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Generative AI holds the promise of enabling a range of sought-after capabilities and revolutionizing workflows in various consumer and enterprise verticals. However, putting a model in production involves much more than just generating an output. It involves ensuring the model is reliable, safe, performant and also adheres to the policy of operation in a particular domain. Guardrails as a necessity for models has evolved around the need to enforce appropriate behavior of models, especially when they are in production. In this paper, we use education as a use case, given its stringent requirements of the appropriateness of content in the domain, to demonstrate how a guardrail model can be trained and deployed in production. Specifically, we describe our experience in building a production-grade guardrail model for a K-12 educational platform. We begin by formulating the requirements for deployment to this sensitive domain. We then describe the training and benchmarking of our domain-specific guardrail model, which outperforms competing open- and closed- instruction-tuned models of similar and larger size, on proprietary education-related benchmarks and public benchmarks related to general aspects of safety. Finally, we detail the choices we made on architecture and the optimizations for deploying this service in production; these range across the stack from the hardware infrastructure to the serving layer to language model inference optimizations. We hope this paper will be instructive to other practitioners looking to create production-grade domain-specific services based on generative AI and large language models.

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

We perform a thorough analysis of de Sitter vacua in O(d,d) invariant cosmologies. Starting with a homogeneous and isotropic framework we examine conditions for the existence of such vacua, non-perturbative in \alpha' in both the string frame and the Einstein frame. We elucidate the nature of the instability in the string frame vacuum. For the Einstein frame, we demonstrate that the de Sitter vacuum cannot be eternal. We then extend our analysis to include Bianchi I universes where the O(d,d) symmetry includes scale factor exchange as well as scale factor duality. We show how the theory can be extended to the anisotropic case so that it admits de Sitter vacua, noting the crucial role played by the O(d,d) symmetry in satisfying any additional constraints., Comment: Paper updated with references added, most notably that of Rost and Nunez which includes details of additional dS solutions

The navigation of complex labyrinths with tens of rooms under visual partially observable state is typically addressed using recurrent deep reinforcement learning architectures. In this work, we show that navigation can be achieved through the emergent evolution of a simple Braitentberg-style heuristic that structures the interaction between agent and labyrinth, i.e. complex behaviour from simple heuristics. To do so, the approach of tangled program graphs is assumed in which programs cooperatively coevolve to develop a modular indexing scheme that only employs 0.8\% of the state space. We attribute this simplicity to several biases implicit in the representation, such as the use of pixel indexing as opposed to deploying a convolutional kernel or image processing operators.

Inspired by the method of smoothed asymptotics developed by Terence Tao, we introduce a new ultra-violet regularisation scheme for loop integrals in quantum field theory which we call $\eta$ regularisation. This allows us to reveal a surprising connection between the elimination of divergences in divergent series of powers and the preservation of gauge invariance in the regularisation of loop integrals in quantum field theory. In particular, we note that a method for regularising the series of natural numbers so that it converges to minus one twelfth inspires a regularisation scheme for non-abelian gauge theories coupled to Dirac fermions that preserves the Ward identity for the vacuum polarisation tensor. We also comment on a possible connection to Schwinger proper time integrals., Comment: 26 pages

Fabrication of superconducting nanomechanical resonators for quantum research, detectors and devices traditionally relies on a lithographic process, resulting in oscillators with sharp edges and a suspended length limited to a few 100 micrometres. We report a low-investment top-down approach to fabricating NbTi nanowire resonators with suspended lengths up to several millimetres and diameters down to 100 nanometres. The nanowires possess high critical currents and fields, making them a natural choice for magnetomotive actuation and sensing. This fabrication technique is independent of the substrate material, dimensions and layout and can readily be adapted to fabricate nanowire resonators from any metal or alloy with suitable ductility and yield strength. Our work thus opens access to a new class of nanomechanical devices with applications including microscopic and mesoscopic investigations of quantum fluids, detecting dark matter and fundamental materials research in one-dimensional superconductors in vacuum., Comment: 19 pages, 4 figures

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Ultracold gases of highly magnetic lanthanide atoms have enabled the realisation of dipolar quantum droplets and supersolids. However, future studies could be limited by the achievable atom numbers and hindered by high three-body loss rates. Here we study density-dependent atom loss in an ultracold gas of ${}^{166}$Er for magnetic fields below $4\ \textrm{G}$, identifying six previously unknown features which display both a strong temperature dependence and also sensitivity to the polarisation and intensity of the light used to optically trap the atoms. This detailed knowledge of the loss landscape allows us to optimise the production of dipolar BECs with more than $2 \times 10^5$ atoms and points towards optimal strategies for the study of large-atom-number dipolar gases in the droplet and supersolid regimes., Comment: 6 pages, 4 figures

Click chemistry, which refers to chemical reactions that are fast, selective, and with high product yields, has become a powerful approach in organic synthesis and chemical biology. Due to the cytotoxicity of the transition metals employed in click chemistry reactions, a search for novel metal-free alternatives continues. Herein we demonstrate that an optical cavity can be utilized as a metal-free alternative in click chemistry cycloaddition reaction between cyanoacetylene and formylazide using the quantum electrodynamics coupled cluster (QED-CC) method. We show that by changing the molecular orientation with respect to the polarization of the cavity mode(s), the reaction can be selectively catalyzed to form a major 1,4-disubstituted or 1,5-disubstituted product. This work highlights that a cavity has the same effect on the investigated cycloaddition as the transition metal catalysts traditionally employed in click chemistry reactions. We expect our findings to further stimulate research in cavity-assisted click chemistry reactions.

