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With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every about 60-90 days with amplitudes of around 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of about 0.83 mpc and would merge in about 70000 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy., Comment: 11 pages, 5 figures, 2 tables, plus appendices. Accepted for publication in Astronomy & Astrophysics. NASA press release available at https://science.nasa.gov/missions/swift/nasas-swift-studies-gas-churning-monster-black-holes/ doi: 10.1051/0004-6361/202451305

We analyze the proton$\text{-}$box contribution to the hadronic light$\text{-}$by$\text{-}$light part of the muon's anomalous magnetic moment, which is the first reported baryonic contribution to this piece. We follow the quark$\text{-}$loop analysis, incorporating the relevant data$\text{-}$driven and lattice proton form factors. Although the heavy mass expansion would yield a contribution of $\mathcal{O}(10^{-10})$, the damping of the form factors in the regions where the kernel peaks, explains our finding $a_{\mu}^{\rm{p-box}}=1.82 (7)\times 10^{-12}$, two orders of magnitude smaller than the forthcoming uncertainty on the $a_{\mu}$ measurement and on its Standard Model prediction., Comment: 18 pages, 4 figures

This paper reports on a comprehensive study of an ultra-relativistic positron beam deflection by an oriented quasi-mosaic crystal. The analysis was carried out for the positron energy of 530 MeV incident on the quasi-mosaic bent Si(111) crystal. This particular case was chosen because it has recently been studied experimentally at the Mainz Microtron MAMI. The results of the relativistic molecular dynamics simulations were compared with the experimental observations and a good agreement was found. The presence of planar channeling, de-channeling, volume reflection and volume capture processes in the angular distribution of deflected positrons for different beam-crystal alignments has been studied. Predictions have been made for certain crystal orientations for which experimental data are lacking.

The Dice Similarity Coefficient (DSC) is the current de facto standard to determine agreement between a reference segmentation and one generated by manual / auto-contouring approaches. This metric is useful for non-spatially important images; however, radiation therapy requires consideration of nearby Organs-at-Risk (OARs) and their radiosensitivity which are currently unaccounted for with the traditional DSC. In this work, we introduce the OAR-DSC which accounts for nearby OARs and their radiosensitivity when computing the DSC. We illustrate the importance of this through cases where two proposed contours have similar DSC, but lower OAR-DSC when one contour expands closer to the surrounding OARs. This work is important because the OAR-DSC may be used by deep learning auto-contouring algorithms in a radiation therapy specific loss function, thereby progressing on the current disregard for the importance of these differences on the final radiation dose plan generation, delivery, and risks of patient toxicity., Comment: Proceedings of the XX-th international conference on the use of computers in radiation therapy (ICCR)

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

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.

Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like question answering~(QA). While Knowledge Graphs~(KGs) have been shown to help mitigate these issues, research on the integration of LLMs with background KGs remains limited. In particular, user accessibility and the flexibility of the underlying KG have not been thoroughly explored. We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. It integrates knowledge extraction, integration, and real-time visualization. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge, ensuring adaptability to evolving user requirements and data contexts. Our approach demonstrates superior performance in knowledge graph interactions, particularly for complex domain-specific tasks. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines, achieving 95.12\% accuracy in task classification and 90.45\% success rate in task execution. User studies corroborate its effectiveness in real-world scenarios. To showcase versatility, we extended AGENTiGraph to legislation and healthcare domains, constructing specialized KGs capable of answering complex queries in legal and medical contexts., Comment: 30 pages, 7 figures; Submitted to COLING 2025 System Demonstrations Track

We analysed ALMA FIR (1.3 mm) dust continuum and CO emission of 12 starburst galaxies at $z\sim 2.1-3.6$, selected for their extreme brightness in the rest-UV with $M_{\rm UV} = -23.4$ to $-24.7$. We also analysed VLT HAWK-I $H$- and $K_{\rm s}$-band images. The galaxies are characterised by negligible dust attenuations with blue UV spectral slopes ($-2.62$ to $-1.84$), very young stellar populations of $\sim 10$ Myr, and powerful starbursts with a high mean specific star formation rate of $\rm 112~Gyr^{-1}$, placing them $\sim 1.5$~dex above the main sequence at similar redshifts and stellar masses ($M_{\rm stars} \sim (1.5-4.6)\times 10^9~M_{\odot}$). The FIR dust continuum emission revealed in 9 galaxies yields IR luminosities of $(5.9-28.3)\times 10^{11}~L_{\odot}$ and large dust masses barely produced by SNe within the 10~Myr timescale. The CO emission detected in 8 galaxies evidence large molecular gas masses with a mean molecular gas fraction of 82%. The corresponding star formation efficiencies reach $\gtrsim 40$\%, with amazingly short molecular gas depletion timescales between <13 Myr and 71 Myr. These unique properties, never reported in previously studied galaxies, highlight that these galaxies are likely caught at the very beginning of their stellar mass build-up and undergo a very efficient and fast conversion of gas into stars that can only result from the gas collapse within very short free-fall times. We find that the feedback-free starburst model seems to be able to explain the formation of these galaxies. To reconcile the co-spatial FIR dust emission with the UV-bright unattenuated emission, we speculate about radiation-driven outflows which can temporarily remove dust at the location of the starburst and expel dust at large distances in line with the measured large FIR effective radii ($\rm 1.7~kpc - 5~kpc$) in comparison to very compact stellar radii., Comment: 20 pages, 10 figures, 3 tables. Submitted to Astronomy & Astrophysics

