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The challenges of recruiting, strengthening, and retaining the child care and early education (CCEE) workforce are well documented. Members of the CCEE workforce typically have low levels of formal education and compensation; limited opportunities for education, training, and professional development; inconsistent working conditions; and high levels of stress and burnout. The Building and Sustaining the Child Care and Early Education Workforce (BASE) project conducted an environmental scan to identify and review strategies that are currently being implemented across the country to build, advance, and sustain the CCEE workforce in the face of these challenges. In particular, the research team sought to understand the context in which these strategies are implemented and how that might influence their operation. The team also examined how strategies reach or are tailored to prospective and current CCEE educators. This brief summarizes important themes that emerged from the environmental scan, which was conducted between March 2021 and January 2022. The conclusions presented draw from a combination of activities, including a review of the literature, assessment of recommended sources and materials submitted in response to an open call for information, as well as interviews with key informants from the agencies responsible for putting strategies into place. [This report was written with Lisa Rau.]

The NEO Coordination Centre (NEOCC) of the European Space Agency is an operational centre that, among other activities, computes the orbits of near-Earth objects and their probabilities of impact with the Earth. The NEOCC started providing information about near-Earth objects in 2012 on a dedicated web portal, accessible at https://neo.ssa.esa.int/. Since the beginning of the operational phase, many developments and improvements have been implemented regarding the software, the data provided, and the portal. One of the most important upgrades is that the NEOCC is now independently providing data through a newly developed Orbit Determination and Impact Monitoring system, named Aegis. All the data computed by Aegis is publicly available on the NEOCC web portal, and Aegis is also used to maintain all the major services offered. The most important services comprise an orbital catalogue of all known asteroids, a list of possible future impacts with the Earth (also called Risk List), a list of forthcoming close approaches, a set of graphical toolkits, and an on-demand ephemerides service. Many of the services are also available through dedicated APIs, which can be used to automatically retrieve data. Here we give an overview of the algorithms implemented in the Aegis software, and provide a summary of the services offered by the NEOCC that are supported by Aegis., Comment: Accepted for publication on CMDA

End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses significant challenges, including negative interference - where the model learns conflicting scene-specific parameters - and limited domain generalization, which often necessitates expensive fine-tuning to adapt the models to new domains. In response to these challenges, we introduce Parameter-efficient Scenario-specific Tracking Architecture (PASTA), a novel framework that combines Parameter-Efficient Fine-Tuning (PEFT) and Modular Deep Learning (MDL). Specifically, we define key scenario attributes (e.g, camera-viewpoint, lighting condition) and train specialized PEFT modules for each attribute. These expert modules are combined in parameter space, enabling systematic generalization to new domains without increasing inference time. Extensive experiments on MOTSynth, along with zero-shot evaluations on MOT17 and PersonPath22 demonstrate that a neural tracker built from carefully selected modules surpasses its monolithic counterpart. We release models and code., Comment: NeurIPS 2024

Recently, it has been proposed a new method [arXiv:2405.21029] to detect quantum gravity effects, based on generating gravitational entanglement between two nano-diamonds with Nitrogen-Vacancy defects, in a magnetically trapped configuration. Here we analyse in detail the proposed experimental setup, with a particular focus on implementing the detection of the gravitationally-induced entanglement using an optical readout based on measuring the position of the nano-diamonds and its complementary basis. We also summarise some of the key theoretical and experimental ideas on which this proposed scheme is based., Comment: 8 pages, 2 figures

We prove that $(a)$ any finitely smooth axially symmetric strictly convex domain, with everywhere positive curvature and sufficiently close to an ellipse, and $(b)$ any finitely smooth centrally symmetric strictly convex domain, even-rationally integrable, with everywhere positive curvature and sufficiently close to an ellipse is area spectrally rigid. This means that any area-isospectral family of domains in these classes is necessarily equi-affine. We use the same technique -- adapted to symplectic billiards -- of the paper by De Simoi, Kaloshin, and Wei (2017). The novelty is that the result holds for axially symmetric domains, as in the cited paper, as well as for a subset of centrally symmetric ones.

In both animal and cell populations, the presence of leaders often underlies the success of collective migration processes, which we characterise by a group maintaining a cohesive configuration that consistently moves toward a target. We extend a recent non-local hyperbolic model for follower-leader systems to investigate different degrees of leadership. Specifically, we consider three levels of leadership: indifferent leaders, who do not alter their movement according to followers; observant leaders, who attempt to remain connected with the followers, but do not allow followers to affect their desired alignment; and persuadable leaders, who integrate their attempt to reach some target with the alignment of all neighbours, both followers and leaders. A combination of analysis and numerical simulations is used to investigate under which conditions each degree of leadership allows successful collective movement to a destination. We find that the indifferent leaders' strategy can result in a cohesive and target-directed migration only for short times. Observant and persuadable leaders instead provide robust guidance, showing that the optimal leader behavior depends on the connection between the migrating individuals: if alignment is low, greater follower influence on leaders is beneficial for successful guidance; otherwise, it can be detrimental and may generate various unsuccessful swarming dynamics.

