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Nonlinearities in King plots (KP) of isotope shifts (IS) can reveal the existence of beyond-Standard-Model (BSM) interactions that couple electrons and neutrons. However, it is crucial to distinguish higher-order Standard Model (SM) effects from BSM physics. We measure the IS of the transitions ${{}^{3}P_{0}~\rightarrow~{}^{3}P_{1}}$ in $\mathrm{Ca}^{14+}$ and ${{}^{2}S_{1/2} \rightarrow {}^{2}D_{5/2}}$ in $\mathrm{Ca}^{+}$ with sub-Hz precision as well as the nuclear mass ratios with relative uncertainties below $4\times10^{-11}$ for the five stable, even isotopes of calcium (${}^{40,42,44,46,48}\mathrm{Ca}$). Combined, these measurements yield a calcium KP nonlinearity with a significance of $\sim 900 \sigma$. Precision calculations show that the nonlinearity cannot be fully accounted for by the expected largest higher-order SM effect, the second-order mass shift, and identify the little-studied nuclear polarization as the only remaining SM contribution that may be large enough to explain it. Despite the observed nonlinearity, we improve existing KP-based constraints on a hypothetical Yukawa interaction for most of the new boson masses between $10~\mathrm{eV/c^2}$ and $10^7~\mathrm{eV/c^2}$.

Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, especially those requiring compositional and multi-step reasoning. Given such complex instructions, SOTA models often make mistakes in faithfully modeling object attributes, and relationships among them. In this work, we present an alternate paradigm for T2I synthesis, decomposing the task of complex multi-step generation into three steps, (a) Generate: we first generate an image using existing diffusion models (b) Plan: we make use of Multi-Modal LLMs (MLLMs) to identify the mistakes in the generated image expressed in terms of individual objects and their properties, and produce a sequence of corrective steps required in the form of an edit-plan. (c) Edit: we make use of an existing text-guided image editing models to sequentially execute our edit-plan over the generated image to get the desired image which is faithful to the original instruction. Our approach derives its strength from the fact that it is modular in nature, is training free, and can be applied over any combination of image generation and editing models. As an added contribution, we also develop a model capable of compositional editing, which further helps improve the overall accuracy of our proposed approach. Our method flexibly trades inference time compute with performance on compositional text prompts. We perform extensive experimental evaluation across 3 benchmarks and 10 T2I models including DALLE-3 and the latest -- SD-3.5-Large. Our approach not only improves the performance of the SOTA models, by upto 3 points, it also reduces the performance gap between weaker and stronger models. $\href{https://dair-iitd.github.io/GraPE/}{https://dair-iitd.github.io/GraPE/}$

Side channels have become an essential component of many modern information-theoretic schemes. The emerging field of cross technology communications (CTC) provides practical methods for creating intentional side channels between existing communications technologies. This paper describes a theoretical foundation for one such, recently proposed, CTC scheme: Ghost Modulation (GM). Designed to modulate a low-data-rate message atop an existing network stream, GM is particularly suited for transmitting identification or covert information. The implementation only requires firmware updates to existing hardware, making it a cost-effective solution. However, GM provides an interesting technical challenge due to a highly asymmetric binary crossover erasure channel (BCEC) that results from packet drops and network delays. In this work, we provide a mathematical description of the signal and channel models for GM. A heuristic decision rule based on maximum-likelihood principles for simplified channel models is proposed. We describe an algorithm for GM packet acquisition and timing synchronization, supported by simulation results. Several well known short block codes are applied, and bit error rate (BER) results are presented., Comment: 7 Pages, 5 Figures

Numerical calculations of the electron self-energy without any expansion in the binding nuclear field are required in order to match the rapidly advancing precision of experimental spectroscopy. For the lightest elements, particularly hydrogen, these computations are complicated by large numerical cancellations and the slow convergence of the partial-wave expansion. Methods with accelerated convergence of the partial-wave expansion have been recently put forward [V. A. Yerokhin, K. Pachucki, V. M. Shabaev, Phys. Rev. A 72, 042502 (2005); J. Sapirstein and K. T. Cheng, Phys. Rev. A 108, 042804 (2023)]. In our work we extend the accelerated-convergence methods to the previously hardly accessible region of nuclear charges $Z < 5$ and higher excited states.

