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Multimodal language models can exhibit hallucinations in their outputs, which limits their reliability. The ability to automatically detect these errors is important for mitigating them, but has been less explored and existing efforts do not localize hallucinations, instead framing this as a classification task. In this work, we first pose multimodal hallucination detection as a sequence labeling task where models must localize hallucinated text spans and present a strong baseline model. Given the high cost of human annotations for this task, we propose an approach to improve the sample efficiency of these models by creating corrupted grounding data, which we use for pre-training. Leveraging phrase grounding data, we generate hallucinations to replace grounded spans and create hallucinated text. Experiments show that pre-training on this data improves sample efficiency when fine-tuning, and that the learning signal from the grounding data plays an important role in these improvements.

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

Generative Flow Networks (GFlowNets) are a class of generative models that sample objects in proportion to a specified reward function through a learned policy. They can be trained either on-policy or off-policy, needing a balance between exploration and exploitation for fast convergence to a target distribution. While exploration strategies for discrete GFlowNets have been studied, exploration in the continuous case remains to be investigated, despite the potential for novel exploration algorithms due to the local connectedness of continuous domains. Here, we introduce Adapted Metadynamics, a variant of metadynamics that can be applied to arbitrary black-box reward functions on continuous domains. We use Adapted Metadynamics as an exploration strategy for continuous GFlowNets. We show three continuous domains where the resulting algorithm, MetaGFN, accelerates convergence to the target distribution and discovers more distant reward modes than previous off-policy exploration strategies used for GFlowNets., Comment: 10 pages

The dynamics of domain walls in a square of permalloy (Ni$_{81}$Fe$_{19}$; Py) upon excitation with an oscillating magnetic field of 4 mT amplitude were recorded by pump-probe ptychography with X-ray magnetic circular dichroism (XMCD) at the Ni L$_3$-edge. The 2.5 $\mu$m Py square of 160 nm thickness forms a vortex flux-closure pattern with domain walls that fall into alternating out-of-plane magnetization states due to the interplay of in-plane shape and growth-induced perpendicular anisotropies. Dynamic modes of the domain wall structure were excitable along with the vortex core gyration with frequencies of 500 MHz and 1 GHz. Micromagnetic simulations served to corroborate the imaged domain wall motion.

We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in ${}^3$He-${}^4$He elastic scattering. The parameters are calibrated to data from a recent set of $^{3}$He-${}^4$He elastic scattering differential cross section measurements. Analysis of these data for $E_{\rm lab} \leq 4.3$ MeV yields a unimodal posterior for which both methods obtain the same structure. However, the effective-range expansion amplitude does not account for the $7/2^-$ state of ${}^7$Be so, even after calibration, the description of data at the upper end of this energy range is poor. The data up to $E_{\rm lab}=2.6$ MeV can be well described, but calibration to this lower-energy subset of the data yields a bimodal posterior. After adapting VI to treat such a multi-modal posterior we find good agreement between the VI results and those obtained with parallel-tempered Monte Carlo sampling., Comment: 13 Pages, 6 Figures

The standard theory of musical scales since antiquity has been based on harmony, rather than melody. Some recent analyses support either view, and we lack a comparative test on cross-cultural data. We address this longstanding problem through a rigorous, computational comparison of the main theories against 1,314 scales from 96 countries. There is near-universal support for melodic theories, which predict step-sizes of 1-3 semitones. Harmony accounts for the prevalence of some simple-integer-ratio intervals, particularly for music-theoretic scales from Eurasian societies, which may explain their dominance amongst Western scholars. However, harmony poorly predicts scales measured from ethnographic recordings, particularly outside of Eurasia. Overall, we show that the historical emphasis on harmony is misguided and that melody is the primary determinant of the world's musical scales.