Language-based AI systems are diffusing into society, bringing positive and negative impacts. Mitigating negative impacts depends on accurate impact assessments, drawn from an empirical evidence base that makes causal connections between AI usage and impacts. Interconnected post-deployment monitoring combines information about model integration and use, application use, and incidents and impacts. For example, inference time monitoring of chain-of-thought reasoning can be combined with long-term monitoring of sectoral AI diffusion, impacts and incidents. Drawing on information sharing mechanisms in other industries, we highlight example data sources and specific data points that governments could collect to inform AI risk management., Comment: 7 pages, 2 figures, 1 table

The clusters Abell 2061 and Abell 2067 in the Corona Borealis supercluster have been studied at different radio frequencies and are both known to host diffuse radio emission. The aim of this work is to investigate the radio emission in between them, suggested by low resolution observations. We analyse deep LOFAR HBA observations at 144 MHz to follow up on the possible intercluster filament suggested by previous 1.4 GHz observations. We investigate the radial profiles and the point-to-point surface brightness correlation of the emission in A2061 with radio and Xray observations, to describe the nature of the diffuse emission. We report the detection of diffuse radio emission on 800 kpc scale, more extended than previously known, reaching beyond the radio halo in A2061 towards A2067 and over the separation outside the two clusters R500 radii. We confirm the presence of a radio halo in A2061, while do not find evidence of diffuse emission in A2067. The surface brightness profile from the centre of A2061 shows an excess of emission with respect to the azimuthally averaged radio halo profile and X-ray background. We explore three different dynamical scenario to explain the nature of the diffuse emission. We analyse a trail of emission of 760 kpc between the radio halo and radio relic in A2061. This pre merger system closely resembles the two other cluster pairs where radio bridges connecting the radio halos on Mpc scales have been detected. The diffuse emission extends beyond each cluster R500 radius but in this unique case, the absence of the radio halo in A2067 is likely the reason for the observed 'gap' between the two systems. However, the point-to-point correlation results are challenging to explain. The classification of the emission remains unclear, and detailed spectral analysis and further Xray observations are required to understand the origin of the diffuse emission., Comment: Accepted for publication on Astronomy & Astrophysics 12 pages, 6 figures, 1 appendix

AI systems are assisting humans with an increasingly broad range of intellectual tasks. Humans could be over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify real-world instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. We introduce a pipeline that organisations could use to implement these drills. As an example, we explain how this approach could be used to limit over-reliance on AI in a medical setting. We conclude by arguing that reliance drills could become a key tool for ensuring humans remain appropriately involved in AI-assisted decisions.

We study the effect of Gaussian perturbations on a class of model hyperbolic partial differential equations with double symplectic characteristics in low spatial dimensions, extending some recent work in [5]. The coefficients of our partial differential operators contain harmonic oscillators in the space variables, while the noise is additive, white in time and colored in space. We provide sufficient conditions on the spectral measure of the covariance functional describing the noise that allows for the existence of a random field solution for the resulting stochastic partial differential equation. Furthermore we show how the symplectic structure of the set of multiple points affects the regularity of the noise needed to build a measurable process solution. Our approach is based on some explicit computations for the fundamental solutions of several model partial differential operators together with their explicit Fourier transforms.

Superclusters are the largest-scale environments where a number of galaxy clusters interact with each other through minor/major mergers and grow via accretion along cosmic filaments. We focus on the A3528 complex in the core of the Shapley Supercluster. This system includes three clusters, A3528 (composed itself by two sub-clusters, namely A3528N and A3528S), A3532 and A3530, and presents a mildly active dynamical state. We study how minor mergers affect the evolution of radio galaxies and whether they are able to re-accelerate relativistic electrons in the ICM. We used observations from the uGMRT (Band 3, 4 and 5) and MeerKAT (L-band) telescopes to obtain images and spectral index maps over a wide frequency band and spatial resolutions. We compare these data with those from the SRG/eROSITA X-ray telescope. We detect faint diffuse radio emission associated with the radio galaxies. The BCGs in A3528S and A3532 show filaments of diffuse radio emission which extend for $\sim200-400$ kpc out of the radio galaxy. The spectral index of these filaments is extremely steep and almost constant ($\alpha\sim -2, -2.5$). Contrary to the radio tails in A3528N, the spectral properties of these radio filaments are not consistent with standard models of plasma ageing. We also detect roundish diffuse radio emission around the BCG in A3528S which could be classified as a radio mini-halo. The radio tail in this cluster appears longer that in earlier detections, being $\sim300$ kpc long at all frequencies. We linked the presence of extended radio emission in the form of filaments and threads in the A3528 complex with the effect of minor mergers. This is reinforced by the increasing X-ray fluctuations in correspondence with the radio extended emission in A3528S. Despite the less energy involved, our findings support the hypothesis that these events can re-energise plasma originating from radio galaxies., Comment: 28 pages, 23 figures and 5 tables; accepted for publication in Astronomy & Astrophysics

