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Software reuse is a crucial external quality attribute targeted by open-source and commercial projects. Despite that software reuse has experienced an increased adoption throughout the years, little is known about what aspects of code reuse developers discuss. In this paper, we present an empirical study of 1,409 posts to better understand the challenges developers face when reusing code. Our findings show that 'visual studio' is the top occurring bigrams for question posts, and there are frequent design patterns utilized by developers for the purpose of reuse. We envision our findings enabling researchers to develop guidelines to be utilized to foster software reuse., Comment: 2023 Conference on Systems Engineering Research

The connection between galaxies and dark matter halos is often quantified using the stellar mass-halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys at $z \gtrsim 2$ can also constrain the SMHM relation via the galaxy autocorrelation function and through the cross-correlation between galaxies and Ly$\alpha$ absorption measured in transverse sightlines; however, such studies are very few and have produced some unexpected or inconclusive results. We use $\sim$3000 spectra of $z\sim2.5$ galaxies from the Lyman-alpha Tomography IMACS Survey (LATIS) to measure the galaxy-galaxy and galaxy-Ly$\alpha$ correlation functions in four bins of stellar mass spanning $10^{9.2} \lesssim M_* / M_{\odot} \lesssim 10^{10.5}$. Parallel analyses of the MultiDark N-body and ASTRID hydrodynamic cosmological simulations allow us to model the correlation functions, estimate covariance matrices, and infer halo masses. We find that results of the two methods are mutually consistent and are broadly in accord with standard SMHM relations. This consistency demonstrates that we are able to accurately measure and model Ly$\alpha$ transmission fluctuations $\delta_F$ in LATIS. We also show that the galaxy-Ly$\alpha$ cross-correlation, a free byproduct of optical spectroscopic galaxy surveys at these redshifts, can constrain halo masses with similar precision to galaxy-galaxy clustering., Comment: Accepted to ApJ

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans. This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make? In this work, we posit that this tension reflects a divergence in the configuration of intelligence in today's generative models relative to intelligence in humans. Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs. This contrasts with humans, for whom basic understanding almost always precedes the ability to generate expert-level outputs. We test this hypothesis through controlled experiments analyzing generation vs. understanding in generative models, across both language and image modalities. Our results show that although models can outperform humans in generation, they consistently fall short of human capabilities in measures of understanding, as well as weaker correlation between generation and understanding performance, and more brittleness to adversarial inputs. Our findings support the hypothesis that models' generative capability may not be contingent upon understanding capability, and call for caution in interpreting artificial intelligence by analogy to human intelligence.

We present the initial sample of redshifts for 3,839 galaxies in the MeerKAT DEEP2 field -- the deepest $\sim$1.4\,GHz radio field yet observed. Using a spectrophotometric technique combining coarse optical spectra with broadband photometry, we obtain redshifts with $\sigma_z \leq 0.01(1+z)$. The resulting radio luminosity functions between $0.2

We present a system which grows and manages a network of remote viewpoints during the natural installation cycle for a newly installed camera network or a newly deployed robot fleet. No explicit notion of camera position or orientation is required, neither global - i.e. relative to a building plan - nor local - i.e. relative to an interesting point in a room. Furthermore, no metric relationship between viewpoints is required. Instead, we leverage our prior work in effective remote control without extrinsic or intrinsic calibration and extend it to the multi-camera setting. In this, we memorise, from simultaneous robot detections in the tracker thread, soft pixel-wise topological connections between viewpoints. We demonstrate our system with repeated autonomous traversals of workspaces connected by a network of six cameras across a productive office environment., Comment: Paper accepted to the 18th International Symposium on Experimental Robotics (ISER 2023)

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life.

Recalling the most relevant visual memories for localisation or understanding a priori the likely outcome of localisation effort against a particular visual memory is useful for efficient and robust visual navigation. Solutions to this problem should be divorced from performance appraisal against ground truth - as this is not available at run-time - and should ideally be based on generalisable environmental observations. For this, we propose applying the recently developed Visual DNA as a highly scalable tool for comparing datasets of images - in this work, sequences of map and live experiences. In the case of localisation, important dataset differences impacting performance are modes of appearance change, including weather, lighting, and season. Specifically, for any deep architecture which is used for place recognition by matching feature volumes at a particular layer, we use distribution measures to compare neuron-wise activation statistics between live images and multiple previously recorded past experiences, with a potentially large seasonal (winter/summer) or time of day (day/night) shift. We find that differences in these statistics correlate to performance when localising using a past experience with the same appearance gap. We validate our approach over the Nordland cross-season dataset as well as data from Oxford's University Parks with lighting and mild seasonal change, showing excellent ability of our system to rank actual localisation performance across candidate experiences., Comment: 18th International Symposium on Experimental Robotics (ISER 2023)