In recent years, multi-agent teaming has garnered considerable interest since complex objectives, such as intelligence, surveillance, and reconnaissance, can be divided into multiple cluster-level sub-tasks and assigned to a cluster of agents with the appropriate functionality. Yet, coordination and information dissemination between clusters may be necessary to accomplish a desired objective. Distributed consensus protocols provide a mechanism for spreading information within clustered networks, allowing agents and clusters to make decisions without requiring direct access to the state of the ensemble. Hence, we propose a strategy for achieving system-wide consensus in the states of identical linear time-invariant systems coupled by an undirected graph whose directed sub-graphs are available only at sporadic times. Within this work, the agents of the network are organized into pairwise disjoint clusters, which induce sub-graphs of the undirected parent graph. Some cluster sub-graph pairs are linked by an inter-cluster sub-graph, where the union of all cluster and inter-cluster sub-graphs yields the undirected parent graph. Each agent utilizes a distributed consensus protocol with components that are updated intermittently and asynchronously with respect to other agents. The closed-loop ensemble dynamics is modeled as a hybrid system, and a Lyapunov-based stability analysis yields sufficient conditions for rendering the agreement subspace (consensus set) globally exponentially stable. Furthermore, an input-to-state stability argument demonstrates the consensus set is robust to a class of perturbations. A numerical simulation considering both nominal and perturbed scenarios is provided for validation purposes.

Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diffusion is difficult to achieve. Using light-dimerizable kinesin motors to spatially control the formation and contraction of a microtubule network, we deliberately photobleach a grid pattern onto the filament network serving as a transient and dynamic coordinate system to observe the deformation and translation of the remaining fluorescent squares of microtubules. We find that the network contracts at a rate set by motor speed but is accompanied by a diffusive-like spread throughout the bulk of the contracting network with effective diffusion constant two orders of magnitude lower than that for a freely-diffusing microtubule. We further find that on micron scales, the diffusive timescale is only a factor of approximately 3 slower than that of advection regardless of conditions, showing that the global contraction and long-time relaxation from this diffusive behavior are both motor-driven but exhibit local competition within the network bulk., Comment: 8 page manuscript, 6 figures, 43 page SI

The ferroelectric domain structure of a freestanding BiFeO$_3$ film was visualized by ptychographic dichroic imaging with linearly polarized X-rays at the O K-edge around 530 eV. The dichroic contrast is maximized at the energy of the hybridization of the O 2p state and the Fe 3d orbitals, which is split by the octahedral crystal field of the perovskite structure. The microscopy images thus obtained complement the ptychographic imaging of the antiferromagnetic contribution at the Fe L$_3$-edge. The approach can be extended to the separation of different ferroic contributions in other multiferroic oxides.

We report the first observation of the nearby giant radio galaxy NGC 315 using a global VLBI array consisting of 22 radio antennas located across five continents, including high-sensitivity stations, at 22 GHz. Utilizing the extensive $(u,v)$-coverage provided by the array, coupled with the application of a recently developed super-resolution imaging technique based on the regularized maximum likelihood method, we were able to transversely resolve the NGC 315 jet at parsec scales for the first time. Previously known for its central ridge-brightened morphology at similar scales in former VLBI studies, the jet now clearly exhibits a limb-brightened structure. This finding suggests an inherent limb-brightening that was not observable before due to limited angular resolution. Considering that the jet is viewed at an angle of $\sim50^\circ$, the observed limb-brightening is challenging to reconcile with the magnetohydrodynamic models and simulations, which predict that the Doppler-boosted jet edges should dominate over the non-boosted central layer. The conventional jet model that proposes a fast spine and a slow sheath with uniform transverse emissivity may pertain to our observations. However, in this model, the relativistic spine would need to travel at speeds of $\Gamma\gtrsim6.0-12.9$ along the de-projected jet distance of (2.3-10.8) $\times 10^3$ gravitational radii from the black hole. We propose an alternative scenario that suggests higher emissivity at the jet boundary layer, resulting from more efficient particle acceleration or mass loading onto the jet edges, and consider prospects for future observations with even higher angular resolution., Comment: 25 pages, 12 figures, 1 table, accepted for publication in the Astrophysical Journal Letters