Calculations of the two-loop electron self-energy for the $1S$ Lamb shift are reported, performed to all orders in the nuclear binding strength parameter $Z\alpha$ (where $Z$ is the nuclear charge number and $\alpha$ is the fine structure constant). Our approach allows calculations to be extended to nuclear charges lower than previously possible and improves the numerical accuracy by more than an order of magnitude. Extrapolation of our all-order results to hydrogen yields a result twice as precise as the previously accepted value [E. Tiesinga et al. Rev. Mod. Phys. 93, 025010 (2021)], differing from it by 2.8 standard deviations. The resulting shift in the theoretical prediction for the $1S$-$2S$ transition frequency in hydrogen decreases the value of the Rydberg constant by one standard deviation.

Drone detection has benefited from improvements in deep neural networks, but like many other applications, suffers from the availability of accurate data for training. Synthetic data provides a potential for low-cost data generation and has been shown to improve data availability and quality. However, models trained on synthetic datasets need to prove their ability to perform on real-world data, known as the problem of sim-to-real transferability. Here, we present a drone detection Faster-RCNN model trained on a purely synthetic dataset that transfers to real-world data. We found that it achieves an AP_50 of 97.0% when evaluated on the MAV-Vid - a real dataset of flying drones - compared with 97.8% for an equivalent model trained on real-world data. Our results show that using synthetic data for drone detection has the potential to reduce data collection costs and improve labelling quality. These findings could be a starting point for more elaborate synthetic drone datasets. For example, realistic recreations of specific scenarios could de-risk the dataset generation of safety-critical applications such as the detection of drones at airports. Further, synthetic data may enable reliable drone detection systems, which could benefit other areas, such as unmanned traffic management systems. The code is available https://github.com/mazqtpopx/cranfield-synthetic-drone-detection alongside the datasets https://huggingface.co/datasets/mazqtpopx/cranfield-synthetic-drone-detection., Comment: 12 pages, 8 figures

We consider the transport properties of non-interacting, gapless one-dimensional quantum systems and of the edge modes of two-dimensional topological insulators, in the presence of time-dependent perturbations. We prove the validity of Kubo formula, in the zero temperature and infinite volume limit, for a class of perturbations that are weak and slowly varying in space and in time, in an Euler-like scaling. The proof relies on the representation of the real time Duhamel series in imaginary time, which allows to prove its convergence uniformly in the scaling parameter and in the size of system, at low temperatures. Furthermore, it allows to exploit a suitable cancellation for the scaling limit of the model, related to the emergent anomalous chiral gauge symmetry of relativistic one-dimensional fermions. The cancellation implies that, as the temperature and the scaling parameter are sent to zero, the linear response is the only contribution to the full response of the system. The explicit form of the leading contribution to the response function is determined by lattice conservation laws. In particular, the method allows to prove the quantization of the edge conductance of $2d$ quantum Hall systems from quantum dynamics., Comment: 56 pages, 3 figures

The two-loop self-energy correction to the bound-electron $g$-factor in hydrogenlike ions is investigated, taking into account the electron-nucleus interaction exactly. This all-order calculation is required to improve the total theoretical uncertainty of the $g$-factor, which is limited by the fact that two-loop self-energy corrections have only been calculated so far in the form of an expansion in $Z\alpha$. Here, $Z$ is the nuclear charge number and $\alpha$ is the fine-structure constant. In this work, we report calculations of the last missing parts of the total two-loop self-energy correction, exactly in $Z\alpha$. We apply our theory to the recently measured $g$-factor of the hydrogenlike $^{118}$Sn$^{49+}$ ion [J. Morgner et al., Nature 622, 53 (2023)] and, with a factor of 8, improve the accuracy of its state-of-the-art theoretical value by almost one order of magnitude, enabling more detailed tests of quantum electrodynamics and new physics in strong fields., Comment: 6 pages, 4 figures

New observational facilities are beginning to enable insights into the three-dimensional (3D) nature of exoplanets. Transmission spectroscopy is the most widely used method for characterizing transiting temperate exoplanet's atmospheres, but because it only provides a glimpse of the planet's limb and nightside for a typical orbit, its ability to probe 3D characteristics is still an active area of research. Here, we use the ROCKE-3D general circulation model to test the impact of rotation rate, a ``low-order'' 3D characteristic previously shown to drive differences in planetary phase curves, on the transmission spectrum of a representative super-Earth across temperate-to-warm instellations (S$_p$=0.8, 1, 1.25, 1.66, 2, 2.5, 3, 4, 4.56 S$_\oplus$). We find that different rotation regimes do display differences in their transmission spectra, primarily driven by clouds and humidity, and that the differences shrink or disappear in hotter regimes where water clouds are unable to condense (though our simulations do not consider haze formation). The small size of the feature differences and potential for degeneracy with other properties, like differing water content or atmospheric structure, mean that we do not specifically claim to have identified a single transmission diagnostic for rotation rate, but our results can be used for holistic spectrum interpretation and sample creation, and suggest the need for more modelling in this area., Comment: 15 pages, 11 figures. Submitted to PSJ. Comments welcome