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

The decomposition locus of a tensor is the set of rank-one tensors appearing in a minimal tensor-rank decomposition of the tensor. For tensors lying on the tangential variety of any Segre variety, but not on the variety itself, we show that the decomposition locus consists of all rank-one tensors except the tangency point only. We also explicitly compute decomposition loci of all tensors belonging to tensor spaces with finitely many orbits with respect to the action of product of general linear groups., Comment: 25 pages. Comments are welcome

We construct a weakly compact convex subset of $\ell^2$ with nonempty interior that has an isolated maximal element, with respect to the lattice order $\ell _+^2$. Moreover, the maximal point cannot be supported by any strictly positive functional, showing that the Arrow-Barankin-Blackwell theorem fails. This example discloses the pertinence of the assumption that the cone has a bounded base for the validity of the result in infinite dimensions. Under this latter assumption, the equivalence of the notions of strict maximality and maximality is established

Serotonergic neurons in the raphe nuclei exhibit diverse electrophysiological properties and functional roles, yet conventional identification methods rely on restrictive criteria that likely overlook atypical serotonergic cells. The use of convolutional neural network (CNN) for comprehensive classification of both typical and atypical serotonergic neurons is an interesting one, but the key challenge is often given by the limited experimental data available for training. This study presents a procedure for synthetic data generation that combines smoothed spike waveforms with heterogeneous noise masks from real recordings. This approach expanded the training set while mitigating overfitting of background noise signatures. CNN models trained on the augmented dataset achieved high accuracy (96.2% true positive rate, 88.8% true negative rate) on non-homogeneous test data collected under different experimental conditions than the training, validation and testing data., Comment: Contribution for EPIA 2024 Progress in Artificial Intelligence

In our study, we first constructed a dataset from the tweets of the top 100 medical influencers with the highest Influencer Score during the COVID-19 pandemic. This dataset was then used to construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding their impact on public health discourse. To achieve this, we developed a few-shot multi-label classifier to identify influencers and their network actors' identities, employed BERTopic for extracting thematic content, and integrated these components into a network model to analyze their impact on health discourse. To ensure the reproducibility of our results, we have made the code available at https://github.com/ZhijinGuo/Medinfluencer., Comment: ACM SIGKDD 2024 Workshop epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

There is an increasing trend towards evaluating NLP models with LLM-generated judgments instead of human judgments. In the absence of a comparison against human data, this raises concerns about the validity of these evaluations; in case they are conducted with proprietary models, this also raises concerns over reproducibility. We provide JUDGE-BENCH, a collection of 20 NLP datasets with human annotations, and comprehensively evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations. Our evaluations show that each LLM exhibits a large variance across datasets in its correlation to human judgments. We conclude that LLMs are not yet ready to systematically replace human judges in NLP.

Questions are essential tools for acquiring the necessary information to complete information-seeking tasks. However, large language models (LLMs), especially open-source models, often perform poorly in generating informative questions, as measured by expected information gain (EIG). In this paper, we propose a method to enhance the informativeness of LLM-generated questions in 20-question game dialogues. We sample multiple questions from the same model (LLAMA 2-CHAT 7B) for each game and create pairs of low-EIG and high-EIG questions to apply a Direct Preference Optimization (DPO) algorithm. Our results show that this method produces more effective questions (in terms of EIG), even in domains different from those used to train the DPO model., Comment: Accepted to EMNLP 2024 (Findings)

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

The reasoning abilities of Large Language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in logic and cognitive psychology. Previous research has shown that pre-trained LLMs exhibit reasoning biases, such as $\textit{content effects}$, avoid answering that $\textit{no conclusion follows}$, display human-like difficulties, and struggle with multi-step reasoning. We contribute to this research line by systematically investigating the effects of chain-of-thought reasoning, in-context learning (ICL), and supervised fine-tuning (SFT) on syllogistic reasoning, considering syllogisms with conclusions that support or violate world knowledge, as well as ones with multiple premises. Crucially, we go beyond the standard focus on accuracy, with an in-depth analysis of the conclusions generated by the models. Our results suggest that the behavior of pre-trained LLMs can be explained by heuristics studied in cognitive science and that both ICL and SFT improve model performance on valid inferences, although only the latter mitigates most reasoning biases without harming model consistency., Comment: Accepted to EMNLP 2024 (main conference)

Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a major systematic that must be overcome in order to detect the 21-cm signal, and simulations that reproduce effects seen in the data can guide strategies for mitigating mutual coupling. In this paper, we analyse 12 nights of data from the Hydrogen Epoch of Reionization Array and compare the data against simulations that include a computationally efficient and physically motivated semi-analytic treatment of mutual coupling. We find that simulated coupling features qualitatively agree with coupling features in the data; however, coupling features in the data are brighter than the simulated features, indicating the presence of additional coupling mechanisms not captured by our model. We explore the use of fringe-rate filters as mutual coupling mitigation tools and use our simulations to investigate the effects of mutual coupling on a simulated cosmological 21-cm power spectrum in a "worst case" scenario where the foregrounds are particularly bright. We find that mutual coupling contaminates a large portion of the "EoR Window", and the contamination is several orders-of-magnitude larger than our simulated cosmic signal across a wide range of cosmological Fourier modes. While our fiducial fringe-rate filtering strategy reduces mutual coupling by roughly a factor of 100 in power, a non-negligible amount of coupling cannot be excised with fringe-rate filters, so more sophisticated mitigation strategies are required., Comment: 19 pages, 12 figures, submitted to MNRAS

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals. We introduce a direct electrode-electronics interface with custom fully dry soft electrodes to enhance comfort for long wear. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 uJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 24 uJ per inference and a total system power of only 16.28 mW, allowing for continuous operation of more than 12 h with a small 75 mAh battery., Comment: 10 pages, 5 figures, 5 tables. This paper has been submitted to IEEE Transactions on Biomedical Circuits and Systems

In a hierarchical, dark matter-dominated Universe, stellar mass functions (SMFs), galaxy merger rates, star formation histories (SFHs), satellite abundances, and intracluster light, should all be intimately connected observables. However, the systematics affecting observations still prevent universal and uniform measurements of, for example, the SMF and the SFHs, inevitably preventing theoretical models to compare with multiple data sets robustly and simultaneously. We here present our holistic semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl) that converts via abundance matching dark matter merger trees into galaxy assembly histories, using different SMFs in input and predicting all other observables in output in a fully data-driven and self-consistent fashion with minimal assumptions. We find that: 1) weakly evolving or nearly constant SMFs below the knee ($M_\star \lesssim 10^{11} \, M_\odot$) are the best suited to generate star formation histories aligned with those inferred from MaNGA, SDSS, GAMA, and, more recently, JWST; 2) the evolution of satellites after infall only affects the satellite abundances and star formation histories of massive central galaxies but not their merger histories; 3) the resulting SFR-$M_\star$ relation is lower in normalization by a factor of $\sim 2$ with respect to observations, with a flattening at high masses more pronounced in the presence of mergers; 4) the latest data on intracluster light can be reproduced if mass loss from mergers is included in the models. Our findings are pivotal in acting as pathfinder to test the self-consistency of the high-quality data from, e.g., JWST and Euclid., Comment: 21 pages, 12 figures, MNRAS accepted

Hydrogen is a promising alternative to fossil fuel, however storing it efficiently poses challenges. One promising solution is to adsorb hydrogen on solid materials demonstrating quasi-molecular bonding with hydrogen. The hydrogen adsorption energy can be tuned by changing the morphology or stoichiometry of bimetallic nanoparticles. Here we used complementary techniques to unveil the chemical compositional and morphological transformation undergone by PdxNi100-x nanoparticles during H2 adsorption. Our findings reveal NiO-rich shell and Pd-rich core as confirmed by X-ray photoelectron spectroscopy, X-ray scattering, and electron energy loss spectroscopy. During hydrogen adsorption, which mainly occurs on Pd atoms, the Pd-rich core fragments into small pockets, increasing its surface area. This process is more pronounced for nanoparticles with lower Pd loading, emphasizing the role of NiO. These results shed light on the atomic changes occurring in the PdxNi100-x nanoparticles during hydrogen adsorption and can be applicable on multi-metallic systems to improve hydrogen storage properties.