Disruptions in clinical trials may be due to external events like pandemics, warfare, and natural disasters. Resulting complications may lead to unforeseen intercurrent events (events that occur after treatment initiation and affect the interpretation of the clinical question of interest or the existence of the measurements associated with it). In Uschner et al. (2023), several example clinical trial disruptions are described: treatment effect drift, population shift, change of care, change of data collection, and change of availability of study medication. A complex randomized controlled trial (RCT) setting with (planned or unplanned) intercurrent events is then described, and randomization tests are presented as a means for non-parametric inference that is robust to violations of assumption typically made in clinical trials. While estimation methods like Targeted Learning (TL) are valid in such settings, we do not see where the authors make the case that one should be going for a randomization test in such disrupted RCTs. In this discussion, we comment on the appropriateness of TL and the accompanying TL Roadmap in the context of disrupted clinical trials. We highlight a few key articles related to the broad applicability of TL for RCTs and real-world data (RWD) analyses with intercurrent events. We begin by introducing TL and motivating its utility in Section 2, and then in Section 3 we provide a brief overview of the TL Roadmap. In Section 4 we recite the example clinical trial disruptions presented in Uschner et al. (2023), discussing considerations and solutions based on the principles of TL. We request in an authors' rejoinder a clear theoretical demonstration with specific examples in this setting that a randomization test is the only valid inferential method relative to one based on following the TL Roadmap., Comment: This article is an un-refereed, Authors Original Version

The First Large Absorption Survey in HI (FLASH) is a large-area radio survey for neutral hydrogen in the redshift range 0.41$, and appear to be a mixture of intervening and associated systems. The overall detection rate for HI absorption lines in the Pilot Surveys (0.3 to 0.5 lines per ASKAP field) is a factor of two below the expected value. There are several possible reasons for this, but one likely factor is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper will discuss the host galaxies of the HI absorption systems identified here., Comment: 46 pages, 25 figures, 10 tables. Submitted to PASA

We perform Bayesian model calibration of two-nucleon ($NN$) low-energy constants (LECs) appearing in an $NN$ interaction based on pionless effective field theory (EFT). The calibration is carried out for potentials constructed using naive dimensional analysis in $NN$ relative momenta ($p$) up to next-to-leading order [NLO, $O(p^2)$] and next-to-next-to-next-to-leading order [N3LO, $O(p^4)$]. We consider two classes of pionless EFT potential: one that acts in all partial waves and another that is dominated by $s$-wave physics. The two classes produce broadly similar results for calibrations to $NN$ data up to $E_{\rm lab}=5$ MeV. Our analysis accounts for the correlated uncertainties that arise from the truncation of the pionless EFT. We simultaneously estimate both the EFT LECs and the parameters that quantify the truncation error. This permits the first quantitative estimates of the pionless EFT breakdown scale, $\Lambda_b$: the 95% intervals are $\Lambda_b \in [50.11,63.03]$ MeV at NLO and $\Lambda_b \in [72.27, 88.54]$ MeV at N3LO. Invoking naive dimensional analysis for the $NN$ potential, therefore, does not lead to consistent results across orders in pionless EFT. This exemplifies the possible use of Bayesian tools to identify inconsistencies in a proposed EFT power counting., Comment: 26 pages, 12 figures, 1 table

