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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)

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.]

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.

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: submitted to "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

High-energy neutrinos could be produced in the interaction of charged cosmic rays with matter or radiation surrounding astrophysical sources. To look for transient sources associated with neutrino emission, a follow-up program of neutrino alerts has been operating within the ANTARES Collaboration since 2009. This program, named TAToO, has triggered robotic optical telescopes (MASTER, TAROT, ROTSE and the SVOM ground based telescopes) immediately after the detection of any relevant neutrino candidate and scheduled several observations in the weeks following the detection. A subset of ANTARES events with highest probabilities of being of cosmic origin has also been followed by the Swift and the INTEGRAL satellites, the Murchison Widefield Array radio telescope and the H.E.S.S. high-energy gamma-ray telescope. The results of twelve years of observations are reported. No optical counterpart has been significantly associated with an ANTARES candidate neutrino signal during image analysis. Constraints on transient neutrino emission have been set. In September 2015, ANTARES issued a neutrino alert and during the follow-up, a potential transient counterpart was identified by Swift and MASTER. A multi-wavelength follow-up campaign has allowed to identify the nature of this source and has proven its fortuitous association with the neutrino. The return of experience is particularly important for the design of the alert system of KM3NeT, the next generation neutrino telescope in the Mediterranean Sea., Comment: 27 pages, 14 figures, submitted to JCAP

Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large-language model (LLM) to enhance the planning quality. We propose a pipeline to convert unstructured images to a structured graph consisting of image-patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human-AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity-modulated radiotherapy (IMRT) planning. In comparison to the widely-employed DL models used in radiotherapy, DoseGNN achieved mean square errors that were 80$\%$, 76$\%$ and 41.0$\%$ of those predicted by Swin U-Net Transformer, 3D U-Net CNN and vanilla MLP, respectively. Moreover, the LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language.

Wave turbulence is by nature a multiple time scale problem for which there is a natural asymptotic closure. The main result of this analytical theory is the kinetic equation that describes the long-time statistical behaviour of such turbulence composed of a set of weakly nonlinear interacting waves. In the case of gravitational waves, it involves four-wave interactions and two invariants, energy and wave action. Although the kinetic equation of gravitational wave turbulence has been published with the Hadad-Zakharov metric, along with their physical properties, the detailed derivation has not been shown. Following the seminal work of Newell (1968) for gravity/surface waves, we present the multiple time scale method, rarely used to derive the kinetic equations, and clarify the underlying assumptions and methodology. This formalism is applied to a wave amplitude equation obtained using an Eulerian approach. It leads to a kinetic equation slightly different from the one originally published, with a wave equation obtained using a Hamiltonian approach; we verify, however, that the two formulations are fully compatible when the number of symmetries used is the same. We also show that the exact solutions (Kolmogorov-Zakharov spectra) exhibit the same power laws and cascade directions. Furthermore, the use of the multiple time scale method reveals that the system retains the memory of the initially condition up to a certain level (second order) of development in time., Comment: 38 pages, 2 figures

The possibilities of combining several degrees of freedom inside a unique material have recently been highlighted in their dynamics and proposed as information carriers in quantum devices where their cross-manipulation by external parameters such as electric and magnetic fields could enhance their functionalities. An emblematic example is that of electromagnons, spin-waves dressed with electric dipoles, that are fingerprints of multiferroics. Point-like objects have also been identified, which may take the form of excited quasiparticles. This is the case for magnetic monopoles, the exotic excitations of spin ices, that have been recently proposed to carry an electric dipole although experimental evidences remain elusive. Presently, we investigate the electrical signature of a classical spin ice and a related compound that supports quantum fluctuations. Our in-depth study clearly attributes magnetoelectricity to the correlated spin ice phase distinguishing it from extrinsic and single-ion effects. Our calculations show that the proposed model conferring magnetoelectricity to monopoles is not sufficient, calling for higher order contributions.

The increasing complexity of advanced machine learning models requires innovative approaches to manage computational resources effectively. One such method is the Early Exit strategy, which allows for adaptive computation by providing a mechanism to shorten the processing path for simpler data instances. In this paper, we propose EERO, a new methodology to translate the problem of early exiting to a problem of using multiple classifiers with reject option in order to better select the exiting head for each instance. We calibrate the probabilities of exiting at the different heads using aggregation with exponential weights to guarantee a fixed budget .We consider factors such as Bayesian risk, budget constraints, and head-specific budget consumption. Experimental results, conducted using a ResNet-18 model and a ConvNext architecture on Cifar and ImageNet datasets, demonstrate that our method not only effectively manages budget allocation but also enhances accuracy in overthinking scenarios.

The 2022 Online Learning Consortium (OLC) Blended Learning Symposium was a response to the events of the past decade, such as an absence of a concerted blended learning program, the pandemic, flagging student interest in higher education, technology's explosive development, and pressures to reconsider what constitutes a responsive and effective university. Primarily, however, the symposium chronicled in this report is future-oriented, illustrating that long-held educational value systems need reconsideration. This work is a metaphoric Rosetta Stone codifying ideas of the most forward-thinking and thoughtful educators in the blended learning space. Learning independently offline or online is no longer a viable concept. The report makes it clear that interaction among learning spaces and people is the new blended paradigm.