Unifying quantum theory and general relativity is the holy grail of contemporary physics. Nonetheless, the lack of experimental evidence driving this process led to a plethora of mathematical models with a substantial impossibility of discriminating among them or even establishing if gravity really needs to be quantized or if, vice versa, quantum mechanics must be "gravitized" at some scale. Recently, it has been proposed that the observation of the generation of entanglement by gravitational interaction, could represent a breakthrough demonstrating the quantum nature of gravity. A few experimental proposals have been advanced in this sense, but the extreme technological requirements (e.g., the need for free-falling gravitationally-interacting masses in a quantum superposition state) make their implementation still far ahead. Here we present a feasibility study for a table-top nanodiamond-based interferometer eventually enabling easier and less resource-demanding quantum gravity tests. With respect to the aforementioned proposals, by relying on quantum superpositions of steady massive (mesoscopic) objects our interferometer may allow exploiting just small-range electromagnetic fields (much easier to implement and control) and, at the same time, the re-utilization of the massive quantum probes exploited, inevitably lost in free-falling interferometric schemes., Comment: 8 pages, 5 figures

Entanglement is one of the fundamental properties of a quantum state and is a crucial differentiator between classical and quantum computation. There are many ways to define entanglement and its measure, depending on the problem or application under consideration. Each of these measures may be computed or approximated by multiple methods. However, hardly any of these methods can be run on near-term quantum hardware. This work presents a quantum adaptation of the iterative higher-order power method for estimating the geometric measure of entanglement of multi-qubit pure states using rank-1 tensor approximation. This method is executable on current (hybrid) quantum hardware and does not depend on quantum memory. We study the effect of noise on the algorithm using a simple theoretical model based on the standard depolarising channel. This model allows us to post hoc mitigate the effects of noise on the results of the computation., Comment: 14 pages, 9 figures

We examine a mean-reverting Ornstein-Uhlenbeck process that perturbs an unknown Lipschitz-continuous drift and aim to estimate the drift's value at a predetermined time horizon by sampling the path of the process. Due to the time varying nature of the drift we propose an estimation procedure that involves an online, time-varying optimization scheme implemented using a stochastic gradient ascent algorithm to maximize the log-likelihood of our observations. The objective of the paper is to investigate the optimal sample size/rate for achieving the minimum mean square distance between our estimator and the true value of the drift. In this setting we uncover a trade-off between the correlation of the observations, which increases with the sample size, and the dynamic nature of the unknown drift, which is weakened by increasing the frequency of observation. The mean square error is shown to be non monotonic in the sample size, attaining a global minimum whose precise description depends on the parameters that govern the model. In the static case, i.e. when the unknown drift is constant, our method outperforms the arithmetic mean of the observations in highly correlated regimes, despite the latter being a natural candidate estimator. We then compare our online estimator with the global maximum likelihood estimator., Comment: 14 pages, 5 figures

Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal adaptation to advanced AI, that is, reducing the expected negative impacts from a given level of diffusion of a given AI capability. We introduce a conceptual framework which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems, illustrated with examples in election manipulation, cyberterrorism, and loss of control to AI decision-makers. We discuss a three-step cycle that society can implement to adapt to AI. Increasing society's ability to implement this cycle builds its resilience to advanced AI. We conclude with concrete recommendations for governments, industry, and third-parties.

Exploration and advancements in ultra-wide bandgap (UWBG) semiconductors are pivotal for next-generation high-power electronics and deep-ultraviolet (DUV) optoelectronics. A critical challenge lies in finding a semiconductor that is highly transparent to DUV wavelengths yet conductive with high mobility at room temperature. Here, we achieved both high transparency and high conductivity by employing a thin heterostructure design. The heterostructure facilitated high conductivity by screening phonons using free carriers, while the atomically thin films ensured high transparency. We utilized a heterostructure comprising SrSnO3/La:SrSnO3/GdScO3 (110) and applied electrostatic gating to effectively separate electrons from their dopant atoms. This led to a modulation of carrier density from 1018 cm-3 to 1020 cm-3, with room temperature mobilities ranging from 40 to 140 cm2V-1s-1. The phonon-limited mobility, calculated from first principles, closely matched experimental results, suggesting that room-temperature mobility could be further increased with higher electron density. Additionally, the sample exhibited 85% optical transparency at a 300 nm wavelength. These findings highlight the potential of heterostructure design for transparent UWBG semiconductor applications, especially in deep-ultraviolet regime., Comment: 32 pages