We present a model to estimate the average primary masses, companion mass ranges, the inclination limit for recognizing a rotational variable, and the primary mass spreads for populations of binary stars. The model fits a population's binary mass function distribution and allows for a probability that some mass functions are incorrectly estimated. Using tests with synthetic data, we assess the model's sensitivity to each parameter, finding that we are most sensitive to the average primary mass and the minimum companion mass, with less sensitivity to the inclination limit and little to no sensitivity to the primary mass spread. We apply the model to five populations of binary spotted rotational variables identified in ASAS-SN, computing their binary mass functions using RV data from APOGEE. Their average primary mass estimates are consistent with our expectations based on their CMD locations ($\sim0.75 M_{\odot}$ for lower main sequence primaries and $\sim 0.9$--$1.2 M_{\odot}$ for RS CVn and sub-subgiants). Their companion mass range estimates allow companion masses down to $M_2/M_1\simeq0.1$, although the main sequence population may have a higher minimum mass fraction ($\sim0.4$). We see weak evidence of an inclination limit $\gtrsim50^{\circ}$ for the main sequence and sub-subgiant groups and no evidence of an inclination limit in the other groups. No groups show strong evidence for a preferred primary mass spread. We conclude by demonstrating that the approach will provide significantly better estimates of the primary mass and the minimum mass ratio and reasonable sensitivity to the inclination limit with 10 times as many systems., Comment: 10 pages, 7 figures, 4 tables, submitted to MNRAS

Effective public health messaging benefits from understanding antecedents to unstable attitudes that are more likely to be influenced. This work investigates the relationship between moral and emotional bases for attitudes towards COVID-19 vaccines and variance in stance. Evaluating nearly 1 million X users over a two month period, we find that emotional language in tweets about COVID-19 vaccines is largely associated with more variation in stance of the posting user, except anger and surprise. The strength of COVID-19 vaccine attitudes associated with moral values varies across foundations. Most notably, liberty is consistently used by users with no or less variation in stance, while fairness and sanctity are used by users with more variation. Our work has implications for designing constructive pro-vaccine messaging and identifying receptive audiences., Comment: Accepted to SBP-Brims 2024

The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they violate these assumptions and entail biases such as residual confounding, selection bias, and misalignment between treatment and measurement times. Although researchers are aware of these pitfalls, they continue to occur because anticipating and addressing them in the context of a specific study can be challenging without a large, often unwieldy, interdisciplinary team with extensive expertise. To address this expertise gap, we explore the use of large language models (LLMs) as co-pilot tools to assist researchers in identifying study design flaws that undermine the validity of causal inferences. We propose a conceptual framework for LLMs as causal co-pilots that encode domain knowledge across various fields, engaging with researchers in natural language interactions to provide contextualized assistance in study design. We provide illustrative examples of how LLMs can function as causal co-pilots, propose a structured framework for their grounding in existing causal inference frameworks, and highlight the unique challenges and opportunities in adapting LLMs for reliable use in epidemiological research.

Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional review of medical images by clinicians remains subjective, time-consuming, and prone to human error. With the recent availability of medical imaging data, quantification have become important goals in the field. Radiomics, a methodology aimed at extracting quantitative information from imaging data, has emerged as a promising approach to uncover hidden biological information and support decision-making in clinical practice. This paper presents a review of the radiomic pipeline from the clinical neuroimaging perspective, providing a detailed overview of each step with practical advice. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis. Although radiomics shows great potential for increasing diagnostic precision and improving treatment quality in neurology, several limitations hinder its clinical implementation. Addressing these challenges requires collaborative efforts, advancements in image harmonization methods, and the establishment of reproducible and standardized pipelines with transparent reporting. By overcoming these obstacles, radiomics can significantly impact clinical neurology and enhance patient care.

We report on frequency tripling of high energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab Yb:YAG laser system Bivoj/DiPOLE, using a type-I phase matched LBO crystal for second harmonic generation and type-II phase matched LBO crystal for sum frequency generation. We achieved conversion to 343 nm with stable energy of 50 J at repetition rate of 10 Hz and conversion efficiency of 53%.