The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phenomenon, dubbed as ''safety alignment degradation'' in this paper, and show that the challenge arises from the representation gap that emerges when introducing vision modality to VLMs. In particular, we show that the representations of multi-modal inputs shift away from that of text-only inputs which represent the distribution that the LLM backbone is optimized for. At the same time, the safety alignment capabilities, initially developed within the textual embedding space, do not successfully transfer to this new multi-modal representation space. To reduce safety alignment degradation, we introduce Cross-Modality Representation Manipulation (CMRM), an inference time representation intervention method for recovering the safety alignment ability that is inherent in the LLM backbone of VLMs, while simultaneously preserving the functional capabilities of VLMs. The empirical results show that our framework significantly recovers the alignment ability that is inherited from the LLM backbone with minimal impact on the fluency and linguistic capabilities of pre-trained VLMs even without additional training. Specifically, the unsafe rate of LLaVA-7B on multi-modal input can be reduced from 61.53% to as low as 3.15% with only inference-time intervention. WARNING: This paper contains examples of toxic or harmful language., Comment: Preprint

We study large classes of real-valued analytic functions that naturally emerge in the understanding of Dulac's problem, which addresses the finiteness of limit cycles in planar differential equations. Building on a Maximum Modulus-type result we got, our main statement essentially follows. Namely, for any function belonging to these classes, the following dichotomy holds: either it has isolated zeros or it coincides with the identity. As an application, we prove that the non-accumulation of limit cycles holds around a specific class of the so-called superreal polycycles., Comment: 19 pages, 4 figures

6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into micro VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated.

The problem of imputing multivariate time series spans a wide range of fields, from clinical healthcare to multi-sensor systems. Initially, Recurrent Neural Networks (RNNs) were employed for this task; however, their error accumulation issues led to the adoption of Transformers, leveraging attention mechanisms to mitigate these problems. Concurrently, the promising results of diffusion models in capturing original distributions have positioned them at the forefront of current research, often in conjunction with Transformers. In this paper, we propose replacing time-oriented Transformers with State-Space Models (SSM), which are better suited for temporal data modeling. Specifically, we utilize the latest SSM variant, S6, which incorporates attention-like mechanisms. By embedding S6 within Mamba blocks, we develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced spatiotemporal representations. Implementing these architectural modifications, previously unexplored in this field, we present Time series Imputation with Bi-directional mamba blocks and diffusion models (TIMBA). TIMBA achieves superior performance in almost all benchmark scenarios and performs comparably in others across a diverse range of missing value situations and three real-world datasets. We also evaluate how the performance of our model varies with different amounts of missing values and analyse its performance on downstream tasks. In addition, we provide the original code to replicate the results., Comment: 14 pages, 7 tables and 2 figures

This research presents a framework for analyzing the dynamics of online communities in social media platforms, utilizing a temporal fusion of text and network data. By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution, revealing the influence of real-world events. We introduced fourteen key elements based on social science theories to evaluate social media dynamics, validating our framework through a case study of Twitter data during major U.S. events in 2020. Our analysis centers on discrimination discourse, identifying sexism, racism, xenophobia, ableism, homophobia, and religious intolerance as main fragments. Results demonstrate rapid community emergence and dissolution cycles representative of discourse fragments. We reveal how real-world circumstances impact discourse dominance and how social media contributes to echo chamber formation and societal polarization. Our comprehensive approach provides insights into discourse fragmentation, opinion dynamics, and structural aspects of online communities, offering a methodology for understanding the complex interplay between online interactions and societal trends.

Some giant radio galaxies selected at X-rays with an AGN show signs of a restarted nuclear activity. One object in this peculiar class is Mrk1498, a giant low-frequency double radio source that shows extended emission in [OIII]. This emission is likely related to the history of the nuclear activity of the galaxy. We investigate whether this bubble-like emission might trace an outflow from either present or past AGN activity. Using MEGARA/GTC, medium-resolution spectroscopy (R 10000) data, we obtained the kinematics and fluxes of the ionised gas from modelling the [OIII] and Hbeta features.with three kinematic components. All the components show an overall blue to red velocity pattern, with similar peak-to-peak velocities but a different velocity dispersion. At a galactocentric distance of 2.3 kpc, we found a blob with a velocity up to 100km/s, and a high velocity dispersion (170km/s) that is spatially coincident with the direction of the radio jet. The observed [OIII]/Hbeta line ratio indicates possible ionisation from AGN or shocks nearly everywhere. The clumpy structure visibile in HST images at kpc scales show the lowest values of log[OIII]/Hbeta , which is likely not related to the photoionisation by the AGN. Taking optical and radio activity into account, we propose a scenario of two different ionised gas features over the radio AGN lifecycle of Mrk 1498. The radio emission suggests at least two main radio activity episodes: an old episode at Mpc scales (formed during a time span of 100Myr), and a new episode from the core (>2000yr ago). At optical wavelengths, we observe clumps and a blob that are likely associated with fossil outflow. The latter is likely powered by past episodes of the flickering AGN activity that may have occurred between the two main radio phases., Comment: 10 pages, 6 Figures

