamics of stars and black holes within the mAGN disk are modeled, with a focus on gas-induced migration and gas dynamical friction. These dynamical processes can lead to several potentially observable phenomena, including the alignment of stellar orbits into the disk plane, the enhancement of gravitational wave mergers (particularly IMRIs and EMRIs), and the occurrence of mili/centi-tidal disruption events (mTDEs/cTDEs) with unique observational signatures. We find that gas hardening can significantly accelerate the inspiral of binaries within the disk, potentially leading to a frequency shift in the emitted gravitational waves. Additionally, we explore the possibility of forming accreting IMBH systems from captured binaries within the mAGN disk, potentially resulting in the formation of ultraluminous X-ray sources (ULXs). The observational implications of such accreting systems, including X-ray emission, optical signatures, and transient phenomena, are discussed. Furthermore, we investigate the possibility of large-scale jets emanating from gas-embedded IMBHs in GCs. While several caveats and uncertainties exist, our work highlights the potential for mAGN disks to provide unique insights into IMBH demographics, accretion physics, and the dynamics of GCs., Comment: Comments welcome!

Layered transition metal dichalcogenides (TMDs) stabilize in multiple structural forms with profoundly distinct and exotic electronic phases. Interfacing different layer types is a promising route to manipulate TMDs' properties, not only as a means to engineer quantum devices, but also as a route to explore fundamental physics in complex matter. Here we use angle-resolved photoemission (ARPES) to investigate a strong layering-dependent enhancement of superconductivity in TaS$_2$, in which the superconducting transition temperature, $T_c$, of its $2H$ structural phase is nearly tripled when insulating $1T$ layers are inserted into the system. The study is facilitated by a novel vanadium-intercalation approach to synthesizing various TaS$_2$ polytypes, which improves the quality of ARPES data while leaving key aspects of the electronic structure and properties intact. The spectra show the clear opening of the charge density wave gap in the pure $2H$ phase and its suppression when $1T$ layers are introduced to the system. Moreover, in the mixed-layer $4Hb$ system, we observe a strongly momentum-anisotropic increase in electron-phonon coupling near the Fermi level relative to the $2H$ phase. Both phenomena help to account for the increased $T_c$ in mixed $2H$/$1T$ layer structures.

The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory for their ability to achieve a goal. However, when feedback is delayed and sparse, the learning signal is poor, and action evaluation becomes harder. Canonical solutions, such as reward shaping and options, require extensive domain knowledge and manual intervention, limiting their scalability and applicability. In this work, we lay the foundations for Credit Assignment with Language Models (CALM), a novel approach that leverages Large Language Models (LLMs) to automate credit assignment via reward shaping and options discovery. CALM uses LLMs to decompose a task into elementary subgoals and assess the achievement of these subgoals in state-action transitions. Every time an option terminates, a subgoal is achieved, and CALM provides an auxiliary reward. This additional reward signal can enhance the learning process when the task reward is sparse and delayed without the need for human-designed rewards. We provide a preliminary evaluation of CALM using a dataset of human-annotated demonstrations from MiniHack, suggesting that LLMs can be effective in assigning credit in zero-shot settings, without examples or LLM fine-tuning. Our preliminary results indicate that the knowledge of LLMs is a promising prior for credit assignment in RL, facilitating the transfer of human knowledge into value functions., Comment: 9 pages

Music-text multimodal systems have enabled new approaches to Music Information Research (MIR) applications such as audio-to-text and text-to-audio retrieval, text-based song generation, and music captioning. Despite the reported success, little effort has been put into evaluating the musical knowledge of Large Language Models (LLM). In this paper, we demonstrate that LLMs suffer from 1) prompt sensitivity, 2) inability to model negation (e.g. 'rock song without guitar'), and 3) sensitivity towards the presence of specific words. We quantified these properties as a triplet-based accuracy, evaluating the ability to model the relative similarity of labels in a hierarchical ontology. We leveraged the Audioset ontology to generate triplets consisting of an anchor, a positive (relevant) label, and a negative (less relevant) label for the genre and instruments sub-tree. We evaluated the triplet-based musical knowledge for six general-purpose Transformer-based models. The triplets obtained through this methodology required filtering, as some were difficult to judge and therefore relatively uninformative for evaluation purposes. Despite the relatively high accuracy reported, inconsistencies are evident in all six models, suggesting that off-the-shelf LLMs need adaptation to music before use.