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Boyle's 1662 observation that the volume of a gas is, at constant temperature, inversely proportional to pressure, offered a prototypical example of how an equation of state (EoS) can succinctly capture key properties of a many-particle system. Such relations are now cornerstones of equilibrium thermodynamics. Extending thermodynamic concepts to far-from-equilibrium systems is of great interest in various contexts including glasses, active matter, and turbulence, but is in general an open problem. Here, using a homogeneous ultracold atomic Bose gas, we experimentally construct an EoS for a turbulent cascade of matter waves. Under continuous forcing at a large length scale and dissipation at a small one, the gas exhibits a non-thermal, but stationary state, which is characterised by a power-law momentum distribution sustained by a scale-invariant momentum-space energy flux. We establish the amplitude of the momentum distribution and the underlying energy flux as equilibrium-like state variables, related by an EoS that does not depend on the details of the energy injection or dissipation, or the history of the system. Moreover, we show that the equations of state for a wide range of interaction strengths and gas densities can be empirically scaled onto each other. This results in a universal dimensionless EoS that sets benchmarks for the theory and should also be relevant for other turbulent systems., Comment: 6 pages, 5 figures

We provide an overview of the effects of interactions in Bose-condensed gases. We focus on phenomena that have been explored in ultracold atom experiments, covering both tuneable contact interactions and dipolar interactions. Our discussion includes: modifications to the ground state and excitation spectrum, critical behaviour near the Bose--Einstein condensation temperature, the unitary regime where the interactions are as strong as allowed by quantum mechanics, quantum droplets in mixtures, and supersolids in dipolar gases., Comment: 9 pages, 8 figures, invited contribution to Encyclopedia of Condensed Matter Physics, 2nd edition

Lanthanide (Ln) based systems in the ZrSiS-type nodal-line semimetals have been subjects of research investigations as grounds for studying the interplay of topology with possible magnetic ordering and electronic correlations that may originate from the presence of Ln 4f electrons. In this study, we carried out a thorough study of a LnSbTe system - NdSbTe - by using angle-resolved photoemission spectroscopy along with first-principles calculations and thermodynamic measurements. We experimentally detect the presence of multiple gapless nodal-line states, which is well supported by first-principles calculations. A dispersive and an almost non-dispersive nodal-line exist along the bulk X-R direction. Another nodal-line is present well below the Fermi level across the G- M direction, which is formed by bands with high Fermi velocity that seem to be sensitive to light polarization. Our study provides an insight into the electronic structure of a new LnSbTe material system that will aid towards understanding the connection of Ln elements with topological electronic structure in these systems., Comment: 34 pages, 12 figures; Supplemental Material included

We implement a trust region method on the GPU for nonlinear least squares curve fitting problems using a new deep learning Python library called JAX. Our open source package, JAXFit, works for both unconstrained and constrained curve fitting problems and allows the fit functions to be defined in Python alone -- without any specialized knowledge of either the GPU or CUDA programming. Since JAXFit runs on the GPU, it is much faster than CPU based libraries and even other GPU based libraries, despite being very easy to use. Additionally, due to JAX's deep learning foundations, the Jacobian in JAXFit's trust region algorithm is calculated with automatic differentiation, rather than than using derivative approximations or requiring the user to define the fit function's partial derivatives.

Achieving control over chemical reaction's rate and stereoselectivity realizes one of the Holy Grails in chemistry that can revolutionize chemical and pharmaceutical industries. Strong light-matter interaction in optical or nanoplasmonic cavities might provide the knob to reach such control. In this work, we demonstrate the catalytic and selectivity control of an optical cavity for two selected Diels-Alder cycloaddition reactions using the quantum electrodynamics coupled cluster (QED-CC) method. Herein, we find that by changing the molecular orientation with respect to the polarization of the cavity mode the reactions can be significantly inhibited or selectively enhanced to produce major endo or exo products on demand. This work highlights the potential of utilizing quantum vacuum fluctuations of an optical cavity to modulate the rate of Diels-Alder cycloaddition reactions and to achieve stereoselectivity in a practical and non-intrusive way. We expect that the present findings will be applicable to a larger set of relevant click chemical reactions under strong light-matter coupling conditions.

