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The purpose of this study is to determine the effectiveness of audiovisual aids in the instruction of junior-level preservice teachers. The tapes used were vignettes of persons demonstrating specific teaching skills in the areas of lesson organization, questioning, using student ideas, praise and corrective feedback, and variety and variation in teaching styles. Ninety-six subjects were assigned to one of four groups. All groups were pretested and posttested with identical measures. For each of the five topics, the treatment groups were: tapes only; tapes plus instruction; instruction only; and control. It was hypothesized that preservice teachers who receive instruction and view protocol tapes will score significantly higher on related posttests than teachers who only receive instruction or who only view tapes. An unexpected consequence of this study was the finding that the control group, which received neither verbal nor visual instruction related to the topics of the tapes, performed just as well on related posttests as groups that did receive such instruction. The results of this study support the contention that no generalizations can be made concerning the effectiveness of media in instruction and that each type of media must be evaluated individually. (JD)

Motivated by practical concerns in cryptography, we study pseudorandomness properties of permutations on $\{0,1\}^n$ computed by random circuits made from reversible $3$-bit gates (permutations on $\{0,1\}^3$). Our main result is that a random circuit of depth $\sqrt{n} \cdot \tilde{O}(k^3)$, with each layer consisting of $\Theta(n)$ random gates in a fixed two-dimensional nearest-neighbor architecture, yields approximate $k$-wise independent permutations. Our result can be seen as a particularly simple/practical block cipher construction that gives provable statistical security against attackers with access to $k$~input-output pairs within few rounds. The main technical component of our proof consists of two parts: 1. We show that the Markov chain on $k$-tuples of $n$-bit strings induced by a single random $3$-bit one-dimensional nearest-neighbor gate has spectral gap at least $1/n \cdot \tilde{O}(k)$. Then we infer that a random circuit with layers of random gates in a fixed one-dimensional gate architecture yields approximate $k$-wise independent permutations of $\{0,1\}^n$ in depth $n\cdot \tilde{O}(k^2)$ 2. We show that if the $n$ wires are layed out on a two-dimensional lattice of bits, then repeatedly alternating applications of approximate $k$-wise independent permutations of $\{0,1\}^{\sqrt n}$ to the rows and columns of the lattice yields an approximate $k$-wise independent permutation of $\{0,1\}^n$ in small depth. Our work improves on the original work of Gowers, who showed a gap of $1/\mathrm{poly}(n,k)$ for one random gate (with non-neighboring inputs); and, on subsequent work improving the gap to $\Omega(1/n^2k)$ in the same setting., Comment: Merge of arXiv:2404.14648 and arXiv:2409.14614. Results in arXiv:2404.14648 on candidate constructions of computationally pseudorandom permutations from one-way functions have been withdrawn due to an error

The computational challenges posed by many-particle quantum systems are often overcome by mixed quantum-classical (MQC) models in which certain degrees of freedom are treated as classical while others are retained as quantum. One of the fundamental questions raised by this hybrid picture involves the characterization of the information associated to MQC systems. Based on the theory of dynamical invariants in Hamiltonian systems, here we propose a family of hybrid entropy functionals that consistently specialize to the usual R\'enyi and Shannon entropies. Upon considering the MQC Ehrenfest model for the dynamics of quantum and classical probabilities, we apply the hybrid Shannon entropy to characterize equilibrium configurations for simple Hamiltonians. The present construction also applies beyond Ehrenfest dynamics., Comment: First version. For submission to Lecture Notes in Comput. Sci

A new geometric approach to systems with boundary energy flow is developed using infinite-dimensional Dirac structures within the Lagrangian formalism. This framework satisfies a list of consistency criteria with the geometric setting of finite-dimensional mechanics. In particular, the infinite-dimensional Dirac structure can be constructed from the canonical symplectic form on the system's phase space; the system's evolution equations can be derived equivalently from either a variational perspective or a Dirac structure perspective; the variational principle employed is a direct extension of Hamilton's principle in classical mechanics; and the approach allows for a process of system interconnection within its formulation. This is achieved by developing an appropriate infinite dimensional version of the previously developed Lagrange-Dirac systems. A key step in this construction is the careful choice of a suitable dual space to the configuration space, specifically, a subspace of the topological dual that captures the system's behavior in both the interior and the boundary, while allowing for a natural extension of the canonical geometric structures of mechanics. This paper focuses on systems where the configuration space consists of differential forms on a smooth manifold with a boundary. To illustrate our theory, several examples, including nonlinear wave equations, the telegraph equation, and the Maxwell equations are presented.

