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We present Firefly, a new browser-based interactive tool for visualizing 3D particle data sets. On a typical personal computer, Firefly can simultaneously render and enable real-time interactions with > ~10 million particles, and can interactively explore datasets with billions of particles using the included custom-built octree render engine. Once created, viewing a Firefly visualization requires no installation and is immediately usable in most modern internet browsers simply by visiting a URL. As a result, a Firefly visualization works out-of-the-box on most devices including smartphones and tablets. Firefly is primarily developed for researchers to explore their own data, but can also be useful to communicate results to researchers/collaborators and as an effective public outreach tool. Every element of the user interface can be customized and disabled, enabling easy adaptation of the same visualization for different audiences with little additional effort. Creating a new Firefly visualization is simple with the provided Python data pre-processor (PDPP) that translates input data to a Firefly-compatible format and provides helpful methods for hosting instances of Firefly both locally and on the internet. In addition to visualizing the positions of particles, users can visualize vector fields (e.g., velocities) and also filter and color points by scalar fields. We share three examples of Firefly applied to astronomical datasets: 1) the FIRE cosmological zoom-in simulations, 2) the SDSS galaxy catalog, and 3) Gaia DR3. A gallery of additional interactive demos is available at https://alexbgurvi.ch/Firefly., Comment: 26 pages, 9 figures

We introduce Flux, which shows how logical refinements can work hand in glove with Rust's ownership mechanisms to yield ergonomic type-based verification of low-level pointer manipulating programs. First, we design a novel refined type system for Rust that indexes mutable locations, with pure (immutable) values that can appear in refinements, and then exploits Rust's ownership mechanisms to abstract sub-structural reasoning about locations within Rust's polymorphic type constructors, while supporting strong updates. We formalize the crucial dependency upon Rust's strong aliasing guarantees by exploiting the stacked borrows aliasing model to prove that ``well-borrowed evaluations of well-typed programs do not get stuck''. Second, we implement our type system in Flux, a plug-in to the Rust compiler that exploits the factoring of complex invariants into types and refinements to efficiently synthesize loop annotations -- including complex quantified invariants describing the contents of containers -- via liquid inference. Third, we evaluate Flux with a benchmark suite of vector manipulating programs and parts of a previously verified secure sandboxing library to demonstrate the advantages of refinement types over program logics as implemented in the state-of-the-art Prusti verifier. While Prusti's more expressive program logic can, in general, verify deep functional correctness specifications, for the lightweight but ubiquitous and important verification use-cases covered by our benchmarks, liquid typing makes verification ergonomic by slashing specification lines by a factor of two, verification time by an order of magnitude, and annotation overhead from up to 24% of code size (average 9%) to nothing at all.

Recent excitement in observation of non-reciprocal critical current (NRC) is motivated by a suggestion that "superconducting diode effect" may be an intrinsic property of non-centrosymmetric superconductors with strong spin-orbit interactions[1]. Theoretically it has been understood that linear in the Cooper pair momentum terms, caused by the Rashba spin-orbit and Zeeman interactions or, more generally, any symmetry-allowed Lifshitz invariants[2] in uniform singlet superconductors, do not contribute to the supercurrent, although the role of higher-order terms remains unclear[3, 4]. In this work we show that critical current non-reciprocity is a generic property of multilayered superconductor structures in the presence of magnetic field-generated diamagnetic currents. In the regime of an intermediate coupling between the layers, the Josephson vortices are predicted to form at high fields and currents. We report the observation of NRC in nanowires fabricated from InAs/Al heterostructures. The effect is independent of the crystallographic orientation of the wire, ruling out an intrinsic origin of NRC. Non-monotonic NRC evolution with magnetic field is consistent with the generation of diamagnetic currents and formation of the Josephson vortices. This extrinsic NRC mechanism can be used to design novel devices for superconducting circuits., Comment: 32 pages, 14 figures