Electronic spectroscopy of zero-dimensional (0D) quantum systems, including point defects in solids, atomic states, and small molecules, is a critical tool for developing a fundamental understanding of these systems, with applications ranging from solid-state and molecular materials development to emerging technologies rooted in quantum information science. Toward this end, scanning tunneling spectroscopy (STS) has demonstrated atomic-scale sensitivity, but is not easily scalable for applications, whereas device-based approaches rely on embedding these systems within a solid-state tunnel junction (TJ) and are not generally applicable. Here we demonstrate an all-electrical readout mechanism for these quasi-0D states that is modular and general, dramatically expanding the phase space of accessible quantum systems and providing an approach that is amenable to scaling and integration with other solid-state quantum technologies. Our approach relies on the creation of high-quality tunnel junctions via the mechanical exfoliation and stacking of multi-layer graphene (MLG) and hexagonal boron nitride (hBN) to encapsulate the target quantum system (QS) in an MLG/hBN/QS/hBN/MLG heterostructure. This structure allows for electronic spectroscopy and readout of candidate quantum systems through a combination of Coulomb and spin-blockade, providing access to entire classes of quantum systems that have previously only been accessible via optical spectroscopy or magnetic resonance measurements of large ensembles, if at all.

We have developed a new all-optical method to directly measure the pressure-volume-temperature (PVT) equation of state (EOS) of fluids and transparent solids in the diamond anvil high pressure cell by measuring the volume of the sample chamber. Our method combines confocal microscopy and white light interference with a new analysis method which exploits the mutual dependence of sample density and refractive index: Experimentally, the refractive index determines the measured sample chamber thickness (and therefore the measured sample volume/density), yet the sample density is by far the dominant factor in determining the variation in refractive index with pressure. Our analysis method allows us to obtain a set of values for the density and refractive index which are mutually consistent, and agree with the experimental data within error. We have conducted proof-of-concept experiments on a variety of samples (H$_{2}$O, CH$_{4}$, C$_{2}$H$_{6}$, C$_{3}$H$_{8}$, KCl and NaCl) at ambient temperature, and at high temperatures up to just above 500 K. Our proof-of-concept data demonstrate that our method is able to reproduce known fluid and solid EOS within error. Furthermore, we demonstrate that our method allows us to directly and routinely measure the PVT EOS of simple fluids at GPa pressures up to, at least, 514 K (the highest temperature reached in our study). A reasonable estimation of the known sources of error in our volume determinations indicates that the error is currently $\pm$ 2.7% at high temperature, and that it is feasible to reduce it to ca. $\pm$ 1% in future work., Comment: Accepted for publication in Physics of Fluids

Wide-field magnetic microscopy using nitrogen-vacancy (NV) centers in diamond can yield high-quality magnetic images of DC and AC magnetic fields. The unique combination of micron-scale spatial resolution of scalar or vector fields at room temperature and parallel camera readout make this an appealing technique for applications in biology, geology, condensed-matter physics, and electronics. However, while NV magnetic microscopy has achieved great success in these areas, historically the accessible frequency range has been limited. In this paper, we overcome this limitation by implementing the recently developed technique of quantum frequency mixing. With this approach, we generate wide-field magnetic images of test structures driven by alternating currents up to 70 MHz, well outside the reach of DC and Rabi magnetometry methods. With further improvements, this approach could find utility in hyperspectral imaging for electronics power spectrum analysis, electronics diagnostics and troubleshooting, and quantum computing hardware validation., Comment: 8 pages main text, 3 pages supplement

Cerebral aneurysm rupture, leading to subarachnoid hemorrhage with a high mortality rate, disproportionately affects younger populations, resulting in a significant loss of productive life years. A significant proportion of these deaths is due to aneurysmal re-bleeding within the first three days following the initial bleed, prior to treatment. While early aneurysm treatment is recommended, there is no consensus on the ideal timing, and emergency treatment offers only an incremental benefit at a significant cost. Although various multivariable prediction models have been proposed to provide personalized risk assessments, no validated patient-specific predictor is available to rationalize emergency treatment. Furthermore, no model has yet incorporated emerging computational biomechanics-based biomarkers such as wall tension. In this paper, we proposed and validated an efficient semi-automatic pipeline to compute patient-specific cerebral aneurysm wall tension as a potential biomarker for the likelihood of re-bleeding. Our pipeline uses the patient's computed tomography angiography (CTA) image obtained at the time of subarachnoid hemorrhage diagnosis to create a patient-specific biomechanical model of the cerebral aneurysm using the finite element method. A distinctive feature of our approach is the straightforward model creation and wall tension computation using shell finite elements, without requiring patient-specific material properties or aneurysm wall thickness. Our non-invasive, patient-specific method for cerebral aneurysm wall tension can potentially provide individualized risk prediction and enhance clinical decision-making.