Recent research describes a gap between the workforce skills employers need and the skills college graduates bring to the workforce. Making strides toward closing the workforce skills gap means that institutions, employers, and students must work together to cultivate a shared understanding of how strategic collaboration and communication are mutually beneficial. As such, the purpose of this study was to examine whether, or to what extent, institutions are using workforce trends to align and measure learning outcomes and help students communicate their levels of workplace skill proficiencies to potential employers. The research questions included: (1) How is your institution using workforce trends to align and measure learning outcomes?; (2) How is your institution utilizing workforce trends to design and measure learning experiences connected to learning outcomes?; and (3) What methods are used at your institution to teach students to communicate their level of skill proficiencies? This Research Brief presents five key findings on a student workforce skills communication gap, whereby students have appropriate workforce skills upon graduation but may lack the communication skills to effectively articulate these to potential employers. In addition, a Playbook is available that presents strategies for college and university leaders, employers, and students to overcome this workforce skills gap. These publications provide institutions of higher education with strategies and insights to better apply workforce trends to institutional decision-making, in terms of their programmatic and course design efforts. [For the corresponding Playbook, see ED633660.]

In this report, GLSEN, COLAGE, and Family Equality aim to address a significant gap in our understanding of school climate by focusing on the experiences of harassment, bullying, discrimination, and/or hostile school climates for students who have LGBTQ+parent(s) or caregiver(s). These experiences are a crucial part of the larger tapestry of discrimination and bias that LGBTQ+ communities face at school and may contribute to a hostile learning environment, yet the phenomena is largely erased and ignored. Based on surveys we conducted with students and parents/caregivers in 2022, this study seeks to redress the erasure and undercounting of experiences of harassment, bullying, discrimination, and/or hostile school climates in school based on having an LGBTQ+parent or caregiver and to make visible the experience of LGBTQ+ families in our schools. [This report was produced in partnership with COLAGE and the Family Equality Council.]

Pairing of electrons is ubiquitous in electronic systems featuring attractive inter-electron interactions, as exemplified in superconductors. Counter-intuitively, it can also be mediated in certain circumstances by the repulsive Coulomb interaction alone. Quantum Hall (QH) Fabry-P\'erot interferometers (FPIs) tailored in two-dimensional electron gas under a perpendicular magnetic field has been argued to exhibit such unusual electron pairing seemingly without attractive interaction. Here, we show evidence in graphene QH FPIs revealing not only a similar electron pairing at bulk filling factor nu=2 but also an unforeseen emergence of electron tripling characterized by a fractional Aharonov-Bohm flux period h/3e (h is the Planck constant and e the electron charge) at nu=3. Leveraging a novel plunger-gate spectroscopy, we demonstrate that electron pairing (tripling) involves correlated charge transport on two (three) entangled QH edge channels. This spectroscopy indicates a quantum interference flux-periodicity determined by the sum of the phases acquired by the distinct QH edge channels having slightly different interfering areas. While recent theory invokes the dynamical exchange of neutral magnetoplasmons -- dubbed neutralons -- as mediator for electron pairing, our discovery of three entangled QH edge channels with apparent electron tripling defies understanding and introduces a new three-body problem for interacting fermions.

In order to alleviate the computational costs of fully quantum nonadiabatic dynamics, we present a mixed quantum-classical (MQC) particle method based on the theory of Koopman wavefunctions. Although conventional MQC models often suffer from consistency issues such as the violation of Heisenberg's principle, we overcame these difficulties by blending Koopman's classical mechanics on Hilbert spaces with methods in symplectic geometry. The resulting continuum model enjoys both a variational and a Hamiltonian structure, while its nonlinear character calls for suitable closures. Benefiting from the underlying action principle, here we apply a regularization technique previously developed within our team. This step allows for a singular solution ansatz which introduces the trajectories of computational particles - the koopmons - sampling the Lagrangian classical paths in phase space. In the case of Tully's nonadiabatic problems, the method reproduces the results of fully quantum simulations with levels of accuracy that are not achieved by standard MQC Ehrenfest simulations. In addition, the koopmon method is computationally advantageous over similar fully quantum approaches, which are also considered in our study. As a further step, we probe the limits of the method by considering the Rabi problem in both the ultrastrong and the deep strong coupling regimes, where MQC treatments appear hardly applicable. In this case, the method succeeds in reproducing parts of the fully quantum results., Comment: Second version. 40 pages, 15 figures

In dense astrophysical environments, notably core-collapse supernovae and neutron star mergers, neutrino-neutrino forward scattering can spawn flavor conversion on very short scales. Scattering with the background medium can impact collective flavor conversion in various ways, either damping oscillations or possibly setting off novel collisional flavor instabilities (CFIs). A key feature in this process is the slowness of collisions compared to the much faster dynamics of neutrino-neutrino refraction. Assuming spatial homogeneity, we leverage this hierarchy of scales to simplify the description accounting only for the slow dynamics driven by collisions. We illustrate our new approach both in the case of CFIs and in the case of fast instabilities damped by collisions. In both cases, our strategy provides new equations, the slow-dynamics equations, that simplify the description of flavor conversion and allow us to qualitatively understand the final state of the system after the instability, either collisional or fast, has saturated., Comment: Added footnote 2 to correct an algebraic error; version corresponding to Erratum in PRD

This article proposes a novel indoor magnetic field-based place recognition algorithm that is accurate and fast to compute. For that, we modified the generalized ''Hough Transform'' to process magnetic data (MagHT). It takes as input a sequence of magnetic measures whose relative positions are recovered by an odometry system and recognizes the places in the magnetic map where they were acquired. It also returns the global transformation from the coordinate frame of the input magnetic data to the magnetic map reference frame. Experimental results on several real datasets in large indoor environments demonstrate that the obtained localization error, recall, and precision are similar to or are better than state-of-the-art methods while improving the runtime by several orders of magnitude. Moreover, unlike magnetic sequence matching-based solutions such as DTW, our approach is independent of the path taken during the magnetic map creation.

Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction., Comment: This publication has been accepted at ECCV'24