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Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.

We consider the periodic fractional nonlinear Schr\"{o}dinger equation $$ iu_t -(-\Delta)^{\frac{s}{2}} u + \mathcal{N}(|u|)u=0, \quad x\in \mathbb{T}^N,\, \, t \in \mathbb R, \, \, s>0, $$ where the nonlinearity term is expressed in two ways: the first one $\mathcal{N}\in C^J(\mathbb R^+)$, whose derivatives have a certain polynomial decay, e.g., $\mathcal{N}(|u|)=\log(|u|)$; the second one is given by a sum of powers, possibly infinite, $$ \mathcal{N}(|u|) = \sum a_k |u|^{\gamma_k}, \quad \gamma_k \in \mathbb{R}, ~~ a_k \in \mathbb{C}, $$ which includes examples such as $\mathcal{N}(|u|) \, u =\frac{u}{|u|^{\gamma}},$ $\gamma>0$. By using standard properties of periodic Sobolev spaces $H^J(\mathbb{T}^N)$, $J>0$, we study the local well-posedness for the Cauchy problems of the above equations when initial data satisfy a non-vanishing condition $\inf\limits_{x\in \mathbb{T}^N}|u_0(x)|>0$., Comment: 21 pages

We present new ALMA continuum and spectral observations of the MUSE Ultra Deep Field (MUDF), a $2\times 2$ arcmin$^2$ region with ultradeep multiwavelength imaging and spectroscopy hosting two bright $z\approx 3.22$ quasars used to study intervening gas structures in absorption. Through a blind search for dusty galaxies, we identified a total of seven high-confidence sources, six of which with secure spectroscopic redshifts. We estimate galaxy dust and stellar masses ($M_{\rm dust}\simeq 10^{7.8-8.6}\,M_{\odot}$, $M_{\star}\simeq 10^{10.2-10.7}\,M_{\odot}$), as well as star formation rates (${\rm SFR}\simeq 10^{1.2-2.0}\,M_{\odot}\,{\rm yr^{-1}} $) which show that most of these galaxies are massive and dust-obscured similar to coeval (sub-)millimeter galaxies. All six spectroscopically-confirmed galaxies are within $500~\rm km~s^{-1}$ of metal absorption lines observed in the quasar sightlines, corresponding to $100\%$ association rate. We also find that four of these galaxies belong to groups in which they are among the most massive members. Within the multiple group galaxies associated to the same absorption system, the ALMA sources are not always the closest in projection, but they are often aligned with the gaseous structures in velocity space. This suggests that these massive galaxies occupy the center of the potential well of the gas structures traced in absorption. However, albeit the low number density of sources identified with ALMA, our study may indicate that absorbers seem to infrequently originate in the inner circumgalactic medium of these galaxies. Instead, they appear to be better tracers of the gas distributed in the large-scale structure that host them., Comment: 13 pages, 9 figures, 1 Appendix. Submitted to A&A

We provide some examples of Killing superalgebras on 2-dimensional pseudo-Riemannian manifolds within the theoretical framework established in arXiv:2409.11306. We compute the Spencer cohomology group $\mathsf{H}^{2,2}(\mathfrak{s}_-;\mathfrak{s})$ and filtered deformations of the non-chiral flat model (Euclidean and Poincar\'e) superalgebra $\mathfrak{s}$ for various squaring maps and show these arise as Killing superalgebras for (imaginary) geometric Killing and skew-Killing spinors in both Riemannian and Lorentzian signature., Comment: 18 pages, previously appeared in earlier form as part of the author's PhD thesis at https://era.ed.ac.uk/handle/1842/41754

We generalise the notion of a Killing superalgebra which arises in the physics literature on supergravity to general dimension, signature and choice of spinor module and squaring map, and also allowing for Lie algebras as well as superalgebras, capturing a set of examples of such algebras on higher-dimensional spheres. We demonstrate that the definition requires a connection on a spinor bundle -- provided by supersymmetry transformations in the supergravity examples and by the Killing spinor equation on the spheres -- and obtain a set of sufficient conditions on such a connection for the Killing (super)algebra to exist. We show that these (super)algebras are filtered deformations of graded subalgebras of (a generalisation of) the Poincar\'e superalgebra and then study such deformations abstractly using Spencer cohomology. In the highly supersymmetric Lorentzian case, we describe the filtered subdeformations which are of the appropriate form to arise as Killing superalgebras, lay out a classification scheme for their odd-generated subalgebras and prove that, under certain technical conditions, there exist homogeneous Lorentzian spin manifolds on which these deformations are realised as Killing superalgebras. Our results generalise previous work in the 11-dimensional supergravity literature., Comment: 61 pages, previously appeared in earlier form as part of the author's PhD thesis at https://era.ed.ac.uk/handle/1842/41754 . v2: revised introduction, fixed a LaTeX error, added new references, fixed bibliographical errors, clarified terminology in a number of places