The rare-earth monopnictide (REM) family, which hosts magnetic ground states with extreme magnetoresistance, has established itself as a fruitful playground for the discovery of interesting topological phases. Here, by using high-resolution angle-resolved photoemission spectroscopy complemented by first-principles density functional-theory based modeling, we examine the evolution of the electronic structure of the candidate REM Dirac semimetal NdSb across the magnetic transition. A complex angel-wing-like band structure near the zone center and three arc-like features at the zone corner have been observed. This dramatic reconstruction of the itinerant bands around the zone center is shown to be driven by the magnetic transition: Specifically,, the Nd 5d electron band backfolds at the Gamma point and hybridizes with the Sb 5p hole bands in the antiferromagnetic phase. Our study indicates that antiferromagnetism plays an intricate role in the electronic structure of the REM family., Comment: 13 pages, 13 figures; Supplemental Materials included

Weyl semimetal (WSM), a novel state of quantum matter, hosts Weyl fermions as emergent quasiparticles resulting from the breaking of either inversion or time-reversal symmetry. Magnetic WSMs that arise from broken time-reversal symmetry provide an exceptional platform to understand the interplay between magnetic order and Weyl physics, but few WSMs have been realized. Here, we identify CeAlSi as a new non-centrosymmetric magnetic WSM via angle-resolved photoemission spectroscopy (ARPES) and first-principles, density-functional theory based calculations. Our surface-sensitive vacuum ultraviolet ARPES data confirms the presence of surface Fermi arcs as, the smoking gun evidence for the existence of the Weyl semimetallic state in CeAlSi. We also observe bulk Weyl cones in CeAlSi using bulk-sensitive soft-X-ray ARPES measurements. In addition, Ce 4f at bands are found near the Fermi level, indicating that CeAlSi is a unique platform for investigating exotic quantum phenomena resulting from the interaction of topology, magnetism and electronic correlations., Comment: 12 pages, 4 figures; supplementary information included

A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with experimental laser beams, generated using a spatial light modulator, are used to train and evaluate the NN. After training on the simulated dataset the NN achieves beam parameter rootmean-square-errors (RMSEs) of less than 3.4% on the experimental dataset. Subsequent training on the experimental dataset causes the RMSEs to fall below 1.1%. The NN method can be used as a stand-alone measurement of the beam parameters or can compliment other beam profiling methods by providing an accurate region-of-interest., Comment: 6 pages, 4 figures

The two-fluid model is fundamental for the description of superfluidity. In the nearly-incompressible-liquid regime, it successfully describes first and second sound, corresponding, respectively, to density and entropy waves, in both liquid helium and unitary Fermi gases. Here, we study the two sounds in the opposite regime of a highly compressible fluid, using an ultracold $^{39}$K Bose gas in a three-dimensional box trap. We excite the longest-wavelength mode of our homogeneous gas, and observe two distinct resonant oscillations below the critical temperature, of which only one persists above it. In a microscopic mode-structure analysis, we find agreement with the hydrodynamic theory, where first and second sound involve density oscillations dominated by, respectively, thermal and condensed atoms. Varying the interaction strength, we explore the crossover from hydrodynamic to collisionless behavior in a normal gas., Comment: Main text (5 pages, 4 figures), Supplemental Material (14 pages, 8 figures)

Homogeneous quantum gases open up new possibilities for studying many-body phenomena and have now been realised for a variety of systems. For gases with short-range interactions the way to make the cloud homogeneous is, predictably, to trap it in an ideal (homogeneous) box potential. We show that creating a close to homogeneous dipolar gas in the roton regime, when long-range interactions are important, actually requires trapping particles in soft-walled (inhomogeneous) box-like potentials. In particular, we numerically explore a dipolar gas confined in a pancake trap which is harmonic along the polarisation axis and a cylindrically symmetric power-law potential $r^p$ radially. We find that intermediate $p$'s maximise the proportion of the sample that can be brought close to the critical density required to reach the roton regime, whereas higher $p$'s trigger density oscillations near the wall even when the bulk of the system is not in the roton regime. We characterise how the optimum density distribution depends on the shape of the trapping potential and find it is controlled by the trap wall steepness., Comment: 7 pages, 3 figures

There is growing interest in the role of sentiment in economic decision-making. However, most research on the subject has focused on positive and negative valence. Conviction Narrative Theory (CNT) places Approach and Avoidance sentiment (that which drives action) at the heart of real-world decision-making, and argues that it better captures emotion in financial markets. This research, bringing together psychology and machine learning, introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning. It does this by comparing word-lists, previously constructed to capture these concepts in text data, across a large range of semantic features. The results demonstrate that Avoidance in particular is well defined as a separate type of emotion, which is evaluative/cognitive and action-orientated in nature. Refining the Avoidance word-list according to these features improves macroeconomic models, suggesting that they capture the essence of Avoidance and that it plays a crucial role in driving real-world economic decision-making.