We present the stellar mass - stellar metallicity relation for 3491 star-forming galaxies at $2 \lesssim z \lesssim 3$ using rest-frame far-ultraviolet (FUV) spectra from the Ly$\alpha$ Tomography IMACS Survey (LATIS). We fit stellar population synthesis models from the Binary Population And Spectral Synthesis code (BPASS v$2.2.1$) to medium resolution (R $\sim 1000$) and high signal-to-noise ($>30$ per 100 km/s over a wavelength range of 1221 - 1800 \r{A}) composite spectra of galaxies in bins of stellar mass to determine their stellar metallicity, primarily tracing $\rm Fe/H$. We find a strong correlation between stellar mass and stellar metallicity, with stellar metallicity monotonically increasing with stellar mass at low masses and flattening at high masses ($M_* \gtrsim 10^{10.3} M_\odot$). Additionally, we compare our stellar metallicity measurements with the gas-phase oxygen abundance of galaxies at similar redshift and estimate the average $\rm [\alpha/Fe] \sim 0.6$. Such high $\alpha$-enhancement indicates that high-redshift galaxies have not yet undergone significant iron enrichment through Type Ia supernovae. Moreover, we utilize an analytic chemical evolution model to constrain the mass loading parameter of galactic winds as a function of stellar mass. We find that as the stellar mass increases, the mass loading parameter decreases. The parameter then flattens or reaches a turning point at around $M_* \sim 10^{10.5} M_\odot$. Our findings may signal the onset of black hole-driven outflows at $z \sim 2.5$ for galaxies with $M_* \gtrsim 10^{10.5} M_\odot$., Comment: 21 pages, 17 figures, 3 tables, accepted for publication in ApJ

As evidenced by recent survey results, majority of asteroids are slow rotators (P>12 h), but lack spin and shape models due to selection bias. This bias is skewing our overall understanding of the spins, shapes, and sizes of asteroids, as well as of their other properties. Also, diameter determinations for large (>60km) and medium-sized asteroids (between 30 and 60 km) often vary by over 30% for multiple reasons. Our long-term project is focused on a few tens of slow rotators with periods of up to 60 hours. We aim to obtain their full light curves and reconstruct their spins and shapes. We also precisely scale the models, typically with an accuracy of a few percent. We used wide sets of dense light curves for spin and shape reconstructions via light-curve inversion. Precisely scaling them with thermal data was not possible here because of poor infrared data: large bodies are too bright for WISE mission. Therefore, we recently launched a campaign among stellar occultation observers, to scale these models and to verify the shape solutions, often allowing us to break the mirror pole ambiguity. The presented scheme resulted in shape models for 16 slow rotators, most of them for the first time. Fitting them to stellar occultations resolved previous inconsistencies in size determinations. For around half of the targets, this fitting also allowed us to identify a clearly preferred pole solution, thus removing the ambiguity inherent to light-curve inversion. We also address the influence of the uncertainty of the shape models on the derived diameters. Overall, our project has already provided reliable models for around 50 slow rotators. Such well-determined and scaled asteroid shapes will, e.g. constitute a solid basis for density determinations when coupled with mass information. Spin and shape models continue to fill the gaps caused by various biases., Comment: Accepted to Astronomy & Astrophysics. 12 pages + appendices

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

Let $K$ be a quadratic field and $E$ an elliptic curve defined over $K$ such that $E(K)[2]\simeq C_2.$ In this paper, we study the effect of quadratic base change on $E(K)_{\text{tor}}.$ In particular, we examine the growth of $E(K)_{\text{tor}}$ upon quadratic base change when $K$ is any quadratic cyclotomic field. In addition, for a given elliptic curve $E/K$ with prescribed torsion group over $K,$ (no restriction on its $2$-torsion part) we describe an algorithm to find all quadratic extensions $L/K$ in which $E(K)_{\text{tor}} \subsetneq E(L)_{\text{tor}}$ and describe $E(L)_{\text{tor}}$ in each such case., Comment: 14 pages, comments welcome!