We present intial results associating galaxies in the MUSE Ultra Deep Field (MUDF) with gas seen in absorption along the line-of-sight to two bright quasars in this field, to explore the dependence of metals in the circumgalactic medium (CGM) on galaxy properties. The MUDF includes $\sim$140h of VLT/MUSE data and 90 orbits of HST/G141M grism observations alongside VLT/UVES spectroscopy of the two quasars and several bands of HST imaging. We compare the metal absorption around galaxies in this field as a function of impact parameter, azimuthal angle and galaxy metallicity across redshifts 0.5 $<$ z $<$ 3.2. Due to the depth of our data and a large field-of-view, our analysis extends to low stellar masses ($<$ $10^{7}$ M$_{\odot}$) and high impact parameters ($>$ 600 kpc). We find a correlation between absorber equivalent width and number of nearby galaxies, but do not detect a significant anti-correlation with impact parameter. Our full sample does not show any significant change in absorber incidence as a function of azimuthal angle. However, we do find a bimodality in the azimuthal angle distribution of absorption at small impact parameters ($<$2 r$_{vir}$) and around highly-star-forming galaxies, possibly indicating disk-like accretion and biconical outflows. Finally, we do not detect any systematic deviation from the fundamental metallicity relation (FMR) among galaxies with detected absorption. This work is limited by gaps in the wavelength coverage of our current data; broader-wavelength observations with JWST will allow us to unlock the full potential of the MUDF for studying the CGM., Comment: Accepted for publication in The Astrophysical Journal. 31 pages, 17 figures

Efficient and accurate prediction of physical systems is important even when the rules of those systems cannot be easily learned. Reservoir computing, a type of recurrent neural network with fixed nonlinear units, is one such prediction method and is valued for its ease of training. Organic electrochemical transistors (OECTs) are physical devices with nonlinear transient properties that can be used as the nonlinear units of a reservoir computer. We present a theoretical framework for simulating reservoir computers using OECTs as the non-linear units as a test bed for designing physical reservoir computers. We present a proof of concept demonstrating that such an implementation can accurately predict the Lorenz attractor with comparable performance to standard reservoir computer implementations. We explore the effect of operating parameters and find that the prediction performance strongly depends on the pinch-off voltage of the OECTs., Comment: 10 pages, 8 figures

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts across various sectors. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field encouraging cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

We present a Python package called Modular Petri Net Assembly Toolkit (MPAT) that empowers users to easily create large-scale, modular Petri Nets for various spatial configurations, including extensive spatial grids or those derived from shape files, augmented with heterogeneous information layers. Petri Nets are powerful discrete event system modeling tools in computational biology and engineering. However, their utility for automated construction of large-scale spatial models has been limited by gaps in existing modeling software packages. MPAT addresses this gap by supporting the development of modular Petri Net models with flexible spatial geometries.

Petri nets are a promising modeling framework for epidemiology, including the spread of disease across populations or within an individual. In particular, the Susceptible-Infectious-Recovered (SIR) compartment model is foundational for population epidemiological modeling and has been implemented in several prior Petri net studies. However, the SIR model is generally stated as a system of ordinary differential equations (ODEs) with continuous time and variables, while Petri nets are discrete event simulations. To our knowledge, no prior study has investigated the numerical equivalence of Petri net SIR models to the classical ODE formulation. We introduce crucial numerical techniques for implementing SIR models in the GPenSim package for Petri net simulations. We show that these techniques are critical for Petri net SIR models and show a relative root mean squared error of less than 1% compared to ODE simulations for biologically relevant parameter ranges. We conclude that Petri nets provide a valid framework for modeling SIR-type dynamics using biologically relevant parameter values provided that the other PN structures we outline are also implemented., Comment: 27 pages, 11 figures, six equations, and associated code can be found in GitHub repository https://github.com/trevorreckell/Numerical-Comparison-of-PN-vs-ODE-for-SIR

Filaments connecting halos are a long-standing prediction of cold dark matter theories. We present a novel detection of the cosmic web emission connecting two massive quasar-host galaxies at cosmic noon in the MUSE Ultra Deep Field (MUDF) using unprecedentedly deep observations that unlock a high-definition view of the filament morphology, a measure of the transition radius between the intergalactic and circumgalactic medium, and the characterization of the surface brightness profiles along the filament and in the transverse direction. Through systematic comparisons with simulations, we validate the filaments' typical density predicted in the current cold dark matter model. Our analysis of the MUDF field, an excellent laboratory for quantitatively studying filaments in emission, opens a new avenue to understanding the cosmic web that, being a fundamental prediction of cosmology, bears key information on the essence of dark matter., Comment: submitted, comments welcome!

