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We have discovered the stellar counterpart to the ALFALFA Virgo 7 cloud complex, which has been thought to be optically dark and nearly star-free since its discovery in 2007. This ~190 kpc long chain of enormous atomic gas clouds ($M_\mathrm{HI} \sim 10^9 \; \mathrm{M_\odot}$) is embedded in the hot intracluster medium of the Virgo galaxy cluster but is isolated from any galaxy. Its faint, blue stellar counterpart, BC6, was identified in a visual search of archival optical and UV imaging. Follow-up observations with the Green Bank Telescope, Hobby-Eberly Telescope, and Hubble Space Telescope demonstrate that this faint counterpart is at the same velocity as the atomic gas, is actively forming stars, and is metal-rich ($12 + \mathrm{(O/H)} = 8.58 \pm 0.25$). We estimate its stellar mass to be only $\log ( M_\ast/\mathrm{M_\odot}) \sim 4.4$, making it one of the most gas-rich stellar systems known. Aside from its extraordinary gas content, the properties of BC6 are entirely consistent with those of a recently identified class of young, low-mass, isolated, and star-forming clouds in Virgo, that appear to have formed via extreme ram pressure stripping events. We expand the existing discussion of the origin of this structure and suggest NGC 4522 as a likely candidate, however, the current evidence is not fully consistent with any of our proposed progenitor galaxies. We anticipate that other "dark" gas clouds in Virgo may have similarly faint, star-forming counterparts. We aim to identify these through the help of a citizen science search of the entire cluster., Comment: Accepted to ApJL

The reformulation of the Met Office's dynamical core for weather and climate prediction previously described by the authors is extended to spherical domains using a cubed-sphere mesh. This paper updates the semi-implicit mixed finite-element formulation to be suitable for spherical domains. In particular the finite-volume transport scheme is extended to take account of non-uniform, non-orthogonal meshes and uses an advective-then-flux formulation so that increment from the transport scheme is linear in the divergence. The resulting model is then applied to a standard set of dry dynamical core tests and compared to the existing semi-implicit semi-Lagrangian dynamical core currently used in the Met Office's operational model., Comment: 26 Pages, 8 Figures, 1 Table

Stochastic electronic structure theories, e.g., Quantum Monte Carlo methods, enable highly accurate total energy calculations which in principle can be used to construct highly accurate potential energy surfaces. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged-elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method - previously developed for QMC structural optimization - to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and on saddle points, we show that it is possible to identify MEPs and TSs using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia molecule and an SN2 reaction. We validate our results using Density Functional Theory- and coupled cluster-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities - free energy differences, rate constants, and equilibrium constants - that incorporates stochastically-optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations, at significantly reduced computational expense.

We present the angular diameter distance measurement obtained with the Baryonic Acoustic Oscillation feature from galaxy clustering in the completed Dark Energy Survey, consisting of six years (Y6) of observations. We use the Y6 BAO galaxy sample, optimized for BAO science in the redshift range 0.6<$z$<1.2, with an effective redshift at $z_{\rm eff}$=0.85 and split into six tomographic bins. The sample has nearly 16 million galaxies over 4,273 square degrees. Our consensus measurement constrains the ratio of the angular distance to sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51$\pm$0.41 (at 68.3% confidence interval), resulting from comparing the BAO position in our data to that predicted by Planck $\Lambda$CDM via the BAO shift parameter $\alpha=(D_M/r_d)/(D_M/r_d)_{\rm Planck}$. To achieve this, the BAO shift is measured with three different methods, Angular Correlation Function (ACF), Angular Power Spectrum (APS), and Projected Correlation Function (PCF) obtaining $\alpha=$ 0.952$\pm$0.023, 0.962$\pm$0.022, and 0.955$\pm$0.020, respectively, which we combine to $\alpha=$ 0.957$\pm$0.020, including systematic errors. When compared with the $\Lambda$CDM model that best fits Planck data, this measurement is found to be 4.3% and 2.1$\sigma$ below the angular BAO scale predicted. To date, it represents the most precise angular BAO measurement at $z$>0.75 from any survey and the most precise measurement at any redshift from photometric surveys. The analysis was performed blinded to the BAO position and it is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics., Comment: Submitted to PRD, 39 pages, 12 figures