Recent observations on the large-scale structure of the universe indicate that the cosmological constant cannot be the definitive answer to the nature of dark energy. Therefore, it is a good time to propose alternatives to understand the late-accelerated expansion of the universe. In this work we study the possibility that the acceleration of space-time is due to the topology of the universe. We assume that the topology of the universe is a principal fiber bundle whose base space is our 4-dimensional spacetime and the fiber is an $N$-dimensional Lie group that evolves with time. For the base space we consider a homogeneous and isotropic spacetime, we find that the base space is currently accelerating for $1 < N$ compact semi-simple Lie groups whose scale-factors are equal and for $1 < N$ non-Abelian Lie groups whose scale-factors are different and as long as its structure constants satisfy some conditions. However when we study the evolution of the density parameters these differ from the evolution within the $\Lambda$CDM model, this led us to think in the possibility of use a different group as fiber in order to obtain the right evolution of the density parameters. We conclude that it is possible that the accelerated expansion of the universe is due to consider a different topology of the universe as a principal fiber bundle., Comment: 16 pages, 5 figures

3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene reconstruction and tracking, aimed at enhancing surgical scene understanding and assisting interventions. Our method dynamically extends a canonical scene representation using Gaussian splatting, while modeling tissue deformations through a sparse set of control points. We introduce an efficient online fitting algorithm that optimizes the scene parameters, enabling consistent tracking and accurate reconstruction. Through experiments on the StereoMIS dataset, we demonstrate the effectiveness of our approach, outperforming state-of-the-art tracking methods and achieving comparable performance to offline reconstruction techniques. Our work enables various downstream applications thus contributing to advancing the capabilities of surgical assistance systems.

Investigating the metal enrichment in the early universe helps us constrain theories about the first stars and study the ages of galaxies. The lensed galaxy MACS0647$-$JD at $z=10.17$ is the brightest galaxy known at $z > 10$. Previous work analyzing JWST NIRSpec and MIRI data yielded a direct metallicity $\rm{12+log(O/H)}=7.79\pm0.09$ ($\sim$ 0.13 $Z_\odot$) and electron density $\rm{log}(n_e / \rm{cm^{-3}}) = 2.9 \pm 0.5$, the most distant such measurements to date. Here we estimate the direct C/O abundance for the first time at $z > 10$, finding a sub-solar ${\rm log(C/O)}=-0.44^{+0.06}_{-0.07}$. This is higher than other $z>6$ galaxies with direct C/O measurements, likely due to higher metallicity. It is also slightly higher than galaxies in the local universe with similar metallicity. This may suggest a very efficient and rapid burst of star formation, a low effective oxygen abundance yield, or the presence of unusual stellar populations including supermassive stars. Alternatively, the strong CIII]${\rm {\lambda}{\lambda}}$1907,1909 emission ($14\pm 3\,{\r{A}}$ rest-frame EW) may originate from just one of the two component star clusters JDB ($r \sim 20$ pc). Future NIRSpec IFU spectroscopic observations of MACS0647$-$JD will be promising for disentangling C/O in the two components to constrain the chemistry of individual star clusters just 460 Myr after the Big Bang., Comment: 9 pages, 2 figures, submitted

The $n$ queens problem considers the maximum number of safe squares on an $n \times n$ chess board when placing $n$ queens; the answer is only known for small $n$. Miller, Sheng and Turek considered instead $n$ randomly placed rooks, proving the proportion of safe squares converges to $1/e^2$. We generalize and solve when randomly placing $n$ hyper-rooks and $n^{k-1}$ line-rooks on a $k$-dimensional board, using combinatorial and probabilistic methods, with the proportion of safe squares converging to $1/e^k$. We prove that the proportion of safe squares on an $n \times n$ board with bishops in 2 dimensions converges to $2/e^2$. This problem is significantly more interesting and difficult; while a rook attacks the same number of squares wherever it's placed, this is not so for bishops. We expand to the $k$-dimensional chessboard, defining line-bishops to attack along $2$-dimensional diagonals and hyper-bishops to attack in the $k-1$ dimensional subspace defined by its diagonals in the $k-2$ dimensional subspace. We then combine the movement of rooks and bishops to consider the movement of queens in 2 dimensions, as well as line-queens and hyper-queens in $k$ dimensions., Comment: 19 pages, 6 figures

Historically, spectroscopic techniques have been essential for studying the optical properties of thin solid films. However, existing formulae for both normal transmission and reflection spectroscopy often rely on simplified theoretical assumptions, which may not accurately align with real-world conditions. For instance, it is common to assume (1) that the thin solid layers are deposited on completely transparent thick substrates and (2) that the film surface forms a specular plane with a relatively small wedge angle. While recent studies have addressed these assumptions separately, this work presents an integrated framework that eliminates both assumptions simultaneously. In addition, the current work presents a deep review of various formulae from the literature, each with their corresponding levels of complexity. Our review analysis highlights a critical trade-off between computational complexity and expression accuracy, where the newly developed formulae offer enhanced accuracy at the expense of increased computational time. Our user-friendly code, which includes several classical transmittance and reflectance formulae from the literature and our newly proposed expressions, is publicly available in both Python and Matlab at this link.