We use the Ultraviolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields (UVCANDELS) to measure half-light radii in the rest-frame far-UV for $\sim$16,000 disk-like galaxies over $0.5\leq z \leq 3$. We compare these results to rest-frame optical sizes that we measure in a self-consistent way and find that the stellar mass-size relation of disk galaxies is steeper in the rest-frame UV than in the optical across our entire redshift range. We show that this is mainly driven by massive galaxies ($\gtrsim10^{10}$M$_\odot$), which we find to also be among the most dusty. Our results are consistent with the literature and have commonly been interpreted as evidence of inside-out growth wherein galaxies form their central structures first. However, they could also suggest that the centers of massive galaxies are more heavily attenuated than their outskirts. We distinguish between these scenarios by modeling and selecting galaxies at $z=2$ from the VELA simulation suite in a way that is consistent with UVCANDELS. We show that the effects of dust alone can account for the size differences we measure at $z=2$. This indicates that, at different wavelengths, size differences and the different slopes of the stellar mass-size relation do not constitute evidence for inside-out growth., Comment: Accepted for publication in ApJ. 22 pages, 12 figures, and 4 tables

Using more than 100 galaxies in the MUSE Ultra Deep Field with spectroscopy from the Hubble Space Telescope's Wide Field Camera 3 and the Very Large Telescope's Multi Unit Spectroscopic Explorer, we extend the gas-phase mass-metallicity relation (MZR) at $z\approx\,$1$\,$-$\,$2 down to stellar masses of M$_{\star}$ $\approx$ 10$^{7.5}$ M$_{\odot}$. The sample reaches six times lower in stellar mass and star formation rate (SFR) than previous HST studies at these redshifts, and we find that galaxy metallicities decrease to log(O/H) + 12 $\approx$ 7.8 $\pm$ 0.1 (15% solar) at log(M$_{\star}$/M$_{\odot}$) $\approx$ 7.5, without evidence of a turnover in the shape of the MZR at low masses. We validate our strong-line metallicities using the direct method for sources with [O III] $\lambda$4363 and [O III] $\lambda$1666 detections, and find excellent agreement between the techniques. The [O III] $\lambda$1666-based metallicities double existing measurements with S/N $\geq$ 5 for unlensed sources at $z~>$ 1, validating the strong-line calibrations up to $z \sim$2.5. We confirm that the MZR resides $\sim$0.3 dex lower in metallicity than local galaxies and is consistent with the fundamental metallicity relation (FMR) if the low mass slope varies with SFR. At lower redshifts ($z\sim$0.5) our sample reaches $\sim$0.5 dex lower in SFR than current calibrations and we find enhanced metallicities that are consistent with extrapolating the MZR to lower SFRs. Finally, we detect only a $\sim$0.1 dex difference in the metallicities of galaxies in groups versus isolated environments. These results are based on robust calibrations and reach the lowest masses and SFRs that are accessible with HST, providing a critical foundation for studies with the Webb and Roman Space Telescopes., Comment: Accepted for publication in ApJ on March 23, 2024. The paper has 29 pages, 12 figures, and 6 tables. The calibrated data are available through MAST at: https://archive.stsci.edu/hlsp/mudf

We present oracle-based testing a new technique for automatic black-box testing of network protocol implementations. Oracle-based testing leverages recent advances in LLMs to build rich models of intended protocol behavior from knowledge embedded in RFCs, blogs, forums, and other natural language sources. From these models it systematically derives exhaustive test cases using symbolic program execution. We realize oracle-based testing through Eywa, a novel protocol testing framework implemented in Python. To demonstrate Eywa's effectiveness, we show its use through an extensive case study of the DNS protocol. Despite requiring minimal effort, applying Eywa to the DNS resulting in the discovery of 26 unique bugs across ten widely used DNS implementations, including 11 new bugs that were previously undiscovered despite elaborate prior testing with manually crafted models.

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory's ten-year science mission scheduled to begin in 2025. Rubin's 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320-1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory., Comment: 8 pages, 2 figures, 26th International Conference on Computing in High Energy & Nuclear Physics

Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heuristic and the optimum or between two heuristics in realistic scenarios. We present MetaOpt, a system that helps analyze heuristics. Users specify the heuristic and the optimal (or another heuristic) as input, and MetaOpt automatically encodes these efficiently for a solver to find performance gaps and their corresponding adversarial inputs. Its suite of built-in optimizations helps it scale its analysis to practical problem sizes. To show it is versatile, we used MetaOpt to analyze heuristics from three domains (traffic engineering, vector bin packing, and packet scheduling). We found a production traffic engineering heuristic can require 30% more capacity than the optimal to satisfy realistic demands. Based on the patterns in the adversarial inputs MetaOpt produced, we modified the heuristic to reduce its performance gap by 12.5$\times$. We examined adversarial inputs to a vector bin packing heuristic and proved a new lower bound on its performance.