Large language models (LLMs) have been widely applied to assist test generation with the source code under test provided as the context. This paper aims to answer the question: If the source code under test is incorrect, will LLMs be misguided when generating tests? The effectiveness of test cases is measured by their accuracy, coverage, and bug detection effectiveness. Our evaluation results with five open- and six closed-source LLMs on four datasets demonstrate that incorrect code can significantly mislead LLMs in generating correct, high-coverage, and bug-revealing tests. For instance, in the HumanEval dataset, LLMs achieve 80.45% test accuracy when provided with task descriptions and correct code, but only 57.12% when given task descriptions and incorrect code. For the APPS dataset, prompts with correct code yield tests that detect 39.85% of the bugs, while prompts with incorrect code detect only 19.61%. These findings have important implications for the deployment of LLM-based testing: using it on mature code may help protect against future regression, but on early-stage immature code, it may simply bake in errors. Our findings also underscore the need for further research to improve LLMs resilience against incorrect code in generating reliable and bug-revealing tests., Comment: 23 pages

Many powerful tests of the Standard Model of particle physics and searches for new physics with precision atomic spectroscopy are plagued by our lack of knowledge of nuclear properties. Ideally, such properties may be derived from precise measurements of the most sensitive and theoretically best-understood observables, often found in hydrogen-like systems. While these measurements are abundant for the electric properties of nuclei, they are scarce for the magnetic properties, and precise experimental results are limited to the lightest of nuclei. Here, we focus on $^9$Be which offers the unique possibility to utilize comparisons between different charge states available for high-precision spectroscopy in Penning traps to test theoretical calculations typically obscured by nuclear structure. In particular, we perform the first high-precision spectroscopy of the $1s$ hyperfine and Zeeman structure in hydrogen-like $^9$Be$^{3+}$. We determine its effective Zemach radius with an uncertainty of $500$ ppm, and its bare nuclear magnetic moment with an uncertainty of $0.6$ parts-per-billion (ppb) - uncertainties unmatched beyond hydrogen. Moreover, we compare to measurements conducted on the three-electron charge state $^9$Be$^{+}$, which, for the first time, enables testing the calculation of multi-electron diamagnetic shielding effects of the nuclear magnetic moment at the ppb level. In addition, we test quantum electrodynamics (QED) methods used for the calculation of the hyperfine splitting. Our results serve as a crucial benchmark essential for transferring high-precision results of nuclear magnetic properties across different electronic configurations.

For the Laplacian in spherical and hyperbolic spaces, Robin eigenvalues in two dimensions and Dirichlet eigenvalues in higher dimensions are shown to satisfy scaling inequalities analogous to the standard scale invariance of the Euclidean Laplacian. These results extend work of Langford and Laugesen to Robin problems and to Dirichlet problems in higher dimensions. In addition, scaled Robin eigenvalues behave exotically as the domain expands to a 2-sphere, tending to the spectrum of an exterior Robin problem., Comment: 38 pages, 14 figures

Representing speech as discrete tokens provides a framework for transforming speech into a format that closely resembles text, thus enabling the use of speech as an input to the widely successful large language models (LLMs). Currently, while several speech tokenizers have been proposed, there is ambiguity regarding the properties that are desired from a tokenizer for specific downstream tasks and its overall generalizability. Evaluating the performance of tokenizers across different downstream tasks is a computationally intensive effort that poses challenges for scalability. To circumvent this requirement, we present STAB (Speech Tokenizer Assessment Benchmark), a systematic evaluation framework designed to assess speech tokenizers comprehensively and shed light on their inherent characteristics. This framework provides a deeper understanding of the underlying mechanisms of speech tokenization, thereby offering a valuable resource for expediting the advancement of future tokenizer models and enabling comparative analysis using a standardized benchmark. We evaluate the STAB metrics and correlate this with downstream task performance across a range of speech tasks and tokenizer choices., Comment: 5 pages