We numerically simulate, in both the forced and decay regimes, a fourth-order nonlinear diffusion equation derived from the kinetic equation of gravitational wave turbulence in the limit of strongly local quartic interactions. When a forcing is applied to an intermediate wavenumber $k_i$, we observe a dual cascade of energy and wave action. In the stationary state, the associated flux ratio is proportional to $k_i$, and the Kolmogorov-Zakharov spectra are recovered. In decaying turbulence, the study reveals that the wave action spectrum can extend to wavenumbers greater than the initial excitation $k_i$ with constant negative flux, while the energy flux is positive with a power law dependence in $k$. This leads to an unexpected result: a single inertial range with a Kolmogorov-Zakharov wave action spectrum extending progressively to wavenumbers larger than $k_i$. We also observe a wave action decay in time in $t^{-1/3}$ while the front of the energy spectrum progresses according to a $t^{1/3}$ law. These properties can be understood with simple theoretical arguments., Comment: 11 pages, 4 figures

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La R{\'e}union. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts.

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La R\'eunion. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts., Comment: Preprint. in French language

We derive variational integrators for stochastic Hamiltonian systems on Lie groups using a discrete version of the stochastic Hamiltonian phase space principle. The structure-preserving properties of the resulting scheme, such as symplecticity, preservation of the Lie-Poisson structure, preservation of the coadjoint orbits, and conservation of Casimir functions, are discussed, along with a discrete Noether theorem for subgroup symmetries. We also consider in detail the case of stochastic Hamiltonian systems with advected quantities, studying the associated structure-preserving properties in relation to semidirect product Lie groups. A full convergence proof for the scheme is provided for the case of the Lie group of rotations. Several numerical examples are presented, including simulations of the free rigid body and the heavy top.

We show that the fundamental group of framed circles in $S^1 \times D^3$ injects into the fundamental group of framed spheres in $S^2\times D^2$, so that the cokernel is the fundamental group of framed neat disks in $D^4$. In particular, grasper families of circles give rise to countably many nontrivial families of spheres. Ambient extensions of either of these two types of families induce the same barbell diffeomorphisms of $S^1\times S^2\times I$. We give two proofs that these diffeomorphisms are nontrivial and pairwise distinct. This implies infinite generation of the abelian group of isotopy classes of diffeomorphisms of $S^1\times S^2\times I$ that are pseudo-isotopic to the identity, recovering a result of Singh., Comment: 21 pages, 14 figures

With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming increasingly unsustainable. Existing approaches, which rely heavily on first-principles Monte Carlo simulations for modeling event showers in calorimeters, are projected to require millions of CPU-years annually -- far exceeding current computational capacities. This bottleneck presents an exciting opportunity for advancements in computational physics by integrating deep generative models with quantum simulations. We propose a quantum-assisted hierarchical deep generative surrogate founded on a variational autoencoder (VAE) in combination with an energy conditioned restricted Boltzmann machine (RBM) embedded in the model's latent space as a prior. By mapping the topology of D-Wave's Zephyr quantum annealer (QA) into the nodes and couplings of a 4-partite RBM, we leverage quantum simulation to accelerate our shower generation times significantly. To evaluate our framework, we use Dataset 2 of the CaloChallenge 2022. Through the integration of classical computation and quantum simulation, this hybrid framework paves way for utilizing large-scale quantum simulations as priors in deep generative models., Comment: Neurips ML4PS 2024. 5 Figs, 8 pp

Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at these detectors have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run \cite{collaboration2022atlas}. Simulating a single LHC event with \textsc{Geant4} currently devours around 1000 CPU seconds, with simulations of the calorimeter subdetectors in particular imposing substantial computational demands \cite{rousseau2023experimental}. To address this challenge, we propose a conditioned quantum-assisted deep generative model. Our model integrates a conditioned variational autoencoder (VAE) on the exterior with a conditioned Restricted Boltzmann Machine (RBM) in the latent space, providing enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave's Pegasus-structured \textit{Advantage} quantum annealer (QA) for sampling. We introduce a novel method for conditioning the quantum-assisted RBM using \textit{flux biases}. We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers. The effectiveness of our framework is illustrated using Dataset 2 of the CaloChallenge \cite{calochallenge}., Comment: 27 pages, 10 figures, 8 appendices

It has been claimed that graphite hosts superconductivity at room temperature, although all efforts to isolate it have been vain. Here we report a separation method that uses magnetic field gradients to sort the superconducting from normal grains out of industrial graphite powders. We have obtained a concentrate of above room temperature superconducting particles. Electrical resistance measurements on agglomerates of sorted grains of three types of graphite show transition temperatures up to T$_{c{_{onset}}} \sim$ 700K with zero resistance up to $\sim$ 500K. Magnetization measurements confirm these values through jumps at \textit{T$_c$} in the zero field cooled curves, and by the occurrence diamagnetic hysteretic cycles shrinking with temperature. Our results open the door towards the study of above room temperature superconducting ill-stacked graphite phases., Comment: 21 pages, 9 figures

We continue the study of the approximate $k$-wise independence of random reversible circuits as permutations of $\{\pm1\}^n$. Our main result is the first construction of a natural class of random reversible circuits with a sublinear-in-$n$ dependence on depth. Our construction is motivated by considerations in practical cryptography and is somewhat inspired by the design of practical block ciphers, such as DES and AES. Previous constructions of He and O'Donnell [HO24], which were built with gate architectures on one-dimensional lattices, suffered from an inherent linear-in-$n$ dependence on depth. The main novelty of our circuit model is a gate architecture built on higher-dimensional lattices., Comment: Merged with arXiv:2404.14648. For merged paper see arXiv:2502.07159

We develop a Lie group geometric framework for the motion of fluids with permeable boundaries that extends Arnold's geometric description of fluid in closed domains. Our setting is based on the classical Hamilton principle applied to fluid trajectories, appropriately amended to incorporate bulk and boundary forces via a Lagrange-d'Alembert approach, and to take into account only the fluid particles present in the fluid domain at a given time. By applying a reduction by relabelling symmetries, we deduce a variational formulation in the Eulerian description that extends the Euler-Poincar\'e framework to open fluids. On the Hamiltonian side, our approach yields a bracket formulation that consistently extends the Lie-Poisson bracket of fluid dynamics and contains as a particular case a bulk+boundary bracket formulation proposed earlier. We illustrate the geometric framework with several examples, and also consider the extension of this setting to include multicomponent fluids, the consideration of general advected quantities, the analysis of higher-order fluids, and the incorporation of boundary stresses. Open fluids are found in a wide range of physical systems, including geophysical fluid dynamics, porous media, incompressible flow and magnetohydrodynamics. This new formulation permits a description based on geometric mechanics that can be applied to a broad class of models.

Much of the cost and effort required during the software testing process is invested in performing test maintenance - the addition, removal, or modification of test cases to keep the test suite in sync with the system-under-test or to otherwise improve its quality. Tool support could reduce the cost - and improve the quality - of test maintenance by automating aspects of the process or by providing guidance and support to developers. In this study, we explore the capabilities and applications of large language models (LLMs) - complex machine learning models adapted to textual analysis - to support test maintenance. We conducted a case study at Ericsson AB where we explored the triggers that indicate the need for test maintenance, the actions that LLMs can take, and the considerations that must be made when deploying LLMs in an industrial setting. We also proposed and demonstrated implementations of two multi-agent architectures that can predict which test cases require maintenance following a change to the source code. Collectively, these contributions advance our theoretical and practical understanding of how LLMs can be deployed to benefit industrial test maintenance processes., Comment: Under submission to ACM TOSEM