We present an atmospheric retrieval analysis on a set of young, cloudy, red L-dwarfs -- CWISER J124332.12+600126.2 and WISEP J004701.06+680352.1 -- using the \textit{Brewster} retrieval framework. We also present the first elemental abundance measurements of the young K-dwarf (K0) host star, BD+60 1417 using high resolution~(R = 50,000) spectra taken with PEPSI/LBT. In the complex cloudy L-dwarf regime the emergence of condensate cloud species complicates retrieval analysis when only near-infrared data is available. We find that for both L dwarfs in this work, despite testing three different thermal profile parameterizations we are unable to constrain reliable abundance measurements and thus the C/O ratio. While we can not conclude what the abundances are, we can conclude that the data strongly favor a cloud model over a cloudless model. We note that the difficulty in retrieval constraints persists regardless of the signal to noise of the data examined (S/N $\sim$ 10 for CWISER J124332.12+600126.2 and~40 for WISEP J004701.06+680352.1). The results presented in this work provide valuable lessons about retrieving young, low-surface gravity, cloudy L-dwarfs. This work provides continued evidence of missing information in models and the crucial need for JWST to guide and inform retrieval analysis in this regime., Comment: Accepted to ApJ

Cytoskeletal networks have a self-healing property where networks can repair defects to maintain structural integrity. However, both the mechanisms and dynamics of healing remain largely unknown. Here we report an unexplored healing mechanism in microtubule-motor networks by active crosslinking. We directly generate network cracks using a light-controlled microtubule-motor system, and observe that the cracks can self-heal. Combining theory and experiment, we find that the networks must overcome internal elastic resistance in order to heal cracks, giving rise to a bifurcation of dynamics dependent on the initial opening angle of the crack: the crack heals below a critical angle and opens up at larger angles. Simulation of a continuum model reproduces the bifurcation dynamics, revealing the importance of a boundary layer where free motors and microtubules can actively crosslink and thereby heal the crack. We also formulate a simple elastic-rod model that can qualitatively predict the critical angle, which is found to be tunable by two dimensionless geometric parameters, the ratio of the boundary layer and network width, and the aspect ratio of the network. Our results provide a new framework for understanding healing in cytoskeletal networks and designing self-healable biomaterials.

Though the effects of metal dopants on the electrostructural transition of rutile VO$_2$ have been studied for many decades, there is still no consensus explanation for the observed trends. A major challenge has been to separate the impact of a dopant's size from other factors such as its electronic configuration, which stems from the difficulty in directly probing the local bonding environment around a dopant atom. This work addresses the special case of larger dopant ions by combining X-ray total scattering experiments on V$_{0.83}$Mo$_{0.17}$O$_2$ and V$_{0.89}$Nb$_{0.11}$O$_2$ single crystals with multiple Monte Carlo method models to simulate local size effects in the high-temperature tetragonal phase (R). We find that sufficiently long apical metal-oxygen bonds (M$-$O$_{ap}$) induce a strain field in the neighboring chains that locally resembles the metal-metal dimer formation present in the low-temperature distorted structure of VO$_2$ (M1). The dimer mode in the M1 structure is antisymmetric along M$-$O$_{ap}$, however, while the strain-induced pseudodimer motif is symmetric. The implied direct competition between motifs is verified experimentally. This finding provides a new mechanistic parameter toward understanding the phase transition. More generally, the work highlights how local strain fields around dopants can lead to complex distortions that are ordinarily attributed to electronic origins., Comment: Article: 19 pages, 9 figures; Appendix: 4 pages, 2 figures, 3 tables

We compute explicitly the low-energy spectral weight (LESW) in the lower Hubbard band using the orbital Hatsugai-Kohmoto (OHK) model. We show that the dynamical mixing enters the LESW with a universal slope independent of the number of orbitals. This independence on orbital number corroborates the rapid convergence of the OHK model to the Hubbard model. As a result, OHK is an effective simulator of Hubbard physics.