The power spectrum of the nonlinearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D "slabs" of the mass distribution averaged over $\approx100$~$h^{-1}$Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo (HMC) method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the \textit{Quijote} $N$-body suite at $z=0.5$ and at a transverse resolution of 2~$h^{-1}$Mpc, the method recovers $\sim 30$ times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of $0.01$~$h^3$Mpc$^{-3}$, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4~$h^{-1}$Mpc or smaller., Comment: 13 pages, 11 figures. Matches published version

We provide a suite of public open-source spectral data-reduction software to rapidly obtain scientific products from all forms of long-slit-like spectroscopic observations. Automated SpectroPhotometric REDuction (ASPIRED) is a Python-based spectral data-reduction toolkit. It is designed to be a general toolkit with high flexibility for users to refine and optimize their data-reduction routines for the individual characteristics of their instruments. The default configuration is suitable for low-resolution long-slit spectrometers and provides a quick-look quality output. However, for repeatable science-ready reduced spectral data, some moderate one-time effort is necessary to modify the configuration. Fine-tuning and additional (pre)processing may be required to extend the reduction to systems with more complex setups. It is important to emphasize that although only a few parameters need updating, ensuring their correctness and suitability for generalization to the instrument can take time due to factors such as instrument stability. We compare some example spectra reduced with ASPIRED to published data processed with iraf-based and STARLINK-based pipelines, and find no loss in the quality of the final product. The Python-based, iraf-free ASPIRED can significantly ease the effort of an astronomer in constructing their own data-reduction workflow, enabling simpler solutions to data-reduction automation. This availability of near-real-time science-ready data will allow adaptive observing strategies, particularly important in, but not limited to, time-domain astronomy., Comment: 23 pages, 10 figures, 1 table, 1 appendix

We derive expressions for the survey-window convolved galaxy power spectrum in real space for a full sky and deep redshift survey, but taking into account the geometrical lightcone effect. We investigate the impact of using the standard mean redshift approximation as a function of survey depth, and show that this assumption can lead to both an overall amplitude suppression and scale-dependent error when compared to the `true' spectrum. However, we also show that by using a carefully chosen `effective fixed-time', one can find a range of scales where the approximation to the full model is highly accurate, but only on a more restricted set of scales. We validate the theory by constructing dark matter and galaxy lightcone mock surveys from a large $N$-body simulation with a high cadence of snapshots. We do this by solving the light cone equation exactly for every particle, where the particle worldlines are obtained in a piecewise fashion with cubic interpolation between neighbouring snapshots. We find excellent agreement between our measurements and the theory ($\sim \pm 5\%$) over scales $(0.004 h{\rm Mpc}^{-1} \leq k \leq 0.54 h {\rm Mpc}^{-1})$ and for a variety of magnitude limits. Finally, we look to see how accurately we can measure the turnover scale of the galaxy power spectrum $k_0$. Using the lightcone mocks we show that one can detect the turnover scale with a probability $P \geq 95\%$ in an all-sky catalogue limited to an apparent magnitude $m_{\rm lim}\sim 21$. We also show that the detection significance would remain high for surveys with $m_{\rm lim}\sim22$ and $20\%$ sky coverage., Comment: 31 + 11 pages, 12 figures. Closely matches version published in JCAP -- 2022JCAP...08..019P

This exploratory study was commissioned by the Education Endowment Foundation (the EEF). The report presents findings on the challenges facing schools in disadvantaged circumstances (characterised by the EEF as 'Priority Schools'). Priority Schools, which include both primary and secondary schools, have a high proportion of pupils eligible for free school meals (FSM) and have been identified as being most in need of support to raise overall levels of attainment while also closing the gap in attainment between disadvantaged pupils and their peers. The study also explored schools' 'readiness' to engage with external support on evidence-informed approaches to school improvement, including the conditions that are necessary to support this. This report identifies and explores two key issues: (1) The challenges faced by Priority Schools in raising attainment (and any variations according to phase, school type or Ofsted category); and (2) Priority Schools' 'readiness' to engage with evidence-informed school improvement support (including the conditions enabling and hindering engagement). A key theme emerging through the research is that of agency. System leaders agreed with headteachers that the external challenges mentioned placed their schools under considerable pressure; and that it was difficult for school leaders to improve their schools when facing such obstacles. However, they also believed that headteachers sometimes failed to see themselves as critical change agents -- demonstrating a tendency to focus on the enormity of the external challenge, rather than on the possibilities to make specific, targeted, changes to those elements of a child's school day, over which they could exercise control.

With intricate lattice structures, kagome materials are an excellent platform to study various fascinating topological quantum states. In particular, kagome materials, revealing large responses to external stimuli such as pressure or magnetic field, are subject to special investigation. Here, we study the kagome-net HoMn$_6$Sn$_6$ magnet that undergoes paramagnetic to ferrimagnetic transition (below 376 K) and reveals spin-reorientation transition below 200 K. In this compound, we observe the topological Hall effect and substantial contribution of anomalous Hall effect above 100 K. We unveil the pressure effects on magnetic ordering at a low magnetic field from the pressure tunable magnetization measurement. By utilizing high-resolution angle-resolved photoemission spectroscopy, Dirac-like dispersion at the high-symmetry point K is revealed in the vicinity of the Fermi level, which is well supported by the first-principles calculations, suggesting a possible Chern-gapped Dirac cone in this compound. Our investigation will pave the way to understand the magneto-transport and electronic properties of various rare-earth-based kagome magnets.