The Gay, Lesbian and Straight Education Network's (GLSEN) research has demonstrated that LGBTQ students who attend schools with instruction that is inclusive of LGBTQ people, history, and events experience a better school climate and improved academic outcomes. We also know from the experiences of students, educators and advocates (as well as what is being attacked and undermined) that learning outside of the classroom also has a tremendous impact on LGBTQ+ youth experiences in schools. This resource focuses on "inclusive learning," which encompasses and extends beyond GLSEN's historic focus on classroom instruction. We synthesize over 20 years of research conducted by GLSEN and others in the field and contribute original and new analyses of our National School Climate Survey (NSCS) 2021 data on the impact of inclusive learning on LGBTQ+ youth experiences and outcomes in k-12 schools in the US. We also clarify how state policies and curricular standards, locally adopted curriculum, and the practices of individual educators can shape whether or not students receive inclusive instruction. For the first time, NSCS data is used to evaluate the impacts of LGTBQI+ inclusive school libraries, instructional materials, and school internet access and to specifically assess the impacts of LGBTQI+ inclusive sex education.

As the higher education landscape undergoes rapid transformation, educators continue to seek best practices for creating quality digital, blended, and online learning experiences. However, disparate definitions of quality and differing instructor and student preferences complicate this work. This report addresses the pressing need to bridge the gap between student and faculty expectations, highlighting the critical role of student voices in defining quality education. In seeking to better understand what quality digital learning looks like from a student perspective and how students think quality digital learning can best center diversity, equity, and inclusion (DEI) principles, data was analyzed from four student panels at recent Online Learning Consortium conferences and a framework was developed based on insights students shared. During these panels, students called for digital learning educators, practitioners, and leaders to: (1) get beyond face-to-face replication; (2) take collaborative action on DEI; (3) keep humans and connection at the center of learning; (4) incorporate intentional and deep design; (5) structure learning experiences for success; (6) support professional development opportunities for their instructors; and (7) nurture a culture of belonging. Readers of the report will access actionable strategies tailored for leaders, instructors, and support staff which can be used to leverage digital, blended, and online learning as a part of their institution's strategy.

The mental health landscape in higher education has reached a critical juncture, with an increasing number of students experiencing severe psychological issues. Recent studies have found that over 60% of college students meet the criteria for at least one mental health problem, with significant portions reporting symptoms of depression, anxiety, and suicidal ideation (Flannery, 2023; Alonso, 2023). Encouragingly, more students are seeking mental health counseling, yet a substantial gap remains, with over 60% not receiving the care they need (Alonso, 2023; Sutton, 2023). The COVID-19 pandemic has further complicated these trends, significantly impacting student enrollment patterns and increasing the need for quality online education and telemental health services. The study aims to address the mental health crisis by focusing specifically on the needs of online students and support strategies that can more effectively support this population. Through a comprehensive, global survey of 338 respondents from diverse roles in higher education, the researchers sought to understand the changing demand and utilization of mental health services among online students, the steps institutions are taking to customize these services, the unique mental health needs online students face, and the barriers preventing them from seeking help. This research reveals that online student demand and utilization of mental health services have significantly increased over the past year, with key challenges including isolation, diverse schedules, and less visible mental health issues. Also, educators identify barriers like lack of awareness, limited service availability, and time constraints in seeking support as especially significant for online students. [This study was collaborated with Uwill.]