We present herein the derivation of a lubrication-mediated (LM) quasi-potential model for droplet rebounds off deep liquid baths, assuming the presence of a persistent dynamic air layer which acts as a lubricating pressure transfer. We then present numerical simulations of the LM model for axisymmetric rebounds of solid spheres and compare quantitatively to current results in the literature, including experimental data in the low speed impact regime. In this regime the LM model has the advantage of being far more computationally tractable than DNS and is also able to provide detailed behaviour within the micro-metric thin lubrication region. The LM system has an interesting mathematical structure, with the lubrication layer providing a free boundary elliptic problem mediating the drop and bath free-boundary evolutionary equations.

In order for a droplet to rebound rather than coalesce from a liquid surface, there must be a layer of air persisting throughout the impact. This air layer acts as a lubricant to the system and permits a pressure transfer between the two liquid bodies. Through considering the bath, air, and drop regions of fluid, we introduce a fully coupled reduced dynamic model of two-dimensional droplets rebounding off liquid baths, which incorporates a evolving lubricating air layer. Numerical solutions of the lubrication-mediated model are compared to widely used direct numerical simulation (DNS) of the Navier-Stokes equations. The reduced model captures rebounding dynamics well in the regime where it is most relevant: for low speed impacts of small droplets, where capillary forces are important. Numerically, the reduced model is efficient, allowing for the computation of multiple rebounds and of long time dynamics of droplets rebounding on a vibrating bath. Furthermore, the lubrication-mediated model is able to provide detailed information within the air layer such as pressure and lubrication-layer geometry, which is often omitted from reduced models, and typically obtained by higher resolution but higher cost DNS methods.

The origin of strong sodium absorption, which has been observed for a few nearby Type Ia supernovae (SNe Ia), remains elusive. Here we analyse two high-signal-to-noise, intermediate-resolution VLT/X-shooter spectra at epochs $+$18 and $+$27 days past peak brightness of the strongly lensed and multiply-imaged Type Ia SN 2016geu which exploded at a redshift of $z = 0.4$. We show that SN 2016geu exhibits very strong, multiple Na I and Ca II absorption lines with a large total Na I D restframe equivalent width of 5.2 $\pm$ 0.2 A, among the highest ever detected for a SN Ia and similar to only a handful of nearby SNe Ia with extraordinary large Na I D EWs. The absorption system is time-invariant and extends over a large velocity span $\sim$ 250 km s$^{-1}$. The majority of the absorption is blueshifted relative to the strongest component, while there are both blueshifted and redshifted components relative to the systemic redshift of the galaxy. The column density ratios and widths of the absorption lines indicate that the absorption likely arises from a combination of interstellar dusty molecular clouds and circumgalactic in- and outflowing material, rather than circumstellar matter around the SN., Comment: 12 pages, 6 figures, accepted for publication in ApJ

The front-end electronics of silicon detectors are typically designed to ensure optimal noise performance for the expected input charge. A combination of preamplifiers and shaper circuits result in a nontrivial response of the front-end to injected charge, and the magnitude of the response may be sizeable in readout windows subsequent to that in which the charge was initially injected. The modulation of the discriminator threshold due to the superposition of the front-end response across multiple readout windows is coined "threshold bounce". In this paper, we report a measurement of threshold bounce using silicon modules built for the Phase-II Upgrade of the ATLAS detector at the Large Hadron Collider. These modules utilize ATLAS Binary Chips for their hit readout. The measurement was performed using a micro-focused 15 keV photon beam at the Diamond Light Source synchrotron. The effect of the choice of photon flux and discriminator threshold on the magnitude of the threshold bounce is studied. A Monte Carlo simulation which accounts for the front-end behaviour of the silicon modules is developed, and its predicted hit efficiency is found to be in good agreement with the measured hit efficiency., Comment: 22 pages; accepted to JINST