Certain relations among neutron-star observables that are insensitive to the equation of state are known to exist. Such universal relations have been shown to be valid for cold and stationary neutron stars. Here, we study these relations in more dynamic scenarios: protoneutron stars and hypermassive neutron stars. First, we study protoneutron stars. We use an effective equation of state, extracted from three-dimensional core-collapse supernova simulations, to obtain the structure of spherically symmetric protoneutron stars. We then consider nonradial oscillations to compute their $f$-mode frequency ($f$), as well as slow rotation and small tidal deformation, to compute their moment of inertia ($I$), spin-induced quadrupole moment ($Q$), and Love number. We find that well-established universal relations for cold neutron stars involving these observables ($I$-Love-$Q$ and $f$-Love relations) are approximately valid for protoneutron stars, with a deviation below $\approx$ 10$\%$ for a postbounce time above $\approx$ 0.5 s, considering eight different supernova progenitors and the SFHo equation of state. Next, we study hypermassive neutron stars. We obtain a new universal relation between the $f$-mode frequency and the compactness of cold and nonrotating neutron stars, using bulk quantities. We show that this relation has an equation-of-state-variation of $\approx$ $3\%$, considering a set of ten equations of state. Using results from binary neutron star merger simulations, we study the evolution of hypermassive neutron stars on the $f$-$C$ plane, considering two different mass ratios and the SFHo equation of state. We find that the relation between the peak frequency of the gravitational-wave signal and the compactness from these hypermassive neutron stars deviates from the universal $f$-$C$ relation by 70 $-$ 80$\%$, when the peak frequency is taken directly as a proxy for the $f$-mode.

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to distant massive black-hole mergers and the expansion of the Universe. This definition study report, or Red Book, presents a summary of the very large body of work that has been undertaken on the LISA mission over the LISA definition phase., Comment: 155 pages, with executive summary and table of contents

Password-based authentication faces various security and usability issues. Password managers help alleviate some of these issues by enabling users to manage their passwords effectively. However, malicious client-side scripts and browser extensions can steal passwords after they have been autofilled by the manager into the web page. In this paper, we explore what role the password manager can take in preventing the theft of autofilled credentials without requiring a change to user behavior. To this end, we identify a threat model for password exfiltration and then use this threat model to explore the design space for secure password entry implemented using a password manager. We identify five potential designs that address this issue, each with varying security and deployability tradeoffs. Our analysis shows the design that best balances security and usability is for the manager to autofill a fake password and then rely on the browser to replace the fake password with the actual password immediately before the web request is handed over to the operating system to be transmitted over the network. This removes the ability for malicious client-side scripts or browser extensions to access and exfiltrate the real password. We implement our design in the Firefox browser and conduct experiments, which show that it successfully thwarts malicious scripts and extensions on 97\% of the Alexa top 1000 websites, while also maintaining the capability to revert to default behavior on the remaining websites, avoiding functionality regressions. Most importantly, this design is transparent to users, requiring no change to user behavior.

The nature of the insulator-to-metal phase transition in vanadium dioxide (VO2) is one of the longest-standing problems in condensed-matter physics. Ultrafast spectroscopy has long promised to determine whether the transition is primarily driven by the electronic or structural degree of freedom, but measurements to date have been stymied by their sensitivity to only one of these components and/or their limited temporal resolution. Here we use ultra-broadband few-femtosecond pump-probe spectroscopy to resolve the electronic and structural phase transitions in VO2 at their fundamental time scales. We find that the system transforms into a bad-metallic phase within 10 fs after photoexcitation, but requires another 100 fs to complete the transition, during which we observe electronic oscillations and a partial re-opening of the bandgap, signalling a transient semi-metallic state. Comparisons with tensor-network simulations and density-functional theory calculations show these features originate from oscillations around the equilibrium high-symmetry atomic positions during an unprecedentedly fast structural transition, in which the vanadium dimers separate and untwist with two different timescales. Our results resolve the complete structural and electronic nature of the light-induced phase transition in VO2 and establish ultra-broadband few-femtosecond spectroscopy as a powerful new tool for studying quantum materials out of equilibrium., Comment: 20 pages, 12 figures

We demonstrate the generation of photons within a fiber-cavity quantum memory, followed by later on-demand readout. Signal photons are generated by spontaneous four-wave mixing in a fiber cavity comprising a birefringent fiber with dichroic reflective end facets. The detection of the partner herald photon indicates the creation of the stored signal photon. After a delay, the signal photon is switched out of resonance with the fiber cavity by intracavity frequency translation using Bragg scattering four-wave mixing, driven by ancillary control pulses. We measure sub-Poissonian statistics in the output signal mode, with $g^{(2)}_{AC}=0.54(1)$ in the first readout bin and a readout frequency translation efficiency of $\approx$80%. The 1/e memory lifetime is $\approx$67 cavity cycles, or 1.68$\mu$s. In an alternate fiber cavity, we show a strategy for noise reduction and measure $g^{(2)}_{AC}=0.068(10)$ after one cavity cycle., Comment: 6 pages, 3 figures