We explore the redshift evolution of the dynamical properties of massive clusters and their brightest cluster galaxies (BCGs) at $z < 2$ based on the IllustrisTNG-300 simulation. We select 270 massive clusters with $M_{200} < 10^{14}~{\rm M}_{\odot}$ at $z = 0$ and trace their progenitors based on merger trees. From 67 redshift snapshots covering $z < 2$, we compute the 3D subhalo velocity dispersion as a cluster velocity dispersion ($\sigma_{\rm cl}$). We also calculate the 3D stellar velocity dispersion of the BCGs ($\sigma_{\rm *,~BCG}$). Both $\sigma_{\rm cl}$ and $\sigma_{\rm *,~BCG}$ increase as universe ages. The BCG velocity dispersion grows more slowly than the cluster velocity dispersion. Furthermore, the redshift evolution of the BCG velocity dispersion shows dramatic changes at some redshifts resulting from dynamical interaction with neighboring galaxies (major mergers). We show that $\sigma_{\rm *,~BCG}$ is comparable with $\sigma_{\rm cl}$ at $z > 1$, offering an interesting observational test. The simulated redshift evolution of $\sigma_{\rm cl}$ and $\sigma_{\rm *,~BCG}$ generally agrees with an observed cluster sample for $z < 0.3$, but with large scatter. Future large spectroscopic surveys reaching to high redshift will test the implications of the simulations for the mass evolution of both clusters and their BCGs., Comment: 16 pages, 13 figures, 1 table, accepted for publication in The Astrophysical Journal

The use of mid-circuit measurement and qubit reset within quantum programs has been introduced recently and several applications demonstrated that perform conditional branching based on these measurements. In this work, we go a step further and describe a next-generation implementation of classical computation embedded within quantum programs that enables the real-time calculation and adjustment of program variables based on the mid-circuit state of measured qubits. A full-featured Quantum Intermediate Representation (QIR) model is used to describe the quantum circuit including its embedded classical computation. This integrated approach eliminates the need to evaluate and store a potentially prohibitive volume of classical data within the quantum program in order to explore multiple solution paths. It enables a new type of quantum algorithm that requires fewer round-trips between an external classical driver program and the execution of the quantum program, significantly reducing computational latency, as much of the classical computation can be performed during the coherence time of quantum program execution. We review practical challenges to implementing this approach along with developments underway to address these challenges. An implementation of this novel and powerful quantum programming pattern, a random walk phase estimation algorithm, is demonstrated on a physical quantum computer with an analysis of its benefits and feasibility as compared to existing quantum computing methods., Comment: 13 pages, 5 figures

Clinical trials disruption has always represented a non negligible part of the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only.

We measure the anisotropy of the Milky Way stellar halo traced by a dense sample of 18

Primordial black holes (PBHs) provide an exciting prospect for accounting for dark matter. In this paper, we consider inflationary models that incorporate realistic features from high-energy physics -- including multiple interacting scalar fields and nonminimal couplings to the spacetime Ricci scalar -- that could produce PBHs with masses in the range required to address the present-day dark matter abundance. Such models are consistent with supersymmetric constructions, and only incorporate operators in the effective action that would be expected from generic effective field theory considerations. The models feature potentials with smooth large-field plateaus together with small-field features that can induce a brief phase of ultra-slow-roll evolution. Inflationary dynamics within this family of models yield predictions for observables in close agreement with recent measurements, such as the spectral index of primordial curvature perturbations and the ratio of power spectra for tensor to scalar perturbations. As in previous studies of PBH formation resulting from a period of ultra-slow-roll inflation, we find that at least one dimensionless parameter must be highly fine-tuned to produce PBHs in the relevant mass-range for dark matter. Nonetheless, we find that the models described here yield accurate predictions for a significant number of observable quantities using a smaller number of relevant free parameters., Comment: 22 pages, 9 figures. References added to match published version

We establish the undecidability of 2019 puzzle game Baba is You through a reduction from the Post correspondence problem. In particular, we consider a restricted form of the Post correspondence problem introduced by Neary (arXiv:1312.6700) that is limited to five pairs of words. Baba is You is an award winning tile-based game in which the player can reprogram the game's mechanisms by pushing blocks that spell out the rules. We achieve undecidability through a generalization of the size of the playfield in the horizontal direction, adding a "hallway" to one side of the level. The undecidability of Baba is You has been claimed several times online using different source problems, including the simulation of Turing machines and Conway's Game of Life, however, this contribution appears to be the first formal proof of the result.