Advances in light shaping for optical trapping of neutral particles have led to the development of box traps for ultracold atoms and molecules. These traps have allowed the creation of homogeneous quantum gases and opened new possibilities for studies of many-body physics. They simplify the interpretation of experimental results, provide more direct connections with theory, and in some cases allow qualitatively new, hitherto impossible experiments. Here we review progress in this emerging field., Comment: 11 pages, 6 figures. Comments welcome

We explore an alternative method to the usual shear correlation function approach for the estimation of aperture mass statistics in weak lensing survey data. Our approach builds on the direct estimator method. In this paper, we extend our analysis to statistics of arbitrary order and to the multiscale aperture mass statistics. We show that there always exists a linear order algorithm to retrieve any of these generalised aperture mass statistics from shape catalogs when the direct estimator approach is adopted. We validate our approach through application to a large number of Gaussian mock lensing surveys where the true answer is known and we do this up to 10th order statistics. We then apply our estimators to an ensemble of real-world mock catalogs obtained from N-body simulations - the SLICS mocks, and show that one can expect to retrieve detections of higher order clustering up to fourth order in a KiDS-1000 like survey. We expect that these methods will be of most utility for future wide-field surveys like Euclid and the Rubin Telescope., Comment: 13 pages + 11 pages appendix, 10 figures. Matches version published in MNRAS

We perform a detailed calculation of the various contributions to the fluctuation conductivity of a granular metal close to its superconducting transition. We find three distinct regions of power law behavior in reduced temperature, $\eta=(T-T_c)/T_c$, with crossovers at $\Gamma/T_c$ and $E_{Th}/T_c$, where $\Gamma$ is the electron tunneling rate, and $E_{Th}$ is the Thouless energy of a grain. The calculation includes both intergrain and intragrain degrees of freedom. This complete theory of the fluctuation region in granular superconductors is then compared to experimental results from boron-doped nanocrystalline diamond, using the assumption of a constant phase breaking rate, $\tau_{\phi}^{-1}$. We find a semi-quantitative agreement between the theoretical and experimental results only in the case of large phase breaking. We argue that there may be a novel phase breaking mechanism in granular metals worthy of further experimental and theoretical investigation.

Timed Automata (TA) are a very popular modeling formalism for systems with time-sensitive properties. A common task is to verify if a network of TA satisfies a given property, usually expressed in Linear Temporal Logic (LTL), or in a subset of Timed Computation Tree Logic (TCTL). In this paper, we build upon the TACK bounded model checker for TA, which supports a signal-based semantics of TA and the richer Metric Interval Temporal Logic (MITL). TACK encodes both the TA network and property into a variant of LTL, Constraint LTL over clocks (CLTLoc). The produced CLTLoc formula can then be solved by tools such as Zot, which transforms CLTLoc properties into the input logics of Satisfiability Modulo Theories (SMT) solvers. We present a novel method that preserves TACK's encoding of MITL properties while encoding the TA network directly into the SMT solver language, making use of both the BitVector logic and the logic of real arithmetics. We also introduce several optimizations that allow us to significantly outperform the CLTLoc encoding in many practical scenarios.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

We present a joint likelihood analysis of the real-space power spectrum and bispectrum measured from a variety of halo and galaxy mock catalogs. A novel aspect of this work is the inclusion of nonlinear triangle configurations for the bispectrum, made possible by a complete next-to-leading order ("one-loop") description of galaxy bias, as is already common practice for the power spectrum. Based on the goodness-of-fit and the unbiasedness of the parameter posteriors, we accomplish a stringent validation of this model compared to the leading order ("tree-level") bispectrum. Using measurement uncertainties that correspond to an effective survey volume of $6\,(\mathrm{Gpc}/h)^3$, we determine that the one-loop corrections roughly double the applicable range of scales, from $\sim 0.17\,h/\mathrm{Mpc}$ (tree-level) to $\sim 0.3\,h/\mathrm{Mpc}$. This converts into a $1.5 - 2$x improvement on constraints of the linear bias parameter at fixed cosmology, and a $1.5 - 2.4$x shrinkage of uncertainties on the amplitude of fluctuations $A_s$, which clearly demonstrates the benefit of extracting information from nonlinear scales despite having to marginalize over a larger number of bias parameters. Besides, our precise measurements of galaxy bias parameters up to fourth order allow for thorough comparisons to coevolution relations, showing excellent agreement for all contributions generated by the nonlocal action of gravity. Using these relations in the likelihood analysis does not compromise the model validity and is crucial for obtaining the quoted improvements on $A_s$. We also analyzed the impact of higher-derivative and scale-dependent stochastic terms, finding that for a subset of our tracers the former can boost the performance of the tree-level model with constraints on $A_s$ that are only slightly degraded compared to the one-loop model., Comment: 33 pages, 13 figures, supporting Mathematica notebook available at https://doi.org/10.5281/zenodo.4529886, Python package for Delaunay binning correction available at https://github.com/aegge/BispTools

Models based on the transformer architecture, such as BERT, have marked a crucial step forward in the field of Natural Language Processing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Unfortunately, the space only exists for a small dataset of 535 words, limiting its uses. Previous work (Utsumi, 2018, 2020, Turton, Vinson & Smith, 2020) has shown that Binder features can be derived from static embeddings and successfully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides contextualised Binder embeddings, which can aid in understanding semantic differences between words in context. It additionally provides insights into how semantic features are represented across the different layers of the BERT model.