Despite the established role of additive manufacturing (AM) in aerospace and medical fields, its adoption in astronomy remains low. Encouraging AM integration in a risk-averse community necessitates documentation and dissemination of previous case studies. The objective of this study is to create the first review of AM in astronomy hardware, answering: where is AM currently being used in astronomy, what is the status of its adoption, and what challenges are preventing its widespread use? The review starts with an introduction to astronomical instruments size/cost challenges, alongside the role of manufacturing innovation. This is followed by highlighting the benefits/challenges of AM and used materials/processes in both space-based and ground-based applications. The review case studies include mirrors, optomechanical structures, compliant mechanisms, brackets and tooling applications that are either in research phase or are implemented., Comment: 27 pages, 17 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 57)

A "tensor space" is a vector space equipped with a finite collection of multi-linear forms. In previous work, we showed that (for each signature) there exists a universal homogeneous tensor space, which is unique up to isomorphism. Here we generalize that result: we show that each Zariski class of tensor spaces contains a weakly homogeneous space, which is unique up to isomorphism; here, we say that two tensor spaces are "Zariski equivalent" if they satisfy the same polynomial identities. Our work relies on the theory of $\mathbf{GL}$-varieties developed by Bik, Draisma, Eggermont, and Snowden., Comment: 20 pages

Despite the increasing prevalence of vector observations, computation of optimal experimental design for multi-response models has received limited attention. To address this problem within the framework of approximate designs, we introduce mREX, an algorithm that generalizes the randomized exchange algorithm REX (J Am Stat Assoc 115:529, 2020), originally specialized for single-response models. The mREX algorithm incorporates several improvements: a novel method for computing efficient sparse initial designs, an extension to all differentiable Kiefer's optimality criteria, and an efficient method for performing optimal exchanges of weights. For the most commonly used D-optimality criterion, we propose a technique for optimal weight exchanges based on the characteristic matrix polynomial. The mREX algorithm is applicable to linear, nonlinear, and generalized linear models, and scales well to large problems. It typically converges to optimal designs faster than available alternative methods, although it does not require advanced mathematical programming solvers. We demonstrate the application of mREX to bivariate dose-response Emax models for clinical trials, both without and with the inclusion of covariates.

SqlCompose brings generative AI into the data analytics domain. SQL is declarative, has formal table schemas, and is often written in a non-linear manner. We address each of these challenges and develop a set of models that shows the importance of each problem. We first develop an internal SQL benchmark to perform offline tests at Meta. We evaluate how well the Public Llama model performs. We attain a BLEU score of 53% and 24% for single- and multi-line predictions, respectively. This performance is consistent with prior works on imperative languages. We then fine-tune Llama on our internal data and database schemas. SqlComposeSA substantially outperforms Llama by 16 percentage points on BLEU score. SQL is often written with multiple sub queries and in a non-sequential manner. We develop SqlComposeFIM which is aware of the context before and after the line(s) that need to be completed. This fill-in-the-middle model outperform SqlComposeFIM by 35 percentage points. We also measure how often the models get the correct table names, and SqlComposeFIM is able to do this 75% of the time. Aside from our scientific research, we also roll out SqlComposeFIM at Meta. SqlCompose is used on a weekly basis by over 10k users including data scientists and software engineers, less than 1% of users have disabled SqlCompose. We use the feedback from users to improve SqlCompose. Interesting positive themes include completing tedious or repetitive SQL clauses, suggesting boilerplate coding, and help in eliminate the need to remember difficult SQL syntax. The most significant negative themes was table and column name hallucinations, which has been reduced with the release of SqlComposeFIM. The SqlCompose models consistently outperform public and internal LLMs, despite being smaller (7 bn and 13 bn), which provides early indications that smaller specialist models can outperform larger general purpose models., Comment: 11 pages