In a previous work \cite{Buttazzo:2020ibd}, it has been pointed out that heavy new physics (NP) contributions to lepton dipole moments and high-energy cross-sections of lepton pairs into Higgs and $\gamma/Z$ bosons are connected, if the electroweak symmetry breaking is realized linearly. As a consequence, a multi-TeV muon collider would provide a unique test of NP in the muon $g$-2 through the study of high-energy processes such as $\mu^+ \mu^-\to h +\gamma /Z$. Since the analysis involved a SMEFT approach to Higgs-processes, it could also be studied by the more general HEFT formulation. We compute the modification of the high-energy cross-section $\mu^+ \mu^-\to h +\gamma/Z$ and $h\to \mu^{+} \mu^{-} +\gamma /Z$ decay using the dimension six HEFT Lagrangian and compare them with the SMEFT analysis. We find that, in some reasonable cases, the HEFT approach could lead to a higher sensitivity to test the NP contributions in the muon $g$-2, requiring thus a less energetic muon collider for such a purpose., Comment: 26 pages, 11 figures

Motivated by the rich properties and various applications of recurrence relations, we consider the extension of traditional recurrence relations to matrices, where we use matrix multiplication and the Kronecker product to construct matrix sequences. We provide a sharp condition, which when satisfied, guarantees that any fixed-depth matrix recurrence relation defined over a product (with respect to matrix multiplication) will converge to the zero matrix. We also show that the same statement applies to matrix recurrence relations defined over a Kronecker product. Lastly, we show that the dual of this condition, which remains sharp, guarantees the divergence of matrix recurrence relations defined over a consecutive Kronecker product. These results completely determine the stability of nontrivial fixed-depth complex-valued recurrence relations defined over a consecutive product.

Conway Checkers is a game played with a checker placed in each square of the lower half of an infinite checkerboard. Pieces move by jumping over an adjacent checker, removing the checker jumped over. Conway showed that it is not possible to reach row 5 in finitely many moves by weighting each cell in the board by powers of the golden ratio such that no move increases the total weight. Other authors have considered the game played on many different boards, including generalising the standard game to higher dimensions. We work on a board of arbitrary dimension, where we allow a cell to hold multiple checkers and begin with m checkers on each cell. We derive an upper bound and a constructive lower bound on the height that can be reached, such that the upper bound almost never fails to be equal to the lower bound. We also consider the more general case where instead of jumping over 1 checker, each checker moves by jumping over k checkers, and again show the maximum height reachable lies within bounds that are almost always equal., Comment: 17 pages, 7 figures

We study simple Osserman limit linear series (that is, Osserman limit linear series having a simple basis) on curves of compact type with three irreducible components. For compact type curves with two components, every exact limit linear series is simple. But, for the case of three components, this property is no longer true. We study a certain distributivity property related with the existence of a simple basis and we find characterizations of that property at any fixed multidegree. Also, we find a characterization of simple limit linear series among the exact limit linear series. In our approach, we also study other structure similar to a simple basis and we get a certain inequality for any exact limit linear series.

Tensor networks are a popular and computationally efficient approach to simulate general quantum systems on classical computers and, in a broader sense, a framework for dealing with high-dimensional numerical problems. This paper presents a broad literature review of state-of-the-art applications of tensor networks and related topics across many research domains including: machine learning, mathematical optimisation, materials science, quantum chemistry and quantum circuit simulation. This review aims to clarify which classes of relevant applications have been proposed for which class of tensor networks, and how these perform compared with other classical or quantum simulation methods. We intend this review to be a high-level tour on tensor network applications which is easy to read by non-experts, focusing on key results and limitations rather than low-level technical details of tensor networks., Comment: 38 pages, 3 figures

With growing interest in recent years, medical visual question answering (Med-VQA) has rapidly evolved, with multimodal large language models (MLLMs) emerging as an alternative to classical model architectures. Specifically, their ability to add visual information to the input of pre-trained LLMs brings new capabilities for image interpretation. However, simple visual errors cast doubt on the actual visual understanding abilities of these models. To address this, region-based questions have been proposed as a means to assess and enhance actual visual understanding through compositional evaluation. To combine these two perspectives, this paper introduces targeted visual prompting to equip MLLMs with region-based questioning capabilities. By presenting the model with both the isolated region and the region in its context in a customized visual prompt, we show the effectiveness of our method across multiple datasets while comparing it to several baseline models. Our code and data are available at https://github.com/sergiotasconmorales/locvqallm., Comment: Accepted at the MICCAI AMAI Workshop 2024

We developed SaludConectaMX as a comprehensive system to track and understand the determinants of complications throughout chemotherapy treatment for children with cancer in Mexico. SaludConectaMX is unique in that it integrates patient clinical indicators with social determinants and caregiver mental health, forming a social-clinical perspective of the patient's evolving health trajectory. The system is composed of a web application (for hospital staff) and a mobile application (for family caregivers), providing the opportunity for cooperative patient monitoring in both hospital and home settings. This paper presents the system's preliminary design and usability evaluation results from a 1.5-year pilot study. Our findings indicate that while the hospital web app demonstrates high completion rates and user satisfaction, the family mobile app requires additional improvements for optimal accessibility; statistical and qualitative data analysis illuminate pathways for system improvement. Based on this evidence, we formalize suggestions for health system development in LMICs, which HCI researchers may leverage in future work.

We investigate the synchronization behavior and the emergence of chimera states in a system of two interacting populations of maps possessing chaotic neural-like dynamics. We characterize four collective states on the space of coupling parameters of the system: complete synchronization, generalized synchronization, chimera states, and incoherence. We quantify the information exchange between the two neuron populations in chimera states. We have found a well-defined direction of the flow of information in chimera states, from the desynchronized population to the synchronized one. The incoherent population functions as a driver of the coherent neuron population in a chimera state. This feature is independent of the population sizes or population partitions. Our results yield insight into the communication mechanisms arising in brain processes such as unihemispheric sleep and epileptic seizures that have been associated to chimera states., Comment: 8 pages, 8 figures