Fast radio bursts (FRBs) are energetic, millisecond-duration radio pulses observed at extragalactic distances and whose origins are still a subject of heated debate. A fraction of the FRB population have shown repeating bursts, however it's still unclear whether these represent a distinct class of sources. We investigated the bursting behaviour of FRB 20220912A, one of the most active repeating FRBs known thus far. In particular, we focused on its burst energy distribution, linked to the source energetics, and its emission spectrum, with the latter directly related to the underlying emission mechanism. We monitored FRB 20220912A at $408$ MHz with the Northern Cross radio telescope and at $1.4$ GHz using the $32$-m Medicina Grueff radio telescope. Additionally, we conducted $1.2$ GHz observations taken with the upgraded Giant Meter Wave Radio Telescope (uGMRT) searching for a persistent radio source coincident with FRB 20220912A, which included high energy observations in the $0.3-10$ keV, $0.4-100$ MeV and $0.03-30$ GeV energy range. We report $16$ new bursts from FRB 20220912A at $408$ MHz during the period between October 16$^{\rm th}$ 2022 and December 31$^{\rm st}$ 2023. Their cumulative spectral energy distribution follows a power law with slope $\alpha_E = -1.3 \pm 0.2$ and we measured a repetition rate of $0.19 \pm 0.03$ hr$^{-1}$ for bursts having a fluence of $\mathcal{F} \geq 17$ Jy ms. Furthermore, we report no detections at 1.4 GHz for $\mathcal{F} \geq 20$ Jy ms. These non-detections imply an upper limit of $\beta < -2.3$, with $\beta$ being the $408$ MHz $-$ $1.4$ GHz spectral index of FRB 20220912A. This is inconsistent with positive $\beta$ values found for the only two known cases in which an FRB has been detected in separate spectral bands. We find that FRB 20220912A shows a decline of four orders of magnitude in its bursting activity at $1.4$ GHz over a .. (abridged), Comment: accepted for publication, 13 pages, 4 figures

The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied. We propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials., Comment: 34 pages

Rapid shear motion of magnetar crust can launch Alfv\'{e}n waves into the magnetosphere. The dissipation of the Alfv\'{e}n waves has been theorized to power the X-ray bursts characteristic of magnetars. However, the process by which Alfv\'{e}n waves convert their energy to X-rays is unclear. Recent work has suggested that energetic fast magnetosonic (fast) waves can be produced as a byproduct of Alfv\'{e}n waves propagating on curved magnetic field lines; their subsequent dissipation may power X-ray bursts. In this work, we investigate the production of fast waves by performing axisymmetric force-free simulations of Alfv\'{e}n waves propagating in a dipolar magnetosphere. For Alfv\'{e}n wave trains that do not completely fill the flux tube confining them, we find a fast wave dominated by a low frequency component with a wavelength defined by the bouncing time of the Alfv\'{e}n waves. In contrast, when the wave train is long enough to completely fill the flux tube, and the Alfv\'{e}n waves overlap significantly, the energy is quickly converted into a fast wave with a higher frequency that corresponds to twice the Alfv\'{e}n wave frequency. We investigate how the energy, duration, and wavelength of the initial Alfv\'{e}n wave train affect the conversion efficiency to fast waves. For modestly energetic star quakes, we see that the fast waves that are produced will become non-linear well within the magnetosphere, and we comment on the X-ray emission that one may expect from such events., Comment: 12 pages, 13 figures

Recent low-frequency radio observations at 140 MHz discovered a 3 Mpc-long bridge of diffuse emission connecting the galaxy clusters Abell 0399 and Abell 0401. We present follow-up observations at 60 MHz to constrain the spectral index of the bridge, which so far has only been detected at 140 and 144 MHz. We analysed deep (~18 hours) LOw Frequency ARray (LOFAR) Low Band Antenna (LBA) data at 60 MHz to detect the bridge at very low frequencies. We then conducted a multi-frequency study with LOFAR HBA data at 144 MHz and uGMRT data at 400 MHz. Assuming second-order Fermi mechanisms for the re-acceleration of relativistic electrons driven by turbulence in the radio bridge regions, we compare the observed radio spectrum with theoretical synchrotron models. The bridge is detected in the 75'' resolution LOFAR image at 60 MHz and its emission fully connects the region between the two galaxy clusters. Between 60 MHz and 144 MHz we found an integrated spectral index value of -1.44 +\- 0.16 for the bridge emission. For the first time, we produced spectral index and related uncertainties maps for a radio bridge. We produce a radio spectrum, which show significant steepening between 144 and 400 MHz. This detection at low frequencies provides important information on the models of particle acceleration and magnetic field structure on very extended scales. The spectral index gives important clues to the origin of inter-cluster diffuse emission. The steepening of the spectrum above 144 MHz can be explained in a turbulent re-acceleration framework, assuming that the acceleration timescales are longer than ~200 Myr., Comment: Letter to the Editor: 7 pages, 4 figures, 2 tables; accepted for publication in Astronomy & Astrophysics (A&A)

The goal of the Fast Abstracts track is to bring together researchers and practitioners working on dependable computing to discuss work in progress or opinion pieces. Contributions are welcome from academia and industry. Fast Abstracts aim to serve as a rapid and flexible mechanism to: (i) Report on current work that may or may not be complete; (ii) Introduce new ideas to the community; (iii) State positions on controversial issues or open problems; (iv) Share lessons learnt from real-word dependability engineering; and (v) Debunk or question results from other papers based on contra-indications. The Student Forum aims at creating a vibrant and friendly environment where students can present and discuss their work, and exchange ideas and experiences with other students, researchers and industry. One of the key goals of the Forum is to provide students with feedback on their preliminary results that might help with their future research directions.