Corruptive majority attacks, in which mining power is distributed among miners and an attacker attempts to bribe a majority of miners into participation in a majority attack, pose a threat to blockchains. Budish bounded the cost of bribing miners to participate in an attack by their expected loss as a result of attack success. We show that this bound is loose. In particular, an attack may be structured so that under equilibrium play by most miners, a miner's choice to participate only slightly affects the attack success chance. Combined with the fact that most of the cost of attack success is externalized by any given small miner, this implies that if most mining power is controlled by small miners, bribing miners to participate in such an attack is much cheaper than the Budish bound. We provide a scheme for a cheap corruptive majority attack and discuss practical concerns and consequences.

We consider the classic Correlation Clustering problem: Given a complete graph where edges are labelled either $+$ or $-$, the goal is to find a partition of the vertices that minimizes the number of the \pedges across parts plus the number of the \medges within parts. Recently, Cohen-Addad, Lee and Newman [CLN22] presented a 1.994-approximation algorithm for the problem using the Sherali-Adams hierarchy, hence breaking through the integrality gap of 2 for the classic linear program and improving upon the 2.06-approximation of Chawla, Makarychev, Schramm and Yaroslavtsev [CMSY15]. We significantly improve the state-of-the-art by providing a 1.73-approximation for the problem. Our approach introduces a preclustering of Correlation Clustering instances that allows us to essentially ignore the error arising from the {\em correlated rounding} used by [CLN22]. This additional power simplifies the previous algorithm and analysis. More importantly, it enables a new {\em set-based rounding} that complements the previous roundings. A combination of these two rounding algorithms yields the improved bound.

We present initial results from the Dark Energy Spectroscopic Instrument (DESI) Complete Calibration of the Color-Redshift Relation (DC3R2) secondary target survey. Our analysis uses 230k galaxies that overlap with KiDS-VIKING $ugriZYJHK_s$ photometry to calibrate the color-redshift relation and to inform photometric redshift (photo-z) inference methods of future weak lensing surveys. Together with Emission Line Galaxies (ELGs), Luminous Red Galaxies (LRGs), and the Bright Galaxy Survey (BGS) that provide samples of complementary color, the DC3R2 targets help DESI to span 56% of the color space visible to Euclid and LSST with high confidence spectroscopic redshifts. The effects of spectroscopic completeness and quality are explored, as well as systematic uncertainties introduced with the use of common Self Organizing Maps trained on different photometry than the analysis sample. We further examine the dependence of redshift on magnitude at fixed color, important for the use of bright galaxy spectra to calibrate redshifts in a fainter photometric galaxy sample. We find that noise in the KiDS-VIKING photometry introduces a dominant, apparent magnitude dependence of redshift at fixed color, which indicates a need for carefully chosen deep drilling fields, and survey simulation to model this effect for future weak lensing surveys., Comment: 19 pages, 16 figures, published in MNRAS, interactive visualizations at https://jmccull.github.io/DC3R2_Overview

A study is presented of the impact of simulated inclusive Electron Ion Collider Deep Inelastic Scattering data on the determination of the proton and nuclear parton distribution functions (PDFs) at next-to-next-to-leading and next-to-leading order in QCD, respectively. The influence on the proton PDFs is evaluated relative to the HERAPDF2.0 set, which uses inclusive HERA data only, and also relative to the global fitting approach of the MSHT20 PDFs. The impact on nuclear PDFs is assessed relative to the EPPS21 global fit and is presented in terms of nuclear modification ratios. For all cases studied, significant improvements in the PDF uncertainties are observed for several parton species. The most striking impact occurs for the nuclear PDFs in general and for the region of high Bjorken $x$ in the proton PDFs, particularly for the valence quark distributions., Comment: 12 pages, 11 figures, 1 Table

We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo occupation distribution (HOD) from z=0.1-0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey (BGS). In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package, galtab, which enables the rapid, precise prediction of CiC for any HOD model available in halotools. This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias, especially at lower luminosity thresholds: the HOD of central galaxies in $z\sim0.15$ samples with limiting absolute magnitude $M_r < -20.0$ and $M_r < -20.5$ samples is positively correlated with halo concentration with a significance of 99.9% and 99.5%, respectively. Our models also favor positive central assembly bias for the brighter $M_r < -21.0$ sample at $z\sim0.25$ (94.8% significance), but there is no significant evidence for assembly bias with the same luminosity threshold at $z\sim0.15$. We provide our constraints for each threshold sample's characteristic halo masses, assembly bias, and other HOD parameters. These constraints are expected to be significantly tightened with future DESI data, which will span an area 100 times larger than that of SV3.