Recent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most creative and ambitious projects. It also has the potential to help businesses and organizations harness knowledge, increase productivity, innovate, transform, and power shared prosperity. This tremendous potential raises two fundamental questions: (1) Will AI actually advance national and global economic transformation to benefit society at large? and (2) What issues must we get right to fully realize AI's economic value, expand prosperity and improve lives everywhere? We explore these questions by considering the recent history of technology and innovation as a guide for the likely impact of AI and what we must do to realize its economic potential to benefit society. While we do not presume the future will be entirely like that past, for reasons we will discuss, we do believe prior experience with technological change offers many useful lessons. We conclude that while progress in AI presents a historic opportunity to advance our economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless we collectively and purposefully act to enable its potential and address its challenges. We suggest a collective policy agenda - involving developers, deployers and users of AI, infrastructure providers, policymakers, and those involved in workforce training - that may help both realize and harness AI's economic potential and address its risks to our shared prosperity., Comment: 37 pages

Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressive demonstrations. Since then, technopreneurs have sought to leverage NeRF models into a profitable business. Therefore, NeRF models make it worth the risk of plagiarizers illegally copying, re-distributing, or misusing those models. This paper proposes a comprehensive intellectual property (IP) protection framework for the NeRF model in both black-box and white-box settings, namely IPR-NeRF. In the black-box setting, a diffusion-based solution is introduced to embed and extract the watermark via a two-stage optimization process. In the white-box setting, a designated digital signature is embedded into the weights of the NeRF model by adopting the sign loss objective. Our extensive experiments demonstrate that not only does our approach maintain the fidelity (\ie, the rendering quality) of IPR-NeRF models, but it is also robust against both ambiguity and removal attacks compared to prior arts., Comment: Error on result tabulation of state of the art method which might cause misleading to readers

Recent strides in the field of neural computation has seen the adoption of Winner Take All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing. Current research commonly validates the performance of these networks via classification tasks, particularly of the MNIST dataset. However, researchers have not yet reached consensus about how best to translate the stochastic responses from these networks into discrete decisions, a process known as population decoding. Despite being an often underexamined part of SNNs, in this work we show that population decoding has a significanct impact on the classification performance of WTA networks. For this purpose, we apply a WTA network to the problem of cancer subtype diagnosis from multi omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing so we utilise a novel implementation of gene similarity networks, a feature encoding technique based on Kohoens self organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets., Comment: This paper has been accepted in BIOINFORMATICS 2024 (BIOSTEC 2024)

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.100.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025

We address the choice of penalty parameter in the Smoothness-Penalized Deconvolution (SPeD) method of estimating a probability density under additive measurement error. Cross-validation gives an unbiased estimate of the risk (for the present sample size n) with a given penalty parameter, and this function can be minimized as a function of the penalty parameter. Least-squares cross-validation, which has been proposed for the similar Deconvoluting Kernel Density Estimator (DKDE), performs quite poorly for SPeD. We instead estimate the risk function for a smaller sample size n_1 < n with a given penalty parameter, using this to choose the penalty parameter for sample size n_1, and then use the asymptotics of the optimal penalty parameter to choose for sample size n. In a simulation study, we find that this has dramatically better performance than cross-validation, is an improvement over a SURE-type method previously proposed for this estimator, and compares favorably to the classic DKDE with its recommended plug-in method. We prove that the maximum error in estimating the risk function is of smaller order than its optimal rate of convergence.

The planar grasshopper problem, originally introduced in (Goulko & Kent 2017 Proc. R. Soc. A 473, 20170494), is a striking example of a model with long-range isotropic interactions whose ground states break rotational symmetry. In this work we analyze and explain the nature of this symmetry breaking with emphasis on the importance of dimensionality. Interestingly, rotational symmetry is recovered in three dimensions for small jumps, which correspond to the non-isotropic cogwheel regime of the two-dimensional problem. We discuss simplified models that reproduce the symmetry properties of the original system in N dimensions. For the full grasshopper model in two dimensions we obtain quantitative predictions for optimal perturbations of the disk. Our analytical results are confirmed by numerical simulations., Comment: Ancillary files with animations of 3d shapes included