J1316+2614 at z=3.613 is the UV-brightest ($M_{UV}$ = -24.7) and strongest Lyman continuum (LyC, $f_{esc}^{LyC} \approx$ 90%) emitting star-forming galaxy known, showing also signatures of inflowing gas from its blue-dominated Ly$\alpha$ profile. Here, we present high-resolution imaging with the HST and VLT of the LyC, Ly$\alpha$, rest-UV, and optical emission of J1316+2614. Detailed analysis of the LyC and UV light distributions reveals compact yet resolved profiles, with LyC and UV morphologies showing identical half-light radii of $\simeq$ 220 pc. The continuum-subtracted Ly$\alpha$ emission reveals an extended filamentary structure of $\simeq$ 6.0 kpc oriented south-north with only weak/residual flux within the stellar core, suggesting a Ly$\alpha$ "hole". J1316+2614 presents remarkably high SFR and stellar mass surface densities of log($\Sigma_{SFR}$ [$M_{\odot}$/yr/kpc^2]) = 3.47$\pm$0.11 and log($\Sigma_{M}$ [$M_{\odot}$/pc^2]) = 4.20$\pm$0.06, respectively, which are among the highest observed in star-forming galaxies. Our findings indicate that J1316+2614 is a powerful, young, and compact starburst, leaking significant LyC photons due to the lack of gas and dust within the starburst. We explore the conditions for gas expulsion using a simple energetic balance and find that, given the strong binding force in J1316+2614, a high star formation efficiency ($\epsilon_{SF} \geq 0.7$) is necessary to remove the gas and explain its exposed nature. Our results thus suggest a close link between high $\epsilon_{SF}$ and high $f_{esc}^{LyC}$. This high efficiency can also naturally explain the remarkably high SFR, UV-luminosity, and efficient mass growth of J1316+2614, where at least 62% of its mass formed in the last 6 Myr. J1316+2614 may exemplify an intense, feedback-free starburst with a high $\epsilon_{SF}$, similar to those proposed for UV-bright galaxies at high redshifts. (ABRIDGED), Comment: 15 pages and 9 figures. Accepted for publication in A&A

A microscopic nuclear matter formalism with explicit chiral symmetry based on the Nambu Jona-Lasinio model is considered to describe nuclear matter. To reproduce nuclear matter properties adequately at the saturation density, four-point and eight-point interactions are introduced. Within a Bayesian inference approach, the parameters of the model are determined by imposing nuclear matter, both experimental and from {\it ab-initio} calculations, and neutron star observational constraints. Nuclear matter properties are well reproduced with an effective mass of 0.75 to 0.8 nucleon mass at the saturation density. At 90% confidence level, the radius of a $1.4 ~\rm M_\odot$ star varies between 11.48 km and 13.20 km, masses as large as $\sim 2.2 ~\rm M_\odot$ are predicted and the radius of a 2 M$_\odot$ star is above 10.5 km. High-density perturbative QCD (pQCD) results exclude equations of state that predict larger maximum masses and radii. The speed of sound increases monotonically with density and reaches values as large as $\sqrt{0.7}c$-$\sqrt{0.8}c$ in the center of massive stars. Several properties such as the polytropic index or the renormalized trace anomaly, that have been proposed to identify the deconfined phase transition, are analyzed. Interestingly, the radius of the obtained posterior that also meets pQCD constraints aligns closely with the mass-radius measurement of the recent PSR J0437-4715, which contrasts with other relativistic mean field model results., Comment: 15 pages, 7 figures

The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling efficient extraction of structured, actionable data from unstructured text by non-experts. While applying LLMs to materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This review provides a comprehensive overview of LLM-based structured data extraction in materials science, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and materials science expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven materials research. The insights presented here could significantly enhance how researchers across disciplines access and utilize scientific information, potentially accelerating the development of novel materials for critical societal needs.

We present extensive photometric and spectroscopic observations of the nearby Type Ia supernova (SN) 2023wrk at a distance of about 40 Mpc. The earliest detection of this SN can be traced back to a few hours after the explosion. Within the first few days the light curve shows a bump feature, while the B - V color is blue and remains nearly constant. The overall spectral evolution is similar to that of an SN 1991T/SN 1999aa-like SN Ia, while the C II $\lambda6580$ absorption line appears to be unusually strong in the first spectrum taken at $t \approx -$15.4 days after the maximum light. This carbon feature disappears quickly in subsequent evolution but it reappears at around the time of peak brightness. The complex evolution of the carbon line and the possible detection of Ni III absorption around 4700 {\AA} and 5300 {\AA} in the earliest spectra indicate macroscopic mixing of fuel and ash. The strong carbon lines is likely related to collision of SN ejecta with unbound carbon, consistent with the predictions of pulsational delayed-detonation or carbon-rich circumstellar-matter interaction models. Among those carbon-rich SNe Ia with strong C II $\lambda6580$ absorption at very early times, the line-strength ratio of C II to Si II and the B-V color evolution are found to exhibit large diversity, which may be attributed to different properties of unbound carbon and outward-mixing $^{56}$Ni., Comment: Accepted for publication in the Astrophysical Journal (27 pages, 14 figures, 7 tables)

We utilized photometric data from the space telescope AKARI to identify potential planetary nebulae (PNe) and proto-planetary nebulae (PPNe) candidates. Using the colour-colour diagram, we found a region with a high concentration of established PNe and PPNe, comprising about 95% of the objects. Based on this, we identified 67 objects within this region that lack definitive classification in existing literature, suggesting they are promising candidates. We conducted Spectral Energy Distribution (SED) analysis and morphological investigations using imagery from various observatories and satellites. Finally, we present a list of 65 potential PNe and PPNe candidates., Comment: 26 pages, 19 figures, 1 table