We present two galaxy samples, selected from DESI Legacy Imaging Surveys (LS) DR9, with approximately 20,000 square degrees of coverage and spectroscopic redshift distributions designed for cross-correlations such as with CMB lensing, galaxy lensing, and the Sunyaev-Zel'dovich effect. The first sample is identical to the DESI Luminous Red Galaxy (LRG) sample, and the second sample is an extended LRG sample with 2-3 times the DESI LRG density. We present the improved photometric redshifts, tomographic binning and their spectroscopic redshift distributions and imaging systematics weights, and magnification bias coefficients. The catalogs and related data products will be made publicly available. The cosmological constraints using this sample and Planck lensing maps are presented in a companion paper. We also make public the new set of general-purpose photometric redshifts trained using DESI spectroscopic redshifts, which are used in this work, for all galaxies in LS DR9., Comment: Matches the journal version. Associated data files: https://data.desi.lbl.gov/public/papers/c3/lrg_xcorr_2023/. General-purpose photo-z catalogs: https://www.legacysurvey.org/dr9/files/#photo-z-sweeps-9-1-photo-z-sweep-brickmin-brickmax-pz-fits

Heterogeneous and monolithic integration of the versatile low loss silicon nitride platform with low temperature materials such as silicon electronics and photonics, III-V compound semiconductors, lithium niobate, organics, and glasses, has been inhibited by the need for high temperature annealing as well as the need for different process flows for thin and thick waveguides. New techniques are needed to maintain the state-of-the-art losses, nonlinear properties, and CMOS compatible processes while enabling this next generation of 3D silicon nitride integration. We report a significant advance in silicon nitride integrated photonics, demonstrating the lowest losses to date for an anneal-free process at a maximum temperature of 250 C, with the same deuterated silane based fabrication flow, for nitride and oxide, for an order of magnitude range in nitride thickness without requiring stress mitigation or polishing. We report record low losses for anneal-free nitride core and oxide cladding, enabling 1.77 dB/m loss and 14.9 million Q for 80 nm nitride core waveguides, more than half an order magnitude lower loss than previously reported 270 C processes, and 8.66 dB/m loss and 4.03 million Q for 800 nm thick nitride. We demonstrate laser stabilization with over 4 orders of magnitude frequency noise reduction using a thin nitride reference cavity. And using a thick nitride micro-resonator, we demonstrate parametric gain and Optical Parametric Oscillation (OPO) with the lowest reported OPO threshold per unit resonator length for low temperature fabricated nitride, and supercontinuum generation over two octaves. These results represent a significant step towards a uniform ultra-low loss silicon nitride homogeneous and heterogeneous platform for both thin and thick waveguides capable of linear and nonlinear photonic circuits and integration with low temperature materials and processes., Comment: 33 pages, 20 figures; V3 - Added explanation of loss/thickness regimes (Fig 2) and thin resonator PDH locking experiment (Fig 4c,d)

Nebular-phase observations of peculiar Type Ia supernovae (SNe Ia) provide important constraints on progenitor scenarios and explosion dynamics for both these rare SNe and the more common, cosmologically useful SNe Ia. We present observations from an extensive ground-based and space-based follow-up campaign to characterize SN 2022pul, a "super-Chandrasekhar" mass SN Ia (alternatively "03fg-like" SN), from before peak brightness to well into the nebular phase across optical to mid-infrared (MIR) wavelengths. The early rise of the light curve is atypical, exhibiting two distinct components, consistent with SN Ia ejecta interacting with dense carbon-oxygen rich circumstellar material (CSM). In the optical, SN 2022pul is most similar to SN 2012dn, having a low estimated peak luminosity ($M_{B}=-18.9$ mag) and high photospheric velocity relative to other 03fg-like SNe. In the nebular phase, SN 2022pul adds to the increasing diversity of the 03fg-like subclass. From 168 to 336 days after peak $B$-band brightness, SN 2022pul exhibits asymmetric and narrow emission from [O I] $\lambda\lambda 6300,\ 6364$ (${\rm FWHM} \approx 2{,}000$ km s$^{-1}$), strong, broad emission from [Ca II] $\lambda\lambda 7291,\ 7323$ (${\rm FWHM} \approx 7{,}300$ km s$^{-1}$), and a rapid Fe III to Fe II ionization change. Finally, we present the first-ever optical-to-mid-infrared (MIR) nebular spectrum of an 03fg-like SN Ia using data from JWST. In the MIR, strong lines of neon and argon, weak emission from stable nickel, and strong thermal dust emission (with $T \approx 500$ K), combined with prominent [O I] in the optical, suggest that SN 2022pul was produced by a white dwarf merger within carbon/oxygen-rich CSM., Comment: 23 pages, 11 figures, submitted to ApJ