The hot phase of the circumgalactic medium (CGM) allows us to probe the inflow and outflow of gas within a galaxy, which is responsible for dictating the evolution of the galaxy. Studying the hot CGM sheds light on a better understanding of gas physics, which is crucial to inform and constrain simulation models. With the recent advances in observational measurements probing the hot CGM in X-rays and tSZ, we have a new avenue for widening our knowledge of gas physics and feedback by exploiting the information from current/future observations. In this paper, we use the TNG300 hydrodynamical simulations to build a fully self-consistent forward model for the hot CGM. We construct a lightcone and generate mock X-ray observations. We quantify the projection effects, namely the locally correlated large-scale structure in X-rays and the effect due to satellite galaxies misclassified as centrals which affects the measured hot CGM galactocentric profiles in stacking experiments. We present an analytical model that describes the intrinsic X-ray surface brightness profile across the stellar and halo mass bins. The increasing stellar mass bins result in decreasing values of $\beta$, the exponent quantifying the slope of the intrinsic galactocentric profiles. We carry forward the current state-of-the-art by also showing the impact of the locally correlated environment on the measured X-ray surface brightness profiles. We also present, for the first time, the effect of misclassified centrals in stacking experiments for three stellar mass bins: $10^{10.5-11}\ M_\odot$, $10^{11-11.2}\ M_\odot$, and $10^{11.2-11.5}\ M_\odot$. We find that the contaminating effect of the misclassified centrals on the stacked profiles increases when the stellar mass decreases., Comment: 14 pages, 10 figures, Submitted to A&A, comments welcome

Aquaculture robotics is receiving increased attention and is subject to unique challenges and opportunities for research and development. Guidance, navigation and control are all important aspects for realizing aquaculture robotics solutions that can greatly benefit the industry in the future. Sensor technologies, navigation methods, motion planners and state control all have a role to play, and this paper introduces some technologies and methods that are currently being applied in research and industry before providing some examples of challenges that can be targeted in the future.

We give an exact formula for the number of distinct square patterns of a given size that occur in the Squiral tiling., Comment: 19 pages, 11 figures. arXiv admin note: text overlap with arXiv:2409.03068

End-to-end neural diarization has evolved considerably over the past few years, but data scarcity is still a major obstacle for further improvements. Self-supervised learning methods such as WavLM have shown promising performance on several downstream tasks, but their application on speaker diarization is somehow limited. In this work, we explore using WavLM to alleviate the problem of data scarcity for neural diarization training. We use the same pipeline as Pyannote and improve the local end-to-end neural diarization with WavLM and Conformer. Experiments on far-field AMI, AISHELL-4, and AliMeeting datasets show that our method substantially outperforms the Pyannote baseline and achieves new state-of-the-art results on AMI and AISHELL-4, respectively. In addition, by analyzing the system performance under different data quantity scenarios, we show that WavLM representations are much more robust against data scarcity than filterbank features, enabling less data hungry training strategies. Furthermore, we found that simulated data, usually used to train endto-end diarization models, does not help when using WavLM in our experiments. Additionally, we also evaluate our model on the recent CHiME8 NOTSOFAR-1 task where it achieves better performance than the Pyannote baseline. Our source code is publicly available at https://github.com/BUTSpeechFIT/DiariZen., Comment: Submitted to ICASSP 2025; New results are updated but conclusions are exactly the same as the original one