To accurately compute data-based prediction of Hamiltonian systems, especially the long-term evolution of such systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a case that is particularly challenging for data-based methods: systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations. For example, any discretization of a continuum elastic rod can be viewed as interacting elements that can move and rotate in space, with each discrete element moving on the group of rotations and translations $SE(3)$. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of \emph{SympNets} (Jin et al, 2020) building the neural network from canonical phase space mappings, and transformations that preserve the Lie-Poisson structure (\emph{LPNets}) as in (Eldred et al, 2024). We derive a novel system of mappings that are built into neural networks for coupled systems. We call such networks Coupled Lie-Poisson Neural Networks, or \emph{CLPNets}. We consider increasingly complex examples for the applications of CLPNets: rotation of two rigid bodies about a common axis, the free rotation of two rigid bodies, and finally the evolution of two connected and interacting $SE(3)$ components. Our method preserves all Casimir invariants of each system to machine precision, irrespective of the quality of the training data, and preserves energy to high accuracy. Our method also shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied, with the effective dimension varying from three to eighteen. Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered., Comment: 52 pages, 9 figures

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables

Watanabe's theta graph diffeomorphism, constructed using Watanabe's clasper surgery construction which turns trivalent graphs in 4-manifolds into parameterized families of diffeomorphisms of 4-manifolds, is a diffeomorphism of $S^4$ representing a potentially nontrivial smooth mapping class of $S^4$. The "(1,2)-subgroup" of the smooth mapping class group of $S^4$ is the subgroup represented by diffeomorphisms which are pseudoisotopic to the identity via a Cerf family with only index 1 and 2 critical points. This author and Hartman showed that this subgroup is either trivial or has order 2 and explicitly identified a diffeomorphism that would represent the nontrivial element if this subgroup is nontrivial. Here we show that the theta graph diffeomorphism is isotopic to this one possibly nontrivial element of the (1,2)-subgroup. To prove this relation we develop a diagrammatic calculus for working in the smooth mapping class group of $S^4$., Comment: 16 pages, 11 figures

Monitoring energy behaviors in AI data centers is crucial, both to reduce their energy consumption and to raise awareness among their users which are key actors in the AI field. This paper shows a proof of concept of easy and lightweight monitoring of energy behaviors at the scale of a whole data center, a user or a job submission. Our system uses software wattmeters and we validate our setup with per node accurate external wattmeters. Results show that there is an interesting potential from the efficiency point of view, providing arguments to create user engagement thanks to energy monitoring., Comment: accepted at ICT4S 2024

High-significance evidences of the existence of a high-energy diffuse flux of cosmic neutrinos have emerged in the last decade from several observations by the IceCube Collaboration. The ANTARES neutrino telescope took data for 15 years in the Mediterranean Sea, from 2007 to 2022, and collected a high-purity all-flavour neutrino sample. The search for a diffuse cosmic neutrino signal using this dataset is presented in this article. This final analysis did not provide a statistically significant observation of the cosmic diffuse flux. However, this is converted into limits on the properties of the cosmic neutrino spectrum. In particular, given the sensitivity of the ANTARES neutrino telescope between 1 and 50 TeV, constraints on single-power-law hypotheses are derived for the cosmic diffuse flux below 20 TeV, especially for power-law fits of the IceCube data with spectral index softer than 2.8.

An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be developed specifically for each component to support the depth of inquiry and interactions required by research scientists. This work introduces Conversational Papers (cPAPERS), a dataset of conversational question-answer pairs from reviews of academic papers grounded in these paper components and their associated references from scientific documents available on arXiv. We present a data collection strategy to collect these question-answer pairs from OpenReview and associate them with contextual information from LaTeX source files. Additionally, we present a series of baseline approaches utilizing Large Language Models (LLMs) in both zero-shot and fine-tuned configurations to address the cPAPERS dataset., Comment: 14 pages, 1 figure

The discretization of Cartan's exterior calculus of differential forms has been fruitful in a variety of theoretical and practical endeavors: from computational electromagnetics to the development of Finite-Element Exterior Calculus, the development of structure-preserving numerical tools satisfying exact discrete equivalents to Stokes' theorem or the de Rham complex for the exterior derivative have found numerous applications in computational physics. However, there has been a dearth of effort in establishing a more general discrete calculus, this time for differential forms with values in vector bundles over a combinatorial manifold equipped with a connection. In this work, we propose a discretization of the exterior covariant derivative of bundle-valued differential forms. We demonstrate that our discrete operator mimics its continuous counterpart, satisfies the Bianchi identities on simplicial cells, and contrary to previous attempts at its discretization, ensures numerical convergence to its exact evaluation with mesh refinement under mild assumptions., Comment: 58 pages, 20 figures, Fix erroneous line break