We are reporting the updates in version 0.2.0 of the Automated SpectroPhotometric REDuction (ASPIRED) pipeline, designed for common use on different instruments. The default settings support many typical long-slit spectrometer configurations, whilst it also offers a flexible set of functions for users to refine and tailor-make their automated pipelines to an instrument's individual characteristics. Such automation provides near real-time data reduction to allow adaptive observing strategies, which is particularly important in the Time Domain Astronomy. Over the course of last year, significant improvement was made in the internal data handling as well as data I/O, accuracy and repeatability in the wavelength calibration., Comment: 4 pages, 2 figures, conference proceeding article for the ADASS XXX to be published in ASP Conference Series

We use a deep neural network to detect and place region-of-interest boxes around ultracold atom clouds in absorption and fluorescence images---with the ability to identify and bound multiple clouds within a single image. The neural network also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds' Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing.

The microelectronics industry is pushing the fundamental limit on the physical size of individual elements to produce faster and more powerful integrated chips. These chips have nanoscale features that dissipate power resulting in nanoscale hotspots leading to device failures. To understand the reliability impact of the hotspots, the device needs to be tested under the actual operating conditions. Therefore, the development of high-resolution thermometry techniques is required to understand the heat dissipation processes during the device operation. Recently, several thermometry techniques have been proposed,such as radiation thermometry, thermocouple based contact thermometry, scanning thermal microscopy (SThM), scanning transmission electron microscopy (STEM) and transition based threshold thermometers. However, most of these techniques have limitations including the need for extensive calibration, perturbation of the actual device temperature, low throughput, and the use of ultra-high vacuum. Here, we present a facile technique, which uses a thin film contact thermometer based on the phase change material Ge2Sb2Te5, to precisely map thermal contours from the nanoscale to the microscale. Ge2Sb2Te5 undergoes a crystalline transition at Tg with large changes in its electric conductivity, optical reflectivity and density. Using this approach, we map the surface temperature of a nanowire and an embedded micro-heater on the same chip where the scales of the temperature contours differ by three orders of magnitude. The spatial resolution can be as high as 20 nanometers thanks to the continuous nature of the thin film.

Ensuring there are enough high-quality teachers in England's schools is crucial for delivering a first-class education for young people. Attracting and retaining enough secondary teachers is a key challenge facing school leaders today. Providing more opportunities for part-time and flexible working may provide part of the answer. This NFER research investigated the demand for part-time and flexible working, the barriers for secondary school leaders and how these can be overcome. It comprised an analysis of national data, survey questions and interviews with school leaders. Key findings are: (1) Part-time and flexible working is strongly influenced by the extent to which senior leaders take a proactive approach to encouraging different working patterns. This can include being systematic about asking for annual submissions to change working patterns, checking these with timetables and staffing forecasts, and then negotiating further with staff, who also need to be flexible around their requests; (2) Around one in six secondary school teachers would like to reduce their hours and could afford to do so. While it is unlikely that all of these teachers would actually reduce their hours if given the opportunity, the finding is consistent with NFER's previous research that illustrated a significant number of teachers who leave the state sector, do so to take up part-time positions elsewhere; (3) The main barriers for school leaders are: ensuring continuity for pupils and timetabling; the constraints on flexible working patterns other than part-time working; communication issues; and the additional costs involved; and (4) The main benefits are: increased teacher retention and recruitment; improved staff wellbeing; ability to retain specialist expertise and curriculum breadth; and an opportunity to reduce staff costs (by reducing the total number of teaching hours required). [Support for this report was provided by the Association for School and College Leaders (ASCL).]

Molecular spin qubits with long spin coherence time as well as non-invasive operation methods on such qubits are in high demand. It was shown that both molecular electronic and nuclear spin levels can be used as qubits. In solid state systems with dopants, an electric field was shown to effectively change the spacing between the nuclear spin qubit levels when the electron spin density is high at the nucleus of the dopant. Inspired by such solid-state systems, we propose that divalent lanthanide (Ln) complexes with an unusual electronic configuration of Ln$^{2+}$ have a strong interaction between the Ln nuclear spin and the electronic degrees of freedom, which renders electrical tuning of the interaction. As an example, we study electronic structure and hyperfine interaction of the $^{159}$Tb nucleus in a neutral Tb(II)(Cp$^{\rm{iPr5}}$)$_2$ single-molecule magnet (SMM) using the complete active space self-consistent field method with spin-orbit interaction included within the restricted active space state interaction. Our calculations show that the low-energy states arise from $4f^8(6s,5d_{z^2})^1$, 4$f^8$(5$d_{x^2-y^2}$)$^1$, and $4f^8(5d_{xy})^1$ configurations. We compute the hyperfine interaction parameters and the electronic-nuclear spectrum within our multiconfigurational approach. We find that the hyperfine interaction is about one order of magnitude greater than that for Tb(III)Pc$_2$ SMMs. This stems from the strong Fermi contact interaction between the Tb nuclear spin and the electron spin density at the nucleus that originates from the occupation of the $(6s,5d)$ orbitals. We also uncover that the response of the Fermi contact term to electric field results in electrical tuning of the electronic-nuclear level separations. This hyperfine Stark effect may be useful for applications of molecular nuclear spins for quantum computing., Comment: 8 pages, 6 figures