We present the first results of eight Globular Clusters (GCs) from the AstroSat/UVIT Legacy Survey program GlobULeS based on the observations carried out in two FUV filters (F148W and F169M). The FUV-optical and FUV-FUV color-magnitude diagrams (CMDs) of GCs with the proper motion membership were constructed by combining the UVIT data with HST UV Globular Cluster Survey (HUGS) data for inner regions and Gaia Early Data Release (EDR3) for regions outside the HST's field. We detect sources as faint as F148W $\sim$ 23.5~mag which are classified based on their locations in CMDs by overlaying stellar evolutionary models. The CMDs of 8 GCs are combined with the previous UVIT studies of 3 GCs to create stacked FUV-optical CMDs to highlight the features/peculiarities found in the different evolutionary sequences. The FUV (F148W) detected stellar populations of 11 GCs comprises 2,816 Horizontal Branch (HB) stars (190 Extreme HB candidates), 46 post-HB (pHB), 221 Blue Straggler Stars (BSS), and 107 White Dwarf (WD) candidates. We note that the blue HB color extension obtained from F148W$-$G color and the number of FUV detected EHB candidates are strongly correlated with the maximum internal Helium (He) variation within each GC, suggesting that the FUV-optical plane is the most sensitive to He abundance variations in the HB. We discuss the potential science cases that will be addressed using these catalogues including HB morphologies, BSSs, pHB, and, WD stars., Comment: Accepted for publication in MNRAS; 24 pages, 20 Figures, 6 Tables

For many quantum systems intended for information processing, one detects the logical state of a qubit by integrating a continuously observed quantity over time. For example, ion and atom qubits are typically measured by driving a cycling transition and counting the number of photons observed from the resulting fluorescence. Instead of recording only the total observed count in a fixed time interval, one can observe the photon arrival times and get a state detection advantage by using the temporal structure in a model such as a Hidden Markov Model. We study what further advantage may be achieved by applying pulses to adaptively transform the state during the observation. We give a three-state example where adaptively chosen transformations yield a clear advantage, and we compare performances on an ion example, where we see improvements in some regimes. We provide a software package that can be used for exploration of temporally resolved strategies with and without adaptively chosen transformations., Comment: Submitted for publication in Quantum Science and Technology. 26 pages, 8 figures. Corrected typos in appendix, updated acknowledgements

Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention. However, the limited size of event-based datasets makes SNNs prone to overfitting and unstable convergence. This issue remains unexplored by previous academic works. In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance. The proposed method is simple and compatible with existing SNN training pipelines. Using the proposed augmentation, for the first time, we demonstrate the feasibility of unsupervised contrastive learning for SNNs. We conduct comprehensive experiments on prevailing neuromorphic vision benchmarks and show that NDA yields substantial improvements over previous state-of-the-art results. For example, the NDA-based SNN achieves accuracy gain on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively. Code is available on GitHub https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN, Comment: Accepted to the 17th European Conference on Computer Vision (ECCV 2022)

We study the observable implications of an incomplete first order phase transition during inflation. In such a phase transition, the nucleated bubbles do not percolate and instead are continuously produced until the onset of reheating. The process creates an inhomogeneity with a distinct power spectrum that depends on both the physics of the phase transition and the inflationary dynamics. Upon horizon re-entry, this spectrum generates gravitational waves through non-linear effects. This stochastic gravitational wave background is predicted to have unique signatures that may be detectable by future experiments spanning a wide frequency range. The discovery of such a gravitational wave signal would shed a light on the detailed dynamics of inflation., Comment: 12 pages, 4 figures

Clusters of galaxies are powerful probes with which to study cosmology and astrophysics. However, for many applications an accurate measurement of a cluster's mass is essential. A systematic underestimate of hydrostatic masses from X-ray observations (the so-called hydrostatic bias) may be responsible for tension between the results of different cosmological measurements. We compare X-ray hydrostatic masses with masses estimated using the caustic method (based on galaxy velocities) in order to explore the systematic uncertainties of both methods and place new constraints on the level of hydrostatic bias. Hydrostatic and caustic mass profiles were determined independently for a sample of 44 clusters based on Chandra observations of clusters from the Hectospec Cluster Survey. This is the largest systematic comparison of its kind. Masses were compared at a standardised radius ($R_{500}$) using a model that includes possible bias and scatter in both mass estimates. The systematics affecting both mass determination methods were explored in detail. The hydrostatic masses were found to be systematically higher than caustic masses on average, and we found evidence that the caustic method increasingly underestimates the mass when fewer galaxies are used to measure the caustics. We limit our analysis to the 14 clusters with the best-sampled caustics where this bias is minimised ($\ge210$ galaxies), and find that the average ratio of hydrostatic to caustic mass at $R_{500}$ is $M_X/M_C=1.12^{+0.11}_{-0.10}$. We interpret this result as a constraint on the level of hydrostatic bias, favouring small or zero levels of hydrostatic bias (less than $20\%$ at the $3\sigma$ level). However, we find systematic uncertainties associated with both mass estimation methods remain at the $10-15\%$ level, which would permit significantly larger levels of hydrostatic bias., Comment: 15 pages plus appendices. Updated to match version accepted for publication in A&A. Updates include additional tests of systematics. Main results are unchanged