This paper critically evaluates the HESA (Higher Education Statistics Agency) Data Report for the Employer Justified Retirement Age (EJRA) Review Group at the University of Cambridge (\cite{CambridgeHESA2024}), identifying significant methodological flaws and misinterpretations. Our analysis reveals issues such as unclear application of data filters, inconsistent variable treatment, and erroneous statistical conclusions. The Report suggests that the EJRA increased job creation rates at Cambridge, but we show Cambridge consistently had lower job creation rates for Established Academic Careers compared to other Russell Group universities, both before and after EJRA implementation in 2011, with no evidence for a significant change in this deficit post implementation. This suggests that EJRA is not a significant factor driving job creation rates. Since other universities without an EJRA exhibit higher job creation rates, this suggests job creation can be sustained without such a policy. We conclude that the EJRA did not achieve its intended goal of increasing opportunities for young academics and may have exacerbated existing disparities compared to other leading universities. We recommend EJRA be abolished at Cambridge since it does not meet its aims and could be viewed as unlawful age discrimination.\newline \it{This version is a revision reflecting some of the comments made by members of the university EJRA review group in public discussion, see \cite{Holmes24}.}, Comment: This is a revision of our earlier paper

The restricted Siegel disc is a homogeneous space related to the connected component $T_0(1)$ of the Universal Teichm\"uller space via the period mapping. In this paper we show that it is a coadjoint orbit of the universal central extension of the restricted symplectic group or, equivalently, an affine coadjoint orbit of the restricted symplectic group with cocycle given by the Schwinger term., Comment: 16 pages

Position calibration in the deep sea is typically done by means of acoustic multilateration using three or more acoustic emitters installed at known positions. Rather than using hydrophones as receivers that are exposed to the ambient pressure, the sound signals can be coupled to piezo ceramics glued to the inside of existing containers for electronics or measuring instruments of a deep sea infrastructure. The ANTARES neutrino telescope operated from 2006 until 2022 in the Mediterranean Sea at a depth exceeding 2000m. It comprised nearly 900 glass spheres with 432mm diameter and 15mm thickness, equipped with photomultiplier tubes to detect Cherenkov light from tracks of charged elementary particles. In an experimental setup within ANTARES, piezo sensors have been glued to the inside of such - otherwise empty - glass spheres. These sensors recorded signals from acoustic emitters with frequencies from 46545 to 60235Hz. Two waves propagating through the glass sphere are found as a result of the excitation by the waves in the water. These can be qualitatively associated with symmetric and asymmetric Lamb-like waves of zeroth order: a fast (early) one with $v_e \approx 5$mm/$\mu$s and a slow (late) one with $v_\ell \approx 2$mm/$\mu$s. Taking these findings into account improves the accuracy of the position calibration. The results can be transferred to the KM3NeT neutrino telescope, currently under construction at multiple sites in the Mediterranean Sea, for which the concept of piezo sensors glued to the inside of glass spheres has been adapted for monitoring the positions of the photomultiplier tubes., Comment: Published by "Experimental Astronomy"

This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations situated in tables from scientific articles. This dataset is designed to facilitate human-AI collaborative problem-solving through AI-powered multi-task tabular capabilities. In contrast to prior work that models interactions as factoid QA or procedure synthesis, iTBLS broadens the scope of interactions to include mathematical reasoning, natural language manipulation, and expansion of existing tables from natural language conversation by delineating interactions into one of three tasks: interpretation, modification, or generation. Additionally, the paper presents a suite of baseline approaches to iTBLS, utilizing zero-shot prompting and parameter-efficient fine-tuning for different computing situations. We also introduce a novel multi-step approach and show how it can be leveraged in conjunction with parameter-efficient fine-tuning to achieve the state-of-the-art on iTBLS; outperforming standard parameter-efficient fine-tuning by up to 15% on interpretation, 18% on modification, and 38% on generation., Comment: 14 pages, 4 figures