We explore an alternative method to the usual shear correlation function approach for the estimation of aperture mass statistics in weak lensing survey data. Our approach builds on the direct estimator method of Schneider (1998). In this paper, to test and validate the methodology, we focus on the aperture mass dispersion. After computing the signal and noise for a weighted set of measured ellipticites we show how the direct estimator can be made into a linear order algorithm that enables a fast and efficient computation. We then investigate the applicability of the direct estimator approach in the presence of a real survey mask with holes and chip gaps. For this we use a large ensemble of full ray-tracing mock simulations. By using various weighting schemes for combining information from different apertures we find that inverse variance weighting the individual aperture estimates with an aperture completeness greater than 70 per cent coverage yields an answer that is in close agreement with the standard correlation function approach. We then apply this approach to the CFHTLenS as pilot scheme and find that our method recovers to high accuracy the Kilbinger (2013) result for the variance of both the E and B mode signal, after we correct the catalogue for the shear bias in the lensfit algorithm for pairs closer than 9". We then explore the cosmological information content of the direct estimator using the Fisher information approach. We show that there is a only modest loss in cosmological information from the rejection of apertures that are of low completeness. This method unlocks the door to fast and efficient methods for recovering higher order aperture mass statistics in linear order operations., Comment: 26 pages, 15 figures. Submitted to MNRAS, comments welcome. v2: updated MNRAS template

Understanding and classifying nonequilibrium many-body phenomena, analogous to the classification of equilibrium states of matter into universality classes, is an outstanding problem in physics. Any many-body system, from stellar matter to financial markets, can be out of equilibrium in a myriad of ways; since many are also difficult to experiment on, it is a major goal to establish universal principles that apply to different phenomena and physical systems. At the heart of the classification of equilibrium states is the universality seen in the self-similar spatial scaling of systems close to phase transitions. Recent theoretical work, and first experimental evidence, suggest that isolated many-body systems far from equilibrium generically exhibit dynamic (spatiotemporal) self-similar scaling, akin to turbulent cascades and the Family-Vicsek scaling in classical surface growth. Here we observe bidirectional dynamic scaling in an isolated quench-cooled atomic Bose gas; as the gas thermalises and undergoes Bose-Einstein condensation, it shows self-similar net flows of particles towards the infrared (smaller momenta) and energy towards the ultraviolet (smaller lengthscales). For both infrared (IR) and ultraviolet (UV) dynamics we find that the scaling exponents are independent of the strength of the interparticle interactions that drive the thermalisation., Comment: 5 pages, 4 figures

Polarization plays an important role in various time-domain astrophysics to understand the magnetic fields, geometry, and environments of spatially unresolved variable sources. In this paper we present the results of laboratory and on-sky testing of a novel dual-beam, dual-camera optical imaging polarimeter (MOPTOP) exploiting high sensitivity, low-noise CMOS technology and designed to monitor variable and transient sources with low systematic errors and high sensitivity. We present a data reduction algorithm that corrects for sensitivity variations between the cameras on a source-by-source basis. Using our data reduction algorithm, we show that our dual-beam, dual-camera technique delivers the benefits of low and stable instrumental polarization ($<0.05$\% for lab data and $<0.25$\% for on sky data) and high throughput while avoiding the additional sky brightness and image overlap problems associated with dual-beam, single-camera polarimeters., Comment: 12 pages, 11 figures, Accepted for publication in MNRAS

Quilc is an open-source, optimizing compiler for gate-based quantum programs written in Quil or QASM, two popular quantum programming languages. The compiler was designed with attention toward NISQ-era quantum computers, specifically recognizing that each quantum gate has a non-negligible and often irrecoverable cost toward a program's successful execution. Quilc's primary goal is to make authoring quantum software a simpler exercise by making architectural details less burdensome to the author. Using Quilc allows one to write programs faster while usually not compromising---and indeed sometimes improving---their execution fidelity on a given hardware architecture. In this paper, we describe many of the principles behind Quilc's design, and demonstrate the compiler with various examples.

Highly Pathogenic Avian Influenza A H5N6 is a mutated virus of Influenza A H5N1 and a new emerging infection that recently caused an outbreak in the Philippines. The 2017 H5N6 outbreak resulted in a depopulation of 667,184 domestic birds. In this study, we incorporate half-saturated incidence in our mathematical models and investigate three intervention strategies against H5N6: isolation with treatment, vaccination and modified culling. We determine the direction of the bifurcation when $\mathcal{R}_0 = 1$ and show that all the models exhibit forward bifurcation. We administer optimal control and perform numerical simulations to compare the consequences and implementation cost of utilizing different intervention strategies in the poultry population. Despite the challenges of applying each control strategy, we show that culling both infected and susceptible birds is a better control strategy in prohibiting an outbreak and avoiding further recurrence of the infection from the population compared to confinement and vaccination., Comment: 27 pages, 14 figures