Modern cloud infrastructure is powered by cluster management systems such as Kubernetes and Docker Swarm. While these systems seek to minimize users' operational burden, the complex, dynamic, and non-deterministic nature of these systems makes them hard to reason about, potentially leading to failures ranging from performance degradation to outages. We present Kivi, the first system for verifying controllers and their configurations in cluster management systems. Kivi focuses on the popular system Kubernetes, and models its controllers and events into processes whereby their interleavings are exhaustively checked via model checking. Central to handling autoscaling and large-scale deployments is our design that seeks to find violations in a smaller and reduced topology. We also develop several model optimizations in Kivi to scale to large clusters. We show that Kivi is effective and accurate in finding issues in realistic and complex scenarios and showcase two new issues in Kubernetes controller source code., Comment: 18 pages

Land-use decision-making processes have a long history of producing globally pervasive systemic equity and sustainability concerns. Quantitative, optimization-based planning approaches, e.g. Multi-Objective Land Allocation (MOLA), seemingly open the possibility to improve objectivity and transparency by explicitly evaluating planning priorities by the type, amount, and location of land uses. Here, we show that optimization-based planning approaches with generic planning criteria generate a series of unstable "flashpoints" whereby tiny changes in planning priorities produce large-scale changes in the amount of land use by type. We give quantitative arguments that the flashpoints we uncover in MOLA models are examples of a more general family of instabilities that occur whenever planning accounts for factors that coordinate use on- and between-sites, regardless of whether these planning factors are formulated explicitly or implicitly. We show that instabilities lead to regions of ambiguity in land-use type that we term "gray areas". By directly mapping gray areas between flashpoints, we show that quantitative methods retain utility by reducing combinatorially large spaces of possible land-use patterns to a small, characteristic set that can engage stakeholders to arrive at more efficient and just outcomes., Comment: 9 pages, 5 figures

Gender inequity is one of the biggest challenges facing the STEM workforce. While there are many studies that look into gender disparities within STEM and academia, the majority of these have been designed and executed by those unfamiliar with research in sociology and gender studies. They adopt a normative view of gender as a binary choice of 'male' or 'female,' leaving individuals whose genders do not fit within that model out of such research entirely. This especially impacts those experiencing multiple axes of marginalization, such as race, disability, and socioeconomic status. For STEM fields to recruit and retain members of historically excluded groups, a new paradigm must be developed. Here, we collate a new dataset of the methods used in 119 past studies of gender equity, and recommend better survey practices and institutional policies based on a more complex and accurate approach to gender. We find that problematic approaches to gender in surveys can be classified into 5 main themes - treating gender as white, observable, discrete, as a statistic, and as inconsequential. We recommend allowing self-reporting of gender and never automating gender assignment within research. This work identifies the key areas of development for studies of gender-based inclusion within STEM, and provides recommended solutions to support the methodological uplift required for this work to be both scientifically sound and fully inclusive., Comment: 33 pages (15 pages plus references), 1 table. Follows work of arXiv:1907.04893 and arXiv:1907.04893

We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topology, syntax, and semantics. Our strategy, that we call Verified Prompt Programming, is to combine GPT-4 with verifiers, and use localized feedback from the verifier to automatically correct errors. Verification requires a specification and actionable localized feedback to be effective. We show results for two use cases: translating from Cisco to Juniper configurations on a single router, and implementing no-transit policy on multiple routers. While human input is still required, if we define the leverage as the number of automated prompts to the number of human prompts, our experiments show a leverage of 10X for Juniper translation, and 6X for implementing no-transit policy, ending with verified configurations.

Low-iron, manganese-enriched (LIME) olivine grains are found in cometary samples returned by the Stardust mission to comet 81P/Wild 2. Similar grains are found in primitive meteoritic clasts and unequilibrated meteorite matrix. LIME olivine is thermodynamically stable in a vapor of solar composition at high temperature at total pressures of a millibar to a microbar, but enrichment of solar composition vapor in a dust of chondritic composition causes the FeO/MnO ratio of olivine to increase. The compositions of LIME olivines in primitive materials indicate oxygen fugacities close to that of a very reducing vapor of solar composition. The compositional zoning of LIME olivines in amoeboid olivine aggregates is consistent with equilibration with nebular vapor in the stability field of olivine, without reequilibration at lower temperatures. A similar history is likely for LIME olivines found in comet samples and in interplanetary dust particles. LIME olivine is not likely to persist in nebular conditions in which silicate liquids are stable., Comment: 15 pages, 5 figures