Multimode optical fibres are hair-thin strands of glass that efficiently transport light. They promise next-generation medical endoscopes that provide unprecedented sub-cellular image resolution deep inside the body. However, confining light to such fibres means that images are inherently scrambled in transit. Conventionally, this scrambling has been compensated by pre-calibrating how a specific fibre scrambles light and solving a stationary linear matrix equation that represents a physical model of the fibre. However, as the technology develops towards real-world deployment, the unscrambling process must account for dynamic changes in the matrix representing the fibre's effect on light, due to factors such as movement and temperature shifts, and non-linearities resulting from the inaccessibility of the fibre tip when inside the body. Such complex, dynamic and nonlinear behaviour is well-suited to approximation by neural networks, but most leading image reconstruction networks rely on convolutional layers, which assume strong correlations between adjacent pixels, a strong inductive bias that is inappropriate for fibre matrices which may be expressed in a range of arbitrary coordinate representations with long-range correlations. We introduce a new concept that uses self-attention layers to dynamically transform the coordinate representations of varying fibre matrices to a basis that admits compact, low-dimensional representations suitable for further processing. We demonstrate the effectiveness of this approach on diverse fibre matrix datasets. We show our models significantly improve the sparsity of fibre bases in their transformed bases with a participation ratio, p, as a measure of sparsity, of between 0.01 and 0.11. Further, we show that these transformed representations admit reconstruction of the original matrices with < 10% reconstruction error, demonstrating the invertibility.

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction. Physical DBT data is acquired with a Siemens Mammomat scanner of a structured breast phantom with ICA inserts. Results are shown for both directional TV minimization and filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the ICA objects., Comment: Proceedings paper for accepted contribution to the 8th International Conference on Image Formation in X-Ray Computed Tomography (https://www.ct-meeting.org)

Bots are automated social media users that can be used to amplify (mis)information and sow harmful discourse. In order to effectively influence users, bots can be generated to reproduce human user behavior. Indeed, people tend to trust information coming from users with profiles that fit roles they expect to exist, such as users with gender role stereotypes. In this work, we examine differences in the types of identities in profiles of human and bot accounts with a focus on combinations of identities that represent gender role stereotypes. We find that some types of identities differentiate between human and bot profiles, confirming this approach can be a useful in distinguishing between human and bot accounts on social media. However, contrary to our expectations, we reveal that gender bias is expressed more in human accounts than bots overall. Despite having less gender bias overall, we provide examples of identities with strong associations with gender identities in bot profiles, such as those related to technology, finance, sports, and horoscopes. Finally, we discuss implications for designing constructive social media bot detection training materials., Comment: 19 pages, 3 figures

Binary code similarity detection is an important problem with applications in areas such as malware analysis, vulnerability research and license violation detection. This paper proposes a novel graph neural network architecture combined with a novel graph data representation called call graphlets. A call graphlet encodes the neighborhood around each function in a binary executable, capturing the local and global context through a series of statistical features. A specialized graph neural network model operates on this graph representation, learning to map it to a feature vector that encodes semantic binary code similarities using deep-metric learning. The proposed approach is evaluated across five distinct datasets covering different architectures, compiler tool chains, and optimization levels. Experimental results show that the combination of call graphlets and the novel graph neural network architecture achieves comparable or state-of-the-art performance compared to baseline techniques across cross-architecture, mono-architecture and zero shot tasks. In addition, our proposed approach also performs well when evaluated against an out-of-domain function inlining task. The work provides a general and effective graph neural network-based solution for conducting binary code similarity detection., Comment: 13 pages, Under-Review