Large language models are often expected to constantly adapt to new sources of knowledge and knowledge editing techniques aim to efficiently patch the outdated model knowledge, with minimal modification. Most prior works focus on monolingual knowledge editing in English, even though new information can emerge in any language from any part of the world. We propose the Cross-Lingual Multi-Hop Knowledge Editing paradigm, for measuring and analyzing the performance of various SoTA knowledge editing techniques in a cross-lingual setup. Specifically, we create a parallel cross-lingual benchmark, CROLIN-MQUAKE for measuring the knowledge editing capabilities. Our extensive analysis over various knowledge editing techniques uncover significant gaps in performance between the cross-lingual and English-centric setting. Following this, we propose a significantly improved system for cross-lingual multi-hop knowledge editing, CLEVER-CKE. CLEVER-CKE is based on a retrieve, verify and generate knowledge editing framework, where a retriever is formulated to recall edited facts and support an LLM to adhere to knowledge edits. We develop language-aware and hard-negative based contrastive objectives for improving the cross-lingual and fine-grained fact retrieval and verification process used in this framework. Extensive experiments on three LLMs, eight languages, and two datasets show CLEVER-CKE's significant gains of up to 30% over prior methods., Comment: Paper on Cross-Lingual Multi-Hop Knowledge Editing

As JWST begins to return observations, it is more important than ever that exoplanet climate models can consistently and correctly predict the observability of exoplanets, retrieval of their data, and interpretation of planetary environments from that data. Model intercomparisons play a crucial role in this context, especially now when few data are available to validate model predictions. The CUISINES Working Group of NASA's Nexus for Exoplanet System Science (NExSS) supports a systematic approach to evaluating the performance of exoplanet models, and provides here a framework for conducting community-organized exoplanet Model Intercomparison Projects (exoMIPs). The CUISINES framework adapts Earth climate community practices specifically for the needs of exoplanet researchers, encompassing a range of model types, planetary targets, and parameter space studies. It is intended to help researchers to work collectively, equitably, and openly toward common goals. The CUISINES framework rests on five principles: 1) Define in advance what research question(s) the exoMIP is intended to address. 2) Create an experimental design that maximizes community participation, and advertise it widely. 3) Plan a project timeline that allows all exoMIP members to participate fully. 4) Generate data products from model output for direct comparison to observations. 5) Create a data management plan that is workable in the present and scalable for the future. Within the first years of its existence, CUISINES is already providing logistical support to 10 exoMIPs, and will continue to host annual workshops for further community feedback and presentation of new exoMIP ideas., Comment: 14 pages, 2 figures

As large language models (LLMs) see increasing adoption across the globe, it is imperative for LLMs to be representative of the linguistic diversity of the world. India is a linguistically diverse country of 1.4 Billion people. To facilitate research on multilingual LLM evaluation, we release IndicGenBench - the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set 29 of Indic languages covering 13 scripts and 4 language families. IndicGenBench is composed of diverse generation tasks like cross-lingual summarization, machine translation, and cross-lingual question answering. IndicGenBench extends existing benchmarks to many Indic languages through human curation providing multi-way parallel evaluation data for many under-represented Indic languages for the first time. We evaluate a wide range of proprietary and open-source LLMs including GPT-3.5, GPT-4, PaLM-2, mT5, Gemma, BLOOM and LLaMA on IndicGenBench in a variety of settings. The largest PaLM-2 models performs the best on most tasks, however, there is a significant performance gap in all languages compared to English showing that further research is needed for the development of more inclusive multilingual language models. IndicGenBench is released at www.github.com/google-research-datasets/indic-gen-bench, Comment: ACL 2024

This paper reports the results of the deployment of Rich-State Simulated Populations at Meta for both automated and manual testing. We use simulated users (aka test users) to mimic user interactions and acquire state in much the same way that real user accounts acquire state. For automated testing, we present empirical results from deployment on the Facebook, Messenger, and Instagram apps for iOS and Android Platforms. These apps consist of tens of millions of lines of code, communicating with hundreds of millions of lines of backend code, and are used by over 2 billion people every day. Our results reveal that rich state increases average code coverage by 38\%, and endpoint coverage by 61\%. More importantly, it also yields an average increase of 115\% in the faults found by automated testing. The rich-state test user populations are also deployed in a (continually evolving) Test Universe; a web-enabled simulation platform for privacy-safe manual testing, which has been used by over 21,000 Meta engineers since its deployment in November 2022., Comment: ICSE 2024

This paper describes a generalizable model evaluation method that can be adapted to evaluate AI/ML models across multiple criteria including core scientific principles and more practical outcomes. Emerging from prediction competitions in Psychology and Decision Science, the method evaluates a group of candidate models of varying type and structure across multiple scientific, theoretic, and practical criteria. Ordinal ranking of criteria scores are evaluated using voting rules from the field of computational social choice and allow the comparison of divergent measures and types of models in a holistic evaluation. Additional advantages and applications are discussed., Comment: Paper presented at AAAI 2022 Fall Symposium on Knowledge Guided Machine Learning, Washington D.C