The Model-X knockoffs is a practical methodology for variable selection, which stands out from other selection strategies since it allows for the control of the false discovery rate (FDR), relying on finite-sample guarantees. In this article, we propose a Truncated D-vine Copula Knockoffs (TDCK) algorithm for sampling approximate knockoffs from complex multivariate distributions. Our algorithm enhances and improves features of previous attempts to sample knockoffs under the multivariate setting, with the three main contributions being: 1) the truncation of the D-vine copula, which reduces the dependence between the original variables and their corresponding knockoffs, improving the statistical power; 2) the employment of a straightforward non-parametric formulation for marginal transformations, eliminating the need for a specific parametric family or a kernel density estimator; 3) the use of the "rvinecopulib'' R package offers better flexibility than the existing fitting vine copula knockoff methods. To eliminate the randomness in distinct realizations resulting in different sets of selected variables, we wrap the TDCK method with an existing derandomizing procedure for knockoffs, leading to a Derandomized Truncated D-vine Copula Knockoffs with e-values (DTDCKe) procedure. We demonstrate the robustness of the DTDCKe procedure under various scenarios with extensive simulation studies. We further illustrate its efficacy using a gene expression dataset, showing it achieves a more reliable gene selection than other competing methods, when the findings are compared with those of a meta-analysis. The results indicate that our Truncated D-vine copula approach is robust and has superior power, representing an appealing approach for variable selection in different multivariate applications, particularly in gene expression analysis., Comment: 31 pages, 9 figures, 1 table

We propose an original technique for the HF radar estimation of the main sea state parameters by exploiting the amplitude modulation of the radar signal time series. While the classical method for ocean wave measurement is based on the second-order ocean Doppler spectrum, this alternative approach makes use of the dominant first-order Bragg echo in the radar signal, which has a larger signal-to-noise ratio and allows for a farther spatial coverage. We apply this method to a yearly set of HF radar data in the vicinity of Toulon (Mediterranean coast of France) and compare it with the classical second-order method, where we use a nearby buoy record as ground truth. The Doppler spectrum-based method is slightly more accurate in estimating the significant wave height and the mean period at a short distance but the temporal approach allows for a significant increase in range and better captures the variability of wave parameters over time., Comment: 12 pages, 13 figures, 2 tables

To design semiconductor-based optical devices, the optical properties of the used semiconductor materials must be precisely measured over a large band. Transmission spectroscopy stands out as an inexpensive and widely available method for this measurement but requires model assumptions and reconstruction algorithms to convert the measured transmittance spectra into optical properties of the thin films. Amongst the different reconstruction techniques, inverse synthesis methods generally provide high precision but rely on rigid analytical models of a thin film system. In this paper, we demonstrate a novel flexible inverse synthesis method that uses angular spectrum wave propagation and does not rely on rigid model assumptions. Amongst other evaluated parameters, our algorithm is capable of evaluating the geometrical properties of thin film surfaces, which reduces the variance caused by inverse synthesis optimization routines and significantly improves measurement precision. The proposed method could potentially allow for the characterization of "uncommon" thin film samples that do not fit the current model assumptions, as well as the characterization of samples with higher complexity, e.g., multi-layer systems., Comment: 10 pages, 7 figures, 5 tables

The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is crucial for reliable deep learning models. Despite considerable attention, theoretically-motivated approaches are few and far between, with most methods building on top of some form of heuristic. Recently, U-OOD was formalized in the context of data invariants, allowing a clearer understanding of how to characterize U-OOD, and methods leveraging affine invariants have attained state-of-the-art results on large-scale benchmarks. Nevertheless, the restriction to affine invariants hinders the expressiveness of the approach. In this work, we broaden the affine invariants formulation to a more general case and propose a framework consisting of a normalizing flow-like architecture capable of learning non-linear invariants. Our novel approach achieves state-of-the-art results on an extensive U-OOD benchmark, and we demonstrate its further applicability to tabular data. Finally, we show our method has the same desirable properties as those based on affine invariants., Comment: Accepted at ECCV 2024

The structures and properties of organic-inorganic perovskites are influenced by the hydrogen bonding between the organic cations and the inorganic octahedral networks. This study explores the dynamics of hydrogen bonds in CH$_3$NH$_3$PbBr$_3$ across a temperature range from 70 K to 350 K, using molecular dynamics simulations with machine-learning force fields. The results indicate that the lifetime of hydrogen bonds decreases with increasing temperature from 7.6 ps (70 K) to 0.16 ps (350 K), exhibiting Arrhenius-type behaviour. The geometric conditions for hydrogen bonding, which include bond lengths and angles, maintain consistency across the full temperature range. The relevance of hydrogen bonds for the vibrational states of the material is also evidenced through a detailed analysis of the vibrational power spectra, demonstrating their significant effect on the physical properties for this class of perovskites., Comment: 26 pages, 14 figures