We present an analysis of ground-based and JWST observations of SN~2022pul, a peculiar "03fg-like" (or "super-Chandrasekhar") Type Ia supernova (SN Ia), in the nebular phase at 338d post explosion. Our combined spectrum continuously covers 0.4--14 $\mu$m and includes the first mid-infrared spectrum of an 03fg-like SN Ia. Compared to normal SN Ia 2021aefx, SN 2022pul exhibits a lower mean ionization state, asymmetric emission-line profiles, stronger emission from the intermediate-mass elements (IMEs) argon and calcium, weaker emission from iron-group elements (IGEs), and the first unambiguous detection of neon in a SN Ia. Strong, broad, centrally peaked [Ne II] line at 12.81 $\mu$m was previously predicted as a hallmark of "violent merger'' SN Ia models, where dynamical interaction between two sub-$M_{ch}$ white dwarfs (WDs) causes disruption of the lower mass WD and detonation of the other. The violent merger scenario was already a leading hypothesis for 03fg-like SNe Ia; in SN 2022pul it can explain the large-scale ejecta asymmetries seen between the IMEs and IGEs and the central location of narrow oxygen and broad neon. We modify extant models to add clumping of the ejecta to better reproduce the optical iron emission, and add mass in the innermost region ($< 2000$ km s$^{-1}$) to account for the observed narrow [O I]~$\lambda\lambda6300$, 6364 emission. A violent WD-WD merger explains many of the observations of SN 2022pul, and our results favor this model interpretation for the subclass of 03fg-like SN Ia., Comment: 20 pages, 10 figures, published in ApJ

This work explores Doppler information from a millimetre-Wave (mm-W) Frequency-Modulated Continuous-Wave (FMCW) scanning radar to make odometry estimation more robust and accurate. Firstly, doppler information is added to the scan masking process to enhance correlative scan matching. Secondly, we train a Neural Network (NN) for regressing forward velocity directly from a single radar scan; we fuse this estimate with the correlative scan matching estimate and show improved robustness to bad estimates caused by challenging environment geometries, e.g. narrow tunnels. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets., Comment: Accepted to ITSC 2023

The Standard Model (SM) of particle physics is in such good agreement with experiment that it is currently accepted as providing an accurate model of reality, with the role of chiral symmetry breaking in electro-weak unification regarded as one of its major achievements. This work focusses on weaknesses in the algebraic formulation of the SM, especially the introduction of chirality, which is shown to be inconsistent with neutrinos being fermions, and conflicts with the experimental evidence that they have finite mass. The SM description of parity is also shown to be erroneous. It is argued that these errors in its algebraic formulation have had the effect of making theoretical extensions of the SM impossible, as well as wasting considerable experimental effort in trying to understand erroneous predictions of the theory., Comment: 7 pages. The paper was retitled to make it more explicit. Some typos were corrected

This paper considers correlation clustering on unweighted complete graphs. We give a combinatorial algorithm that returns a single clustering solution that is simultaneously $O(1)$-approximate for all $\ell_p$-norms of the disagreement vector; in other words, a combinatorial $O(1)$-approximation of the all-norms objective for correlation clustering. This is the first proof that minimal sacrifice is needed in order to optimize different norms of the disagreement vector. In addition, our algorithm is the first combinatorial approximation algorithm for the $\ell_2$-norm objective, and more generally the first combinatorial algorithm for the $\ell_p$-norm objective when $1 < p < \infty$. It is also faster than all previous algorithms that minimize the $\ell_p$-norm of the disagreement vector, with run-time $O(n^\omega)$, where $O(n^\omega)$ is the time for matrix multiplication on $n \times n$ matrices. When the maximum positive degree in the graph is at most $\Delta$, this can be improved to a run-time of $O(n\Delta^2 \log n)$., Comment: 27 pages, 2 figures