The Standard Model of particle physics describes the properties of elementary particles and their interactions remarkably well, but in particular does not account for dark matter. Isotope-shift spectroscopy is a sensitive probe of fifth forces and new particles that illuminate the dark matter sector. This method sets bounds on new bosons that couple neutrons and electrons with masses in the keV/c2 to MeV/c2 range. With increasing spectroscopic precision, such searches are limited by uncertainties of isotope masses and the understanding of nuclear structure. Here, we report on high-precision mass-ratio and isotope-shift measurements of the ytterbium isotopes $^{168,170,172,174,176}$Yb that exceed previous measurements by up to two orders of magnitude. From these measurements, we extract higher-order changes in the nuclear charge distribution along the Yb isotope chain and use these to benchmark novel ab initio calculations. Our measurements set new bounds on the existence of the proposed boson.

Ab initio QED calculations of the nuclear magnetic shielding constant in helium-like ions are presented. We combine the nonrelativistic QED approach based on an expansion in powers of the fine-structure constant $\alpha$ and the so-called ``all-order'' QED approach which includes all orders in the parameter $Z\alpha$ but uses a perturbation expansion in the parameter $1/Z$ (where $Z$ is the nuclear charge number). The combination of the two complementary methods makes our treatment applicable both to low-$Z$ and high-$Z$ ions. Our calculations confirm the presence of a rare antiscreening effect for the relativistic shielding correction and demonstrate the importance of the inclusion of the negative-energy part of the Dirac spectrum.

This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on Reels and Stories products for Instagram, 75% of TestGen-LLM's test cases built correctly, 57% passed reliably, and 25% increased coverage. During Meta's Instagram and Facebook test-a-thons, it improved 11.5% of all classes to which it was applied, with 73% of its recommendations being accepted for production deployment by Meta software engineers. We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement., Comment: 12 pages, 8 figures, 32nd ACM Symposium on the Foundations of Software Engineering (FSE 24)

The investigation of the absolute scale of the effective neutrino mass remains challenging due to the exclusively weak interaction of neutrinos with all known particles in the standard model of particle physics. Currently, the most precise and least model-dependent upper limit on the electron antineutrino mass is set by the KATRIN experiment from the analysis of the tritium \b{eta}-decay. Another promising approach is the electron capture in $^{163}\mathrm{Ho}$, which is under investigation using microcalorimetry within the ECHo and HOLMES collab orations. An independently measured Q-value of this process is vital for the assessment of systematic uncertainties in the neutrino mass determination. Here, we report a direct, independent determination of this $Q$-value by measuring the free-space cyclotron frequency ratio of highly charged ions of $^{163}\mathrm{Ho}$ and $^{163}\mathrm{Dy}$ in the Penning trap experiment \textsc{Pentatrap}. Combining this ratio with atomic physics calculations of the electronic binding energies yields a $Q$-value of $2863.2(0.6)\,\mathrm{eV}/c^{2}$ - a more than 50-fold improvement over the state-of-the-art. This will enable the determination of the electron neutrino mass on a sub-eV level from the analysis of the electron capture in $^{163}\mathrm{Ho}$.

TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. We describe the development and deployment of TestGen at Meta. In particular, we focus on the scalability challenges overcome during development in order to deploy observation-based test carving at scale in industry. So far, TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Meta is currently in the process of more widespread deployment. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86\% of the classes covered by end-to-end tests. Testing on 16 Kotlin Instagram app-launch-blocking tasks demonstrated that the TestGen tests would have trapped 13 of these before they became launch blocking., Comment: 12 pages, 8 figures, FSE 2024, Mon 15 - Fri 19 July 2024, Porto de Galinhas, Brazil

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal., Comment: 6 pages, 1 figure, InteNSE 24: ACM International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, April, 2024, Lisbon, Portugal

In this paper, the quotient of a Leavitt path algebra of an arbitrary graph by an $I$-basic graded ideal, and the quotient of a Leavitt path algebra of a row-finite graph by an arbitrary graded ideal are considered. The result of the quotient of a Leavitt path algebra by an arbitrary graded ideal is extended by using the function $\varphi$. Examples are given to illustrate the results.

Motivated by recent work of Peluse and Soundararajan on divisibility properties of the entries of the character tables of symmetric groups, we investigate the question: For a finite group G, when are two columns of the character table of G congruent to one another modulo a power of a prime?, Comment: 12 pages. Comments welcome!