Metapopulation models have been instrumental in demonstrating the ecological impact of landscape structure on the survival of a focal species in complex environments. However, extensions to multiple species with arbitrary dispersal networks often rely on phenomenological assumptions limiting their scope. Here, we develop a multilayer network model of competitive dispersing metacommunities to investigate how spatially structured environments impact species coexistence and ecosystem stability. We show that homogeneous environments always lead to monodominance unless all species' fitness parameters are in an exact trade-off. However, this precise fine-tuning does not guarantee coexistence in generic heterogeneous environments. By introducing general spatial disorder in the model, we solve it exactly in the mean-field limit, finding that stable coexistence becomes possible in the presence of strong disorder. Crucially, coexistence is supported by the spontaneous localization of species through the emergence of ecological niches. Our results remain qualitatively valid in arbitrary dispersal networks, where topological features can improve species coexistence. Finally, we employ our model to study how correlated disorder promotes spatial ecological patterns in realistic terrestrial and riverine landscapes. Our work provides a novel framework to understand how landscape structure enables coexistence in metacommunities by acting as the substrate for ecological interactions., Comment: 11 pages, 4 figures

The spectral and transport properties of strongly correlated metals, such as SrVO$_3$ (SVO), are widely attributed to electron-electron ($e$-$e$) interactions, with lattice vibrations (phonons) playing a secondary role. Here, using first-principles electron-phonon ($e$-ph) and dynamical mean field theory calculations, we show that $e$-ph interactions play an essential role in SVO: they govern the electron scattering and resistivity in a wide temperature range down to 30 K, and induce an experimentally observed kink in the spectral function. In contrast, the $e$-$e$ interactions control quasiparticle renormalizations and low temperature transport, and enhance the $e$-ph coupling. We clarify the origin of the near $T^2$ temperature dependence of the resistivity by analyzing the $e$-$e$ and $e$-ph limited transport regimes. Our work disentangles the electronic and lattice degrees of freedom in a prototypical correlated metal, revealing the dominant role of $e$-ph interactions in SVO., Comment: 7 pages, 4 figures

We write down the force-free electrodynamics (FFE) equations in dipole coordinates, and solve for normal modes corresponding to Alfv\'enic perturbations in the magnetosphere of a neutron star. We show that a single Alfv\'en wave propagating on dipole field lines spontaneously sources a fast magnetosonic (fms) wave at the next order in the perturbation expansion, without needing 3-wave interaction. The frequency of the sourced fms wave is twice the original Alfv\'en wave frequency, and the wave propagates spherically outwards. The properties of the outgoing fms wave can be computed exactly using the usual devices of classical electrodynamics. We extend the calculation to the closed zone of a rotating neutron star magnetosphere, and show that the Alfv\'en wave also sources a spherical fms wave but at the same frequency as the primary Alfv\'en wave., Comment: 15 pages, 5 figures, comments welcome!

In the current paradigm, high redshift radio halos are expected to be scarce due to inverse Compton energy losses and redshift dimming, which cause them to be intrinsically faint. This low occurrence fraction is predicted by cosmic ray electron turbulent re-acceleration models. To date, only a handful of radio halos have been detected at redshift z > 0.8. We report the MeerKAT detection of a radio halo hosted by a galaxy cluster ACT-CL J0329.2-2330 at z = 1.23, making it the highest redshift halo detected thus far. Using L-band and UHF-band observations, we derive a radio halo spectral index of $\alpha^{1.3GHz}_{0.8GHz}$ = 1.3 $\pm$ 0.4 and a radio power of P$_{1.4GHz}$ = (4.4 $\pm$ 1.5) $\times$ 10$^{24}$ W Hz$^{-1}$. This result further confirms that there is rapid magnetic field amplification in galaxy clusters at high redshift., Comment: 8 pages, 8 figures, and 2 tables

Optical turbulence modelling and simulation are crucial for developing astronomical ground-based instruments, laser communication, laser metrology, or any application where light propagates through a turbulent medium. In the context of spectrum-based optical turbulence Monte-Carlo simulations, we present an alternative approach to the methods based on the Fast Fourier Transform (FFT) using a quasi-random frequency sampling heuristic. This approach provides complete control over the spectral information expressed in the simulated measurable, without the drawbacks encountered with FFT-based methods such as high-frequency aliasing, low-frequency under-sampling, and static sampling statistics. The method's heuristics, implementation, and an application example from the study of differential piston fluctuations are discussed., Comment: 13 pages, 6 figures, to be published in Optics Continuum