In Communication Theory, intermedia agenda-setting refers to the influence that different news sources may have on each other, and how this subsequently affects the breadth of information that is presented to the public. Several studies have attempted to quantify the impact of intermedia agenda-setting in specific countries or contexts, but a large-scale, data-driven investigation is still lacking. Here, we operationalise intermedia agenda-setting by putting forward a methodology to infer networks of influence between different news sources on a given topic, and apply it on a large dataset of news articles published by globally and locally prominent news organisations in 2016. We find influence to be significantly topic-dependent, with the same news sources acting as agenda-setters (i.e., central nodes) with respect to certain topics and as followers (i.e., peripheral nodes) with respect to others. At the same time, we find that the influence networks associated with most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network. In particular, we find clustering and density of influence networks to act as competing forces in this respect, with the former increasing and the latter reducing diversity., Comment: 21 pages, 9 figures

Randomized clinical trials are considered the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, such trials are often impractical because of ethical and financial considerations. In observational studies, matching on the generalized propensity scores was proposed as a possible solution to estimate the treatment effects of multiple interventions. However, the derivation of point and interval estimates for these matching procedures can become complex with non-continuous or censored outcomes. We propose a novel Approximate Bayesian Bootstrap algorithm that result in statistically valid point and interval estimates of the treatment effects with categorical outcomes. The procedure relies on the estimated generalized propensity scores and multiply imputes the unobserved potential outcomes for each unit. In addition, we describe a corresponding interpretable sensitivity analysis to examine the unconfoundedness assumption. We apply this approach to examines the cardiovascular safety of common, real-world anti-diabetic treatment regimens for Type 2 diabetes mellitus in a large observational database.

In order to support near-term applications of quantum computing, a new compute paradigm has emerged--the quantum-classical cloud--in which quantum computers (QPUs) work in tandem with classical computers (CPUs) via a shared cloud infrastructure. In this work, we enumerate the architectural requirements of a quantum-classical cloud platform, and present a framework for benchmarking its runtime performance. In addition, we walk through two platform-level enhancements, parametric compilation and active qubit reset, that specifically optimize a quantum-classical architecture to support variational hybrid algorithms (VHAs), the most promising applications of near-term quantum hardware. Finally, we show that integrating these two features into the Rigetti Quantum Cloud Services (QCS) platform results in considerable improvements to the latencies that govern algorithm runtime., Comment: 21 pages, 8 figures; updated references to match published version

We present a complete derivation of the granular analogue to Drude conductivity using diagrammatic methods. The convergence issues arising when changing the order of momentum and frequency summation are more severe than in the homogeneous case. This is because there are now two momentum sums rather than one, due to the intragrain momentum scrambling in tunnelling events. By careful analytic continuation of the frequency sum, and use of integration by parts, we prove that the system is in the normal (non-superconducting) state, and derive the formula for the granular Drude conductivity expected from Einstein's relation and Fermi's golden rule. We also show that naively performing the momentum sums first gives the correct result, provided that we interpret a divergent frequency sum by analytic continuation using the Hurwitz zeta function., Comment: 18 pages, 5 figures

We aim to provide a suite of publicly available spectral data reduction software to facilitate rapid scientific outcomes from time-domain observations. For time resolved observations, an automated pipeline frees astronomers from performance of the routine data analysis tasks to concentrate on interpretation, planning future observations and communication with international collaborators. The project consists of two parts: data processing (ASPIRED) and a graphical user interface (gASPIRED).ASPIREDis written in python 3, and was intentionally developed as a self-consistent reduction pipeline with its own diagnostics and error handling. The pipeline can reduce2D spectral data from raw image to wavelength and flux calibrated 1D spectrum automatically without any user input.gASPIREDis a cross-platform software developedwithElectronon a single code base. It brings interactivity to the software with a well-maintained and user-friendly environment., Comment: 4 pages, 1 figure, 29th Astronomical Data Analysis Software & Systems (ADASS) conference

Three-body recombination in quantum gases is traditionally associated with heating, but it was recently found that it can also cool the gas. We show that in a partially condensed three-dimensional homogeneous Bose gas three-body loss could even purify the sample, that is, reduce the entropy per particle and increase the condensed fraction $\eta$. We predict that the evolution of $\eta$ under continuous three-body loss can, depending on small changes in the initial conditions, exhibit two qualitatively different behaviours - if it is initially above a certain critical value, $\eta$ increases further, whereas clouds with lower initial $\eta$ evolve towards a thermal gas. These dynamical effects should be observable under realistic experimental conditions., Comment: 4 pages, 3 figures

For a native gate set which includes all single-qubit gates, we apply results from symplectic geometry to analyze the spaces of two-qubit programs accessible within a fixed number of gates. These techniques yield an explicit description of this subspace as a convex polytope, presented by a family of linear inequalities themselves accessible via a finite calculation. We completely describe this family of inequalities in a variety of familiar example cases, and as a consequence we highlight a certain member of the "XY-family" for which this subspace is particularly large, i.e., for which many two-qubit programs admit expression as low-depth circuits., Comment: Updated after initial publication to fix increasing/decreasing conventions and an incorrect Figure 6