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

We present JWST/MIRI MRS spatially resolved $\sim 5-28\,\mu$m observations of the central ~4-8kpc of the ultraluminous infrared galaxy and broad absorption line quasar Mrk231. These are part of the Mid-Infrared Characterization of Nearby Iconic galaxy Centers (MICONIC) program of the MIRI European Consortium guaranteed time observations. No high excitation lines (i.e., [MgV] at 5.61$\mu$m or [NeV] at 14.32$\mu$m) typically associated with the presence of an active galactic nucleus (AGN) are detected in the nuclear region of Mrk231. This is likely due to the intrinsically X-ray weak nature of its quasar. Some intermediate ionization potential lines, for instance, [ArIII] at 8.99$\mu$m and [SIV] at 10.51$\mu$m, are not detected either, even though they are clearly observed in a star-forming region ~920pc south-east of the AGN. Thus, the strong nuclear mid-infrared (mid-IR) continuum is also in part hampering the detection of faint lines in the nuclear region. The nuclear [NeIII]/[NeII]line ratio is consistent with values observed in star-forming galaxies. Moreover, we resolve for the first time the nuclear starburst in the mid-IR low-excitation line emission (size of ~400pc, FWHM). Several pieces of evidence also indicate that it is partly obscured even at these wavelengths. At the AGN position, the ionized and warm molecular gas emission lines have modest widths (W_80~300km/s). There are, however, weak blueshifted wings reaching velocities v_02~-400km/s in [NeII]. The nuclear starburst is at the center of a large (~8kpc), massive rotating disk with widely-spread, low velocity outflows. Given the high star formation rate of Mrk231, we speculate that part of the nuclear outflows and the large-scale non-circular motions observed in the mid-IR are driven by its powerful nuclear starburst., Comment: Accepted for publication in Astronomy & Astrophysics

Thanks to decades of observations using HST, the structure of galaxies at redshift $z>2$ has been widely studied in the rest-frame ultraviolet regime, which traces recent star formation from young stellar populations. But, we still have little information about the spatial distribution of the older, more evolved, stellar populations, constrained by the rest-frame infrared portion of galaxies' spectral energy distribution. We present the morphological characterization of a sample of 21 massive galaxies ($\log(M_{\star}/M_{\odot})>9.5$) at redshift $33$ massive galaxies show a smooth distribution of their rest-infrared light, strongly supporting the increasing number of regular disk galaxies already in place at early epochs. On the contrary, the ultraviolet structure obtained from HST observations is generally more irregular, catching the most recent episodes of star formation. Importantly, we find a segregation of morphologies across cosmic time, having massive galaxies at redshift $z>4$ later-type morphologies compared to $z\sim3$ galaxies. These findings suggest a transition phase in galaxy assembly and central mass build up already taking place at $z\sim3-4$. MIRI provides unique information about the structure of the mature stellar population of high-redshift galaxies, unveiling that massive galaxies beyond cosmic noon are prevalently compact disk galaxies with smooth mass distribution., Comment: Submitted to A&A. 7 pages, 4 figures. Comments welcome

We present Virgil, a MIRI extremely red object (MERO) detected with the F1000W filter as part of the MIRI Deep Imaging Survey (MIDIS) observations of the Hubble Ultra Deep Field (HUDF). Virgil is a Lyman-$\alpha$ emitter (LAE) at $z_{spec} = 6.6312\pm 0.0019$ (from VLT/MUSE) with a rest-frame UV-to-optical spectral energy distribution (SED) typical of LAEs at similar redshifts. However, MIRI observations reveal an unexpected extremely red color at rest-frame near-infrared wavelengths, $\rm F444W - F1000W = 2.33 \pm 0.06$. Such steep rise in the near-infrared, completely missed without MIRI imaging, is poorly reproduced by models including only stellar populations and hints towards the presence of an Active Galactic Nucleus (AGN). Interestingly, the overall SED shape of Virgil resembles that of the recently discovered population of Little Red Dots (LRDs) but does not meet their compactness criterion: at rest-frame UV-optical wavelengths Virgil's morphology follows a 2D-S\'ersic profile with average index $n = 0.93^{+0.85}_{-0.31}$ and $r_e = 0.43$~pkpc. Only at MIRI wavelengths Virgil is unresolved due to the coarser PSF. We also estimate a bolometric luminosity $L_{\rm bol} = (8.4-11.1)\times 10^{44}\rm~erg~s^{-1}$ and a supermassive black hole mass $M_{\rm BH} = (4-7)\times 10^7\rm ~ M_\odot$ in agreement with recently reported values for LRDs. This discovery demonstrates the crucial importance of deep MIRI surveys to find AGN amongst high-$z$ galaxies that otherwise would be completely missed and raises the question of how common Virgil-like objects could be in the early Universe., Comment: 17 pages, 10 figures, 3 tables. Submitted to ApJ

Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative AI method capable of predicting optical galaxy spectra from photometric broad-band images alone. This method draws from the latest advances in diffusion models in combination with contrastive networks. We pass multi-band galaxy images into the architecture to obtain optical spectra. From these, robust values for galaxy properties can be derived with any methods in the spectroscopic toolbox, such as standard population synthesis techniques and Lick indices. When trained and tested on 64x64-pixel images from the Sloan Digital Sky Survey, the global bimodality of star-forming and quiescent galaxies in photometric space is recovered, as well as a mass-metallicity relation of star-forming galaxies. The comparison between the observed and the artificially created spectra shows good agreement in overall metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V) values. Photometric redshift estimates of our generative algorithm can compete with other current, specialized deep-learning techniques. Moreover, this work is the first attempt in the literature to infer velocity dispersion from photometric images. Additionally, we can predict the presence of an active galactic nucleus up to an accuracy of 82%. With our method, scientifically interesting galaxy properties, normally requiring spectroscopic inputs, can be obtained in future data sets from large-scale photometric surveys alone. The spectra prediction via AI can further assist in creating realistic mock catalogs., Comment: Accepted by The Astrophysical Journal. Code is available at https://github.com/LarsDoorenbos/generate-spectra