Continuous quantum phase transitions are widely assumed and frequently observed in various systems of quantum particles or spins. Their characteristic trait involves scaling laws governing a second-order, gradual suppression of the order parameter as the quantum critical point is approached. The localization of Cooper pairs in disordered superconductors and the resulting breakdown of superconductivity have long stood as a prototypical example. Here, we show a departure from this paradigm, showcasing that amorphous superconducting films of indium oxide undergo a distinctive, discontinuous first-order quantum phase transition tuned by disorder. Through systematic measurements of the plasmon spectrum in superconducting microwave resonators, we provide evidence for a marked jump of both the zero-temperature superfluid stiffness and the transition temperature at the critical disorder. This discontinuous transition sheds light on the previously overlooked role of repulsive interactions between Cooper pairs and the subsequent competition between superconductivity and insulating Cooper-pair glass. Furthermore, our investigation shows that the critical temperature of the films no longer relates to the pairing amplitude but aligns with the superfluid stiffness, consistent with the pseudogap regime of preformed Cooper pairs. Our findings raise fundamental new questions into the role of disorder in quantum phase transitions and carry implications for superinductances in quantum circuits., Comment: Updated text

Neutron star merger remnants are unique sites for exploring neutrino flavor conversion in dense media. Because of the natural excess of $\bar{\nu}_e$ over $\nu_e$, the neutrino-neutrino potential can cancel the matter potential, giving rise to matter-neutrino resonant flavor conversion. Under the assumption of two (anti)neutrino flavors and spatial homogeneity, we solve the neutrino quantum kinetic equations to investigate the occurrence of the matter-neutrino resonance within a multi-angle framework. We find that isotropy is broken spontaneously, regardless of the mass ordering. Relying on a hydrodynamical simulation of a binary neutron star merger remnant with a black hole of $3\ M_\odot$ and an accretion torus of $0.3\ M_\odot$, we find that complete flavor conversion caused by the matter-neutrino resonance is unlikely, although the matter and neutrino potentials cancel at various locations above the disk. Importantly, the matter-neutrino resonant flavor conversion crucially depends on the shape of the neutrino angular distributions. Our findings suggest that an accurate modeling of the neutrino angular distributions is necessary to understand flavor conversion physics in merger remnants, its implications on the disk physics and synthesis of the elements heavier than iron., Comment: 31 pages, 18 figures. Minor changes, matches version accepted for publication in JCAP. Supplemental material: https://sid.erda.dk/share_redirect/e2zTyjhG3B/index.html

Following the Pluto fly-by of the New Horizons spacecraft, the mission provided a unique opportunity to explore the Kuiper Belt in-situ. The possibility existed to fly-by a Kuiper Belt object (KBO) as well as to observe additional objects at distances closer than are feasible from earth-orbit facilities. However, at the time of launch no KBOs were known about that were accessible by the spacecraft. In this paper we present the results of 10 years of observations and three uniquely dedicated efforts -- two ground-based using the Subaru Suprime Camera, the Magellan MegaCam and IMACS Cameras, and one with the Hubble Space Telescope -- to find such KBOs for study. In this paper we overview the search criteria and strategies employed in our work and detail the analysis efforts to locate and track faint objects in the galactic plane. We also present a summary of all of the KBOs that were discovered as part of our efforts and how spacecraft targetability was assessed, including a detailed description of our astrometric analysis which included development of an extensive secondary calibration network. Overall, these efforts resulted in the discovery of 89 KBOs including 11 which became objects for distant observation by New Horizons and (486958) Arrokoth which became the first post-Pluto fly-by destination., Comment: Accepted to PSJ. 40 pages, 10 figures, 10 tables