We present two graph drawing algorithms based on the recently defined "grand-Schnyder woods", which are a far-reaching generalization of the classical Schnyder woods. The first is a straight-line drawing algorithm for plane graphs with faces of degree 3 and 4 with no separating 3-cycle, while the second is a rectangular drawing algorithm for the dual of such plane graphs. In our algorithms, the coordinates of the vertices are defined in a global manner, based on the underlying grand-Schnyder woods. The grand-Schnyder woods and drawings are computed in linear time. When specializing our algorithms to special classes of plane graphs, we recover the following known algorithms: (1) He's algorithm for rectangular drawing of 3-valent plane graphs, based on transversal structures, (2) Fusy's algorithm for the straight-line drawing of triangulations of the square, based on transversal structures, (3) Bernardi and Fusy's algorithm for the orthogonal drawing of 4-valent plane graphs, based on 2-orientations, (4) Barriere and Huemer's algorithm for the straight-line drawing of quadrangulations, based on separating decompositions. Our contributions therefore provide a unifying perspective on a large family of graph drawing algorithms that were originally defined on different classes of plane graphs and were based on seemingly different combinatorial structures.

In this work, we compare existing non-polarizable force fields developed to study the solid or solution phases of hybrid organic-inorganic halide perovskites with the AMOEBA polarizable force field. The aim is to test whether more computationally expensive polarizable force fields like AMOEBA offer better transferability between solution and solid phases, with the ultimate goal being the study of crystal nucleation, growth and other interfacial phenomena involving these ionic compounds. In the context of hybrid perovskites, AMOEBA force field parameters already exist for several elements in solution and we decided to leave them unchanged and to only parameterize the missing ones (Pb\textsuperscript{2+} and CH\textsubscript{3}NH\textsubscript{3}\textsuperscript{+} ions) in order to maximise transferability and avoid over-fitting to the specific examples studied here. Overall, we find that AMOEBA yields accurate hydration free energies (within 5\%) for typical ionic species while showing the correct ordering of stability for the different crystal polymorphs of CsPbI\textsubscript{3} and CH\textsubscript{3}NH\textsubscript{3}PbI\textsubscript{3}. While the existing parameters do not accurately reproduce all transition temperatures and lattice parameters, AMOEBA offers better transferability between solution and solid states than existing non-polarizable force fields.

The impact of climate change has led to growing global concern about the interaction of temperature and xenobiotics in agricultural toxicological studies. Thus, for the first time, we evaluated the lethal, sublethal and transgerational effects of six insecticides used in the management of stink bug complex in soybean crops on the different life stages of the parasitoid Telenomus podisi (Hymenoptera: Scelionidae) in three temperature levels (15, 25 and 30 {\deg}C). Telenomus podisi adults (F0 generation), when exposed to insecticides based on acephate, spinosad and thiamethoxam + lambda-cyhalothrin, showed accumulated mortality of 100% at all temperature levels tested. On the other hand, methoxyfenozide + spinetoram caused average mortalities of 88.75% at 15 {\deg}C and 38.75% at 25 and 30 {\deg}C. In contrast, the mortality rates caused by chlorfenapyr at 15, 25 and 30 {\deg}C were 1.25, 71.25 and 71.25%. On the other hand, surviving adults in lethal toxicity bioassay did not show differences in egg parasitism (F0 generation) and emergence of F1 generation in all temperature levels studied; however, the insecticide methoxyfenozide + spinetoram showed the lowest level of parasitism and emergence of T. podisi. In addition, our results demonstrated significant changes in the proportion of emerged males and females as the temperature increased; however, we did not find any differences when comparing the insecticides studied. Furthermore, we detected a significant interaction between insecticides and temperatures by contaminating the host's parasitized eggs (parasitoid pupal stage). Generally, the highest emergence reduction values were found at the highest temperature studied (30 {\deg}C). Our results highlighted the temperature-dependent impact of synthetic insecticides on parasitoids, which should be considered in toxicological risk assessments and under predicted climate change scenarios., Comment: 45 pages

Simple Exponential Smoothing is a classical technique used for smoothing time series data by assigning exponentially decreasing weights to past observations through a recursive equation; it is sometimes presented as a rule of thumb procedure. We introduce a novel theoretical perspective where the recursive equation that defines simple exponential smoothing occurs naturally as a stochastic gradient ascent scheme to optimize a sequence of Gaussian log-likelihood functions. Under this lens of analysis, our main theorem shows that -- in a general setting -- simple exponential smoothing converges to a neighborhood of the trend of a trend-stationary stochastic process. This offers a novel theoretical assurance that the exponential smoothing procedure yields reliable estimators of the underlying trend shedding light on long-standing observations in the literature regarding the robustness of simple exponential smoothing., Comment: 12 pages, 6 figures