Large Language Models (LLMs) tend to be unreliable in the factuality of their answers. To address this problem, NLP researchers have proposed a range of techniques to estimate LLM's confidence over facts. However, due to the lack of a systematic comparison, it is not clear how the different methods compare to one another. To fill this gap, we present a survey and empirical comparison of estimators of factual confidence. We define an experimental framework allowing for fair comparison, covering both fact-verification and question answering. Our experiments across a series of LLMs indicate that trained hidden-state probes provide the most reliable confidence estimates, albeit at the expense of requiring access to weights and training data. We also conduct a deeper assessment of factual confidence by measuring the consistency of model behavior under meaning-preserving variations in the input. We find that the confidence of LLMs is often unstable across semantically equivalent inputs, suggesting that there is much room for improvement of the stability of models' parametric knowledge. Our code is available at (https://github.com/amazon-science/factual-confidence-of-llms)., Comment: accepted on the main track of ACL 2024

Mechanically bonded membranes of interlocked ring polymers are a significant generalization of conventional elastic sheets, where connectivity is provided by covalent bonding, and represent a promising class of topological meta-materials. In this context, two open questions regard the large-scale reverberations of the heterogeneous composition of the rings and the inequivalent modes of interlocking neighboring rings. We address these questions with Langevin dynamics simulations of chainmails with honeycomb-lattice connectivity, where the rings are block copolymers with two segments of different rigidity. We considered various combinations of the relative lengths of the two segments and the patterns of the over- and under-passes linking neighboring rings. We find that varying ring composition and linking patterns have independent and complementary effects. While the former sets the overall size of the chainmail, the latter defines the shape, enabling the selection of starkly different conformation types. Notably, one of the considered linking patterns favors saddle-shaped membranes, providing a first example of spontaneous negative Gaussian curvature in mechanically bonded sheets. The results help establish the extent to which mechanically bonded membranes can differ from conventional elastic membranes, particularly for the achievable shape and size tunability., Comment: Main 15 pages, 9 figures

We investigate the correlation between stellar mass (M*) and star formation rate (SFR) across the stellar mass range log10(M*/Msun)~6-11. We consider almost 50,000 star-forming galaxies at z~3-7, leveraging data from COSMOS/SMUVS, JADES/GOODS-SOUTH, and MIDIS/XDF. This is the first study spanning such a wide stellar mass range without relying on gravitational lensing effects. We locate our galaxies on the SFR-M* plane to assess how the location of galaxies in the star-formation main sequence (MS) and starburst (SB) region evolves with stellar mass and redshift. We find that the two star-forming modes tend to converge at log10(M*/Msun) < 7, with all galaxies found in the SB mode. By dissecting our galaxy sample in stellar mass and redshift, we show that the emergence of the star-formation MS is stellar-mass dependent: while in galaxies with log10(M*/Msun) > 9 the MS is already well in place at z = 5-7, for galaxies with log10(M*/Msun)~7-8 it only becomes significant at z<4. Overall, our results are in line with previous findings that the SB mode dominates amongst low stellar-mass galaxies. The earlier emergence of the MS for massive galaxies is consistent with galaxy downsizing., Comment: 15 pages, 4 figures. Submitted to ApJ

Recent large language models (LLMs) can generate and revise text with human-level performance, and have been widely commercialized in systems like ChatGPT. These models come with clear limitations: they can produce inaccurate information, reinforce existing biases, and be easily misused. Yet, many scientists have been using them to assist their scholarly writing. How wide-spread is LLM usage in the academic literature currently? To answer this question, we use an unbiased, large-scale approach, free from any assumptions on academic LLM usage. We study vocabulary changes in 14 million PubMed abstracts from 2010-2024, and show how the appearance of LLMs led to an abrupt increase in the frequency of certain style words. Our analysis based on excess words usage suggests that at least 10% of 2024 abstracts were processed with LLMs. This lower bound differed across disciplines, countries, and journals, and was as high as 30% for some PubMed sub-corpora. We show that the appearance of LLM-based writing assistants has had an unprecedented impact in the scientific literature, surpassing the effect of major world events such as the Covid pandemic., Comment: v2: Updating dataset, figures and numbers to include all PubMed abstracts until end of June 2024

Deep learning models excel when the data distribution during training aligns with testing data. Yet, their performance diminishes when faced with out-of-distribution (OOD) samples, leading to great interest in the field of OOD detection. Current approaches typically assume that OOD samples originate from an unconcentrated distribution complementary to the training distribution. While this assumption is appropriate in the traditional unsupervised OOD (U-OOD) setting, it proves inadequate when considering the place of deployment of the underlying deep learning model. To better reflect this real-world scenario, we introduce the novel setting of continual U-OOD detection. To tackle this new setting, we propose a method that starts from a U-OOD detector, which is agnostic to the OOD distribution, and slowly updates during deployment to account for the actual OOD distribution. Our method uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach. Furthermore, we design a confidence-scaled few-shot OOD detector that outperforms previous methods. We show our method greatly improves upon strong baselines from related fields.

The XXZ spin-half chain has Heisenberg exchange interactions $J_z$ ($J_\perp$) in the $z$ ($x,y$) direction. The model has a transition from the spin-fluid phase for $-J_\perp < J_z < J_\perp$ to the N\'{e}el phase for $J_z > J_\perp >0$. When bond alternation $\delta$ is included, the N\'{e}el phase transitions to the dimer phase for a finite value of $\delta$. We determine the phase diagram using simple topological indicators related to the polarization of both spins. When a staggered magnetic field $B$ is included, a contour plot of these indicators as a function of $\delta$ and $B$ determine the amount of topological quantized spin pumping around closed circuits in the $(\delta,B)$ plane.

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version